A Comparative Study of Clustering Algorithms for Indonesian Provincial Food Security

Ika Nur Laily Fitriana; Emeylia Safitri; Siti Hadijah Hasanah

doi:10.59395/ijadis.v7i3.1661

Authors

Ika Nur Laily Fitriana Statistics Study Program, Universitas Terbuka, Indonesia
Emeylia Safitri Statistics Study Program, Universitas Terbuka, Indonesia
Siti Hadijah Hasanah Statistics Study Program, Universitas Terbuka, Indonesia

DOI:

https://doi.org/10.59395/ijadis.v7i3.1661

Keywords:

Food Security, Clustering Algorithms, Internal Validity Indices, Bootstrap Stability Analysis, Provincial Typology

Abstract

Food security disparities in Indonesia remain multidimensional and spatially uneven, requiring analytical approaches that can capture regional differences beyond single indicators. This study compares multiple clustering algorithms to classify 38 Indonesian provinces using twelve food security indicators representing food availability, affordability, utilization, and consumption outcomes. Provincial secondary data for 2024 were obtained from official sources, including Statistics Indonesia, the National Food Agency, and the Ministry of Health. The analysis began with data standardization and Principal Component Analysis, which retained three components explaining 74.65% of total variance. Ten clustering approaches were then evaluated, including K-Means, hierarchical linkage methods, Fuzzy C-Means, DBSCAN, HDBSCAN, Gaussian Mixture Model, and ensemble clustering. Each solution was assessed using internal validity indices, including Silhouette, Davies–Bouldin, and Dunn Index, as well as degeneracy checks, cluster membership consistency, bootstrap stability analysis based on ARI and NMI, robustness checks under alternative preprocessing and variable subsets, and substantive interpretability. Ward Linkage was selected as the final method because it produced a non-degenerate and interpretable three-cluster typology, supported by similar partitions from Complete Linkage and ensemble clustering. The two-province high-vulnerability cluster persisted under log-transformation of skewed indicators, removal of individual variables, and reduced component dimensionality. The final clusters reveal clear territorial disparities: a relatively food-secure cluster covering most western and central provinces, a moderately vulnerable eastern cluster, and a high-vulnerability cluster consisting of Papua Pegunungan and Papua Selatan. Poverty, electricity access, protein consumption, health service burden, stunting, and life expectancy were the strongest differentiating indicators. These findings demonstrate the value of combining internal validity and stability analysis to support robust provincial food security typologies and targeted regional policy interventions.

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References

[1] FAO, IFAD, UNICEF, WFP, and WHO, The State of Food Security and Nutrition in the World 2023. FAO; IFAD; UNICEF; WFP; WHO, 2023. doi: 10.4060/cc3017en.

[2] C. B. Barrett, “Measuring food insecurity,” Science (1979)., vol. 327, no. 5967, pp. 825–828, 2010, doi: 10.1126/science.1182768.

[3] A. D. Jones, F. M. Ngure, G. Pelto, and S. L. Young, “What are we assessing when we measure food security? A compendium and review of current metrics,” Advances in Nutrition, vol. 4, no. 5, pp. 481–505, 2013, doi: 10.3945/an.113.004119.

[4] J. B. Upton, J. D. Cissé, and C. B. Barrett, “Food security as resilience: Reconciling definition and measurement,” Agricultural Economics, vol. 47, no. S1, pp. 135–147, 2016, doi: 10.1111/agec.12305.

[5] Badan Pangan Nasional, “Peta Ketahanan dan Kerentanan Pangan (Food Security and Vulnerability Atlas/FSVA).” Accessed: Jul. 22, 2026. [Online]. Available: https://fsva.badanpangan.go.id/

[6] E. R. Amrullah, A. Ishida, A. Pullaila, and A. Rusyiana, “Who suffers from food insecurity in Indonesia?,” Int. J. Soc. Econ., vol. 46, no. 10, pp. 1186–1197, Oct. 2019, doi: 10.1108/IJSE-03-2019-0196.

[7] A. Akbar, R. Darma, I. M. Fahmid, and A. Irawan, “Determinants of Household Food Security during the COVID-19 Pandemic in Indonesia,” Sustainability, vol. 15, no. 5, p. 4131, Feb. 2023, doi: 10.3390/su15054131.

[8] E. R. Amrullah, H. Tokuda, A. Rusyiana, and A. Ishida, “Effect of COVID-19 pandemic on food insecurity in Indonesian households,” Int. J. Soc. Econ., vol. 50, no. 12, pp. 1790–1803, 2023, doi: 10.1108/IJSE-03-2023-0186.

[9] R. Abdulah, “Mapping Food Security in Indonesia: Geographic Clusters and Regional Disparities,” Indonesian Journal of Geography, vol. 57, no. 3, p. 440, Nov. 2025, doi: 10.22146/ijg.99419.

[10] R. S. Rahayu, Y. Purwaningsih, and A. Daerobi, “Mapping of provincial food security in Indonesia using based clustering model,” Jurnal Ekonomi Pembangunan: Kajian Masalah Ekonomi dan Pembangunan, vol. 20, no. 1, pp. 69–79, 2019, doi: 10.23917/jep.v20i1.7096.

[11] V. Ratnasari and A. T. R. Dani, “Mapping the provincial food security conditions in Indonesia using cluster ensemble-based mixed data clustering-Robust clustering with links (CEBMDC-ROCK),” Int. J. Adv. Sci. Eng. Inf. Technol., vol. 13, no. 2, pp. 611–617, 2023, doi: 10.18517/ijaseit.13.2.16457.

[12] M. Gagolewski, M. Bartoszuk, and A. Cena, “Are cluster validity measures (in)valid?,” Inf. Sci. (N. Y)., vol. 581, pp. 620–636, 2021, doi: 10.1016/j.ins.2021.10.004.

[13] T. Liu, H. Yu, and R. H. Blair, “Stability estimation for unsupervised clustering: A review,” Wiley Interdiscip. Rev. Comput. Stat., vol. 14, no. 6, p. e1575, 2022, doi: 10.1002/wics.1575.

[14] I. T. Jolliffe and J. Cadima, “Principal component analysis: a review and recent developments,” Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, vol. 374, no. 2065, p. 20150202, Apr. 2016, doi: 10.1098/rsta.2015.0202.

[15] F. L. Gewers et al., “Principal component analysis: A natural approach to data exploration,” ACM Comput. Surv., vol. 54, no. 4, May 2021, doi: 10.1145/3447755.

[16] J. L. Horn, “A rationale and test for the number of factors in factor analysis,” Psychometrika, vol. 30, no. 2, pp. 179–185, 1965, doi: 10.1007/BF02289447.

[17] D. Xu and Y. Tian, “A Comprehensive Survey of Clustering Algorithms,” Annals of Data Science, vol. 2, no. 2, pp. 165–193, Jun. 2015, doi: 10.1007/s40745-015-0040-1.

[18] A. A. Wani, “Comprehensive analysis of clustering algorithms: Exploring limitations and innovative solutions,” PeerJ Comput. Sci., vol. 10, p. e2286, 2024, doi: 10.7717/peerj-cs.2286.

[19] A. M. Ikotun, A. E. Ezugwu, L. Abualigah, B. Abuhaija, and H. Jia, “K-means clustering algorithms: A comprehensive review, variants analysis, and advances in the era of big data,” Inf. Sci. (N. Y)., vol. 622, pp. 178–210, 2023, doi: 10.1016/j.ins.2022.11.139.

[20] A. Strehl and J. Ghosh, “Cluster ensembles: A knowledge reuse framework for combining multiple partitions,” Journal of Machine Learning Research, vol. 3, pp. 583–617, 2002, [Online]. Available: https://www.jmlr.org/papers/v3/strehl02a.html

[21] P. J. Rousseeuw, “Silhouettes: A graphical aid to the interpretation and validation of cluster analysis,” J. Comput. Appl. Math., vol. 20, pp. 53–65, 1987, doi: 10.1016/0377-0427(87)90125-7.

[22] D. L. Davies and D. W. Bouldin, “A cluster separation measure,” IEEE Trans. Pattern Anal. Mach. Intell., vol. PAMI-1, no. 2, pp. 224–227, 1979, doi: 10.1109/TPAMI.1979.4766909.

[23] J. C. Dunn, “Well-separated clusters and optimal fuzzy partitions,” Journal of Cybernetics, vol. 4, no. 1, pp. 95–104, 1974, doi: 10.1080/01969727408546059.

[24] H. Yu et al., “Bootstrapping estimates of stability for clusters, observations and model selection,” Comput. Stat., vol. 34, no. 1, pp. 349–372, Mar. 2019, doi: 10.1007/s00180-018-0830-y.

[25] C. Hennig, “Cluster-wise assessment of cluster stability,” Comput. Stat. Data Anal., vol. 52, no. 1, pp. 258–271, Sep. 2007, doi: 10.1016/j.csda.2006.11.025.

[26] U. von Luxburg, “Clustering Stability: An Overview,” Foundations and Trends® in Machine Learning, vol. 2, no. 3, pp. 235–274, Apr. 2010, doi: 10.1561/2200000008.

[27] L. Hubert and P. Arabie, “Comparing partitions,” J. Classif., vol. 2, no. 1, pp. 193–218, Dec. 1985, doi: 10.1007/BF01908075.

[28] M. D. Ernst, “Permutation methods: A basis for exact inference,” Statistical Science, vol. 19, no. 4, pp. 676–685, 2004, doi: 10.1214/088342304000000396.

[29] B. L. Welch, “On the comparison of several mean values: An alternative approach,” Biometrika, vol. 38, no. 3/4, pp. 330–336, 1951, doi: 10.2307/2332579.

[30] T. Calinski and J. Harabasz, “A dendrite method for cluster analysis,” Communications in Statistics, vol. 3, no. 1, pp. 1–27, 1974, doi: 10.1080/03610927408827101.

[31] R. Tibshirani, G. Walther, and T. Hastie, “Estimating the number of clusters in a data set via the gap statistic,” J. R. Stat. Soc. Series B Stat. Methodol., vol. 63, no. 2, pp. 411–423, 2001, doi: 10.1111/1467-9868.00293.

[32] F. Murtagh and P. Legendre, “Ward’s Hierarchical Agglomerative Clustering Method: Which Algorithms Implement Ward’s Criterion?,” J. Classif., vol. 31, no. 3, pp. 274–295, Oct. 2014, doi: 10.1007/s00357-014-9161-z.

[33] R. J. G. B. Campello, D. Moulavi, and J. Sander, “Density-Based Clustering Based on Hierarchical Density Estimates,” in Advances in Knowledge Discovery and Data Mining, 2013, pp. 160–172. doi: 10.1007/978-3-642-37456-2_14.

[34] E. S. Dalmaijer, C. L. Nord, and D. E. Astle, “Statistical power for cluster analysis,” BMC Bioinformatics, vol. 23, no. 1, p. 205, Dec. 2022, doi: 10.1186/s12859-022-04675-1.