Implementation of Data Mining and Spatial Mapping in Determining National Food Security Clusterization

Sifriyani Sifriyani; I Nyoman Budiantara; M. Fariz Fadillah Mardianto; Eka Riche Febriyani; Nurul Rizky Chairunnisa; Asyifa Charmadya Putri

doi:10.31764/jtam.v8i3.19912

Implementation of Data Mining and Spatial Mapping in Determining National Food Security Clusterization

Sifriyani Sifriyani, I Nyoman Budiantara, M. Fariz Fadillah Mardianto, Eka Riche Febriyani, Nurul Rizky Chairunnisa, Asyifa Charmadya Putri

Abstract

This study proposes a cluster analysis of provinces based on national food security data. The research objective is to determine provincial clusters based on food indicators which include rice harvest area, distribution of rice stocks, percentage of trade margin and transportation of rice distribution, percentage of average per capita expenditure, and total per capita consumption of rice. The source of observation data for the Rice Harvested Area by Province variable is the Ministry of Agriculture, Central Bureau of Statistics and Agriculture Services throughout Indonesia. This study uses data mining techniques in data processing with the K-Medoids algorithm. The K-Medoids method is a clustering method that functions to break down data sets into several groups. The advantage of this method is that it can overcome the weakness of the K-Means method which is sensitive to outliers. Another advantage of this algorithm is that the results of the clustering process do not depend on the order in which the dataset is entered. The k-medoids clustering method can be applied to food security data by province. From grouping the data obtained three clusters, with silhouette coefficient values for cluster 1, cluster 2, and cluster 3 respectively 0.33; 0.32; and 0.44. With the largest silhouette coefficient value obtained in cluster 3 and the cluster has entered into a strong cluster structure. The research results can provide information to the government about food security grouping data in Indonesia which has an impact on the distribution and availability of food in Indonesia.

Keywords

K Medoids Algorithm; Data Mining; Food Security Data; Silhouette Coefficient.

Full Text:

DOWNLOAD [PDF]

References

Aggarwal, C.C. & Reddyck. (2014). Data Clustering Algoritms and Applications. CRC Press. https://doi.org/10.1201/9781315373515

Arbin, N., N.S. Suhaimi, N.Z. Mokhtar & Z. Othman. (2015). Comparative Analysis between K-Means and K-Medoids for Statistical Clustering. 2015 Third International Conference on Artificial Intelligence, Modelling and Simulation, 116–121. doi: https://doi.org/10.1109/AIMS.2015.82

Arora, P. & Varshney, S. (2016). Analysis of K-Means and K-Medoids Algorithm For Big Data. Procedia - Procedia Comput. Sci. 78:507–512. https://doi:10.1016/j.procs.2016.02.095

Bhat, A. (2014). K-Medoids Clustering Using Partitioning Around Medoids for Performing Face Recognition. International Journal of Soft Computing, Mathematics and Control (IJSCMC), 3–4. https://doi.org/10.14810/IJSCMC.2014.3301

Badan Pusat Statistik, Statistik Indonesia - Statistical Yearbook of Indonesia 2022, Badan Pusat Statistik BPS-Statistics Indonesia, 2022 https://www.bps.go.id/id/publication/2022/02/25/0a2afea4fab72a5d052cb315/statistik-indonesia-2022.html

Chen, Y., H. Li, H. Lin, Y. Wang & Y. Zhao. (2023). Computers and Geotechnics Critical Slip Line Recognition and Extraction Method of Slope Based on Modified K-Medoid Clustering Algorithm. Comput. Geotech 15 (105125). doi:https://doi:10.1016/j.compgeo.2022.105125.

Chen, Z, H. Li, H. Lin, Y. Wang, and Y. Zhao. (2022). A New Parallel Adaptive Structural Reliability Analysis Method Based on Importance Sampling and K-Medoids Clustering. Reliab. Eng. Syst. Saf. 218 (108124). doi: https://doi:10.1016/j.ress.2021.108124.

Chikohora, T.T. (2014). A Study of the Factors Considired When Choosing an Appropriate Data Mining Algorithm. International Journal of Soft Computing and Engineering 4 (3):42–45. https://www.ijsce.org/wp-content/uploads/papers/v4i3/C2270074314.pdf

E. Irwansyah, M. F. (2015). Advanced Clustering Teori Dan Aplikasi. Yogyakarta: Deepublish. https://balaiyanpus.jogjaprov.go.id/opac/detail-opac?id=322626

Harikumar, S. & Pv, S. (2015). K-Medoid Clustering for Heterogeneous DataSets, 70:226–237. doi:https://doi:10.1016/j.procs.2015.10.077.

Indraputra, R.A. & Fitriana, R. (2020). K-Means Clustering Data COVID-19. Jurnal Teknik Industri 10, (3):275–282. doi: https://doi.org/10.25105/jti.v10i3.8428.

Kaur, K.N. (2014). K-Medoid Clustering Algorithm- A Review. International Journal of Computer Application and Technology 14. https://www.academia.edu/8446443/K_Medoid_Clustering_Algorithm_A_Review

Khatami, A., S. Mirghasemi, A. Khosravi, C. Peng & S. Nahavandi. (2017). A New PSO-Based Approach to Fire Flame Detection Using K-Medoids Clustering. Expert Syst. Appl, 68:69–80. doi:https://doi:10.1016/j.eswa.2016.09.021.

Maheshwari, A. (2015). Data Analytics Mode Accessible. https://dokumen.pub/data-analytics-made-accessible-t-8692526.html

Pinheiro, D. N., D. Aloise & S. J. Blanchard. (2020). Convex Fuzzy k -Medoids Clustering. Fuzzy Sets Syst, 389:66–92. doi:https://doi:10.1016/j.fss.2020.01.001.

Pramesti, F.D. (2017). Implementasi Metode K-Medoids Clustering Untuk Pengelompokkan Data Potensi Kebakaran Hutan/Lahan Berdasarkan Persebaran Titik Panas (Hotspot). Jurnal Pengembangan Teknologi Informasi Dan Ilmu Komputer, 723–732. https://j-ptiik.ub.ac.id/index.php/j-ptiik/article/view/204

Pusat Data Informasi Pertanian Sekretariat Jenderal, Statistik Ketahanan PanganTahun 2022, (2022) 138–138. https://satudata.pertanian.go.id/assets/docs/publikasi/Statistik_Ketahanan_Pangan_2022.pdf

Pusat Data dan Sistem Informasi Pertanian Sekretariat Jenderal Kementerian Pertanian, Statistik Ketahanan Pangan Tahun 2022 Volume 2 Nomor 1, Pusat Data dan Sistem Informasi Pertanian Sekretariat Jenderal Kementerian Pertanian, 2022. https://satudata.pertanian.go.id/assets/docs/publikasi/Statistik_Ketahanan_Pangan_2022.pdf

Rendy, H.D. (2014). Perbandingan Metode Clustering Menggunakan Metode Single Linkage Dan K-Means Pada Pengelompokkan Dokumen. JSM STMIK Mikroskil, 76–77. https://media.neliti.com/media/publications/280965-perbandingan-metode-clustering-menggunak-bf005dbb.pdf

Shen, Y., D. Zhang, R. Wang, J. Li & Z. Huang. (2023). Transportation Geotechnics SBD-K-Medoids-Based Long-Term Settlement Analysis of Shield Tunnel. Transp. Geotech, 42(101053). doi:https://doi:10.1016/j.trgeo.2023.101053.

Shukur, B.K. & S.Z.N. Alrashid. (2014). Evaluation of Clustering Image Using Steady State Genetic and Hybrid K-Harmonic Clustering Algorithms. IJCCCE, 14 (1):166–173. https://www.iasj.net/iasj/download/4f003c95c33607ee

Sindi, S., W.R.O. Ningse, I.A. Sihombing, F.I. RHzer & D. Hartama. (2020). Analisis Algoritma K-Medoids Clustering Dalam Pengelompokan Penyebaran COVID-19 Di Indonesia. Jurnal Teknologi Informasi, 4 (1):166–173. doi:https://doi.org/10.36294/jurti.v4i1.1296.

Sitompul, B.J.D., O.S. Sitompul & P. Sihombing. (2019). Enhancement Clustering Evaluation Result of Davies-Bouldin Index with Determining Initial Centroid of K-Means Algorithm. IOP Conf. Series: Journal of Physics, 1235(1):6–12. doi:https://doi.org/10.1088/1742-6596/1235/1/012015.

Sucipto, S., M.S. Al-Mubarok & D.T. Setiyawan. (2021). Hierarchical Clustering Bahan Menu Di Kantin Universitas Untuk Menunjang Implementasi Sistem Jaminan Halal. Jurnal Teknologi Industri Pertanian, 31(1):103–124. doi:https://doi.org/10.1201/b19706.

Sun, D., H. Fei & Q. Li. (2018). A Bisecting K-Medoids Clustering Algorithm Based on Cloud Model, 308–315. doi:https://doi:10.1016/j.ifacol.2018.08.301.

Sureja, N., B. Chawda & A. Vasant. (2022). An Improved K -Medoids Clustering Approach Based on the Crow Search Algorithm. J. Comput. Math. Data Sci., 3(100034). doi:https://doi:10.1016/j.jcmds.2022.100034.

Tan, P.N., Steinbach, M. & Karpatne, A. (2019). Introduction to Data Mining Second Edition. Pearson Education Limited. https://link.springer.com/chapter/10.1007/978-1-4302-3325-1_14

Thakare, Y.S. & S.B. Bagal. (2015). Performance Evaluation of K-Means Clustering Algorithm with Various Distance Metrics. International Journal of Computer Applications, 110(11):12–16. doi:https://doi.org/10.5120/19360-0929.

Wira, B., Budianto, E. Alexius, Wiguna & S. Anggri. (2019). Implementasi Metode K-Medoids Clustering Untuk Mengetahui Pola Pemilihan Program Stusi Mahasiswa Baru Tahun 2018 Di Universitas Kanjuruhan Malang. Jurnal Terapan Sains Dan Teknologi, 55–57. https://doi.org/10.21067/jtst.v1i3.3046

Yu, D., G. Liu, M. Guo & X. Liu. (2018). An Improve d K-Medoids Algorithm Based on Step Increasing and Optimizing Medoids, 92:464–473. doi:https://doi:10.1016/j.eswa.2017.09.052.

Zhang, Y. & P. Shang. (2022). Communications in Nonlinear Science and Numerical Simulation KM-MIC : An Improved Maximum Information Coefficient Based on K-Medoids Clustering. Commun. Nonlinear Sci. Nu Mer. Simul, 106418:111. doi:https://doi:10.1016/j.cnsns.2022.106418.

DOI: https://doi.org/10.31764/jtam.v8i3.19912