Model Based Clustering for Regency/City Grouping Based on Community Welfare Indicators in North Sumatra
DOI:
10.29303/jpm.v19i3.6595Published:
2024-05-19Issue:
Vol. 19 No. 3 (2024): May 2024Keywords:
Categorization; Model-Based Clustering; Societal Well-beingArticles
Downloads
How to Cite
Downloads
Metrics
Abstract
This thesis aims to apply model-based clustering in grouping regencies/cities in North Sumatra based on Community Welfare Indicators to determine the number of groups (clusters) formed based on community welfare indicators in regencies/cities in North Sumatra and to understand the level of community welfare from this grouping in planning and managing community welfare in regencies/cities in North Sumatra, with the hope of achieving equal welfare in every region. The research method used is Model-Based Clustering, which uses 5 research data variables: HDI, Poor Population, Unemployment Rate, GDP, and Health. In this millennial era, the assessment of community welfare requires more attention. Rapid social, technological, and environmental changes have created new dynamics that can affect community welfare. The evaluation of community welfare is not only limited to economic parameters but also considers health, unemployment, and other factors. By using Model-Based Clustering, it is possible to determine the optimal number of groups (clusters) from various and possibly correlated variables, and the results are easier to understand, making the analysis and understanding of the results easier. Readers can learn about the level of community welfare, and the community and government can evaluate their welfare for future improvements. The research results show that among the groups of regencies/cities formed, five cities consistently show lower Gross Regional Domestic Product (GDP) and Human Development Index (HDI) than other cities. Therefore, a sustainable approach is needed to improve these cities' economic conditions and social welfare.
References
Sukmasari, D. (2020). Konsep Kesejahteraan Masyarakat Dalam Perspektif Al-Qur’an. At-Tibyan, 3(1), 1–16.
Basofi, A., & Santoso, D. B. (2017). Analisis Pengukuran Kesejahteraan Di Indonesia Jurnal Ilmiah. Jurnal Ilmiah Mahasiswa FEB, 10(2), 1–16.
Qona’ah, N., Devi, A. R., & Dana, I. M. G. M. (2020). Laboratory Clustering using K-Means, K-Medoids, and Model-Based Clustering. Indonesian Journal of Applied Statistics, 3(1), 64.
Martias, L. D. (2021). Statistika Deskriptif Sebagai Kumpulan Informasi. Fihris: Jurnal Ilmu Perpustakaan Dan Informasi, 16(1), 40. https://doi.org/10.14421/fhrs.2021.161.40-59
Nasution, L. E. (2017). STATISTIK DESKRIPTIF. Jurnal Hikmah, 14(1), 49-55.
Ulinnuh, N., & Veriani, R. (2020). Analisis Cluster dalam Pengelompokan Provinsi di Indonesia Berdasarkan Variabel Penyakit Menular Menggunakan Metode Complete Linkage , Average Linkage dan Ward. InfoTekJar : Jurnal Nasional Informatika Dan Teknologi Jaringan, 5(1), 101–108.
Hair, Joseph F., Black, WC, Babin, BJ, et al. 2009. Multivariate Data Analysis (7th ed). Upper Saddle River:Prentice-Hall International, Inc.
Nafisah, Q., & Chandra, N. E. (2017). Analisis Cluster Average Linkage Berdasarkan Faktor-Faktor Kemiskinan di Provinsi Jawa Timur. Zeta - Math Journal, 3(2), 31–36.
Simamora, B. 2005. Multivariate Marketing Analysis. Ed. 1. Jakarta: PT Gramedia Reader.
Nisa, K., Sari, R. F., Cipta, H., & Husein, I. (2020). Cluster Analysis Using the Hierarki Method For Grouping Sub-Districts in The District Steps Based on Health Indicators. ZERO: Jurnal Sains, Matematika Dan Terapan, 4(1), 28.
Agustini, M. (2017). Model-Based clustering dengan Distribusi t Multivariat Menggunakan Kriteria Integrated Completed Likelihood dan Minimum Message Length.
Azhar, M. (2020). Pemetaan Tingkat Konsumsi Energi Bahan Bakar Minyak (BBM) Kabupaten/Kota di Jawa Timur dengan Model Based Clustering.
Akhyar, S. (2017). Pengelompokan Kabupaten/Kota di Jawa Timur Berdasarkan Indikator Pembangunan Ekonomi Menggunakan Model Based Clustering.
Bhagat, A., Kshirsagar, N., Khodke, P., Dongre, K., & Ali, S. (2016). Penalty Parameter Selection for Hierarchical Data Stream Clustering. Procedia Computer Science, 79(May), 24–31.
Melnykov, V., & Maitra, R. (2010). Finite mixture models and model-based clustering. Statistics Surveys, 4(October), 80–116.
Bertoletti, M., Friel, N., & Rastelli, R. (2015). Choosing the number of clusters in a finite mixture model using an exact integrated completed likelihood criterion. Metron, 73(2), 177–199.
Bustaman, N., Yulyanti Shinta, & Dewi, S. K. (2021). Analisis Faktor-Faktor…{Bustamam, dkk }|. 32(1), 85–92. https://journal.uir.ac.id/index.php/kiat
Indonesia, U. I. (2008). INDEKS PEMBANGUNAN MANUSIA INDONESIA Mohammad Bhakti Setiawan & Abdul Hakim. 18–26
Waluyo, D. (2006). Studi Tentang Bentuk Kemiskinan Penduduk di Desa Cindogo Kecamatan Tapen Kab. Bondowoso. Jurnal Humanity, 1(2), 11482.
Mahroji, D., & I. Nurkhasanah. 2019. “Pengaruh Indeks Pembangunan Manusia terhadap Tingkat Pengangguran di Provinsi Banten” Jurnal Ekonomi-Qu, 9(1).
Hasibuan, R. R. A., Kartika, A., Suwito, F. A., & Agustin, L. (2022). Pengaruh Produk Domestik Regional Bruto (PDRB) terhadap Tingkat Kemiskinan Kota Medan. Reslaj : Religion Education Social Laa Roiba Journal, 4(3), 683–693.
Jacob, D. E., & Sandjaya. (2018). Faktor faktor yang mempengaruhi kualitas hidup masyarakat Karubaga district sub district Tolikara propinsi Papua. Jurnal Nasional Ilmu Kesehatan (JNIK), 1(69), 1–16.
Arie, K. B., Hoyyi Abdul, & Abdul, M. M. (2013). Analisis Faktor-Faktor Yang Mempengaruhi Keputusan Pembelian dan Kepuasan Konsumen Pada Notebook Merek Acer. Jurnal Gaussian, 2(1), 29-38
Uly Aldini, & Wara Pramesti. (2020).Pengelompokan Provinsi Di Indonesia Berdasarkan Indikator Mutu Pendidikan Sekolah Menengah Pertama tahun 2016-2018 Menggunakaan Model Based Clustering. J Statistika: Jurnal Ilmiah Teori Dan Aplikasi Statistika, 13(2), 25–38.
Purnomo, Sutadji, E., Utomo, W., Purnawirawan, O., Farich, R., A.S., S., M., R. F., Carina, A., & R., N. G. (2022). Analisis Data Multivariat.
Author Biographies
Muhammad Afif Fauzi Hasibuan, UINSU
Hendra Cipta, Department of Mathematics, Faculty of Science and Technology, Universitas Islam Negeri Sumatera Utara
Sajaratud Dur, Department of Mathematics, Faculty of Science and Technology, Universitas Islam Negeri Sumatera Utara
License
Copyright (c) 2024 Muhammad Afif Fauzi Hasibuan, Hendra Cipta, Sajaratud Dur
This work is licensed under a Creative Commons Attribution 4.0 International License.
The following terms apply to authors who publish in this journal:
1. Authors retain copyright and grant the journal first publication rights, with the work simultaneously licensed under a Creative Commons Attribution License 4.0 International License (CC-BY License) that allows others to share the work with an acknowledgment of the work's authorship and first publication in this journal.
2. Authors may enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., posting it to an institutional repository or publishing it in a book), acknowledging its initial publication in this journal.
3. Before and during the submission process, authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website), as this can lead to productive exchanges as well as earlier and greater citation of published work (See The Effect of Open Access).
