A Novel Design of Error Backpropagation Algorithm for Ingredient Mixing Process Tamarind Turmeric Herb

Mila Fauziyah, Supriatna Adhisuwignjo, Bagus Fajar Afandi, Anindya Dwi Risdhayanti


The goal of this study is to determine the best picture pattern for the tamarind turmeric herb. So far, the taste and color of tamarind turmeric herb have not been consistent, as they are impacted by maturity, and the amount of Turmeric used. The error backpropagation technique, which is commonly used in Content-based image retrieval systems, will be used to recognize image patterns. The main goal is to capture various portions of the tamarind turmeric herb during the extraction procedure. The camera is used to classify the tamarind turmeric herb product, process it into 5x5 pixels, and average the RGB value to obtain stable RGB values in each category, which are then fed into the Error Backpropagation algorithm. The most appropriate and fastest Error Backpropagation algorithm procedure will be found and implemented in a real-time computer. The first way will be to train the algorithm with ten data by changing neurons, layer, momentum, and learning rate, and the second technique will be to test the algorithm with ten data. The results of the training and testing procedure show that the two hidden layers can recognize 100% of inputs, with three input layers for R, G, and B values, ten neurons in the first and second hidden layers, and one output layer with Learning rate 0.5 and Momentum 0.6 as a parameter. Dark yellow is the best image pattern standard for tamarind turmeric herb, with RGB values in the range from 255, 103, 32 to 255, 128, 48.


Tamarind turmeric herb; CBIR systems; error backpropagation; RGB Image

Full Text:



F. Y. Kurniawan, M. Jalil, A. Purwantoro, B. S. Daryono, and Purnomo, “Jamu Kunir Asem: Ethnomedicine Overview by Javanese Herbal Medicine Formers in Yogyakarta,†J. Jamu Indones., vol. 6, no. 1, pp. 8–15, 2021, doi: 10.29244/jji.v6i1.211.

S. A. Astuti, F. Juwita, and A. Fajriyah, “Pengaruh Pemberian Kunyit Asam terhadap Penurunan Intensitas Nyeri Haid,†Indones. J. Midwifery, vol. 3, no. 2, p. 143, 2020, doi: 10.35473/ijm.v3i2.618.

H. Setyoningsih, Y. Pratiwi, A. Rahmawati, H. M. Wijaya, R. N. Lina, and K. Kudus, “Penggunaan Vitamin Untuk Meningkatkan,†vol. 4, no. 2, pp. 136–150, 2021.

S. N. Hidayah, N. Izah, and I. D. Andari, “Peningkatan Imunitas dengan Konsumsi Vitamin C dan Gizi Seimbang Bagi Ibu Hamil Untuk Cegah Corona Di Kota Tegal,†J. ABDINUS J. Pengabdi. Nusant., vol. 4, no. 1, pp. 170–174, 2020, doi: 10.29407/ja.v4i1.14641.

L. Fatmawati, Y. Syaiful, and K. Nikmah, “Kunyit Asam (Curcuma Doemstica Val) Menurunkan Intensitas Nyeri Haid,†Journals Ners Community, vol. 11, no. 1, pp. 10–17, 2020.

N. A. Q. A’yunin, U. Santoso, and E. Harmayani, “Kajian kualitas dan aktivitas antioksidan berbagai formula minuman jamu kunyit asam,†J. Teknol. Pertan. Andalas, vol. 23, no. 1, pp. 37–48, 2019.

R. Mangal, A. V. Nori, and A. Orso, “Robustness of neural networks: A probabilistic and practical approach,†Proc. - 2019 IEEE/ACM 41st Int. Conf. Softw. Eng. New Ideas Emerg. Results, ICSE-NIER 2019, no. i, pp. 93–96, 2019, doi: 10.1109/ICSE-NIER.2019.00032.

R. P. Lippmann, “An introduction to computing with neural nets,†ACM SIGARCH Comput. Archit. News, vol. 16, no. 1, pp. 7–25, 1988, doi: 10.1145/44571.44572.

Y. Miyata and S. Nakajima, “Application of back propagation to hospital patient outcomes,†2020 IEEE 9th Glob. Conf. Consum. Electron. GCCE 2020, pp. 791–792, 2020, doi: 10.1109/GCCE50665.2020.9291829.

L. R. Reddy, P. Patel, and S. K. Rajendra, “Utilization of resilient back propagation algorithm and discrete wavelet transform for the differential protection of three phase power transformer,†2020 21st Natl. Power Syst. Conf. NPSC 2020, 2020, doi: 10.1109/NPSC49263.2020.9331861.

F. Guo, L. Zhang, and X. Liu, “An Optimized Back Propagation Neural Network Based on Genetic Algorithm for Line Loss Calculation in Low-voltage Distribution Grid,†Proc. - 2020 Chinese Autom. Congr. CAC 2020, pp. 663–667, 2020, doi: 10.1109/CAC51589.2020.9327754.

Y. Ayyappa, A. Bekkanti, A. Krishna, P. Neelakanteswara, and C. Z. Basha, “Enhanced and Effective Computerized Multi Layered Perceptron based Back Propagation Brain Tumor Detection with Gaussian Filtering,†Proc. 2nd Int. Conf. Inven. Res. Comput. Appl. ICIRCA 2020, pp. 58–62, 2020, doi: 10.1109/ICIRCA48905.2020.9182921.

J. Yan and H. Zhu, “Image Based Localization Algorithm Using Similarity Measurements and Backpropagation Neural Network,†ICEICT 2020 - IEEE 3rd Int. Conf. Electron. Inf. Commun. Technol., pp. 379–382, 2020, doi: 10.1109/ICEICT51264.2020.9334204.

S. Das, A. Wahi, S. Sundaramurthy, N. Thulasiram, and S. Keerthika, “Classification of knitted fabric defect detection using Artificial Neural Networks,†Proc. 2019 Int. Conf. Adv. Comput. Commun. Eng. ICACCE 2019, 2019, doi: 10.1109/ICACCE46606.2019.9079951.

S. Cao and Y. Zhong, “A Methodology of Determining Weight Ratios of Different Question Types Based on Back Propagation Neural Network,†Proc. 2020 IEEE Int. Conf. Adv. Electr. Eng. Comput. Appl. AEECA 2020, pp. 164–168, 2020, doi: 10.1109/AEECA49918.2020.9213570.

R. Mukhaiyar and R. Safitri, “Implementation of artificial neural network: Back propagation method on face recognition system,†2019 16th Int. Conf. Qual. Res. QIR 2019 - Int. Symp. Electr. Comput. Eng., pp. 1–5, 2019, doi: 10.1109/QIR.2019.8898276.

F. Simmross-Wattenberg et al., “OpenCLIPER: An OpenCL-Based C++ Framework for Overhead-Reduced Medical Image Processing and Reconstruction on Heterogeneous Devices,†IEEE J. Biomed. Heal. Informatics, vol. 23, no. 4, pp. 1702–1709, 2019, doi: 10.1109/JBHI.2018.2869421.

S. Pang et al., “SpineParseNet: Spine Parsing for Volumetric MR Image by a Two-Stage Segmentation Framework with Semantic Image Representation,†IEEE Trans. Med. Imaging, vol. 40, no. 1, pp. 262–273, 2021, doi: 10.1109/TMI.2020.3025087.

O. Huang et al., “MimickNet, Mimicking Clinical Image Post- Processing under Black-Box Constraints,†IEEE Trans. Med. Imaging, vol. 39, no. 6, pp. 2277–2286, 2020, doi: 10.1109/TMI.2020.2970867.

R. Al Mukaddim, R. Ahmed, and T. Varghese, “Subaperture Processing-Based Adaptive Beamforming for Photoacoustic Imaging,†IEEE Trans. Ultrason. Ferroelectr. Freq. Control, vol. 68, no. 7, pp. 2336–2350, 2021, doi: 10.1109/TUFFC.2021.3060371.

R. Malladi, G. Kalamangalam, N. Tandon, and B. Aazhang, “Identifying Seizure Onset Zone from the Causal Connectivity Inferred Using Directed Information,†IEEE J. Sel. Top. Signal Process., vol. 10, no. 7, pp. 1267–1283, 2016, doi: 10.1109/JSTSP.2016.2601485.

M. A. A. Mosleh, A. A. Al-Yamni, and A. Gumaei, “An automatic nuclei cells counting approach using effective image processing methods,†2019 IEEE 4th Int. Conf. Signal Image Process. ICSIP 2019, pp. 865–869, 2019, doi: 10.1109/SIPROCESS.2019.8868753.

A. Van Opbroek, H. C. Achterberg, M. W. Vernooij, and M. De Bruijne, “Transfer learning for image segmentation by combining image weighting and kernel learning,†IEEE Trans. Med. Imaging, vol. 38, no. 1, pp. 213–224, 2019, doi: 10.1109/TMI.2018.2859478.

S. Fadaei and A. Rashno, “A Framework for Hexagonal Image Processing Using Hexagonal Pixel-Perfect Approximations in Subpixel Resolution,†IEEE Trans. Image Process., vol. 30, pp. 4555–4570, 2021, doi: 10.1109/TIP.2021.3073328.

B. Stimpel, C. Syben, F. Schirrmacher, P. Hoelter, A. Dorfler, and A. Maier, “Multi-Modal Deep Guided Filtering for Comprehensible Medical Image Processing,†IEEE Trans. Med. Imaging, vol. 39, no. 5, pp. 1703–1711, 2020, doi: 10.1109/TMI.2019.2955184.

R. D. Myers, “Detection Of Skin Cancer Using Image Processing Techniques Chandrahasa,†Science (80-. )., vol. 179, no. 4080, p. 1349, 2016.

M. Rasamuel, L. Khacef, L. Rodriguez, and B. Miramond, “Specialized visual sensor coupled to a dynamic neural field for embedded attentional process,†SAS 2019 - 2019 IEEE Sensors Appl. Symp. Conf. Proc., 2019, doi: 10.1109/SAS.2019.8705979.

X. Song, S. Jiang, L. Herranz, and C. Chen, “Learning effective RGB-D representations for scene recognition,†IEEE Trans. Image Process., vol. 28, no. 2, pp. 980–993, 2019, doi: 10.1109/TIP.2018.2872629.

I. Kurniastuti and A. Andini, “Determination of RGB in Fingernail Image As Early Detection of Diabetes Mellitus,†Proc. - 2019 Int. Conf. Comput. Sci. Inf. Technol. Electr. Eng. ICOMITEE 2019, vol. 1, pp. 206–210, 2019, doi: 10.1109/ICOMITEE.2019.8920876.

W. Reinert, “A miniaturized RGB-laser light engine,†Proc. - 2020 IEEE 8th Electron. Syst. Technol. Conf. ESTC 2020, 2020, doi: 10.1109/ESTC48849.2020.9229809.

DOI: http://dx.doi.org/10.18517/ijaseit.13.3.17237


  • There are currently no refbacks.

Published by INSIGHT - Indonesian Society for Knowledge and Human Development