Pengelompokan Masyarakat Kurang Mampu Dengan Menggunakan Algoritma K-Means Data Mining
- Evan Edward Siagian Institut Teknologi dan Bisnis Indonesia, Medan, Indonesia
- Irfansyah Nuddin Lubis Institut Teknologi dan Bisnis Indonesia, Medan, Indonesia
- Monita Setya Institut Teknologi dan Bisnis Indonesia, Medan, Indonesia
- Ade Dermawan Sijabat Institut Teknologi dan Bisnis Indonesia, Medan, Indonesia
-
David JM Sembiring
*
Institut Teknologi dan Bisnis Indonesia, Medan,
Indonesia
- Meiliyani Br Ginting Institut Teknologi dan Bisnis Indonesia, Medan, Indonesia (*) Corresponding Author
Abstract
Some villages often experience difficulties in classifying economically disadvantaged communities, resulting in the distribution of social assistance sometimes being misdirected. Various grants are received, such as subsidies provided to the poor. Problems encountered include poorly managed community data, which complicates the analysis process, and the lack of a measurable grouping method, which often misdirects aid. Without an objective, data-driven grouping system, aid distribution errors will continue to recur, resulting in misdirected aid. To address these issues, one solution is the use of data mining techniques. In the past, big data management was often done manually or using conventional methods that required significant time, effort, and expense. Data mining is the process of exploring and analyzing large data sets to discover patterns, relationships, or important information that can support decision-making. The K-Means algorithm is a clustering method in data mining used to group data into groups (clusters) based on similar characteristics. The purpose of this study is to design and implement a system for grouping poor communities based on the K-Means algorithm that can assist village governments in distributing aid precisely to targets, accelerate the data analysis process, and reduce aid distribution errors. This study uses 30 population data with 5 attributes: occupation, income, dependents, home ownership, and assets. The method used in this study is the K-Means Algorithm. From the calculations that have been carried out, it is recommended that there are 3 clusters with the same results, namely cluster 1 with 10 residents, cluster 2 with 10 residents, and cluster 3 with 10 residents as well.
References
R. Aditiya Perdana and P. Aisyiyah Rakhma Devi, “Impelementasi Data Mining Untuk Clustering Penduduk Miskin Di Desa Setrohadi Menggunakan Algoritma Pillar K-Means,” JATI (Jurnal Mhs. Tek. Inform., vol. 8, no. 3, pp. 3780–3787, 2024, doi: 10.36040/jati.v8i3.9796.
V. Hutagaol, D. Anggraeni, and A. Nata, “Penerapan Data Mining Untuk Clustering Masyarakat Kurang Mampu Pada Desa Rawang Pasar Vi Dengan Algoritma K-Means,” JIPI (Jurnal Ilm. Penelit. dan Pembelajaran Inform., vol. 10, no. 1, pp. 304–312, 2025, doi: 10.29100/jipi.v10i1.5741.
S. Amaliyah, Jasmir, and S. Rianti, “Penerapan Data Mining Untuk Menentukan Kelompok Prioritas Penerima Bantuan PKH Menggunakan Metode Clustering K-Means Pada Desa Kuala Dendang,” J. Inform. Dan Rekayasa Komputer(JAKAKOM), vol. 3, no. 1, pp. 453–458, 2023, doi: 10.33998/jakakom.2023.3.1.802.
F. Febriansyah and S. Muntari, “Penerapan Algoritma K-Means untuk Klasterisasi Penduduk Miskin pada Kota Pagar Alam,” JISKA (Jurnal Inform. Sunan Kalijaga), vol. 8, no. 1, pp. 66–77, 2023, doi: 10.14421/jiska.2023.8.1.66-77.
Z. Sitorus, “Penerapan Data Mining Untuk Clustering Penduduk Miskin Di Kota Tanjungbalai Menggunakan Metode Algoritma K-Means,” J. Sci. Soc. Res., vol. 4307, no. 1, pp. 212–218, 2024, [Online]. Available: http://jurnal.goretanpena.com/index.php/JSSR
A. Waruwu, M. Yetri, and F. Setiawan, “Implementasi Data Mining Dalam Mengelompokkan Data penduduk Kurang Mampu Menggunakan Metode K-Means Clustering,” J. Sist. Inf. Triguna Dharma (JURSI TGD), vol. 2, no. 6, p. 945, 2023, doi: 10.53513/jursi.v2i6.8965.
B. H. Prakoso, E. Rachmawati, D. R. P. Mudiono, V. Vestine, and G. E. J. Suyoso, “Klasterisasi Puskesmas dengan K-Means Berdasarkan Data Kualitas Kesehatan Keluarga dan Gizi Masyarakat,” J. Buana Inform., vol. 14, no. 01, pp. 60–68, 2023, doi: 10.24002/jbi.v14i01.7105.
S. Ramadani, I. Ambarita, and A. M. H. Pardede, “Metode K-Means Untuk Pengelompokan Masyarakat Miskin Dengan Menggunakan Jarak Kedekatan Manhattan City Dan Euclidean ( Studi Kasus Kota Binjai ),” Inf. Syst. Dev., vol. 04, no. 2, pp. 15–29, 2019.
S. Suryadi, “Penerapan Metode Clustering K-Means Untuk Pengelompokan Kelulusan Mahasiswa Berbasis Kompetensi,” J. Inform., vol. 6, no. 1, pp. 52–72, 2019, doi: 10.36987/informatika.v6i1.738.
P. A. J. Dina Sunia, Kurniabudi, “Penerapan Data Mining untuk Clustering Data Penduduk Miskin Menggunakan Algoritma K-Means,” J. Ilm. Mhs. Tek. Inform., vol. Vol 1 No 2, no. 2016, pp. 121–134, 2019.
I. Nasution, A. P. Windarto, and M. Fauzan, “Penerapan Algoritma K-Means Dalam Pengelompokan Data Penduduk Miskin Menurut Provinsi,” Build. Informatics, Technol. Sci., vol. 2, no. 2, pp. 76–83, 2020, doi: 10.47065/bits.v2i2.492.
M. I. Zuhendra, R. Hidayat, and H. Hendrawaty, “Penerapan Data Mining Untuk Klasterisasi Tingkat Kemiskinan Berdasarkan Data Terpadu Kesejahteraan Sosial (Dtks),” SKANIKA Sist. Komput. dan Tek. Inform., vol. 7, no. 1, pp. 32–41, 2024, doi: 10.36080/skanika.v7i1.3149.
D. L. Fay, “Pengantar Data Mining,” Angew. Chemie Int. Ed. 6(11), 951–952., 1967.
A. Hidayat, Nurhidayati, and amri muliawan Nur, “Implementasi Algoritma K-Means Untuk Klasterisasi Peserta,” Print. J. Pengemb. Rekayasa Inform. dan Komput., vol. 1, no. 2, pp. 154–166, 2023.
R. R. A. Aria, S. Susilowati, and I. R. Rahadjeng, “Data Mining Menentukan Cluster Penerima Program Bantuan dengan Metode K-Means,” Remik, vol. 7, no. 1, pp. 291–300, 2023, doi: 10.33395/remik.v7i1.12030.
D. Suhardinata, A. K. Ningsih, F. Kasyidi, U. Jenderal, and A. Yani, “Klasterisasi Data Penduduk Untuk Penerimaan Bantuan Pangan Non Tunai (BPNT) Menggunakan K-Means (Studi Kasus : Desa Tanimulya Bandung Barat),” IJESPG (International J. Eng. Econ. Soc. Polit. Gov., vol. 1, no. 3, pp. 221–228, 2023, [Online]. Available: http://ijespgjournal.org/index.php/ijespg/article/view/55
A. Fatlun, S. S. Hilabi, B. Priyatna, and A. L. Hananto, “Penggunaan Algoritma K-Means Clustering CRISP-DM Dalam Mengelompokkan Drama Korea Sebagai Rekomendasi Film,” JATISI (Jurnal Tek. Inform. dan Sist. Informasi), vol. 12, no. 1, pp. 130–140, 2025, doi: 10.35957/jatisi.v12i1.11347.
Egy Andryan, A. Abdullah, and P. Y. Utami, “Clustering Penduduk Kurang Mampu Di Desa Mekar Baru Menggunakan Algoritma K-Means,” J. Tek. Komput. AMIK BSI, vol. 8, no. 2, pp. 174–180, 2022, doi: 10.31294/jtk.v4i2.
A. Setyo Nugroho, “Penerapan Metode K-Means Clustering Untuk Penentuan Eligibilitas Bantuan Langsung Tunai Berbasis Mobile,” J. Inf. Technol. Ampera, vol. 5, no. 1, pp. 2774–2121, 2024, doi: 10.51519/journalita.v5i1.510.
Dayla May Cytry, S. Defit, and G. Nurcahyo, “Penerapan Metode K-Means dalam Klasterisasi Status Desa terhadap Keluarga Beresiko Stunting,” J. KomtekInfo, vol. 10, no. 3, pp. 122–127, 2023, doi: 10.35134/komtekinfo.v10i3.423.
Bila bermanfaat silahkan share artikel ini
Berikan Komentar Anda terhadap artikel Pengelompokan Masyarakat Kurang Mampu Dengan Menggunakan Algoritma K-Means Data Mining
Pages: 194 - 203
Copyright (c) 2025 Evan Edward Siagian, Irfansyah Nuddin Lubis, Monita Setya, Ade Dermawan Sijabat, David JM Sembiring, Meiliyani Br Ginting
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under Creative Commons Attribution 4.0 International License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (Refer to The Effect of Open Access).
