The Application of the K-Means Clustering Method in Grouping the Number of Poor People per Regency/City in Papua Province in 2023

Poverty is a serious problem faced by many developing countries and is still a major challenge in remote areas of Indonesia, especially in Papua Province. The high poverty rate in Papua Province makes this area the poorest province with the highest percentage of poor people in Indonesia in 2023. This study aims to classify Regencies/Cities in Papua Province based on the socio-economic conditions of the community using the K-Means Clustering. The data was obtained from the official source of the Central Statistics Agency of Papua Province using four main indicators, namely the percentage of the poor population, total population, the Human Development Index (IPM), and the Life Expectancy Rate (AHH). The analysis process was carried out iteratively until stability was achieved in the third iteration. The final results showed 3 clusters: Cluster 1 consists of 13 districts/cities with a moderate poverty level, cluster 2 consists of 7 districts/cities with a high poverty level, and cluster 3 consists of 9 districts/cities with a low poverty level. The high poverty levels in cluster 2 include: Jayawijaya Regency, Paniai Regency, Puncak Jaya Regency, Yahukimo Regency, Tolikara Regency, Lanny Jaya Regency, and Puncak Regency. Keyword: Clustering, K-Means, Poverty

Authors

Publication Details

Journal
Journal of Mathematics Technology and Education.
Published
2025-06-30
DOI
https://doi.org/10.32734/jomte.v4i2.22466
Primary Topic
Data Mining and Machine Learning Applications
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

The Application of the K-Means Clustering Method in Grouping the Number of Poor People per Regency/City in Papua Province in 2023

Tulus Joseph Herianto, Enita Dewi Tarigan, Priskilla Sahaduta Simbolon
Journal of Mathematics Technology and Education.
Data Mining and Machine Learning Applications
article

The Application of the K-Means Clustering Method in Grouping the Number of Poor People per Regency/City in Papua Province in 2023

Tulus Joseph Herianto, Enita Dewi Tarigan, Priskilla Sahaduta Simbolon
article en

Abstract

Poverty is a serious problem faced by many developing countries and is still a major challenge in remote areas of Indonesia, especially in Papua Province. The high poverty rate in Papua Province makes this area the poorest province with the highest percentage of poor people in Indonesia in 2023. This study aims to classify Regencies/Cities in Papua Province based on the socio-economic conditions of the community using the K-Means Clustering. The data was obtained from the official source of the Central Statistics Agency of Papua Province using four main indicators, namely the percentage of the poor population, total population, the Human Development Index (IPM), and the Life Expectancy Rate (AHH). The analysis process was carried out iteratively until stability was achieved in the third iteration. The final results showed 3 clusters: Cluster 1 consists of 13 districts/cities with a moderate poverty level, cluster 2 consists of 7 districts/cities with a high poverty level, and cluster 3 consists of 9 districts/cities with a low poverty level. The high poverty levels in cluster 2 include: Jayawijaya Regency, Paniai Regency, Puncak Jaya Regency, Yahukimo Regency, Tolikara Regency, Lanny Jaya Regency, and Puncak Regency. Keyword: Clustering, K-Means, Poverty

Journal of Mathematics Technology and Education.Vol. 4(2)
Openalex Percentile: Top 66%
Data Mining and Machine Learning Applications
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

The Application of the K-Means Clustering Method in Grouping the Number of Poor People per Regency/City in Papua Province in 2023 — Tulus Joseph Herianto, Enita Dewi Tarigan, et al. · Journal of Mathematics Technology and Education. (2025) | TGRS Research Map | TGRS