Analisis Kemiskinan Menggunakan Metode Algoritma Clustering K-Means
Keywords:
Clusters, K-means, PovertyAbstract
Poverty has a broad and serious impact on the lives of individuals and society. When people live in poverty, they may face difficulties meeting basic needs, such as adequate food, adequate housing, and proper education. These limitations can negatively impact physical and mental health, education, employment opportunities, and overall quality of life. The purpose of this study is to find out the grouping of districts/cities that have similar characteristics based on the 2019 poverty indicators. This research uses data obtained from the BPS (Central Bureau of Statistics). The method used is the k-means clustering method which is a clustering partition method for grouping objects into k clusters. Based on the research results, the characteristics of each cluster were grouped based on the poverty indicator values in several districts/cities in 2019 as many as 2 clusters. Formed from 20 districts/cities in cluster 1 and 29 districts/cities in cluster 2. Cluster 1 has the characteristics of Low Work Challenges, with Low Per Capita Expenditure Rates and Low Unemployment Rates while Cluster 2 has the characteristics of High Job Challenges, with Per Capita Expenditure Levels High and High Non Working Rate.
References
A. D. A. F. E. W. Febiyanti Alfiah, “Analisis Clustering K-Medoids Berdasarkan Indikator Kemiskinan di Jawa Timur Tahun 2020,” Jurnal Ilmiah Sains, pp. 1-7, 2022.
A. F. F. P. S. Achmad Bahauddin, “ANALISIS CLUSTERING PROVINSI DI INDONESIA BERDASARKAN TINGKAT KEMISKINAN MENGGUNAKAN ALGORITMA K-MEANS,” MISI(Jurnal Manajemen informatika & Sistem Informasi), 2021.
F. S. Kamila Aprilia, “ANALISIS GARIS KEMISKINAN MAKANAN MENGGUNAKAN METODE ALGORITMA K-MEANS CLUSTERING,” SISMATIK (Seminar Nasional Sistem Informasi dan Manajemen Informatika), 2021.
“ANALISIS KASUS KEMISKINAN DI PROVINSI KALIMANTAN TENGAH DENGAN PENDEKATAN PRINCIPAL COMPONENT ANALYSIS,” JURNAL AL-QARDH, 2022.
D. V. Ferezagia, “Analisis Tingkat Kemiskinan di Indonesia,” Jurnal Sosial Humaniora Terapan, 2018.
I. Juaeni, “DAMPAK PENERAPAN PRINCIPAL COMPONENT ANALYSIS (PCA) DALAM CLUSTERING CURAH HUJAN DI PULAU JAWA, BALI DAN LOMBOK,” 2014.
G. M. Tinungki, “PENENTUAN INDIKATOR KEMISKINAN PENDUDUK INDONESIA TAHUN 2017 DENGAN PEMODELAN SPARSE PRINCIPAL COMPONENT ANALYSIS,” SEMNAS, 2018.
F. R. E. M. Derita Lamtiar Pasaribu, “NALISIS FAKTOR KEMISKINAN KABUPATEN/KOTA DI KALIMANTAN, SULAWESI, BALI DAN NUSA TENGGARA,” Jurnal Dinamika Pertanian Edisi XXXV , pp. 107-116, 2019.
D. K. Noor Zuhdiyaty, “ANALISIS FAKTOR - FAKTOR YANG MEMPENGARUHI KEMISKINAN DI INDONESIA SELAMA LIMA TAHUN TERAKHIR (Studi Kasus Pada 33 Provinsi)”.
“Penerapan Sparse Principal Component Analysis dalam Menghasilkan Matriks Loading yang Spars,” JMSK, pp. 44-54, 2019.
