International Journal on Advanced Science, Engineering and Information Technology
http://insightsociety.org/ojaseit/index.php/ijaseit
International Journal on Advanced Science, Engineering and Information Technology (IJASEIT) is an international peer-reviewed journal dedicated to interchange for the results of high quality research in all aspect of science, engineering and information technology. The journal publishes state-of-art papers in fundamental theory, experiments and simulation, as well as applications, with a systematic proposed method, sufficient review on previous works, expanded discussion and concise conclusion. As our commitment to the advancement of science and technology, the IJASEIT follows the open access policy that allows the published articles freely available online without any subscription.en-USInternational Journal on Advanced Science, Engineering and Information Technology2088-5334<span>Authors who publish with this journal agree to the following terms:</span><br /><ol type="a"><ol type="a"><li>Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a <a href="http://creativecommons.org/licenses/by/3.0/" target="_new">Creative Commons Attribution License</a> that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.</li><li>Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.</li><li>Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See <a href="http://opcit.eprints.org/oacitation-biblio.html" target="_new">The Effect of Open Access</a>).</li></ol></ol>Activity of Natural Compound Pothos tener Wall on Aeromonas hydrophila Infection to Prevent of Antibiotics
http://insightsociety.org/ojaseit/index.php/ijaseit/article/view/19182
This study aims to discover how influential Photos tener Wall is as an anti-bacterial treatment for Aeromonas hydrophila. Methods: Cyprinus Carpio were reared for 28 days, and on the 29th and 30th days before Aeromonas hydrophila infection, the fish were adequately fasted. On the 31st day, they were intramuscularly challenged with A. hydrophila (105 CFU/mL) (the first day in A. hydrophila infection). The treatments given were (I) immersed trial with fresh (live) P tener Wall: (H1) Immersed with 15 g of P. tener Wall plant, (H2) Immersed with 30 g of P. tener Wall plant, and (H3) Immersed with 60 g of P. tener; (II) feeding trials in which the treatments given were (P1) 2% of P. tener Wall powder mixed with 1 kg commercial diet and (P2) 4% of P. tener Wall powder mixed with 1 kg commercial diet; Experiment III combined the best results from experiment I (H2) and experiment II (P2) and Oxytetracycline 5 g/kg feed as a control antibiotic. The result obtained was that the treatment of 30 g of fresh P. tener Wall or adding 4% simplicial P. tener Wall in the diet could increase koi fish's immune response and resistance to A. Hydrophila has a survival rate that reaches 100%. This treatment has the same effect as using antibiotic Oxytetracycline 5 g/kg of feed. An important aspect for further research is that P. tener wall can be tested on other fish diseases caused by bacteria or fungi.Media Fitri Isma NugrahaDwi Asih KurniatiSri RukminiImam TaukhidHessy NovitaBejo SlametIkhsan KhasaniDwi Budiyanto TrisnoharjonoMuh. Alias L RajamuddinBerna Elya2023-12-312023-12-31136Increasing Fe Content in Rice Plants with the Application Liquid Fertilizer of Moringa oleifera and Golden Snail
http://insightsociety.org/ojaseit/index.php/ijaseit/article/view/18501
Increasing iron (Fe) content in rice is needed because Fe is a critical mineral that plays a crucial role in body metabolisms, such as a form of hemoglobin, antibody production, and a catalyst for several compounds. Meanwhile, the Fe absorption by rice plants is relatively low. Moringa oleifera and golden snail contain high Fe, potentially liquid fertilizer ingredients. This research aims to increase Fe uptake and Fe content of rice plants by application of liquid fertilizer of Moringa oleifera and golden snail. The study used a completely randomized design with two factors. The first factor is the composition of liquid organic fertilizer (P), six levels (P0: without fertilizer, P1: fresh extract of Moringa leaf, P2: fermented Moringa leaf, P3: fresh extract of Golden snail, P4: fermented Golden snail, P5: Mixed 1:1 by volume fermented of Moringa leaf and golden snail). The second factor is liquid fertilizer concentrations (K), which are four levels (K1: 2%, K2: 4%, K3: 6%, and K4: 8% concentration). Repetition of treatment three times. The results showed that the treatment affected increasing fresh weight of the plant, dry weight of the plant, 100 grain weight, number of leaves, total chlorophyll, and Fe content. The fermented mixture of Moringa and golden snail at 4% concentration increased the Fe available in the soil by 6,677% or 4,788% higher than the control. The fermented Moringa leaf with an 8% concentration increased Fe in rice, which was 8,165% or 30.50% higher than the control.Srie Juli RachmawatiEdi PurwantoAmalia Tetrani SakyaWidyatmani Sih Dewi2023-12-312023-12-31136Cross Flow Microfiltration System in Separating Fermented Nixtamal Corn for Preparation of Natural Folic Acid
http://insightsociety.org/ojaseit/index.php/ijaseit/article/view/19248
A purification process of biomass of nixtamal yellow corn fermented by Rhizopus oligosporus strain C1 (FNC-A) and Rhizopus sp (FNC-B) was performed using a microfiltration (MF) membrane with pore size 0.15 µm installed in a crossflow microfiltration (CFMF) system to obtain retentate and permeate fractions as natural folic acid preparations. CFMF process was conducted at room temperature with a pump motor frequency (PMF) of 10 Hz (flow rate of 3.5 L/min) and a transmembrane pressure (TMP) of 4 bar for 0, 15, 30, 45, 60, 75, and 90 minutes. Based on optimum folic acid, the results showed that the best treatments of FNC-B and FNC-B were achieved at 90 min, resulting in a folic acid increase in retentate of FNC-A and FNC- B of 72.61 and 95.26%, reducing sugar of 20.41 and 170.93% (1.7-folds), total sugars of 155.51% (1.55-folds) and 426.76% (4.27-folds), and dissolved protein of 55.33 and 39.20%, and decrease in total solids of 88.91 and 91.64%, respectively, compared to initial biomass of FNC-A and FNC-B. The MFCF system effectively separated folic acid in retentates of FNC-A (33.77%) and FNC-B (95.27%) at the optimal condition. Folic acid monomers predominated the characteristics of FNC-A and FNC-B in optimum conditions with molecular weights of 442.10 and 442.18 Dalton, the average particle size of 38.31 μm and 37.97 μm, and distribution of particles at 10, 50, and 90% from the particle size 10.31, 26.32 and 81.55 μm, and 10.37, 28.04 and 76.09 μm, respectively.Agustine Susilowati- AspiyantoHakiki MelanieYati MaryatiPuspa D. Lotulung2023-12-312023-12-31136Optimization and Analysis of Polyhydroxyalkanoate (PHA) by Bacillus sp. Strain CL33 and Bacillus flexus Strain S5a from Palm Oil Mill Waste
http://insightsociety.org/ojaseit/index.php/ijaseit/article/view/18418
Polyhydroxyalkanoate (PHA) is a biodegradable polymer that microorganisms can synthesize amidst non-optimal growth conditions with excess carbon sources. Palm oil, rich in fatty acids, serves as a carbon source for PHA synthesis. The bacterial PHA production can be influenced by carbon concentration in the growth medium. Therefore, determining the optimal concentration of palm oil as a carbon source is crucial for PHA production. Additionally, it is possible to determine the type of PHA generated by bacteria, which can then be utilized as information when processing utilizing the PHA. The experiment employed palm oil concentrations of 0.5%, 1%, and 2% and was carried out for periods of 48, 72, 96 hours. It was discovered that Bacillus sp. strain CL33 and Bacillus flexus strain S5a produced the most effective PHA at a concentration of 25 with an incubation period of 96 hours. The PHA generated by these bacteria was quantitatively analyzed through measurements of total bacterial growth, cell dry weight, and the levels of crotonic acid. PHA types were also analyzed using GC-MS, with monomers including 2-hydroxybutyrate(-2HB), 2-hydroxy-3-phenylpropionate (2H3PhP), 3-Hydroxyhexanoate (3HHx), 3-hydroxyoctanoate (3H2O), and 3-hydroxydecanoate (3HD). The Bacillus sp. strain CL33 yielded a PHA level of 92.23%. Meanwhile, Bacillus flexus strain S5a synthesized a polyhydroxyalkanoate comprising mostly 3-hydroxyhexanoate (3HHx) and polydimethylsiloxane (PDMS). The monomers used were decamethyltetrasiloxane, dodecamethylpentasiloxane, hexamethylcyclotrisiloxane, octamethylpentasiloxane, and dodecamethylcyclohexasiloxane. The type of PHA produced accounted for 85.93% of the total.Nur HaedarMutia Putri Jamaluddin- FahruddinZaraswati DwyanaZarlina ZainuddinFuad GaniMustika Tuwo2023-12-312023-12-31136Utilization of Sargassum Flour Fermented with Bacillus aerius Bacteria in Siganus guttatus Rabbitfish Enlargement Feed
http://insightsociety.org/ojaseit/index.php/ijaseit/article/view/19034
This Sargassum is a staple food of rabbitfish in nature; although its nutritional quality is relatively low if it is used as one of the feed ingredients first carried out in the fermentation process, the results of this fermentation are used as one of the raw feed materials. The study was conducted to look at the use of fermented sargassum flour in feed at the lowest dose of 7.5% to the highest dose of 30%. This study was designed using a completely randomized design (RAL). Using 15 units of floating net cage containers. The study duration was 120 days. During the study, the test fish were fed thrice daily, namely 08.00 am, 1:00 pm, and 03:00 pm. Feeding is carried out. To determine the severity of the treatment of the biological response of test fish, sampling is carried out every 30 days. From several parameters observed, such as weight gain, specific growth rate, and daily feed consumption rate, although statistically not different (P>0.05), the tendency to increase with the increase in sargassum dose by 30%. Similarly, the total digestibility of feed, digestibility of feed protein, and digestibility of feed energy statistically do not show a difference (P>0.05) but also the tendency to increase. Based on the results of this activity, it was concluded that sargassum flour that has been fermented with bacillus aerius bacteria can be used well by rabbitfish, with as much as 30% in the feed.- Kamaruddin- HaryatiSiti AslamyahMuhammad Yusri Karim2023-12-312023-12-31136Increasing Viability of Bacillus subtilis BR610 through Inulin-Loaded Synbiotic Microcapsules
http://insightsociety.org/ojaseit/index.php/ijaseit/article/view/19056
This study aims to determine the growth pattern of Bacillus subtilis BR610 isolated from the intestines of Rabbitfish on broth + inulin nutrient media, the diameter of beads, encapsulation rate, absorption efficiency, and probiotic viability in synbiotic microcapsules when exposed to simulated bile and high temperature. The study was designed with a completely randomized plan. The treatments tested were inulin and alginate concentration, viability in 10% bile, temperatures of 70oC and 90oC. An overview of granular synbiotic microcapsules is presented in the form of images, while the quantitative data obtained was processed using ANOVA with the help of the SPSS application. BR610 synbiotic microcapsule beads are round to oval in shape, transparent white in color, and the granules' elasticity increases with increasing alginate concentration. Statistical test results showed that 1% inulin significantly increased the population of B. subtilis BR610, a diameter of beads 0.9-3 mm, viability of probiotics in beads was 7.776 ± 0.06 log CFU/mL. The highest rate and efficiency of encapsulation and survival of probiotics on simulated were obtained from beads with 2% alginate and 1% inulin concentration. Synbiotic microcapsules can protect probiotics from environmental stress. This research serves as a scholarly resource on the utilization of probiotics in diverse domains, including fish feed production. It is well-established that the temperature within the feed molding apparatus can exceed 70oC during the fabrication of fish feed pellets. However, this is no longer a hindrance due to the implementation of alginate coating on probiotics.Bunga Rante TampangalloHilal Anshary- SriwulanRachman Syah2023-12-312023-12-31136Influence of ZnO on Antibacterial Properties of Portland Cement
http://insightsociety.org/ojaseit/index.php/ijaseit/article/view/19262
This research project focuses on evaluating the antimicrobial properties of a composite material composed of Portland Cement and Zinc Oxide (ZnO). The study assesses the antibacterial activity of this composite by using Escherichia coli (E. coli) as the test microorganism. Bacterial growth assessment is carried out through the Total Plate Count (TPC) method. The investigation involved varying the concentration of ZnO within the Portland Cement composite, specifically at levels of 0%, 1%, 3%, and 5%. The study primarily centers on generating reactive oxygen species (ROS) by ZnO, and this evaluation was conducted under both UV light exposure and without it. This dual approach allows for a comprehensive examination of ROS activity. Furthermore, the research project involves material characterization using X-ray Diffraction (XRD) to determine the nanoparticle size of ZnO and identify the crystal structures present within the composite material. Additionally, morphological analysis is performed using Scanning Electron Microscopy (SEM) to visualize the structural properties of ZnO embedded within the Portland Cement. SEM analysis is conducted at various magnifications, including 1000x, 2500x, 5000x, and 10,000x, to provide a detailed view of the ZnO's structural properties. In summary, this research project explores the antimicrobial potential of a composite material incorporating Portland Cement and ZnO, focusing on ROS generation and the composite material's structural properties. The findings will contribute to our understanding of the material's suitability for applications in antimicrobial environments.Rahadian ZainulRandy Trafino- KrismadinataRemon LapisaPutri AzhariAmalia Putri Lubis- Muhardi2023-12-312023-12-31136Addition of Cd2+ Metal Ions to Conway Culture Medium on Phytoplankton Growth of Chaetoceros calcitrans
http://insightsociety.org/ojaseit/index.php/ijaseit/article/view/19038
The main objective of this study is to examine the impact of Cd2+ metal ions on the growth of marine phytoplankton, specifically focusing on their potential as agents for phytoremediation in marine settings affected by heavy metal pollution. In this study, the behavior of Cd2+ metal ions added to the culture medium in phytoplankton type of Chaetoceros Calcitrans In the culture medium, an investigation was conducted on a series of Chaetoceros Calcitrans cultures, comparing those including and not including the Cd2+ metal ions’ addition. Observations were made on the growth pattern of Chaetoceros Calcitrans. To assess the impact of introducing Cd2+ metal ions into the Conway culture media, various metrics such as definite growth rate, growth inhibition percentage, and test of toxicity were employed. The findings indicated that the growth trajectory of Chaetoceros Calcitrans in the Conway medium, in the absence of Cd2+ metal ions as a control group, exhibited the most substantial growth curve. The growth patterns observed in the culture medium upon the addition of Cd2+ metal ions at a concentration of 0.1 mg/L were found to be comparable to those observed in the samples of control group. Adding Cd2+ metal ions at concentrations exceeding 0.1 mg/L has decreased the inhibited growth rate of Chaetoceros Calcitrans. The concurrent increase in PGI costs further exacerbates this effect. The findings from the statistical analysis of difference tests conducted on blanks investigating the impact of introducing Cd2+ metal ions to Chaetoceros Calcitrans suggest that concentrations ranging from 0.01 to 0.10 ppm of Cd2+ metal ions have no discernible effect on the growth of Chaetoceros Calcitrans. Furthermore, the highest concentration of Cd2+ metal ions that Chaetoceros Calcitrans can withstand is 0.10 ppm, with an EC50 value of 6.13 ppm.Andi MakkasauErma Suryani Sahabuddin2023-12-312023-12-31136Transmutation of Plutonium and Minor Actinide in PWR Thorium-Transuranic Fuel Assembly
http://insightsociety.org/ojaseit/index.php/ijaseit/article/view/18638
Long-lived radioactive waste is often considered a concerning issue on utilizing nuclear power. This waste is in the form of plutonium and minor actinides (MA), formed due to successive neutron capture of 238U. Both are not particularly hazardous radiologically, but their long half-life caused an issue in the public acceptance of radioactive waste disposal. Thereby, this issue must be resolved, either politically or technically. One of the technical solutions to address the issue of long-lived radioactive waste is the incineration of transuranic (TRU) elements in a pressurized water reactor (PWR) fuel assembly. Mixing TRU with thorium in a PWR fuel assembly can theoretically reduce TRU stockpile more effectively than uranium. This paper discusses plutonium and MA transmutation in a Thorium-Transuranic (Th-TRU) PWR fuel assembly using MCNP6 code and ENDF/B-VII library. A Westinghouse 17×17 PWR fuel assembly was chosen to determine the feasibility of TRU incineration. Three assemblies of 50:50, 55:45, and 60:40 mixture of UO2 and Th-TRU fuel rods are compared to one reference UO2 assembly from the criticality and burn-up point of view. From the calculations, the plutonium incineration rate was observed to be 9.47 %, 10.91 %, and 12.2 %, while MA incineration rates were found to be 11.5 %, 11.93 %, and 12.3 % for Th50-TRU50, Th55-TRU45, and Th60-TRU40, respectively. This observation indicates that a higher thorium fraction in the fuel assembly can increase the TRU transmutation rate. Therefore, a high thorium fraction is recommended to increase the transmutation rate of the TRU.- ZuhairWahid LuthfiR. Andika Putra Dwijayanto- Sriyono- Suwoto2023-12-312023-12-31136Effect of Calcination on the Ash from Lokon Volcano and Its Potentially Sustainable Binder Material
http://insightsociety.org/ojaseit/index.php/ijaseit/article/view/18900
The need for cement as a housing construction material has continued to increase due to the growing population. This high demand increases carbon dioxide emissions. Hence, it is necessary to optimize the use of natural pozzolan material. Volcanic ash is a natural pozzolan material in North Sulawesi, but its use could be more optimal. This study aimed to determine the effect of calcination on the physical properties of volcanic ash originating from the eruption of Mount Lokon. The calcination was carried out to determine the potential of Lokon ash at different temperatures to assess the structural characteristics, mineral phases, metal oxide composition, functional group bonding, morphology, and its potential as a binder for concrete mixtures. The ash material used comes from sand taken from the Pasahapen River and filtered through a 325-mesh sieve. Lokon ash was calcined at temperatures of 800, 900, and 1000oC to determine the structural and morphological characteristics. At the same time, the effects were examined using an X-ray diffractometer (XRD), Raman spectroscopy, X-ray fluorescence (XRF), Fourier Transform InfraRed (FTIR), and Scanning Electron Microscopy (SEM). The results showed that calcination triggered the formation of hematite in the ash, which will increase its reactivity as a pozzolan material. This process causes the crystallinity of ash minerals to increase, but the ash material produced is predominantly amorphous. Hence, it has excellent potential as a binder material in concrete mixtures.Maria Daurina BobantoDolfie Paulus PandaraFerdy FerdyVistarani Arini TiwowHenry AritonangAdey TanaumaRezkyana Todingan2023-12-312023-12-31136Multitemporal Acoustic Backscatter Data Analysis to Monitor the Dynamics of Seabed Surface Sediments
http://insightsociety.org/ojaseit/index.php/ijaseit/article/view/18675
Backscatter strength is a product of underwater acoustic remote sensing. This study used a multibeam echosounder as an acoustic remote sensing instrument to collect backscatter strength data. These data are then used to classify the surficial sediment distribution. To monitor the difference in seabed sediment distribution, a time-series survey was performed to obtain multitemporal acoustic backscatter data. An EM 304 multibeam system from Kongsberg was mounted on the Research Vessel Baruna Jaya III from the Indonesia National Research and Innovation Agency (BRIN). It was used to collect backscatter data in the waters of Raja Ampat, Indonesia. The data were collected at different times, April and July 2021. Geometric and radiometric corrections were applied to these backscatter data. Based on the angular response curve analysis from acoustic backscatter strength, the research area can be classified into four seabed sediment types: boulder, gravel, sand, and mud. A comparison of both time series backscatter data shows that the boulder and gravel areas increased by 13.6% and 19.0%, respectively. Elsewhere, the area with sediment types of sand and mud diminished by 30.5% and 2.0%. The change in the sediment type area occurred as much as 50.5%, while the remaining 49.5% area remained unchanged. This resulting value is apparently derived from the steep topography that rapidly changes sediment distribution. One such suggestion is that sediment sampling should be performed to confirm the model from angular response curve analysis.Danar Guruh Pratomo- KhomsinIrena Hana HariyantoNabilla Aprilia PutriImam MuditaDwi HaryantoAhmad Fawaiz Safi’2023-12-312023-12-31136In Silico Docking to Explore the Coronavirus-2 ACE2 Inhibitor Potential in Brown Seaweed Padina sp. from Morotai Island, North Maluku, Indonesia
http://insightsociety.org/ojaseit/index.php/ijaseit/article/view/19110
Efforts to explore new sources of antivirals for coronavirus-2 from abundant marine natural materials are highly encouraged. The study aimed to explore the potential compounds of brown seaweed Padina sp. from Morotai Island extracted using three solvents, i.e., n-hexane, ethyl acetate, and acetone, as an antiviral against coronavirus-2 through an entry inhibitor mechanism using bioinformatics tools. The target protein was Angiotensin-Converting Enzyme-related carboxypeptidase (ACE2) receptor. Protein structure was downloaded from PDB and prepared using Chimera. The interaction of compounds to ACE2 was predicted using AutoDock4 and AutoDockTools. MLN-4760 was used as a standard compound. Results showed that 15 selected compounds were potential as ACE2 inhibitors, resulting in negative binding energies, low inhibition constant, and varying binding modes. The conformation structure of all compounds was occupied on the ACE-2 active site. Four compounds were highly potential as ACE2 inhibitors with binding energy lower than a standard compound, comprised of Neophytadiene (diterpene); 6,9,12,15-Docosatetraenoic acid, methyl ester (fatty acid); N-Dimethylaminomethyl-tert-butyl-isopropylphosphine (alkaloid) and 8,11-Octadecadienoic acid, methyl ester (fatty acid). Ethyl acetate and acetone are suggested to be used as solvents for the extraction to produce compounds as ACE2 inhibitors, but ethyl acetate was found to be the most effective. Brown seaweed of Padina sp. is recommended to be developed as a pharmaceutical and nutraceutical preparation for COVID-19. Further in vivo and in vitro studies are suggested to confirm this study's results and provide stronger evidence.- Sundari- KhadijahAras SyaziliLia HapsariAbdu Mas'ud2023-12-312023-12-31136Measurement Analysis of Non-Invasive Blood Glucose On Sensor Coplanar Waveguide Loaded Square Ring Resonator with Interdigital Coupling Capacitor
http://insightsociety.org/ojaseit/index.php/ijaseit/article/view/18674
This study presents the experimental results of a system with a sensor structure detecting human blood glucose levels. A microwave-based sensor is used for non-invasive blood glucose monitoring. The sensor design uses an asymmetrically loaded CPW structure as a square ring resonator with an interdigital coupling capacitor on the ground side. Simulated with a load of artificial finger tissue made from gelatin, modeled in four layers. The first layer is the skin is the outermost tissue, the next layer is fat, blood and bone. Each layer of tissue has a certain thickness size; skin (0.3mm), fat (0.2mm), blood (1.5mm), and bone (4mm). The measurement simulation is used, HFSS as modeling simulation and VNA as a measurement of the physical representation of the design results with parametric optimization methods. To verify the correlation and the expected sensitivity, media with different dielectrics were mounted on the surface of the sensor resonator with blood glucose levels of 1mg/dl, 72mg/dl, 126mg/dl, 162mg/dl and 216mg/dl. Reflection factor S11 was observed based on dielectric constant blood glucose levels (dB) fluctuations. Analysis of the data on the graph between the independent variables, namely blood glucose concentration and the dependent variable levels of S11 has an “R†correlation value of 0.97. The sensitivity level of the sensor on the S11 reflection factor with HFSS simulation averages 73.36mdB/mgdl-1 and VNA reaches 82.39mdB/mgdl-1. The results are interesting for developing a more optimal glucose sensor system.Catur RatmokoIryanto LaisaMudrik Alaydrus2023-12-312023-12-31136Surface Water Wave Detector for Floating Devices with Capacitive Sensor
http://insightsociety.org/ojaseit/index.php/ijaseit/article/view/19045
Water wave monitoring is essential in collecting marine parameters for oceanography studies and early warning systems on security and safety, such as drowning detection, weather detection, and gas leakage from underwater pipeline detection, because these activities create different water wave patterns that can be further analyzed. The current wave detection methods, such as underwater pressure and resistive sensors, have lower durability as they require direct contact with the water. Monocular camera wave detection can detect the line where water waves propagate. However, a static platform is required to perform monitoring operations. This research aims to develop a continuous capacitive sensor system that a buoy can implement for contactless water surface wave detection and to develop a water wave direction detection algorithm by Principal Component Analysis (PCA) calculation. Capacitive sensors arranged in a circular shape and a compass module are implemented inside the prototype buoy. Each capacitive sensor detects the real-time wave height change by changing the capacitance value. The capacitance values from all the capacitive sensors and the North of the compass sensor are sent to the embedded server for further computations. Processes carried out in the embedded server are initial calibration, centroid calculation, PCA calculations for water wave detection, and data visualization on the webpage. The prototype buoy with a capacitive sensor system and compass sensor developed can detect the four positions tested in the experiment with a mean square error of 38.42° and a mean absolute error of 5.85°.Poh Wei LuiBoon Chin YeoWay Soong Lim2023-12-312023-12-31136Position and Temperature Detector for Autism Spectrum Disorder Children based on Sensor and Using IoT System
http://insightsociety.org/ojaseit/index.php/ijaseit/article/view/19416
Children with Autism Spectrum Disorder (ASD) have characteristics where one cannot control emotions, which can cause tantrums that can impact behavior and body temperature. Based on this, they should be supervised by parents/relatives. To reduce the effects of these circumstances, this study seeks to design a technology system that can measure body temperature and detect the position of ASD children who can later monitor the activities they do. This system applies the ESP32 microcontroller and utilizes the GPS module to read the position of objects detected by the system and the MLX90614 temperature sensor, which can detect the body temperature of ASD children. Then, to facilitate checking, the control system is designed with an IoT system through the Blynk application to make it easier for users to supervise ASD children and can be accessed via smartphones in real time. In this study, detection testing was carried out on 3 ASD child subjects by grouping three conditions: namely, the child exits the location when the child is outside the predetermined location; then the child exits the body temperature when the child's body temperature is abnormal, and the child exits the location and body temperature outside normal. The results obtained show that the detector test results provide notifications to application users in the form of "child out of location," "child out of body temperature," and "child out of location and body temperature outside normal".Yunidar YunidarMelinda Melinda2023-12-312023-12-31136Dynamic QoS: Automatically Modifying QoS Queue's Maximum Bandwidth Rate-Limit of Network Devices for Network Improvement
http://insightsociety.org/ojaseit/index.php/ijaseit/article/view/19409
The heterogeneous data traffic of today's network is a huge challenge to existing best-effort network technology, particularly in the context of large Ethernet, which handles hundreds to thousands of users. The existing conventional best-effort network technology is no longer efficient to handle the diversity of traffic types in the network and requires network management equipment such as Quality of Service (QOS). Usually, QOS is implemented on the gateway router. However, for better network performance and management, to guarantee high priority for sensitive traffic like video conferencing, Voice over Internet Protocol (VoIP), and streaming media within an internal network, it is nice to have QoS implemented on each router in the LAN network, starting from the access router to the gateway router. This paper is to demonstrate the effectiveness of the proposed dynamic QoS that has been developed and deployed in the LAN, purposely to provide adequate bandwidth for sensitive traffic when the network utilization is high and congested, by automatically modifying the QoS Queue's Maximum Bandwidth Rate-Limit of the best-effort traffic queue of the related router. The performance of the proposed developed dynamic QoS was evaluated via a comparison study before and after the dynamic QoS was presented in the network simulation environment that was built using Mininet. Results from the testing show that the developed dynamic QoS can improve the network's performance by automatically giving the appropriate bandwidth for sensitive traffic on the fly while needed/on demand.Muhammad Fendi OsmanMohd Rizal Mohd IsaMohammad Adib KhairuddinMohd Afizi Mohd ShukranNoor Afiza Mat RazaliNur Diyana KamarudinAmin Suharjono2023-12-312023-12-31136Deployment of 5G NR Outdoor-to-Indoor at Midband and mmWave Frequency Implementation in Indonesia's Industrial Area
http://insightsociety.org/ojaseit/index.php/ijaseit/article/view/19414
In the world of telecommunications, there have been significant advancements in broadband access, especially with the introduction of fifth-generation cellular technology, or 5G NR. The presence of 5G may have an impact on performance. This research compares 5G NR network deployment in mid-band at the 3.5 GHz frequency and high-band at 28 GHz frequency in a 5 km2 Pulogadung industrial area. To provide reliable service, link budget calculations were conducted using the downlink outdoor-to-indoor (O2I) and uplink outdoor-to-indoor (O2I) scenarios based online of sight (LOS). The Urban Micro (UMa) propagation model was used for the 3.5 GHz frequency, while the Urban Micro (UMi) model was used for the 28 GHz frequency, both standardized by 3GPP TR 38.901. The calculation results were simulated using the Automatic Site Placement (ASP) feature in Mentum Planet Tools version 7.2.1, which provided recommendations for the new site locations. The simulations showed that the Downlink O2I-LOS scenario, which requires more sites, resulted in stronger signal strength than the Uplink O2I-LOS scenario. The highest signal strength was achieved by the downlink O2I-LOS scenario at the 3.5 GHz frequency, as indicated by an average SS-RSRP value of -91.88 dBm. On the other hand, the lowest signal strength was obtained by the uplink O2I-LOS scenario at the 28 GHz frequency, with an average SS-RSRP value of -98.11 dBm. The difference in predicted 5G SS-RSRP values is influenced by the variation in standard parameter values in the link budget for each frequency.Alfin HikmaturokhmanGhina FahiraRay Nur EsaAsri Wulandari AsriGoh Khang Wen2023-12-312023-12-31136Performance Study of Multipath Effect in 5G Millimeter-Wave Channel
http://insightsociety.org/ojaseit/index.php/ijaseit/article/view/19033
5G has been essentially a buzzword for several years, but according to the experts, from 2022 onward, there will be an inflection point between network maturity and the availability of 5G. To make 5G a reality, we must minimize all propagation losses. One of the possible factors that reduces the performance of 5G transmission is the multipath effect. In this paper, we investigate the severity of the multipath effect in the 5G millimeter-wave (mmWave) channel and mitigate the multipath effect using adaptive equalization based on the least mean square (LMS) algorithm to improve the performance of 5G wireless signal transmission. A mmWave channel simulator, NYUSIM, provides complete data for all resolvable multipaths in a specific channel configuration. An analysis of bit-error-rate (BER) based on the minimum BER (MBER) and minimum mean square error (MMSE) optimization criterion is performed to measure the improved performance of a 5G data channel simulated under line-of-sight (LOS) and non-LOS (NLOS) paths. A good overall performance of BER based on the MBER and MMSE criteria is attained using the LMS equalization method in a micro-urban area at a maximum data rate of 50 Mbps. For both LOS and NLOS conditions, the increase in data rate to 55.56 Mbps and 62.5 Mbps causes a significant decrease in BER performance. In conclusion, the primary factor affecting the BER performance is the data rate, not the frequency or transmitter-to-receiver distance.Gwo Chin ChungPravin Kumar A/L SivakumarJun Jiat TiangWai Leong PangChu Liang Lee2023-12-312023-12-31136Telecommunication Fiber Box Detection Using YOLO in Urban Environment
http://insightsociety.org/ojaseit/index.php/ijaseit/article/view/19027
The Fiber Distribution Panel (FDP) box is an essential piece of internet access hardware because it provides users with high-speed data networking and functions as a cable organizer to reduce wire clutter. After installing the FDP, an inspection must be performed to ensure that all necessary components are present. However, This examination is still done manually; the technician snaps a picture of the panel and sends it to its supervisor for verification, which is time-consuming and often prone to errors. In addition to images captured in low-light and complex environments, it makes it more difficult for humans to identify the components with just a naked eye. On this matter, a much more efficient method to assess the FDP installation work is very much needed. Therefore, using computer vision approaches, we utilize a deep learning algorithm to perform object detection and automate the assessment of FDP installation components based on visual data. One of the deep learning models established in the literature is the You Only Look Once (YOLO) model, a one-stage deep learning object detection algorithm that employs a fully conventional approach to generate highly accurate real-time predictions. This paper uses YOLOv5s to identify the fiber box and its relevant components, even in urban environments. Experimentations show that YOLO successfully identified the installation parts with a mean average precision score of 86% at a 0.5 confidence level, even with limited data.Azib-Jazman AzmawiWan-Noorshahida Mohd-IsaAbdul Aziz Abdul Rahman2023-12-312023-12-31136Analysis of 5.8 GHz Network for Line of Sight (LOS) and Non-Line of Sight (NLOS) in Suburban Environment
http://insightsociety.org/ojaseit/index.php/ijaseit/article/view/19048
This paper presents the findings of radio wave characterization based on the measurement data at 5.8 GHz. The measurement data were collected by a testbed channel, which links with the following scenarios: a single tree, a row of trees, a row of trees and a road, a row of trees, a road, and a building. These experiments were conducted at University Teknologi Malaysia (UTM) Skudai, Johor to represent the suburban environment. The links consist of pairs of transmitting and receiving antennas that deploy the path of a line of sight (LOS) and non-line of sight (NLOS) radio propagation wave networks. Based on the measurement data analysis, the general issue concerning the statistical probability distribution and the characteristics of LOS and NLOS are examined and discussed. Note that 5.8 GHz technology can be used in both LOS and NLOS scenarios, but its performance varies based on the presence of obstacles and signal propagation characteristics. Other prominent experimental analysis methods, such as hypothesis testing and goodness of fit tests, are implemented to consolidate the findings. The analysis found that the empirical probability density function of LOS and NLOS channels follows Gaussian, Rayleigh, and Rician distribution. Predicting specific future technological developments, such as the availability of 5.8 GHz technology, is challenging because it depends on various factors, including research and development efforts, regulatory decisions, market demand, and technological advancements.Ikha Fadzila Md IdrisTan Kim GeokNoor Ziela Abd RahmanMohd Haffizzi Md Idris2023-12-312023-12-31136Fine Tuned of DenseNET121 to Classify NTT Weaving Motifs on Mobile Application
http://insightsociety.org/ojaseit/index.php/ijaseit/article/view/18314
The problem of classifying Woven Fabric Motifs through pattern recognition can be addressed using Convolutional Neural Networks (CNNs). Existing CNN architectures like VGG, ResNet, MobileNet, and DenseNet offer diverse propagation methods. These architectures, trained on datasets like imagenet, have demonstrated competence in solving large-scale classification tasks. The CNN model trained on the ImageNet dataset, hereinafter referred to as the pre-trained model, can be utilized to address the classification issue of NTT woven fabric motifs. This involves retraining the model using a new output layer and dataset, a method known as Transfer Learning. In addition to Transfer Learning, this research employs Fine Tuning, which entails retraining several classification layers. The pre-trained model used in this research is DenseNet121. This model was chosen because it does not require too much storage space and has good classification performance so that it can be embedded in smartphones. The results of this study indicate that of the three pre-trained models tested (DenseNet121, MobileNetV2, and ResNet50V2), the pre-trained Model DenseNet121 is the model that has the highest accuracy and the smallest loss, namely 92.58% accuracy and 29.62% Loss. Tests on mobile devices also show that from 130 test data, this model gets an accuracy of 99.23%. Overall, the classification model of NTT woven fabric motifs embedded in mobile devices can be used as an alternative to help the community or people who want to learn about NTT woven fabric motifs.Yohanes Eudes Hugo MaurAlbertus Joko Santoso- Pranowo2023-12-312023-12-31136Performance Analysis of New 2D Spatial OCDMA Encoding based on HG Modes in Multicore Fiber
http://insightsociety.org/ojaseit/index.php/ijaseit/article/view/19036
This paper presents a pioneering 2D spatial Optical Code-Division Multiple Access (OCDMA) encoding system that exploits Mode Division Multiplexing (MDM) and Multicore Fiber (MCF) technologies. This innovative approach utilizes two spatial dimensions to enhance the performance and security of OCDMA systems. In the first dimension, we employ Hermite-Gaussian modes (HG00, HG01, HG11) to modulate each user's signal individually. This unique approach offers a robust means of data transmission while ensuring minimal interference among users. The second-dimension leverages MCF encoding, introducing two incoherent OCDMA codes: the Zero Cross Correlation (ZCC) code (λc=0) and the ZFD code (λc=1). These codes are thoughtfully designed and simulated, taking into account their cross-correlation properties to guarantee minimal interference and heightened data security. To assess the efficiency of this novel OCDMA encoding system, we implemented simulations with three active users using the Opti system software. At the transmitter end, each user's signal is modulated individually by their designated HG mode (HG00, HG01, HG11), resulting in separate channels. Subsequently, at the multicore fiber, each user's data is encoded with a unique code-word, and they are directed through specific core groups, ensuring data isolation and integrity. In this paper, the BER and eye pattern are examined with respect to different parameters such as data rate and distance. At a distance of 5 km and data rate of 10 Gbit/s, a BER value around 10-70 is achieved.Walid SahraouiAngela AmphawanMuhammed Basheer JasserTse-Kian Neo2023-12-312023-12-31136Dynamic Sign Language Recognition Using Mediapipe Library and Modified LSTM Method
http://insightsociety.org/ojaseit/index.php/ijaseit/article/view/19401
Hand gesture recognition (HGR) is a primary mode of communication and human involvement. While HGR can be used to enhance user interaction in human-computer interaction (HCI), it can also be used to overcome language barriers. For example, HGR could be used to recognize sign language, which is a visual language expressed by hand movements, poses, and faces, and used as a basic communication mode by deaf people around the world. This research aims to create a new method to detect dynamic hand movements, poses, and faces in sign language translation systems. The Long Short-Term Memory Modification (LSTM) approach and the Mediapipe library are used to recognize dynamic hand movements. In this study, twenty dynamic movements that match the context were designed to solve the challenge of identifying dynamic signal movements. Sequences and image processing data are collected using MediaPipe Holistic, processed, and trained using the LSTM Modification method. This model is practiced using training and validation data and a test set to evaluate it. The training evaluation results using the confusion matrix achieved an average accuracy of twenty words trained, which was 99.4% with epoch 150. The results of experiments per word showed detection accurateness of 85%, while experiments using sentences only reached 80%. The research carried out is a significant step forward in advancing the accuracy and practice of the dynamic sign language recognition system, promising better communication and accessibility for deaf people.- RidwangAmil Ahmad IlhamIngrid Nurtanio- Syafaruddin2023-12-312023-12-31136Benefits of Using Technology through the Use of Applications in Integrated Referral Services in Social Welfare Centers (Puskesos)
http://insightsociety.org/ojaseit/index.php/ijaseit/article/view/18463
Integrated social services organized through the SLRT (Integrated Referral Service System) by the Puskesos (Social Welfare Center) were a step forward in answering today's increasingly complex social problems. On the other hand, the development of technology and information provided opportunities and challenges to unite the two technologies and integrate social services into a single unit to strengthen further efforts to solve social problems. The involvement of technology through applications was a step forward and quite visionary to take advantage of technological developments, especially the closer technology and information to today's society. Based on these conditions, the purpose of this study was to examine deeply the benefits of using technology in integrated referral services carried out by Puskesos. In line with these objectives, the method used was qualitative with descriptive type. Data collection techniques include documentation studies, observations, in-depth interviews, and focus group discussions. Furthermore, the sampling technique in this study was purposive sampling, and the number of informants in this study amounted to 70, spread over two locations, namely Sleman Regency and Bandung Regency. The results show four benefits that were quite dominant in the use of technology in integrated social services: effectiveness, efficiency, accountability, and public trust. Realizing this benefit, it is hoped that other supporting programs will be involved in the form of socialization related to the use of technology through the use of applications in these services.Denti Kardeti- PribowoAep Rusmana- MarjukiBambang RustantoAyu Mirah Kirani- Alfrojems2023-12-312023-12-31136The Impact of Online Transportation Services on Indonesian Urban Non-Working Trip Volume and Distribution Pattern: A Case Study in Bandung City
http://insightsociety.org/ojaseit/index.php/ijaseit/article/view/18460
The development of digital technology has made it much easier for most people to access online communication through smartphones. Such a phenomenon has encouraged operators to develop an online transportation service system, which has also been growing in Indonesian urban areas. The urban community accepts the new alternative of the application-based transportation system since it offers a much more efficient way to reach transportation services. Moreover, the online transportation system also offers a much wider range of destination locations. It is believed that the creation of an application-based transportation service system will affect metropolitan areas' social and economic conditions and how urban communities’ travel. The development of this application-based transportation service would change the configuration of urban non-working trip distribution patterns, which should be understood as inputs for the urban transport development plan. This research aims to understand the impact of online transportation services on non-working trip distribution patterns in Indonesian urban areas, with Bandung City as the case study. The method used in this research is statistical descriptive, using respondents' responses to map the influenced trip generation and distribution patterns. The results show that online transportation has enabled the community to travel more easily and reach wider areas. Such changes have formed a new travel pattern that extends the destination locations to a wider peri-urban area. In the future, the government should anticipate urban fringe area land use growth, changing the region into more "urbanized-built-up" areas.Miming MiharjaRenny DesianaDesiree Marlyn Kipuw2023-12-312023-12-31136An Approach to the Utilization of Design Thinking in Artificial Intelligence Education
http://insightsociety.org/ojaseit/index.php/ijaseit/article/view/19042
As artificial intelligence (AI) continues its rapid and relentless progression, the necessity for a comprehensive AI education has become increasingly evident. While South Korea has initiated various policies related to AI education, recent research has underscored the potential adverse repercussions of current instructional approaches on learners. In response to this pressing concern, the present study delves into integrating design thinking principles into AI education and meticulously assesses its impact on learning outcomes. To achieve this objective, we seamlessly amalgamated design thinking principles with AI problem-solving techniques, developing a tailor-made AI education curriculum explicitly crafted for middle school students. Subsequently, this innovative curriculum was implemented among middle school students, and their Computational Thinking (CT) competence was rigorously evaluated. The findings unequivocally establish that the infusion of design thinking into AI education significantly augmented the CT skills of the participating students. In comparison to the control group, it was discerned that middle school students who underwent AI education integrated with design thinking exhibited a statistically substantial enhancement in their Computational Thinking (CT) proficiencies. This study furnishes compelling empirical evidence that unequivocally endorses design thinking as a potent instructional approach within the domain of AI education, particularly for middle school students. Furthermore, it underscores the necessity of embracing innovative pedagogical methodologies in AI education to equip the younger generation with the indispensable skills to adeptly navigate the perpetually evolving landscape of an AI-driven future.Seong-Won KimHakNeung GoSeung-Ju HongYoungjun Lee2023-12-312023-12-31136Teacher Challenges in Designing the Learning after Curriculum Change: An Analysis of Learning Management System
http://insightsociety.org/ojaseit/index.php/ijaseit/article/view/19655
Curriculum change is an essential educational milestone, which often aims to align the education system with the needs of continuously developing society and global trends. This research aims to identify teachers' challenges in designing learning after curriculum changes. The research design uses quantitative description. The respondents were 214 elementary school teachers in six Central Java, Indonesia cities. An online questionnaire form created using Google Forms is used to collect data. Data was taken using a survey and analyzed descriptively. This instrument consists of 3 (three) parts: (1) teacher challenges regarding curriculum changes, (2) the teacher's ability to design learning, and (3) knowledge and competence about and in using the LMS. The study results show that the biggest challenge for teachers lies in management, experience, and references in implementing the new curriculum, even though their readiness is high. Teachers need supporting facilities like a learning management system (LMS) to help them design lessons and develop learning modules. The majority of teachers are familiar with using LMS, so LMS can be a solution to adapt to teacher challenges. Using LMS to create teaching modules can increase the effectiveness and efficiency of all teacher activities at every level of education. It is hoped that the findings of this research can serve as a guide for educators, researchers, educational technology creators, and governments in exploring and developing new ways to support teachers' work, from planning to assessing learning tools. They can easily upload materials, create assignments and tests, and provide feedback to students.Sri MarmoahFatma SukmawatiJenny I S Poerwanti- Supianto- YantoroDiana Sinziana Duca2023-12-312023-12-31136Regression-based Analytical Approach for Speech Emotion Prediction based on Multivariate Additive Regression Spline (MARS)
http://insightsociety.org/ojaseit/index.php/ijaseit/article/view/18603
Using regression analysis techniques for speech-emotion recognition (SER) is an excellent method of resource efficiency. The labeled speech emotion data has high emotional complexity and ambiguity, making this research difficult. The maximum average difference is used to consider the marginal agreement between the source and target domains without focusing on the distribution of the previous classes in the two domains. To address this issue, we propose emotion recognition in speech using a regression analysis technique based on local domain adaptation. The results of this study show that the model's generalization ability with the function of the local additive method is very good for improving speech emotion recognition performance. Even though it provides excellent benefits in resource efficiency, regression analytical techniques are rarely used in the SER field; however, we believe this method is the best solution for SER problems. Using the Multivariate Additive Regression Spline, this study developed a predictive model for the existence of angry and non-angry emotions (MARS). Using probability analysis of error values, this approach can overcome regression on data that is not typically distributed. This method yields an ideal basis function that significantly impacts changes in emotional form. This study generates a prediction model with a Mean Square Error (MSE) of 0.0130, a Generalized Cross Validation (GCV) value of 0.0062, and a R Square (RSQ) value of 0.9721, yielding test results with a 97% accuracy rate.Budi TriandiSyahril EfendiHerman Mawengkang- Sawaluddin2023-12-312023-12-31136Business Category Classification via Indistinctive Satellite Image Analysis Using Deep Learning
http://insightsociety.org/ojaseit/index.php/ijaseit/article/view/19059
Satellite image analysis has numerous useful applications in various domains. Extracting their visual information has been made easier using remote sensing and deep learning technologies that intelligently interpret clear visual cues. However, satellite information has the potential for more complex tasks, such as recommending business locations and categories based on the implicit patterns and structures of the regions of interest. Nonetheless, this task is significantly more challenging due to the absence of obvious visual cues and the highly similar appearance of each location. This study aims to analyze satellite image similarity between business class categories and investigate the capabilities of state-of-the-art deep learning models for learning non-obvious visual cues. Specifically, a satellite image dataset is constructed using business locations and annotated with the business categories for image structural similarity analysis, followed by business category classification via fine-tuning of deep learning classifiers. The models are then analyzed by visualizing the features learned to determine if they could capture hidden information for such a task. Experiments show that business locations have significantly high SSIM regardless of categories, and deep learning models only recorded a top accuracy of 60%. However, feature visualization using Grad-CAM shows that the models learn biased features and disregard highly informative details such as roads. It is concluded that typical learning models and strategies are insufficient to effectively solve this complex visual problem; thus, further research should be done to formulate solutions for such non-obvious classifications with the potential to support business recommendation applications.Injamul Haque SuvonYuen Peng LohNoramiza HashimWan Noorshahida Mohd-IsaChoo-Yee TingKhairil Imran GhauthArpita BhattacharijeeWan Razali Matsah2023-12-312023-12-31136Development of Mobile Learning Based on Digital Entrepreneurs Using Raspberry Pi on TVET
http://insightsociety.org/ojaseit/index.php/ijaseit/article/view/18608
Mobile learning is a method of instruction that uses portable electronics like laptops, smartphones, mobile phones, and PCs so that students may access lesson materials, instructions, and apps whenever and wherever they are without being constrained by time or place. This is an innovation regarding a systematic and structured application-based learning system as an interactive medium for students, especially at TVET. This article aims to develop mobile learning using a mini server Raspberry Pi 3 Model B+ based on digital entrepreneurs (digipreneur) that runs on the Moodle LMS as a source of learning content so that learning can be carried out interactively and flexibly, without having to be connected to the internet, or classrooms that are not effective even in areas with no internet access. This activity goes through 2 stages of system design known as client-server. In designing the server, the Raspberry Pi mini server model B+ configuration is carried out as a source of digital learning resources by using several supporting applications such as MySQL server, SSH, PHPMyAdmin, and Apache in designing the client using the Moodle LMS application, which contains digipreneur-based digital learning materials and resources, all stored in the database server. Three primary users are built into the Mobile Learning Database System: Administrators, Lecturers, and Students. This application is expected to be innovative and the right solution in terms of learning and become an alternative problem-solving tool in education.Rahmat Fadillah- GanefriAsmar YulastriHendra Hidayat2023-12-312023-12-31136Spatiotemporal Analysis for Rainfall Prediction Using Extreme Learning Machine Cluster
http://insightsociety.org/ojaseit/index.php/ijaseit/article/view/18214
Rainfall prediction is an essential study as a guideline for water resources management to manage disasters. Still, earlier research cares much about temporal information, only considering a single spatial location. The earth’s land surface has a large area of spatial location, so to manage spatial information simultaneously as temporal, we use spatiotemporal data to analyze rainfall prediction more accurately. This study uses the spatiotemporal Extreme Learning Machines (ELM) Cluster to forecast rainfall using CHIPRS data from satellites and stations. Data consists of spatial two dimensions and temporal data from 1981 to 2020. The dataset for the experiment contains 480 months. We use focal operation for data preprocessing to the nearest neighbor value. Moreover, the ELM cluster can manage every spatial location by sharing the output weight of ELM, so there is no spatial information left behind. Then, comparing the spatiotemporal Extreme Learning Machines Cluster among SVR, Linear Regression, Gaussian, Ridge, and Lasso are used to predict the data on those timescales. The results indicate that spatiotemporal ELM-Cluster can accurately forecast rainfall. Using ELM-Cluster in hydrological rainfall forecasting is encouraging, and the model can practically be used. Evaluation using MAE with a score of 66.77 and RMSE, 83.77, getting the fastest training with only 28.9 seconds compared to the other methods due to the ELM Cluster does not have backpropagation with spatial improvement.Renaldy FredyanMuhammad Rizki Nur MajiidGede Putra Kusuma2023-12-312023-12-31136Facial Skin Type Analysis Using Few-shot Learning with Prototypical Networks
http://insightsociety.org/ojaseit/index.php/ijaseit/article/view/19040
Facial skin type analysis is a critical task in several fields, including dermatology, cosmetics, and biometrics, and has been the subject of significant research in recent years. Traditional facial skin type analysis approaches rely on large, labeled datasets, which can be time-consuming and costly to collect. This study proposes a novel few-shot learning (FSL) approach for facial skin type analysis that can accurately classify skin types with limited labeled data. A diverse dataset of facial images with varying skin tones and conditions was curated. The proposed approach leverages pre-trained deep neural networks and an FSL algorithm based on prototypical networks (PNs) and matching networks (MNs) to address the challenge of limited labeled data. Importantly, this study has significant implications for improving access to dermatological care, especially in underserved populations, as many individuals are unaware of their skin type, which can lead to ineffective or even harmful skincare practices. Our approach can help individuals quickly determine their skin type and develop a personalized skincare routine based on their unique skin characteristics. The results of our experiments demonstrate the effectiveness of the proposed approach. PNs achieved the highest accuracy in the 2-way, 10-shot, 15-query scenario with an accuracy of 95.78 ± 2.79%, while MNs achieved the highest accuracy of 90.33 ± 4.10% in the 2-way, 5-shot, 10-query scenario. In conclusion, this study highlights the potential of FSL and deep neural networks to overcome the limitations of traditional approaches to facial skin analysis, offering a promising avenue for future research in this field.Quan Fong YeoShih Yin OoiYing Han PangYing Huey Gan2023-12-312023-12-31136Assessment of Maintenance Performance Using the Maintenance Scorecard Method and Prioritization of Problem Control Strategies with the USG Method
http://insightsociety.org/ojaseit/index.php/ijaseit/article/view/18563
Measuring the performance of machine maintenance becomes very important, serves as a monitoring tool, and triggers increased performance in the production section. Losses due to engine damage will impact the company's profit, which is less than optimal because maintenance can contribute as much as 20-50% of the cost composition of the company's operational costs. This study aimed to measure performance in the maintenance section using the maintenance scorecard (MS) and determine the priority of the performance control strategy using the Urgency, Seriousness, and Growth Method (USG). The research was conducted through case studies on chemical manufacturing companies located in Indonesia. The performance assessment results with the maintenance scorecard method show that the total scorecard maintenance value is in the category of need improvement, which is 63.35. There are 3 KPIs in the very bad category: maintenance costs from a cost-efficiency perspective, work completed from a quality perspective, and self-audit from a learning perspective. The first and foremost strategy that can be done based on USG's priority is to implement reliability-centered maintenance (RCM) to reduce time loss and increase the knowledge and competence of employees. This priority is useful when a company encounters a constraint so that it cannot carry out all strategies simultaneously or can only carry out some strategies while still getting optimal benefits from continuous improvement.Rahmat NurcahyoDuhita Wahyu MaulidaDanar Agus SusantoEllia Kristiningrum2023-12-312023-12-31136Mental Health State Classification Using Facial Emotion Recognition and Detection
http://insightsociety.org/ojaseit/index.php/ijaseit/article/view/19055
Analyzing and understanding emotion can help in various aspects, such as realizing one’s attitude, behavior, etc. By understanding one’s emotions, one's mental health state can be calculated, which can help in the medical field by classifying whether one is mentally stable or not. Facial Recognition is one of the many fields of computer vision that utilizes convolutional networks or Conv Nets to perform, train, and learn. Conv Nets and other machine learning algorithms have evolved to adapt better to larger datasets. One of the advancements in Conv Nets and machines is the introduction of various Conv architectures like VGGNet. Thus, this study will present a mental health state classification approach based on facial emotion recognition. The methodology comprises several interconnected components, including preprocessing, feature extraction using Principal Component Analysis (PCA) and VGGNet, and classification using Support Vector Machines (SVM) and Multilayer Perceptron (MLP). The FER2013 dataset tests multiple models’ performances, and the best model is employed in the mental health state classification. The best model, which combines Visual Geometry Group Network (VGGNet) feature extraction with SVM classification, achieved an accuracy of 66%, demonstrating the effectiveness of the proposed methodology. By leveraging facial emotion recognition and machine learning techniques, the study aims to develop an effective method.Adel Aref Ali Al-zanamOmer Hussein Abdou Elsayed Hussein AlhomeryChoo Peng Tan2023-12-312023-12-31136A Healthcare Recommender System Framework
http://insightsociety.org/ojaseit/index.php/ijaseit/article/view/19049
After the pandemic hit every part of the world, healthcare awareness is slowly rising among every human being, especially leaders of each country. Due to a shortage of manpower in the healthcare industry, patients tend to search the internet for some self diagnoses. This way is extremely dangerous as patients might end up using the wrong treatment such as taking the wrong medication to treat their sickness since there are so many different remedies posted on the internet without valid recognition from the healthcare professionals. To aid in overcoming this problem, this research will be building a Healthcare Recommender System. The goal of a Healthcare Recommender System (HRS) aims to supply its user with medical information that is meant to be highly relevant and tailored to an individuals need. Hence, this paper gives an overview of various recommender systems, datasets employed, and evaluation metrics used in the healthcare system. In addition, we propose the framework for the HRS to capture user input on their condition and recommend the next course of action. The steps involved in our recommender system includes choosing the dataset and techniques, data cleaning and preprocessing, building the recommender system, training the recommender engine, and finally performing the prediction. We generate the accuracy of prediction and analyze with some results. From the experimental results, Cosine Similarity has the highest accuracy compared to Jaccard Similarity and Euclidean Distance.Kher-Ning OoiSu-Cheng HawKok-Why Ng2023-12-312023-12-31136Ambiguity Detection and Improvement for Malay Requirements Specification: A Systematic Review
http://insightsociety.org/ojaseit/index.php/ijaseit/article/view/18535
Malaysian public sectors have invested billions in digitizing systems. Electronic government efforts created much software. Our informal interview taught us that many software projects encountered delays, and several failed. One of the main contributions of software failure is ambiguity in requirements specification (RS). Ambiguity is a familiar requirement smell that causes misinterpretation. Thus, we seek to devise a technique for detecting and improving ambiguous RS in the Malaysian public sector. One of our challenges is that the Malaysian public sector RS is developed in Malay, and most available techniques support English and other major languages. Hence, this paper investigates the automated and semi-automated techniques to detect and improve ambiguous RS. Following the standard guidelines for systematic mapping, review, snowballing, and quality assessment, we studied works from 2010 to 2022 on ambiguity detection and improvement techniques. We chose 42 articles as primary studies from 2,549. As a result, Natural Language Processing (NLP) and machine learning (ML) are the most promising techniques for automated and semi-automated ambiguous detection models. Furthermore, the ambiguous improvement technique began using deep learning (DL) in 2019. However, most proposed tools are still in the validation phase and are not widely employed, implying that tool development and validation research are progressing slowly. Apart from the generic linguistic context of RS, some research focuses on industrial domain-based RS. Our study shows that additional strategies have been developed to overcome RS-related issues.Mohd Firdaus ZahrinMohd Hafeez OsmanSa'adah HassanAzlena HaronAlfian Abdul Halin2023-12-312023-12-31136Learning Dayak Literature through Information Systems
http://insightsociety.org/ojaseit/index.php/ijaseit/article/view/18094
This study aims to analyze old poetry (mantras) as part of learning literature during the Corona pandemic that is currently hitting Indonesia. Also, this study revitalizes culture by creating an information system application. This study used descriptive qualitative. There were 16 participants, consisting of the village community of Ranyai, cultures, traditional stakeholders, and village heads. This method provided an overview and described the research results on literary culture and an analysis of the structure of prayer/mantra in the Gawai Dayak tradition and its revitalization efforts. The results showed that the structure of the text in the form of mantra utterances was based on the analysis of syntax, rhyme, and rhythm. The context of the Gawai Dayak tradition consists of cultural, situational, social, and ideological contexts. The context of the Gawai Dayak tradition consisted of motion, proxemic, paralinguistic, and material contexts. The design of an information system on Dayak literature could help realize the revitalization of cultural traditions and teaching and learning processes in a directed, effective, and consistent manner during the Corona pandemic. The implication for further research was the opening of further research developments, such as looking for values from tradition. Learning that used information systems was also still wide open to be developed with other more complete applications and under the times.Sigit WidiyartoDadang SunendarDellia Mila VerniaSiti AlifahHugo Aries SupraptoAri Wahyu LeksonoNur Rizkiyah2023-12-312023-12-31136An Extreme Programming Approach to Streamlining Thesis Writing
http://insightsociety.org/ojaseit/index.php/ijaseit/article/view/18701
Thesis writing is a significant challenge for many university students, often leading to substantial stress, confusion, and distress. Despite numerous studies to reduce academic stress, a specific solution for thesis writing has yet to be identified. This study introduces Penamasy, a thesis writing management system designed to address this issue. Penamasy was developed using the Extreme Programming (XP) methodology, effectively addressing the rapidly changing demands of university students in an academic environment. The result was a user-friendly app delivered promptly and met the needs of students. The study consisted of three research cycles, including user requirements, release planning, and acceptance tests, followed by an extended user acceptance test with students and promoters from three universities. Results indicated a positive response, with 79.6% of respondents expecting a more systematic and fuller online thesis writing experience, enabling them to complete their theses promptly. In conclusion, Penamasy provides a solution for university students facing the stress and difficulties of thesis writing. By streamlining the process and offering a systematic approach, students can focus on their research with confidence in the control of their writing process. Nevertheless, this study can be used as a guide in resolving students' academic stress and many other pragmatic problems that occur, especially in an educational environment. Future studies should involve users choosing UI component libraries, performance evaluation, and possible workflows.Andi Bahtiar SemmaMuh SaeroziKusrini KusriniAbdul SyukurAchmad Maimun2023-12-312023-12-31136Human Age Group Estimation Using Gait Features
http://insightsociety.org/ojaseit/index.php/ijaseit/article/view/19053
In many practical applications, identifying the target age group is essential for marketing products and services. For instance, gaming and entertainment companies need to understand which age groups are most likely to purchase their services. This knowledge allows them to optimize their products and services to better cater to their target audience. This study proposes an age group prediction system using gait features. Gait, in this context, pertains to an individual's unique walking style. A diverse dataset containing subjects from 3 to 70 years old is collected. The age group is classified into three categories: child, adult, and senior. The critical aspect of this research lies in the preprocessing techniques applied to the gait patterns. The gait patterns are extracted from landmark human joint positions' key point values and preprocessed using smoothening techniques. Additionally, dimension reduction techniques enhance computational efficiency and accuracy before feeding the features into a deep learning-based classifier. These preprocessing steps play a pivotal role in the success of the deep learning-based classifier. A promising accuracy of up to 95% is reported for correctly recognizing the human age groups. The outcomes of this investigation underscore the tremendous potential of leveraging machine learning techniques to refine marketing strategies and boost customer satisfaction. The proposed approach can aid companies in aligning their products and services with the preferences and needs of distinct age groups, thereby enhancing their market presence and resonance with their target audience.Qian Fu SooTee ConnieMichael Kah Ong Goh2023-12-312023-12-31136Artificial Intelligence for the Classification of Plastic Waste Utilizing TinyML on Low-Cost Embedded Systems
http://insightsociety.org/ojaseit/index.php/ijaseit/article/view/18958
BCG's implementation of the economy makes Thailand more environmentally conscious. The consolidation policy encourages consumers to eliminate single-use plastics using the 3Rs. This article introduces a solution to reduce plastic waste drastically using artificial intelligence. Utilizing a low-cost Arducam Pico4ML embedded device and TinyML, a plastic waste classifying system prototype is developed for plastic bottle segregation. The grayscale image datasets of PET, HDPE plastic bottles, and unknown objects are adjusted in the image pre-processing state and utilized to create trained models using MobileNetV2 convolutional-based neural network algorithms. Effective feature extraction and model training are performed on the Edge Impulse platform, and the trained model is exported to an embedded device using the optimized compiler. A further RS485 Modbus communication protocol feature enables integration with a programmable logic controller (PLC). The validation results of the trained model indicate a classification performance of 100% accuracy. Based on the average precision results, it is notable that the trained model can recognize the most common waste with an average accuracy of over 90%. The minimum classification rate of the MobileNetV2 quantized model is 249 milliseconds. It is also implemented in low-cost embedded devices for real-time plastic waste classification using fewer processing resources (185.4K ROM and 88K RAM). The findings exhibit sequential contributions that satisfy the criteria for classifying plastic bottles and the machine's integration capacity. These outcomes are anticipated to foster social shifts in behavior and enhance public awareness about plastic waste management.Jutarut ChaoraingernVittaya TipsuwanpornArjin Numsomran2023-12-312023-12-31136Application of Artificial Intelligence in Predicting Oil Production Based on Water Injection Rate
http://insightsociety.org/ojaseit/index.php/ijaseit/article/view/19399
The utilization of artificial intelligence (AI) has become imperative across various domains, including the oil and gas industry, which covers several fields, including reservoirs, drilling, and production. In oil and gas production, conventional methods, such as reservoir simulation, are used to predict the oil production rate. This simulation requires comprehensive data, so each process step takes a long time and is expensive. AI is urgently needed and can be a solution in this case. This research aims to apply AI techniques to forecast oil production rates based on water injection rates from two injection wells. Three wells are connected with a direct line drive pattern. Three different AI methods were applied, including multiple linear polynomial regression (PR), multiple linear regression (MLR), and artificial neural networks (ANN) in constructing oil production rate prediction models. Actual field data of 1180 data are used, including water injection rate data from two injection wells and oil production history data from one production well. The dataset has been split randomly into 80% training and 20% allocated for testing subsets. The training data is used to build predictive models, while the testing data is used to validate model performance. Comparative analysis selects the model with the lowest root mean square error (RMSE) and the highest R^2 test value. Results demonstrate that the ANN model achieves the smallest Root Mean Square Error (RMSE) of 0.142 and the highest R^2 test value of 16.2%, outperforming the PR and MLR methods. The ANN prediction model provides a rapid and efficient approach to estimating oil production rates.Diyah RosianiMuhamad Gibral WalayPradini RahalintarArya Dwi CandraAkhmad SofyanYesaya Arison Haratua2023-12-312023-12-31136Analysis and Evaluation of PointNet for Indoor Office Point Cloud Semantic Segmentation
http://insightsociety.org/ojaseit/index.php/ijaseit/article/view/18887
Indoor modeling is one of the primary sources of information in building management due to the increased use of BIM in the AEC industry. The indoor model can be acquired with several survey instruments, but TLS is the most popular resulting point cloud that can be processed into a 3D model. However, the process commonly still uses inefficient manual methods. Point cloud data have irregular, unordered, unstructured characteristics, making them more challenging to process. The deep learning algorithm can be a solution to solve the problem. PointNet is the first deep learning algorithm that directly accepts point cloud data as input. This study aims to analyze and evaluate the office indoor point cloud segmentation using PointNet. The office indoor point cloud data was acquired using TLS and then pre-processed for deep learning input. Transfer learning strategy is used as a weight initialization technique. The pre-trained model was trained with the S3DIS dataset and then fine-tuned to segment nine indoor classes in this study. The result shows PointNet achieves 85% overall accuracy and 66% average class IoU score to predict indoor classes using this study’s point cloud data. Geometry control shows that the predicted point cloud has an RMSE score of 1.8 cm, meaning the geometries of the segmented point cloud are accurate. Using the transfer learning method has increased the performance of the deep learning model. Further research is needed to evaluate the model thoroughly using more training and evaluation data and different transfer learning strategies.Calvin Wijaya- Harintaka2023-12-312023-12-31136Analysis of Propagation Characteristics in Unmanned Aerial Vehicle (UAV) System
http://insightsociety.org/ojaseit/index.php/ijaseit/article/view/18287
Unmanned Aerial Vehicle (UAV) has gained great attention to the spread of communication in civilian and military applications. UAV communication channel has its own characteristics compared to cellular and satellite systems, which are widely used. Thus, an accurate channel characterization is very important to optimize the performance and design of an efficient UAV communication system. However, several challenges exist in UAV channel modeling. For example, channel propagation characteristics of UAVs are still less explored. Therefore, this research discusses the propagation characteristics of UAV communication systems. Due to the limitation of the measurement tools, the propagation characteristics identified in this research was the pathloss coefficient value and optimum height based on the value of Received Signal Strength Indicator (RSSI) measurement results at different distance and heights. The link communication used 433 MHz telemetry. The results of pathloss coefficient at heights of 10 m, 20 m, and 30 m are 1.56 m, 1.77 m, and 1.99 m. While the results of the optimum height of 10 m, 20 m, and 30 m are 1.39 m, 1.32 m, and 1.47 m.Eni Dwi WardihaniTiara Nira SariThomas Agung SetyawanHany Windri Astuti2023-12-312023-12-31136Comparison of Crossflow Turbine Performance through Nozzle Position Variations Using ANSYS Simulation
http://insightsociety.org/ojaseit/index.php/ijaseit/article/view/19054
The performance comparison of Crossflow turbines is greatly influenced by the position of the nozzle in the conversion of water energy into mechanical energy that occurs through the blades, runners, and shafts of Crossflow turbines. The study aims to directly examine the visualization of water fluid dynamics across the turbine runner blade and enhance the performance of the Crossflow turbine by varying the nozzle position. This study intends to investigate the impact of water flow dynamics and emission on the performance of Crossflow turbines with a combined horizontal-vertical nozzle position, specifically focusing on the magnitude of the number of turbine blades driven and the size of the runner blade area. The objective of investigating nozzle position variations in Crossflow turbines is to determine the specific nozzle position at which the turbine blade may efficiently extract maximum energy from the water flow, hence optimizing turbine performance. The research method using models made using CAD software is AutoCAD by exporting to IGES or IGS format to be compatible with ANSYS. The simulation of this research is with post-processing. There are three, namely making animations, making contours, and taking data to compare cross-turbine performance using variations in nozzle position. Crossflow turbine performance with horizontal nozzle position torque and turbine power is lower, and there is an increase in a vertical position. Then, the horizontal and vertical nozzle position is very good because the nozzle is more effective with maximum turbine performance, namely 13.811-watt turbine power 1,099 turbine torque at 120 rpm.Corvis L RantererungAtus Buku2023-12-312023-12-31136Evaluation and Optimization Based on Exergy in Kamojang Geothermal Power Plant Unit 3
http://insightsociety.org/ojaseit/index.php/ijaseit/article/view/19441
The quality of production well from the Kamojang geothermal power plant unit 3 diminishes annually, whereas there has been a substantial rise in the demand for electrical energy in the region. This research focuses on optimizing the vacuum pressure in the main condenser by employing exergy analysis, a methodology grounded in the principles of the second law of thermodynamics. Exergy analysis offers insights into each system component's exergy efficiency and irreversibility. Furthermore, an energy assessment is conducted to offer insights into each component's energy consumption or utilization. Energy and exergy rates are computed for every state and component within the power plant, encompassing the steam receiving header, separator, demister, turbine, main condenser, inter condenser, after condenser, and cooling tower. The exergy analysis findings reveal that the exergy rate derived from the production well amounts to 95327 kW, generating 52882 kW of electricity and producing a system exergy efficiency of 55.47%. The turbine experiences the highest irreversibility, totaling 12874 kW. Adjustments are made to the main condenser vacuum pressure to optimize the system, aiming to identify the optimal setting that maximizes both exergy efficiency and power output. The optimization outcomes indicate that reducing the vacuum pressure in the main condenser leads to enhanced exergy efficiency and increased power output. The optimal vacuum pressure obtained is 0.1 bar, resulting in the highest exergy efficiency and output power of 57.42% and 54738 kW, respectively, with the lowest irreversibility of 32751.07 kW.Bayu RudiyantoArief WicaksonoMiftah Hijriawan2023-12-312023-12-31136Analysis of Dam Break Wave Using Analytical, Computational Fluid Dynamics, and Experimental Approaches
http://insightsociety.org/ojaseit/index.php/ijaseit/article/view/18927
This research aims to examine the capability of the Computational Fluid Dynamics (CFD) method in simulating the behavior of dam break waves. It begins by building a 2D numerical simulation using OpenFOAM. To overcome the influence of turbulence, we employed the Large Eddy Simulation (LES) turbulent model, specifically the k-Equation and Smagorinsky model. The simulation was developed by applying the Navier-Stokes equations using the finite volume method in OpenFOAM. The analysis focuses on the free surface of a dam break. The results are in good accordance with both analytical and experimental results. The simulation has followed the trend of experimental and analytical free surface profiles at the dam break’s early and late conditions. The low mesh number on the computational domain caused significant differences in the wavefront of the dam break. It reduced the accuracy of the calculation between the water and air interface. This study highlights the importance of understanding dam break wave behavior as part of risk mitigation for dam leakage. The behavior of dam break waves can be observed by determining observation positions at different locations, with the water gate of a dam serving as the reference point. These highly accurate numerical results indicate that the CFD approach employing OpenFOAM can be relatively cost-effective yet accurate in analyzing multiphase problems, such as dam breaks. This CFD approach is expected to contribute to developing mitigation and disaster prevention in the future.Evi NovianiYoga Satria PutraCucu Suheri2023-12-312023-12-31136Investigating an Enhanced Approach for Greenhouse Climate Control: Optimising Cooling and Heating Systems
http://insightsociety.org/ojaseit/index.php/ijaseit/article/view/19384
This study aimed to enhance greenhouse climate regulation by optimizing the efficiency of existing cooling and heating systems while considering external temperature and humidity conditions. We introduced an automated system capable of regulating the internal greenhouse environment, which underwent testing across 30 days in September 2021, with temperatures ranging from 26°C to 31°C and humidity from 65% to 70%. The system consistently monitored and adjusted the microclimate, with sensors capturing temperature and humidity data at 30-second intervals, amassing over 83,000 data entries for enhanced control accuracy. The automated regulation effectively maintained desired humidity, significantly reducing nighttime levels by 80% while carefully increasing daytime humidity to counteract external heat. Temperature control was largely successful, sustaining daytime levels around 32°C, but faced challenges in maintaining the target of 26°C during cooler nights. Energy consumption was optimized, with the automation leading to a significant 4-33% energy saving for cooling and an 8% saving in heating compared to traditional methods. Additionally, the system was accessible via a web interface, allowing for real-time climate tracking and prompt anomaly identification. In conclusion, the developed greenhouse automation system exhibited efficiency in equipment usage and improved temperature and humidity control. Further enhancements are required for lamp-based heating. This research contributes to the efficiency and reliability of greenhouse automation systems, mitigating risks associated with external environmental factors and enhancing stability, productivity, and disease and pest prevention.Endi Sailul HaqShinta SetiadeviEka Mistiko RiniDianni YusufArdito Atmaka Aji2023-12-312023-12-31136Research in Electronic Multi-Sensor Accuracy in the Implementation of Soil Fertility Monitoring System Using LoRA
http://insightsociety.org/ojaseit/index.php/ijaseit/article/view/18836
The use of electronic sensors to track the nutrients in the soil is an interesting tool for farmers. This has led to the sale of many different kinds of electronic sensors with different levels of accuracy. The accuracy of this electronic sensor was figured out by comparing the results of the sensor's measurements with the results of lab tests done in different ways. This study compares the accuracy of electronic devices used to measure soil nutrients like nitrogen, phosphorus, potassium, electrical conductivity, water pH, and humidity to measurements made in the lab using the ICP-OES (Inductively coupled plasma-optical emission spectroscopy) method. We used three electronic sensors and a transmission system based on LoRA (Long Range) to measure the nutrients in the soil and put the results on our website. The similarities between electronic sensors and laboratory test parameters include the standard deviation, accuracy value, and correlation test between sensors and from the sensors to laboratory test results. The standard deviation parameter test showed a big value between the electronic sensor and the lab test results. However, none of the three used electronic sensors had a standard deviation number that differed greatly from the others. Except for the pH value of the soil, the electronic sensor's accuracy tests for the other five parameters were not very good compared to the lab tests. Also, the sensor correlation test showed a high correlation, while the correlation test between sensor data and lab test results showed a low correlation.Wahyu Pamungkas TjiptoyudaMas Aly AfandiSarah AstitiI Ketut Agung EnricoAnis Sirwan ZukhrufiRahmat Hardian Putra- Rohmat2023-12-312023-12-31136Requirements for Domain-Specific Language in Enterprise Architecture-based SDGs Orchestration
http://insightsociety.org/ojaseit/index.php/ijaseit/article/view/17835
<p>Coherence is a crucial issue in orchestrating Sustainable Development Goals (SDGs) at the society level. The various parties involved with different language standards further increase the complexity of communication. Enterprise Architecture (EA), which emphasizes consistency and coherence as the pillars of its concept, can be used to address coherence issues in the SDGs orchestration. This study aims to build a Requirements Framework for developing an architecture description language as a domain-specific language used for EA-based SDGs orchestration. To clarify the purpose of the research, a motivating scenario was built in the form of the SDGs Participation Platform (SDGs-PP). SDGs-PP becomes the context in which EA-based SDGs orchestration involves society-level actors (orchestrators) and enterprise-level actors (enterprise architects). This Requirement Framework is built using design science methodology as a part of ongoing research. The requirements consisting of context, concept, and collaboration domains and nine action requirements (elicit, separate, connect, classify, manifest, map, arrange, separate, and set up) were successfully formulated. The Context domain was developed from the ISO/IEC/IEEE 42010 standard. The Concept domain focuses on elaborating the SDGs means of implementation and the business ecosystem concept. The Collaboration domain discusses the separation in the model canvas between the enterprise and society domains. Apart from being used as a requirement framework for the meta-model development of an architecture description language, the results of this study can also be used further as a research framework in the domain of EA-based SDGs orchestration.</p>Erda Guslinar PerdanaBenhard SitohangHusni Setiawan SastramihardjaMuhammad Zuhri Catur Candra2023-12-312023-12-31136Road Maintenance Management Based on Geographic Information System (GIS)
http://insightsociety.org/ojaseit/index.php/ijaseit/article/view/19390
<span lang="EN-US">This research implements GIS in transportation, specifically road maintenance. The system is built by utilizing 2D/3D models from aerial photographs using UAV as a base map. Attribute data such as the type and dimensions of road damage can be obtained by interpreting high-resolution 2D/3D models, which display each road damage, making it easier to measure the dimensions of road damage. The assessment of road conditions is done using the PCI method, which indicates that 51% of the roads fall under the category of people with low incomes to severely damaged category. These roads are prioritized on a map based on their area and cost of maintenance. The projection calculation of the amount of damage is analyzed with one do-nothing scenario, where the roads have not been maintained for ten years. The progression of the damage is observed each year, and the reactive maintenance cost is calculated from 2023 to 2032. The cost and duration are analyzed using three do-something scenarios: optimistic, moderate, and pessimistic. The research results show that the moderate scenario has the lowest cost among the other scenarios and is the most effective scenario, as it produces road conditions with an International Roughness Index (IRI) value of less than 6. This research can assist the government in making informed decisions regarding road maintenance.</span>Ajeng Meiliana RizkyAnanda Amatory ZahraYackob AstorRidho SeptianGhifari MunawarAtmy Verani SihombingCholid Fauzi2023-12-302023-12-30136