Main Content
Lembaga Kita Journal Portal

Abstract

Poverty stands as a complex structural obstacle within social development frameworks. The COVID-19 pandemic intensified poverty dynamics in Indonesia which saw poverty rates increase by 9.78% in March and reach 10.19% by September. Local Bureau of Statistics data shows that the poverty rate in Cilacap Regency dropped to 10.99% (around 191,000 people) in March 2024 from 10.68% (186,080 people) in March 2023. The study uses k-means clustering methodology for analysis and maps poverty-prone areas utilizing QGIS software. The analysis revealed 12 sub-districts and 14 neighborhood units (RW) alongside a single community unit (RT) that show unique poverty characteristics. The silhouette coefficient evaluation produced a 0.55 score which showed a moderate cluster structure and acceptable cluster placement. The research provides empirical evidence about poverty distribution which shows how data mining methods can enhance spatial socioeconomic studies. The study presents a detailed analysis of poverty stratification across Cilacap Regency through the application of sophisticated computational methods.

Keywords

Poverty Clustering K-Means

How to Cite

Citation Format
Setiawan, K., Kastum, & Pratama, Y. P. (2025). K-Means Clustering Analysis of Poverty Data in Cilacap District. International Journal Software Engineering and Computer Science (IJSECS), 5(1), 53-62. https://doi.org/10.35870/ijsecs.v5i1.3759

References

31 References
  • Solichin, A., & Khairunnisa, K. (2020). Klasterisasi persebaran virus Corona (Covid-19) di DKI Jakarta menggunakan metode K-Means. Fountain of Informatics Journal, 5(2), 52-59.
  • Alfina, T., Santosa, B., & Barakbah, A. R. (2012). Analisa perbandingan metode hierarchical clustering, k-means dan gabungan keduanya dalam cluster data (studi kasus: Problem kerja praktek teknik industri its). Jurnal Teknik ITS, 1(1), A521-A525.
  • Aria, P. (2020, April 7). Globalization and the World Supply Chain Locked by the Covid-19 Pandemic.
  • Anggraini, A. A., Muharom, L. A., & Si, M. (2017). Pengelompokan Kecamatan Menggunakan Metode K-Means Cluster. Universitas Muhammadiyah Jember. http://repository.unmuhjember.ac.id/591
  • Nosra, A., Arifianto, D., & Rahman, M. (2022). Penerapan Metode Cosine Similarity Untuk Meningkatkan Kinerja K-Means Pada Pengelompokan Wilayah Penanganan Covid Di DKI Jakarta. Jurnal Smart Teknologi, 3(5), 560-566.
  • Davies, D. L., & Bouldin, D. W. (1979). A cluster separation measure. IEEE Transactions on Pattern Analysis and Machine Intelligence, (2), 224-227. https://doi.org/10.1109/TPAMI.1979.4766909
  • Nurjannah, M., Hamdani, H., & Astuti, I. F. (2016). Penerapan Algoritma Term Frequency-Inverse Document Frequency (TF-IDF) untuk Text Mining. Informatika Mulawarman: Jurnal Ilmiah Ilmu Komputer, 8(3), 110-113. http://dx.doi.org/10.30872/jim.v8i3.113
  • Noviyanto, N. (2020). Penerapan Data Mining dalam Mengelompokkan Jumlah Kematian Penderita COVID-19 Berdasarkan Negara di Benua Asia. Jurnal Khatulistiwa Informatika, 22(2), 183-188. https://doi.org/10.31294/p.v21i2
  • Noviyanto, N., & Ekasari, P. (2022). Algoritma K-Means Untuk Klasterisasi Jabatan Fungsional Dosen Pada Perguruan Tinggi Swasta Di Lingkungan LLDikti Wilayah III. Paradigma - Jurnal Komputer Dan Informatika, 24(1), 103-107. https://doi.org/10.31294/paradigma.v24i1.1112
  • Fitri, R., & Asyikin, A. N. (2015). Aplikasi penilaian ujian essay otomatis menggunakan metode cosine similarity. Poros Teknik, 7(2), 88-94. https://doi.org/10.31961/porosteknik.v7i2.218
  • Siahaan, D., & Christina, S. (2015). Using Semantic Similarity for Identifying Relevant Page Numbers for Indexed Term of Textual Book. In R. Intan, C. H. Chi, H. Palit, & L. Santoso (Eds.), Intelligence in the Era of Big Data (pp. XX-XX). Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-46742-8_17
  • Triharseno, W., Dhuhita, W. M. P., & Priadana, A. (2020). Sistem Pendukung Keputusan Penerimaan Programer Software House Menggunakan Metode Simple Additive Weighting (SAW). JURNAL PILAR TEKNOLOGI Jurnal Ilmiah Ilmu Ilmu Teknik, 5(1). https://doi.org/10.33319/piltek.v5i1.52
  • Wu, X., Kumar, V., Ross Quinlan, J., Ghosh, J., Yang, Q., Motoda, H., ... & Steinberg, D. (2008). Top 10 algorithms in data mining. Knowledge and Information Systems, 14, 1-37. https://doi.org/10.1007/s10115-007-0114-2
  • Alsharkawi, A., Al-Fetyani, M., Dawas, M., Saadeh, H., & Yaman, M. (2021). Poverty classification using machine learning: The case of Jordan. Sustainability, 13(3), 1412. https://doi.org/10.3390/su13031412
  • Li, G., Cai, Z., Qian, Y., & Chen, F. (2021). Identifying urban poverty using high-resolution satellite imagery and machine learning approaches: Implications for housing inequality. Land, 10(6), 648. https://doi.org/10.3390/land10060648
  • Lopes, M., Silva, L., Silva, S., Lima, W., Barbosa, R., & Fernandes, M. (2023). Self-organising maps applied to the stratification of preterm birth risk in Brazil with socioeconomic data. https://doi.org/10.21203/rs.3.rs-3676862/v1
  • Gao, C., Fei, C., McCarl, B., & Leatham, D. (2020). Identifying vulnerable households using machine learning. Sustainability, 12(15), 6002. https://doi.org/10.3390/su12156002
  • Ramadhan, R., Wijayanto, A., & Pramana, S. (2023). Geospatial big data approaches to estimate granular level poverty distribution in East Java, Indonesia using machine learning and deep learning regressions. Proceedings of the International Conference on Data Science and Official Statistics, 2023(1), 186-200. https://doi.org/10.34123/icdsos.v2023i1.359
  • Nofriani, N. (2020). Machine learning application for classification prediction of household's welfare status. Jitce (Journal of Information Technology and Computer Engineering), 4(2), 72-82. https://doi.org/10.25077/jitce.4.02.72-82.2020
  • Novitasari, M., & Iskandar, D. (2022). Spatial spillover impact of sectoral government expenditure on poverty alleviation in South Kalimantan Province. The Journal of Indonesia Sustainable Development Planning, 3(3), 207-221. https://doi.org/10.46456/jisdep.v3i3.361
  • Nugraha, A., Prayitno, G., Nandhiko, L., & Nasution, A. (2022). Socioeconomic conditions on poverty levels a case study: Central Java Province and Yogyakarta in 2016. Revista De Economia E Sociologia Rural, 60(1). https://doi.org/10.1590/1806-9479.2021.233206
  • Hidayati, S., Darmaliana, A., & Riski, R. (2022). Comparison of k-means, fuzzy c-means, fuzzy gustafson kessel, and DBSCAN for village grouping in Surabaya based on poverty indicators. Jurnal Pendidikan Matematika (Kudus), 5(2), 185. https://doi.org/10.21043/jpmk.v5i2.16552
  • Fitriana, I., & Mabruri, M. (2022). Cluster analysis of COVID-19 impact on poverty in Indonesia using self-organizing map algorithm. Jurnal Aplikasi Statistika & Komputasi Statistik, 14(1), 85-94. https://doi.org/10.34123/jurnalasks.v14i1.389
  • Erda, G., Gunawan, C., & Erda, Z. (2023). Grouping of poverty in Indonesia using K-means with silhouette coefficient. Parameter Journal of Statistics, 3(1), 1-6. https://doi.org/10.22487/27765660.2023.v3.i1.16435
  • Riyono, J., & Pujiastuti, C. (2022). Simulation of the K-means clustering algorithm with the elbow method in making clusters of provincial poverty levels in Indonesia. Jurnal Matematika Mantik, 8(2), 113-123. https://doi.org/10.15642/mantik.2022.8.2.113-123
  • Ardini, A., & Sirait, H. (2023). Comparison of K-medoids and CLARA algorithm in poverty clustering analysis in Indonesia. Operations Research International Conference Series, 4(4), 141-148. https://doi.org/10.47194/orics.v4i4.279
  • Poerwanto, B. (2023). K-means – resilient backpropagation neural network in predicting poverty levels. Jurnal Varian, 6(2), 157-166. https://doi.org/10.30812/varian.v6i2.2756
  • Yuningsih, I., & Aryani, M. (2024). Comparison of methods for applying the data mining clustering concept to Banten provincial government poverty data: Systematic review. Jurnal Ilmu Sosial Mamangan, 13(1), 121-134. https://doi.org/10.22202/mamangan.v13i1.8074
  • Fitriana, I., & Mabruri, M. (2022). Cluster analysis of COVID-19 impact on poverty in Indonesia using self-organizing map algorithm. Jurnal Aplikasi Statistika & Komputasi Statistik, 14(1), 85-94. https://doi.org/10.34123/jurnalasks.v14i1.389
  • Rahman, M., Sani, N., Hamdan, R., Othman, Z., & Bakar, A. (2021). A clustering approach to identify multidimensional poverty indicators for the bottom 40 percent group. Plos One, 16(8), e0255312. https://doi.org/10.1371/journal.pone.0255312
  • Alfiah, F., Almadayani, A., Farizi, D., & Widodo, E. (2021). Analisis clustering K-medoids berdasarkan indikator kemiskinan di Jawa Timur tahun 2020. Jurnal Ilmiah Sains, 22(1), 1. https://doi.org/10.35799/jis.v22i1.35911.

Author Biographies

Kiki Setiawan, Sekolah Tinggi Ilmu Komputer Cipta Karya Informatika

Information Systems Study Program, Faculty of Computer Science, Sekolah Tinggi Ilmu Komputer Cipta Karya Informatika, East Jakarta City, Special Capital Region of Jakarta, Indonesia

Kastum, Sekolah Tinggi Ilmu Komputer Cipta Karya Informatika

Information Systems Study Program, Faculty of Computer Science, Sekolah Tinggi Ilmu Komputer Cipta Karya Informatika, East Jakarta City, Special Capital Region of Jakarta, Indonesia

Yuliya Putri Pratama, Sekolah Tinggi Ilmu Komputer Cipta Karya Informatika

Information Systems Study Program, Faculty of Computer Science, Sekolah Tinggi Ilmu Komputer Cipta Karya Informatika, East Jakarta City, Special Capital Region of Jakarta, Indonesia

License

Copyright (c) 2025 Kiki Setiawan, Kastum, Yuliya Putri Pratama

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

This is an open-access journal distributed under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, reproduction, and adaptation in any medium (even commercially), provided appropriate credit is given to the original author(s), a link to the license is provided, and any changes made are indicated. Authors retain copyright without restrictions and grant the journal the right of first publication as well as non-exclusive publishing rights for archiving, indexing, and scholarly dissemination.

Downloads

OJS UsageStats Plugin
Download data is not yet available.

Similar Articles

You may also start an advanced similarity search for this article.

Most read articles by the same author(s)

Contrimetric

Recommended Articles