CLUSTERING OF CITIES AND REGENCIES IN WEST NUSA TENGGARA BASED ON WELFARE INDICATORS A HIERARCHICAL AGGLOMERATIVE APPROACH
Article Sidebar
Main Article Content
Abstract
Community welfare is a primary development goal emphasized in the Sustainable Development Goals (SDGs) and Indonesia’s RPJPN. However, disparities in welfare persist in regions such as West Nusa Tenggara (NTB), which consistently ranks below the national average in indicators such as poverty, education, and health. This condition underscores the need for data-driven approaches to understand regional characteristics and develop more targeted policies. This study aims to clustering Cities/Regencies in West Nusa Tenggara (NTB) Province based on people's welfare indicators. These indicators are indicators of people's welfare that represent aspects of population, health, education, employment, level and pattern of consumption, poverty and housing. The objects of observation are all Cities/Regencies in the province of NTB consisting of 10 Cities/Regencies, namely Bima City, Central Lombok Regency, East Lombok Regency, North Lombok Regency, West Lombok Regency, Mataram City, Sumbawa Regency, West Sumbawa Regency, Dompu Regency and Bima Regency. The data collection method used is the documentation method, which is obtained from the publication of Central Statistics Agency of NTB for 2022. The data analysis technique used in this study is quantitative data analysis using cluster analysis. The cluster analysis method used is the agglomerative hierarchical technique consisting of average linkage, single linkage, complete linkage, centroid, median and wards method. Based on the analysis that has been carried out, it is known that the grouping of Cities/Regencies in the NTB Province showed various clusters of each method. The optimal group is obtained by using Davies-Bouldin Index value consists of 5 clusters using average linkage, single linkage, centroid and median method; six Regencies joined in cluster 1 which are West Lombok, Central Lombok, East Lombok, Sumbawa, Dompu and Bima, West Sumbawa Regency in cluster 2, North Lombok Regency in cluster 3, Mataram City in cluster 4 and Bima City in cluster 5. The grouping results are expected to be input to the Government to plan strategic programs according to the different characteristics of each cluster to improve the welfare of the people of NTB.
Keywords: Clustering; Welfare; Indicator; Cluster Analysis; Agglomerative Hierarchical
Downloads
Article Details
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
References
Astutik, S., Solimun, & Darmanto. (2018). Analisis Multivariat: Teori dan Aplikasinya dengan SAS. Universitas Brawijaya Press.
Belinda, N. S., Rahmi, I., & Yozza, H. (2019). Penerapan Analisis Cluster Ensemble Dengan Metode Rock Untuk Mengelompokkan Provinsi Di Indonesia Berdasarkan Indikator Kesejahteraan Rakyat. Jurnal Matematika UNAND, VIII(2), 108–119.
Bintaro. (1989). Interaksi Desa-Kota dan Permasalahannya. Ghalia Indonesia.
BPS. (2022). Indikator Kesejahteraan Rakyat Provinsi Nusa Tenggara Barat 2022.
Chelvin Pratama, R. (n.d.). Pengelompokan Kabupaten/Kota Di Provinsi Papua Berdasarkan Indikator Kesejahteraan Rakyat 2020 (Clustering Of Regencies in Papua Based on People’s Welfare Indicators 2020 ).
Dakhi, Y. L., & Ningsi, B. A. (2024). Pengelompokan Kabupaten dan Kota Provinsi Sumatera Utara Berdasarkan Indikator Kesejahteraan Rakyat Menggunakan Algoritma K-Means. MALCOM: Indonesian Journal of Machine Learning and Computer Science, 4(3), 993–1003. https://doi.org/10.57152/malcom.v4i3.1381
Dini, S. K., & Fauzan, A. (2020). Clustering Provinces in Indonesia based on Community Welfare Indicators. EKSAKTA: Journal of Sciences and Data Analysis, 56–63. https://doi.org/10.20885/eksakta.vol1.iss1.art9
Hennig, C., Meila, M., Murtagh, F., & Rocci, R. (2016). Handbook of Cluster Analysis. CRC Press.
Kassambara, A. (2017). Multivariate Analysis I: Practical Guide To Cluster Analysis in R. Unsupervised Machine Learning. In Taylor & Francis Group. STHDA.
Latuhimallo, V. A., Talakua, M. W., & Leleury, Z. A. (2021). Analisis Clustering Untuk Pengelompokan Kabupaten/Kota Berdasarkan Indikator Kesejahteraan Rakyat Di Wilayah Provinsi Maluku. 1(02), 127–138.
Musa, M., & Fallo, S. I. (2023). Hierarchical Cluster Analysis On People’s Welfare In Southeast Sulawesi Province. BAREKENG: Jurnal Ilmu Matematika Dan Terapan, 17(2), 1163–1172. https://doi.org/10.30598/barekengvol17iss2pp1163-1172
Nugroho, S. (2008). Statistika Multivariat Terapan. UNIB Press.
Prasetya Subakti, D., & Kholijah, G. (n.d.). Analisis Klaster Dengan Average Linkage Method Untuk Pengelompokan Provinsi Di Indonesia Berdasarkan Indikator Kesejahteraan Rakyat Clustering Analysis With Average Linkage Method For Grouping Province In Indonesia Based On Welfare Indicators. In Mathematic Science and Application (MSA) Journal (Vol. 1, Issue 1). https://bps.go.id
Ramdhani, F., Hoyyi, A., & Mukid, M. A. (2015). Pengelompokan Provinsi Di Indonesia Berdasarkan Karakteristik Kesejahteraan Rakyat Menggunakan Metode K-MEANS Cluster. Jurnal Gaussian, 4(4), 875–884.
Roestam, S. (1993). Pembangunan Nasional untuk Kesejahteraan Rakyat. Kantor Menteri Koordinator Bidang Kesejahteraan Rakyat Republik Indonesia.
Santoso, S. (2018). Mahir Statistik Multivariat dengan SPSS. Elex Media Komputindo.
Tenriawaru, A. (2018). Clustering Kabupaten/Kota Provinsi Sulawesi Tenggara Berdasarkan Indikator Kesejahteraan Rakyat. 4(2), 175–182. https://doi.org/10.5281/zenodo.2528154
Wahyuni, I., & Wulandari, S. P. (2022). Pemetaan Kabupaten/Kota di Jawa Timur Berdasarkan Indikator Kesejahteraan Rakyat Menggunakan Analisis Cluster Hierarki. Jurnal Sains Dan Seni ITS, 11(1).
Widayat. (2018). STATISTIKA MULTIVARIAT (Pada Bidang Manajemen Dan Bisnis). UMM Press.
Wijaya, T., & Budiman, S. (2016). Analisis Multivariat Untuk Penelitian Manajemen. Penerbit Pohon Cahaya.
Yulianto, S., & Hidayatullah, K. H. (2014). Analisis Klaster Untuk Pengelompokan Kabupaten/Kota Di Provinsi Jawa Tengah Berdasarkan Indikator Kesejahteraan Rakyat. Statistika, 2(1).