Penerapan Algoritma K-Means dalam Pengelompokan Keluarga Penerima Manfaat di Provinsi Lampung

Rahma Deani; Muhammad Herizal Habibi; Elok Dwi Yuliana; Gusti Fajri Nur Alamsyah; Nuari Anisa Sivi

doi:10.55606/juisik.v6i2.2416

Penerapan Algoritma K-Means dalam Pengelompokan Keluarga Penerima Manfaat di Provinsi Lampung

Authors

Rahma Deani Universitas Nahdlatul Ulama Lampung
Muhammad Herizal Habibi Universitas Nahdlatul Ulama Lampung
Elok Dwi Yuliana Universitas Nahdlatul Ulama Lampung
Gusti Fajri Nur Alamsyah Universitas Nahdlatul Ulama Lampung
Nuari Anisa Sivi Universitas Nahdlatul Ulama Lampung

DOI:

https://doi.org/10.55606/juisik.v6i2.2416

Keywords:

Clustering, Data Mining, K-Means, KPM, RapidMiner

Abstract

The distribution of social food assistance to Beneficiary Families (KPM) requires proper data management to ensure that assistance is delivered accurately and equitably. However, several challenges remain, including data inaccuracies and less effective data grouping processes. Based on these conditions, this study was conducted to cluster data on the number of beneficiary families and the allocation of social food assistance budgets across districts and cities in Lampung Province using the K-Means algorithm implemented through RapidMiner. The study utilized secondary data obtained from official publications of the Central Statistics Agency (BPS), covering 15 districts/cities with variables consisting of the number of beneficiaries and the amount of social assistance budgets allocated in each region. The results indicate that the data were grouped into three clusters with a satisfactory level of clustering quality. Three clusters were selected because they provide results that are easier to interpret. The clustering results reveal variations in the number of beneficiaries and the allocation of social assistance budgets across districts and cities, enabling a clearer analysis of regional characteristics and patterns of social assistance distribution.

References

Alifa, T. T., Astuti, R., & Basysyar, F. M. (2024). Implementasi data mining menggunakan algoritma K-means. JATI (Jurnal Mahasiswa Teknik Informatika), 8(1), 602–607. https://doi.org/10.36040/jati.v8i1.8340

Aria, R. R., & Susilowati, S. (2023). Data mining menentukan cluster penerima program bantuan dengan metode K-means. REMIK: Riset dan E-Jurnal Manajemen Informatika Komputer, 7(1), 291–300. https://doi.org/10.33395/remik.v7i1.12030

Badan Pusat Statistik. (2024). Jumlah keluarga penerima manfaat (KPM) dan anggaran bantuan sosial pangan menurut kabupaten/kota di Provinsi Lampung, 2024. Badan Pusat Statistik.

Briliant, D., & Tanti, L. (2025). Model pengelompokkan penerima bantuan sosial PKH dengan teknik data mining. Jurnal JSON, 7(2), 397–410. https://doi.org/10.30865/json.v7i2.9170

Damanik, Y. F. S. Y., Sumarno, Gunawan, I., Hartama, D., & Kirana, I. O. (2021). Penerapan data mining untuk pengelompokan penyebaran COVID-19. Jurnal Ilmu Komputer dan Informatika (JIKI), 1(2), 109–132. https://doi.org/10.54082/jiki.13

Fadhli, K., & Nazila, L. R. (2023). Pengaruh bantuan sosial BPNT dan PKH terhadap efektivitas penanggulangan kemiskinan. Education Journal, 11(2), 196–202. https://doi.org/10.37081/ed.v11i2.4654

Ganda, L., Putra, R., & Anggrawan, A. (2021). Pengelompokan penerima bantuan sosial masyarakat dengan metode K-means. Matrik: Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer, 21(1), 205–214. https://doi.org/10.30812/matrik.v21i1.1554

Hayati, U. (2024). Clustering penerima bantuan sosial menggunakan algoritma K-means. JATI (Jurnal Mahasiswa Teknik Informatika), 8(1), 574–579. https://doi.org/10.36040/jati.v8i1.8365

Kementerian Keuangan Republik Indonesia. (2017). Lembaran Negara Republik Indonesia Nomor 156 Tahun 2017 (pp. 1–22).

Manoppo, E. V., & Laoh, N. A. (2022). Strategi pemanfaatan data terpadu kesejahteraan sosial (DTKS) dalam penyaluran bantuan sosial RS-RTLH oleh Dinas Sosial Provinsi Sulawesi Utara. Jurnal Kebijakan, 4(1), 25–39. https://doi.org/10.33701/jk.v4i1.2598

Novitasari, N., Nuris, N. D., & Herdiana, R. (2023). Jurnal Informatika Terpadu. Jurnal Informatika Terpadu, 9(1), 68–73. https://doi.org/10.54914/jit.v9i1.660

Sholeh, M., & Aeni, K. (2023). Perbandingan evaluasi metode Davies Bouldin, Elbow, dan Silhouette pada model clustering. STRING (Satuan Tulisan Riset dan Inovasi Teknologi), 8(1). https://doi.org/10.30998/string.v8i1.16388

Siregar, A., Buono, A., & Priandana, K. (2022). Perbandingan algoritma K-means dan fuzzy C-means untuk clustering citra daun melon. Building of Informatics, Technology and Science (BITS), 4(3), 1503–1510. https://doi.org/10.47065/bits.v4i3.2534

Susanti, P. (2020). Implementasi Undang-Undang Nomor 13 Tahun 2011 dalam penanganan fakir miskin di bidang pendidikan dan pelayanan. Esensi Hukum, 2(2), 1–13. https://doi.org/10.35586/esensihukum.v2i2.36

Wakhidah, N. (n.d.). Clustering menggunakan K-means algorithm (K-means algorithm clustering).

Downloads

Published

2026-06-24

How to Cite

Rahma Deani, Muhammad Herizal Habibi, Elok Dwi Yuliana, Gusti Fajri Nur Alamsyah, & Nuari Anisa Sivi. (2026). Penerapan Algoritma K-Means dalam Pengelompokan Keluarga Penerima Manfaat di Provinsi Lampung. Jurnal Ilmiah Sistem Informasi Dan Ilmu Komputer, 6(2), 153–165. https://doi.org/10.55606/juisik.v6i2.2416