Mapping the Provincial Food Security Conditions in Indonesia Using Cluster Ensemble-Based Mixed Data Clustering-Robust Clustering with Links (CEBMDC-ROCK)

Vita Ratnasari, Andrea Tri Rian Dani


Problems related to food are indeed an issue that continues to be discussed by the government, both imports, self-sufficiency, the issue food security. Food security conditions have become one of the biggest problems in Indonesia, even though Indonesia is an agricultural country with abundant resources. The problem is not only the availability but also the affordability. It happens due to the social inequality between the rich and the poor, which means the rich can easily relish food. People with low incomes experience food insecurities. Thus, an appropriate strategy and policies can be done for each province in Indonesia to make it equal. Cluster analysis is used to map the provincial profile based on the condition of food security. However, the variable types in this research are numerical and categorical data, which makes general cluster analysis insufficient. This study used the Cluster Ensemble Based Mixed Data Clustering-Robust Clustering Using Links (CEBMDC-ROCK) method to cluster provinces in Indonesia based on food security conditions. The analysis process starts with numerical clustering data using Agglomerative Hierarchical Clustering (AHC) and then with categorical data using Robust Clustering Using Links (ROCK). The result shows that the province in Indonesia is divided into five groups based on the quality of food security, which is from very low to excellent. Based on the clustering results, which provinces need special attention from the government regarding food security can be seen.


Cluster analysis; food security; mixed data; robust clustering

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R. Hakim, T. Haryanto, and D. W. Sari, “Technical efficiency among agricultural households and determinants of food security in East Java, Indonesia,†Sci. Rep., vol. 11, no. 1, pp. 1–9, 2021, doi: 10.1038/s41598-021-83670-7.

M. F. F. Mardianto et al., “Classification of Food Menu and Grouping of Food Potential To Support the Food Security and Nutrition Quality,†Commun. Math. Biol. Neurosci., vol. 2022, pp. 1–31, 2022, doi: 10.28919/cmbn/6801.

V. Trivellone, E. P. Hoberg, W. A. Boeger, and D. R. Brooks, “Food security and emerging infectious disease: Risk assessment and risk management,†R. Soc. Open Sci., vol. 9, no. 2, 2022, doi: 10.1098/rsos.211687.

Z. G. Dessie, T. Zewotir, and D. North, “The spatial modification effect of predictors on household level food insecurity in Ethiopia,†Sci. Rep., vol. 12, no. 1, pp. 1–11, 2022, doi: 10.1038/s41598-022-23918-y.

L. Bizikova, S. Jungcurt, K. McDougal, and S. Tyler, “How can agricultural interventions enhance contribution to food security and SDG 2.1?,†Glob. Food Sec., vol. 26, no. October, p. 100450, 2020, doi: 10.1016/j.gfs.2020.100450.

M. Akbari et al., “The Evolution of Food Security: Where Are We Now, Where Should We Go Next?,†Sustainability, vol. 14, no. 6, pp. 1–27, 2022, doi: 10.3390/su14063634.

K. Pawlak and M. Kołodziejczak, “The role of agriculture in ensuring food security in developing countries: Considerations in the context of the problem of sustainable food production,†Sustain., vol. 12, no. 13, 2020, doi: 10.3390/su12135488.

H. Y. S. H. Nugroho et al., “Toward Water, Energy, and Food Security in Rural Indonesia: A Review,†Water (Switzerland), vol. 14, no. 10, pp. 1–25, 2022, doi: 10.3390/w14101645.

M. Campi, M. Dueñas, and G. Fagiolo, “Specialization in food production affects global food security and food systems sustainability,†World Dev., vol. 141, p. 105411, 2021, doi: 10.1016/j.worlddev.2021.105411.

M. H. Montolalu, M. Ekananda, T. Dartanto, D. Widyawati, and M. Panennungi, “The Analysis of Trade Liberalization and Nutrition Intake for Improving Food Security across Districts in Indonesia,†Sustainability, vol. 14, no. 6, 2022, doi: 10.3390/su14063291.

H. Dharmawan, B. Sartono, A. Kurnia, A. F. Hadi, and E. Ramadhani, “A Study of Machine Learning Algorithms to Measure the Feature Importance In Class-Imbalance Data of Food Insecurity Cases in Indonesia,†Commun. Math. Biol. Neurosci., vol. 2022, pp. 1–25, 2022.

A. Radovanovic, J. Li, J. V. Milanovic, N. Milosavljevic, and R. Storchi, “Application of agglomerative hierarchical clustering for clustering of time series data,†IEEE PES Innov. Smart Grid Technol. Conf. Eur., vol. 2020-Octob, pp. 640–644, 2020, doi: 10.1109/ISGT-Europe47291.2020.9248759.

H. Nouraei, H. Nouraei, and S. W. Rabkin, “Comparison of Unsupervised Machine Learning Approaches for Cluster Analysis to Define Subgroups of Heart Failure with Preserved Ejection Fraction with Different Outcomes,†Bioengineering, vol. 9, no. 4, 2022, doi: 10.3390/bioengineering9040175.

A. M. Jabbar, K. R. Ku-Mahamud, and R. Sagban, “Improved Self-Adaptive ACS Algorithm to Determine the Optimal Number of Clusters,†Int. J. Adv. Sci. Eng. Inf. Technol., vol. 11, no. 3, pp. 1092–1099, 2021, doi: 10.18517/ijaseit.11.3.11723.

A. Munawar et al., “Cluster Application with K-Means Algorithm on the Population of Trade and Accommodation Facilities in Indonesia,†J. Phys. Conf. Ser., vol. 1933, no. 1, 2021, doi: 10.1088/1742-6596/1933/1/012027.

S. Sarumathi, P. Ranjetha, C. Saraswathy, M. Vaishnavi, and S. Geetha, “A Review and Comparative Analysis on Cluster Ensemble Methods,†Int. J. Comput. Inf. Eng., vol. 15, no. 6, pp. 385–394, 2021.

J. Park, K. V. Park, S. Yoo, S. O. Choi, and S. W. Han, “Development of the WEEE grouping system in South Korea using the hierarchical and non-hierarchical clustering algorithms,†Resour. Conserv. Recycl., vol. 161, no. March 2020, p. 104884, 2020, doi: 10.1016/j.resconrec.2020.104884.

W. B. Xie, Y. L. Lee, C. Wang, D. B. Chen, and T. Zhou, “Hierarchical clustering supported by reciprocal nearest neighbors,†Inf. Sci. (Ny)., vol. 527, pp. 279–292, 2020, doi: 10.1016/j.ins.2020.04.016.

L. Ramos Emmendorfer and A. M. de Paula Canuto, “A generalized average linkage criterion for Hierarchical Agglomerative Clustering,†Appl. Soft Comput., vol. 100, p. 106990, 2021, doi: 10.1016/j.asoc.2020.106990.

E. Banjarnahor, A. Bustamam, T. Siswantining, and P. Tampubolon, “Analyzing Kinship in Severe Acute Respiratory Syndrome Coronavirus 2 DNA Sequences Based on Hierarchical and K-Means Clustering Methods Using Multiple Encoding Vector,†Int. J. Adv. Sci. Eng. Inf. Technol., vol. 12, no. 6, pp. 2237–2247, 2022, doi: 10.18517/ijaseit.12.6.15582.

A. Sarica, M. G. Vaccaro, A. Quattrone, and A. Quattrone, “A novel approach for cognitive clustering of parkinsonisms through affinity propagation,†Algorithms, vol. 14, no. 2, 2021, doi: 10.3390/a14020049.

P. Vilas, L. Andreu, and J. L. Sarto, “Cluster analysis to validate the sustainability label of stock indices: An analysis of the inclusion and exclusion processes in terms of size and ESG ratings,†J. Clean. Prod., vol. 330, 2022, doi: 10.1016/j.jclepro.2021.129862.

A. Dogan and D. Birant, “K-centroid link: a novel hierarchical clustering linkage method,†Appl. Intell., vol. 52, no. 5, pp. 5537–5560, 2022, doi: 10.1007/s10489-021-02624-8.

F. Ros and S. Guillaume, “A hierarchical clustering algorithm and an improvement of the single linkage criterion to deal with noise,†Expert Syst. Appl., vol. 128, pp. 96–108, 2019, doi: 10.1016/j.eswa.2019.03.031.

J. Senthilnath, P. B. Shreyas, R. Rajendra, S. Suresh, S. Kulkarni, and J. A. Benediktsson, “Hierarchical clustering approaches for flood assessment using multi-sensor satellite images,†Int. J. Image Data Fusion, vol. 10, no. 1, pp. 28–44, 2019, doi: 10.1080/19479832.2018.1513956.

M. Charikar, V. Chatziafratis, and R. Niazadeh, “Hierarchical clustering better than average-linkage,†Proc. Annu. ACM-SIAM Symp. Discret. Algorithms, pp. 2291–2304, 2019, doi: 10.1137/1.9781611975482.139.

R. Wang and A. Sun, “Research on user clustering algorithm based on improved rock algorithm,†IOP Conf. Ser. Mater. Sci. Eng., vol. 790, no. 1, 2020, doi: 10.1088/1757-899X/790/1/012065.

H. Sofyan, M. Iqbal, M. Marzuki, and M. Muhammad, “The comparison of k-modes clustering and ROCK clustering to the poverty indicator in Samadua Subdistrict, South Aceh,†IOP Conf. Ser. Mater. Sci. Eng., vol. 1087, no. 1, p. 012085, 2021, doi: 10.1088/1757-899x/1087/1/012085.

R. Nooraeni, M. I. Arsa, and N. W. Kusumo Projo, “Fuzzy Centroid and Genetic Algorithms: Solutions for Numeric and Categorical Mixed Data Clustering,†Procedia Comput. Sci., vol. 179, no. 2020, pp. 677–684, 2021, doi: 10.1016/j.procs.2021.01.055.

S. Yin, G. Gan, E. A. Valdez, and J. Vadiveloo, “Applications of clustering with mixed type data in life insurance,†Risks, vol. 9, no. 3, pp. 1–19, 2021, doi: 10.3390/risks9030047.

N. Yuvaraj and C. Suresh Ghana Dhas, “High-performance link-based cluster ensemble approach for categorical data clustering,†J. Supercomput., vol. 76, no. 6, pp. 4556–4579, 2020, doi: 10.1007/s11227-018-2526-z.

Z. He, X. Xu, and S. Deng, “A cluster ensemble method for clustering categorical data,†Inf. Fusion, vol. 6, no. 2, pp. 143–151, 2005, doi: 10.1016/j.inffus.2004.03.001.

L. Wulandari, Y. Farida, A. Fanani, N. Ulinnuha, and P. K. Intan, “Evaluation of disadvantaged regions in east java based-on the 33 indicators of the ministry of villages, development of disadvantaged regions, and transmigration using the ensemble ROCK (Robust clustering using link) method,†Adv. Sci. Technol. Eng. Syst., vol. 5, no. 5, pp. 193–200, 2020, doi: 10.25046/aj050524.

G. Caruso, S. A. Gattone, A. Balzanella, and T. Di Battista, Cluster Analysis: An Application to a Real Mixed-Type Data Set, vol. 179. Springer International Publishing, 2019. doi: 10.1007/978-3-030-00084-4_27.

G. Caruso, S. A. Gattone, F. Fortuna, and T. Di Battista, “Cluster Analysis for mixed data: An application to credit risk evaluation,†Socioecon. Plann. Sci., vol. 73, no. February, p. 100850, 2021, doi: 10.1016/j.seps.2020.100850.

G. Caruso and S. A. Gattone, “Waste management analysis in developing countries through unsupervised classification of mixed data,†Soc. Sci., vol. 8, no. 6, 2019, doi: 10.3390/socsci8060186.

Vijaya, S. Aayushi, and R. Bateja, “A Review on Hierarchical Clustering Algorithms,†Journal of Engineering and Applied Sciences, vol. 12, no. 24. pp. 7501–7507, 2017.

R. S. Pontoh, F. Salsabila, F. C. Garini, R. A. Fatharani, S. Zahroh, and E. Supartini, “Clustering of fishery management areas based on the level of utilization in Indonesia,†Commun. Math. Biol. Neurosci., vol. 2021, pp. 1–19, 2021, doi: 10.28919/cmbn/6171.

S. Zhou, F. Liu, and W. Song, “Estimating the Optimal Number of Clusters Via Internal Validity Index,†Neural Process. Lett., vol. 53, no. 2, pp. 1013–1034, 2021, doi: 10.1007/s11063-021-10427-8.

F. A. Syaani, Irhamah, A. Mukarromah, and K. Fithriasari, “Incident Clustering in the Warehouse Workspaces by Using Text Mining,†IOP Conf. Ser. Mater. Sci. Eng., vol. 1117, no. 1, p. 012023, 2021, doi: 10.1088/1757-899x/1117/1/012023.



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