Analisis Kinerja Algoritma K-Means dan K-Medoids dalam Pengelompokan Penerima Bantuan Sosial di Kelurahan Terjun
DOI:
https://doi.org/10.55606/juisik.v5i2.1293Keywords:
Clustering, Data Mining, K-Means, K-Medoids, Social AssistanceAbstract
The advancement of digital technology has improved data management, including in the distribution of social assistance. However, the large volume of data and the similarity of community characteristics often hinder the manual determination of aid recipients. This study analyzes the performance of two clustering algorithms, K-Means and K-Medoids, in grouping social assistance recipients in Kelurahan Terjun. Using a quantitative approach and data mining techniques based on clustering. The data is divided into three groups: Eligible, Not Eligible, and Requires Validation. The results show that although both algorithms produce similar clustering patterns, K-Medoids demonstrates better performance in cluster distribution and visualization. Cluster visualization using PCA indicates that K-Medoids forms clearer cluster boundaries and more balanced data distribution compared to K-Means. It can be concluded that K-Medoids outperforms in clustering social assistance recipient data and can serve as a more efficient alternative for targeted aid distribution.
References
Alfarizi, M. R. S., Al-farish, M. Z., Taufiqurrahman, M., Ardiansah, G., & Elgar, M. (2023). Penggunaan Python sebagai bahasa pemrograman untuk machine learning dan deep learning. Karya Ilmiah Mahasiswa Bertauhid (KARIMAH TAUHID), 2(1), 1–6.
Alriscki, D. G., & Fauzan, A. (2024). Peningkatan distribusi bantuan sosial di Pangkalpinang dengan pengelompokan berbantuan algoritma K-Means. Jurnal Teknologi dan Sistem Komputer, 24(2), 191–199.
Dewi, D. A. I. C., & Pramita, D. A. K. (2019). Analisis perbandingan metode Elbow dan Silhouette pada algoritma clustering K-Medoids dalam pengelompokan produksi kerajinan Bali. Matrix: Jurnal Manajemen Teknologi dan Informatika, 9(3), 102–109. https://doi.org/10.31940/matrix.v9i3.1662
Dinda Anjani, I., & Bahtiar, A. (2024). Penerapan algoritma K-Means clustering untuk mengelompokkan penerima bantuan sosial tunai (BST) di Jawa Barat. JATI (Jurnal Mahasiswa Teknik Informatika), 8(3), 2743–2747. https://doi.org/10.36040/jati.v8i3.8974
Fathia Palembang, C., Yahya Matdoan, M., Palembang, S. P., & Kunci, K. (2022). Perbandingan algoritma K-Means dan K-Medoids dalam pengelompokkan tingkat kebahagiaan provinsi di Indonesia. BULLET: Jurnal Multidisiplin Ilmu, 1(5), 830–839.
Fauzi Wijaya, Y. (2024). Implementasi data mining untuk penerima bantuan PKH pemerintah dengan menerapkan algoritma klastering K-Medoids. Journal of Computer System and Informatics (JoSYC), 5(3), 506–515. https://doi.org/10.47065/josyc.v5i3.5197
Kamila, I., Khairunnisa, U., & Mustakim, M. (2019). Perbandingan algoritma K-Means dan K-Medoids untuk pengelompokan data transaksi bongkar muat di Provinsi Riau. Jurnal Ilmiah Rekayasa dan Manajemen Sistem Informasi, 5(1), 119. https://doi.org/10.24014/rmsi.v5i1.7381
Khoirunnisa, F., & Rahmawati, Y. (2024). Komparasi 2 metode cluster dalam pengelompokan intensitas bencana alam di Indonesia. Jurnal Informatika dan Teknik Elektro Terapan, 12(1), 68–79. https://doi.org/10.23960/jitet.v12i1.3619
Muharizki, M. I., & Arianto, D. B. (2023). Clustering dengan metode K-Means terhadap statistik permainan pro-player Valorant pada kompetisi Valorant Champions 2022. Serunai: Jurnal Ilmiah Ilmu Komputer dan Teknologi Informasi, 9, 40–47. https://ejournal.stkipbudidaya.ac.id/index.php/ja/article/view/846
Rahayu, S., & Kartini, A. Y. (2021). Algoritma K-Means dan K-Medoids untuk pengelompokan kecamatan penerima bantuan sosial di Kabupaten Bojonegoro. Media Bina Ilmiah, 16(5), 6815–6822.
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