Algoritma K-Means dan Analisis Komponen Utama untuk Mengatasi Multikolinearitas pada Pengelompokan Kabupaten Tertinggal
DOI:
https://doi.org/10.14421/jiska.2025.10.3.294-306Keywords:
K-Means Algorithm, Principal Component Analysis, Multicollinearity, MANOVA, Underdeveloped RegenciesAbstract
Underdeveloped areas are regions that frequently face developmental challenges in various aspects such as infrastructure, education, and healthcare. Presidential Regulation Number 63 of 2020 designates 62 regencies in Indonesia as underdeveloped areas. This study categorizes the 62 underdeveloped regencies based on education and health indicators. The methods used are the k-means algorithm and principal component analysis due to multicollinearity in the data. MANOVA is conducted to determine the influence of the cluster results on the Human Development Index (HDI), Average Years of Schooling (AYS), Expected Years of Schooling (EYS), and Life Expectancy (LE). Due to multicollinearity in the education indicator data, principal component analysis was performed, resulting in three main components. The k-means analysis groups the 62 regencies into three clusters based on education indicators and two clusters based on health indicators. Further analysis using MANOVA shows the influence of the education and health clusters on HDI, AYS, EYS, and LE, indicated by statistical test results showing p-value < a(0.05). Thus, education and health indicators influence the categorization of underdeveloped areas.
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