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Sintawati , Ita Dewi
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Computer Science & IT

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Application of Data Mining for Clustering Human Development Index Based on West Java Province 2017-2022 Widiarina, Widiarina; Mariskhana, Kartika; Sintawati , Ita Dewi
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 1 (2024): Articles Research Volume 8 Issue 1, January 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i1.13148

Abstract

Human development is used as a parameter to see development from the human side. The Human Development Index (HDI) explains how people get sufficient income, adequate health and education. Geographically, Indonesia is an archipelagic country where each province is spread across various islands separated by sea. Making the disparity in human development between provinces relatively high. The gap that occurs is still a problem that must be resolved immediately, because the gap in the human development index can hamper the government's goal of equalizing human welfare in Indonesia. −One of the problems related to population that West Java Province still has to face is the problem of imbalance in population distribution. Incomplete population distribution causes problems with population density and population pressure in an area. This research uses data sources from the West Java Province Central Statistics Agency (BPS). The data used in this research is data from 2017-2022 which consists of 27 regencies and cities of West Java Province. Therefore, researchers utilized the K-Means algorithm in clustering 27 Regencies and Cities of West Java Province. The data will be processed by clustering into 3 clusters, namely the high population area level cluster, the medium population area level cluster and the low population area level cluster. This research classifies population density using Ms. software. Excel and RapidMiner. The iteration process took place 3 times so that the results obtained were 8 regencies and cities with high population area clusters (C0), 0 regencies and cities with medium population area clusters (C1) and 19 regencies and cities with low population area clusters (C1). C2).
Co-Authors Kartika Mariskhana Widiarina .