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All Journal Indonesian Journal of Information System
Megat Norulazmi Megat Mohamed Noor
Universiti Kuala Lumpur
Computer Science & IT

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Machine Learning for Clustering Regencies-Cities Based on Inflation and Poverty Rates in Indonesia Rendra Gustriansyah; Juhaini Alie; Ahmad Sanmorino; Rudi Heriansyah; Megat Norulazmi Megat Mohamed Noor
Indonesian Journal of Information Systems Vol. 5 No. 1 (2022): August 2022
Publisher : Program Studi Sistem Informasi Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/ijis.v5i1.5682

Abstract

The COVID-19 pandemic has increased inflation and poverty rates in many cities, thus requiring considerable attention from the government as a policymaker. Therefore, this study aims to cluster regencies/cities that need mitigation priorities from the Indonesian government based on inflation and poverty rates in 2021. Four machine learning methods, namely k-Means (KM), Partitioning around medoids (PAM), Ward, and Divisive analysis (Diana) are utilized and compared to achieve that purpose. Clustering 90 regencies/cities in Indonesia produced five optimal clusters. Furthermore, the clustering results were validated using the Silhouette width (SW) and Dunn index (DI). The results showed that the k-means method produced the most compact cluster. Hence, this study's results can be utilized as a reference for the government in determining the steps and priorities of economic policy in Indonesia.
Co-Authors Ahmad Sanmorino Juhaini Alie Rendra Gustriansyah Rudi Heriansyah