Implementation of the K-Means Method for Clustering Regency/City in North Sumatra based on Poverty Indicators


  • Syafira Eka Wardani Universitas Labuhanbatu, Indonesia
  • Syaiful Zuhri Harahap Universitas Labuhanbatu, Indonesia
  • Rahma Muti’ah Universitas Labuhanbatu, Indonesia




Clustering; K-Means; North Sumatra; Poverty; Silhouette


Poverty has many negative effects on people's lives, such as difficulty meeting basic needs, limited access to adequate health and education services, and limited economic opportunities. North Sumatra faces significant poverty problems as one of the largest provinces in Indonesia. This requires special attention and a thorough investigation. Reducing poverty is a very important issue for the government of North Sumatra Province. Poverty-alleviation strategies can no longer be applied uniformly. Instead, it is necessary to consider all the factors that cause poverty in each region. This means that the approach that must be given to each regency or city based on its poverty level must be adjusted. To overcome this problem, clustering must be carried out to identify areas with different levels of welfare. The aim of this research is to cluster regencies and cities in North Sumatra Province using the K-means method based on poverty indicator variables. This research only uses three poverty indicators: gross regional domestic product, human development index, and unemployment rate. The optimal number of clusters is determined based on the results of the silhouette coefficient. The research method begins with dataset collection, exploratory data analysis, data preprocessing, and k-means clustering. The value k = 6 produces a silhouette coefficient of 0.4135. This research produced six regency/city clusters. Cluster 1 consists of 11 regencies and 1 city; cluster 2 consists of 1 regency and 2 cities; cluster 3 consists of 4 regencies; cluster 4 consists of 7 regencies; cluster 5 consists of 4 cities; and cluster 6 consists of 2 regencies and 1 city. The variables gross regional domestic product, human development index, and unemployment rate have a big influence on the cluster results. This will enable the government to adopt policies to tackle poverty quickly and effectively.

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Abdul Rahman, M., Sani, N. S., Hamdan, R., Ali Othman, Z., & Abu Bakar, A. (2021). A clustering approach to identify multidimensional poverty indicators for the bottom 40 percent group. PLOS ONE, 16(8), e0255312. Retrieved from

Afira, N., & Wijayanto, A. W. (2021). Analisis Cluster Kemiskinan Provinsi di Indonesia Tahun 2019 dengan Metode Partitioning dan Hierarki. Komputika: Jurnal Sistem Komputer, 10(2), 101–109.

Aldawiyah, N. K., Astuti, A., Kurnia, R. D., Amalia, N. K., Amelia, D., Mardianto, M. F. F., & Ana, E. (2024). Analisis Faktor Kemiskinan di Provinsi Sumatera Utara berdasarkan Regresi Komponen Utama. Variance: Journal of Statistics and Its Applications, 6(1), 63–74.

Andriyani, W., Nasyuha, A. H., Syahra, Y., & Triaji, B. (2023). Clustering Analysis of Poverty Levels in North Sumatra Province Using the Application of Data Mining with the K-Means Algorithm. Jurnal Media Informatika Budidarma, 7(4), 1971–1979.

Annas, S., Poerwanto, B., Sapriani, S., & S, M. F. (2022). Implementation of K-Means Clustering on Poverty Indicators in Indonesia. MATRIK : Jurnal Manajemen, Teknik Informatika Dan Rekayasa Komputer, 21(2), 257–266.

Aprilia, K., & Sembiring, F. (2021). Analisis Garis Kemiskinan Makanan Menggunakan Metode Algoritma K-Means Clustering. SISMATIK (Seminar Nasional Sistem Informasi Dan Manajemen Informatika), 1–10.

Azzahra, A., & Wijayanto, A. W. (2022). Perbandingan Agglomerative Hierarchical dan K-Means Dalam Pengelompokkan Provinsi Berdasarkan Pelayanan Kesehatan Maternal. SISTEMASI: Jurnal Sistem Informasi, 11(2), 481–495. Retrieved from of Agglomerative Hierarchical and K-Means in Grouping Provinces Based on Maternal Health Services

Badan Pusat Statistik Sumatera Utara. (2024). Provinsi Sumatera Utara Dalam Angka 2024. In Badan Pusat Statistik Sumatera Utara (Vol. 52). Medan.

Damanik, R. K., & Sidauruk, S. A. (2020). Pengaruh Jumlah Penduduk Dan PDRB Terhadap Kemiskinan Di Provinsi Sumatera Utara. Jurnal Darma Agung, 28(3), 358.

Hanafiah, M. A., & Wanto, A. (2020). Implementation of Data Mining Algorithms for Grouping Poverty Lines by Regency/City in North Sumatra. International Journal of Information System & Technology, 3(2), 315–322.

Hasibuan, M. A. F., Cipta, H., & Dur, S. (2024). Model Based Clustering for Regency/City Grouping Based on Community Welfare Indicators in North Sumatra. Jurnal Pijar MIPA, 19(3), 479–487.

Indah, I. C., Sari, M. N., & Dar, M. H. (2023). Application of the K-Means Clustering Agorithm to Group Train Passengers in Labuhanbatu. Sinkron: Jurnal Dan Penelitian Teknik Informatika, 7(2), 825–837. e-ISSN

Istiqamah, N., Soesanto, O., & Anggraini, D. (2021). Application of the K-Means algorithm to determine poverty status in Hulu Sungai Tengah. Journal of Physics: Conference Series, 2106(1).

Kasim, R. J., Bahri, S., & Amir, S. (2021). Implementasi Metode K-Means Untuk Clustering Data Penduduk Miskin Dengan Systematic Random Sampling. Prosiding SISFOTEK, 5(1), 95–101.

Lubis, N. J. A., Harahap, S. Z., & Ritonga, I. (2024). Analisis K-Means dan Naive Bayes untuk Pengelompokan Rawan Bencana di Kabupaten Labuhanbatu. Informatika, 12(1), 81–92.

Marito, C., Nisa, F., Pardede, N. N., Erza, D. S. D. Y., Sinaga, D., Hidayat, N., & Suharianto, J. (2023). Analisis Tingkat Pengangguran Terbuka, Human Capital dan Jumlah Penduduk Terhadap Kemiskinan di Sumatera Utara. Jurnal Dinamika Sosial Budaya, 25(2), 287–300.

Masni, N. U. (2023). Analisis Faktor-Faktor yang Mempengaruhi Kemiskinan di Provinsi Sumatera Utara. Universitas Muhammadiyah Sumatera Utara.

Muliani, S. S., Sihombing, V., & Munthe, I. R. (2024). Implementation of Exploratory Data Analysis and Artificial Neural Networks to Predict Student Graduation on-Time. Sinkron: Jurnal Dan Penelitian Teknik Informatika, 8(2), 1188–1199.

Nasution, I., Windarto, A. P., & Fauzan, M. (2020). Penerapan Algoritma K-Means Dalam Pengelompokan Data Penduduk Miskin Menurut Provinsi. Building of Informatics, Technology and Science (BITS), 2(2), 76–83.

Ni’matuzzahroh, L., Andrea Tri Rian, D., & Adrianingsih, N. Y. (2022). Clustering Regencies/Cities in Kalimantan Island Based on Poverty Indicators using Agglomerative Hierarchical Clustering (AHC). Jurnal Matematika, Statistika, Dan Komputasi, 19(1), 79–89.

Novianti, A., Afnan, I. M., Utama, R. I. B., & Widodo, E. (2020). Grouping of Regencies Based on Poverty Factors in Papua Province Uses The K-Medoids Algorithm. Enthusiastic : International Journal of Applied Statistics and Data Science, 1(2), 94–102.

Novitasari, P., & Arovah, I. (2023). Penerapan Metode Clustering Average Linkage Untuk Mengelompokkan Kabupaten/Kota Di Provinsi Sumatera Utara Berdasarkan Indikator Kemiskinan. MathVvsion: Jurnal Matematika, 5(1), 22–27.

Putri, F. I., Damayanti, R., & Kismiantini. (2022). Penerapan Algoritma K-Means Untuk Mengelompokan Kecamatan Di Kabupaten Gunungkidul Berdasarkan Program Keluarga Harapan. Prosiding Seminar Nasional Matematika, Statistika, Dan Aplikasinya, 2, 408–418.

Raharja, F. I., & Trivinata, R. (2020). Klaster Wilayah Penanggulangan Kemiskinan di Kabupaten Malang. Jurnal Ilmiah Administrasi Publisk (JIAP), 6(2), 231–240.

Riani, R. A., & Sofro, A. (2023). Pengelompokan Berdasarkan Garis Kemiskinan Pendekatan Time Series Based Clustering di Provinsi Jawa Timue. MATHUNESA: Jurnal Ilmiah Matematika, 11(03), 478–488.

Rozaini, N., Maharani, S., Azhari, D., & Maisyaroh. (2024). Pengaruh Pertumbuhan Ekonomi dan Tingkat Pengangguran terhadap Kemiskinan di Provinsi Sumatera Utara. JESYA: Jurnal Ekonomi & Ekonomi Syariah, 7(1), 396–402.

Sachrrial, R. H., & Iskandar, A. (2023). Analisa Perbandingan Complate Linkage AHC dan K Medoids Dalam Pengelompokkan Data Kemiskinan di Indonesia. Building of Informatics, Technology and Science (BITS), 5(2).

Saputri, F. W., & Arianto, D. B. (2023). Perbandingan Performa Algoritma K-Means, K- Medoids, dan DBSCAN dalam Penggerombolan Provinsi di Indonesia berdasarkan Indikator Kesejahteraan Masyarakat. Jurnal Teknologi Informasi : Jurnal Keilmuan Dan Aplikasi Bidang Teknik Informatika, 17(2), 138–151.

Sari, J. (2019). Analisis Tingkat Kemiskinan Masyarakat Di Provinsi Sumatera Utara. Al-Masharif: Jurnal Ilmu Ekonomi Dan Keislaman, 7(2), 290–304.

Sari, N. I. (2022). Identifikasi Faktor-Faktor yang Mempengaruhi Kemiskinan Akibat Pandemi COVID-19 di Sumatera Utara dengan Metode Least Absolute Shrinkage and Selection Operator (LASSO) (Universitas Islam Negeri Sumatera Utara). Universitas Islam Negeri Sumatera Utara. Retrieved from

Setiawan, D., & Zahra, A. (2023). Pengelompokan Kemiskinan di Indonesia Menggunakan Time Series Based Clustering. Inferensi, 6(1), 83.

Sibarani, H., Solikhun, Saputra, W., Gunawan, I., & Nasution, Z. M. (2022). Penerapan Metode K-Means Untuk Pengelompokkan Kabupaten/Kota Di Provinsi Sumatera Utara Berdasarkan Indikator Indeks Pembangunan Manusia. JATI (Jurnal Mahasiswa Teknik Informatika), 6(1), 154–161.

Sihombing, S. C., & Sihombing, D. A. (2021). Pengelompokan Tingkat Kesejahteraan Masyarakat di Sumatera Utara dengan Metode K-Means Clustering. Jurnal Matematika Integratif, 17(2), 127.

Sitorus, M., Masrizal, M., & Muti’ah, R. (2023). Decision Making System for Visitor Satisfaction of Labuhanbatu Library Using K-Means Method. JINAV: Journal of Information and Visualization, 4(1), 36–44.

Sujarwo. (2022). K-Means Clustering Gross Participation Rate Regency/City Area In North Sumatra. Jurnal Mantik, 5(36), 2392–2399. Retrieved from

Suyani, N., Arnita, A., Nabila, R. C., & Fitria, A. (2023). Prediction of the Poor Rate K-Means and Generalized Regression Neural Network Algorithms (Case Study: North Sumatra Province). BAREKENG: Jurnal Ilmu Matematika Dan Terapan, 17(1), 0467–0474.

Syuhada, M. R., Yafiz, M., & Irham, M. (2024). Analisis Faktor-Faktor yang Mempengaruhi Tingkat Kemiskinan di Provinsi Sumatera Utara. Jurnal Masharif Al-Syariah: Jurnal Ekonomi Dan Perbankan Syariah, 9(1), 513–526. Volume

Utara, B. S. (2024a). Indeks Pembangunan Manusia (Metode Baru) 2021-2023. Retrieved from website:

Utara, B. S. (2024b). Jumlah Penduduk Miskin Menurut Kabupaten/Kota (Jiwa), 2021-2023. Retrieved from sumut bps website:

Utara, B. S. (2024c). Laju Pertumbuhan PDRB Menurut Lapangan Usaha Atas Dasar Harga Konstan 2022-2023. Retrieved from website:

Utara, B. S. (2024d). Tingkat Pengangguran Terbuka (TPT) Penduduk Umur 15 Tahun Keatas Manurut Kab/Kota (Persen), 2021-2023. Retrieved from website:


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How to Cite

Wardani, S. E. . . ., Harahap, S. Z. ., & Muti’ah, R. . (2024). Implementation of the K-Means Method for Clustering Regency/City in North Sumatra based on Poverty Indicators. Sinkron : Jurnal Dan Penelitian Teknik Informatika, 8(3), 1429-1442.

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