ENHANCED CLUSTERING USING PSO-KMEDOIDS FOR GOVERNMENT AID DISTRIBUTION
DOI:
https://doi.org/10.47080/gegxdv17Keywords:
Aid Priorities, K-Medoids, Particle Swarm Optimization, PovertyAbstract
The distribution of social assistance in Indonesia often experiences problems due to inaccuracies in recipient data between those recorded in government systems and field conditions. In Kalipuro Village, Mojokerto District, data mismatches caused difficulties in screening assistance, requiring village officials to manually re-filter the data. This triggered protests from citizens who should have received assistance but did not get their rights. To overcome this problem, this research proposes the use of the K-Medoids algorithm which is able to overcome sensitivity to outliers. This algorithm is used to cluster data based on criteria such as occupation, number of assets, number of dependents, and income. In addition, this research incorporates the Particle Swarm Optimization (PSO) technique to optimise the clustering process, which is expected to improve accuracy and efficiency in social assistance distribution. The results of clustering analysis using the K-Medoids algorithm show that the best cluster is obtained at the number of clusters K=5, with the distribution of cluster 0 (179 households), cluster 1(89 households), cluster 2 (296 households), cluster 3 (354 households), and cluster 4 (94 households). The Silhouette Score value of 0.6531 indicates good cohesion and separation between clusters. Based on the analysis, cluster 1 is the top priority group of aid recipients, followed by clusters 4, 2, 3, and 0. The K-Medoids algorithm effectively identifies the most needy community groups, supporting targeted and efficient decisions in aid distribution.
References
Alghifari, F., & Juardi, D. (2021). Penerapan Data Mining Pada Penjualan Makanan Dan Minuman Menggunakan Metode Algoritma Naïve Bayes. Jurnal Ilmiah Informatika, 9(02), 75–81. https://doi.org/10.33884/jif.v9i02.3755
BPS. (2024, July). Persentase Penduduk Miskin Maret 2024 turun menjadi 9,03 persen. - Badan Pusat Statistik Indonesia. https://www.bps.go.id/id/pressrelease/2024/07/01/2370/persentase-penduduk-miskin-maret-2024-turun-menjadi-9-03-persen-.html
Buulolo, E., Syahputra, R., & Fau, A. (2020). Algoritma K-Medoids Untuk Menentukan Calon Mahasiswa Yang Layak Mendapatkan Beasiswa Bidikmisi di Universitas Budi Darma. Jurnal Media Informatika Budidarma, 4(3), 797. https://doi.org/10.30865/mib.v4i3.2240
EMedia RI. (2024). Banyak Penyaluran Bansos Tak Tepat Sasaran, Legislator Minta Kemesos Segera Perbaiki DTKS - EMedia DPR RI. https://emedia.dpr.go.id/2024/05/21/banyak-penyaluran-bansos-tak-tepat-sasaran-legislator-minta-kemesos-segera-perbaiki-dtks/
Hu, Z. (2024). Comparison of K-Means, K-Medoids and K-Means++ algorithms based on the Calinski-Harabasz index for COVID-19 epidemic in China. Applied and Computational Engineering, 49(1), 11–20. https://doi.org/10.54254/2755-2721/49/20241046
Kemenko Perekonomian. (2018). Program Bantuan Sosial Untuk Rakyat - Kementerian Koordinator Bidang Perekonomian Republik Indonesia. https://ekon.go.id/publikasi/detail/1281/program-bantuan-sosial-untuk-rakyat
Muhima, R. R., Kurniawan, M., Wardhana, S. R., Yudhana, A., Sunardi, Rahmawati, W. M., & YuliastuƟ, G. E. (2018). Kupas Tuntas Algoritma Clustering Konsep, Perhitungan Manual dan Program.
Musaddad, A. A., & Kriswibowo, A. (2021). Optimalizing Big Data in Reducing Miss-Targeting Family Hope Program (PKH) in Sidoarjo Disctrict with Approach Machine Learning. IJCCS (Indonesian Journal of Computing and Cybernetics Systems), 15(1), 99. https://doi.org/10.22146/ijccs.62589
Novianti, A., Afnan, I. M., Utama, R. I. B., & Widodo, E. (2021). Grouping of Districts 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. https://doi.org/10.20885/enthusiastic.vol1.iss2.art6
Pratiwi, S. H., Witanti, W., Hendro, T., Achmad, U. J., & Abstract, Y. (2024). Optimasi Penentuan Vendor Untuk Material Pesawat Menggunakan Algoritma Particle Swarm Optimization. Jurnal Ilmiah Wahana Pendidikan, Februari, 2024(4), 825–837. https://doi.org/10.5281/zenodo.10537168
Radhi, M., Amalia, A., Sitompul, D. R. H., Sinurat, S. H., & Indra, E. (2022). Analisis Big Data Dengan Metode Exploratory Data Analysis (Eda) Dan Metode Visualisasi Menggunakan Jupyter Notebook. Jurnal Sistem Informasi Dan Ilmu Komputer Prima (JUSIKOM PRIMA), 4(2), 23–27. https://doi.org/10.34012/jurnalsisteminformasidanilmukomputer.v4i2.2475
Rispandi. (2023). Implementasi Metode K-Medoids Untuk Clustring Penerima Bantuan Berdasarkan Normalisasi Data Masyarakat Miskin Dengan Metode Desimal Scaling. Journal of Computing and Informatics Research, 3(1), 141–152. https://doi.org/10.47065/comforch.v3i1.1063
Shahapure, K. R., & Nicholas, C. (2020). Cluster quality analysis using silhouette score. Proceedings - 2020 IEEE 7th International Conference on Data Science and Advanced Analytics, DSAA 2020, 747–748. https://doi.org/10.1109/DSAA49011.2020.00096
Trimono, Farid, N., & Habibi, M. (2023). Statistika Deskriptif Pada Analisis Ketimpangan Kemiskinan (Studi Kasus Data Kemiskinan Wilayah Perkotaan dan Pedesaan Seluruh Provinsi di Indonesia periode tahun 2021-2022). Seminar Nasional Sains Data, 2023(Senada), 253–259.
Wijaya, D. B., Noersasongko, E., & Purwanto, P. (2024). Optimasi Centroid Awal Algoritma K-Medoids Menggunakan Particle Swarm Optimization Untuk Segmentasi Customer. Techno.Com, 23(1), 221–232. https://doi.org/10.62411/tc.v23i1.9516
