Klasterisasi Peserta KB Aktif di Desa Kalirejo Lawang Menggunakan Metode K-Means
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Jan 10, 2025
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The Family Planning (KB) program in Kalirejo Lawang Village faces challenges in the process of clustering active participants, which is time-consuming and prone to errors. Based on these challenges, a clustering solution using the K-Means algorithm was proposed. Experiments were conducted by testing the number of clusters from 2 to 8 and evaluating them using the silhouette score. The results of the study showed that the optimal number of clusters is two, as indicated by a silhouette score of 0.447. This value represents the best clustering quality compared to other cluster numbers, where the scores for clusters 3 to 8 did not exceed this value. This demonstrates that clustering into two groups provides the most optimal results. Lower scores for clusters 3 to 8 indicate that dividing the clusters into more groups did not create clear separations or worsened the cohesion within clusters. The conclusion of this study shows that the K-Means method can be applied and is reliable for clustering active KB participants in Kalirejo Lawang Village. With its speed and accuracy, K-Means offers a significant solution to improving the efficiency of the KB program at the village level. The practical implication of this research is to provide a more structured basis for planning and decision-making in the KB program at the village level. These findings mark an important step in optimizing the management of KB program data, opening opportunities for broader implementation in other areas.
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Ningrum, A. V. F. F., Anshori, M., & Pradini, R. S. (2025). Klasterisasi Peserta KB Aktif di Desa Kalirejo Lawang Menggunakan Metode K-Means. Jurnal Indonesia : Manajemen Informatika Dan Komunikasi, 6(1), 729-741. https://doi.org/10.35870/jimik.v6i1.1273
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de Amorim, R. C., & Makarenkov, V. (2023). On k-means iterations and Gaussian clusters. Neurocomputing, 553, 126547. https://doi.org/10.1016/j.neucom.2023.126547.
Demšar, J., Curk, T., Erjavec, A., Gorup, Č., Hočevar, T., Milutinovič, M., ... & Zupan, B. (2013). Orange: data mining toolbox in Python. the Journal of machine Learning research, 14(1), 2349-2353.
Estupiñán Ricardo, J., Domínguez Menéndez, J. J., Barcos Arias, I. F., Macías Bermúdez, J. M., & Moreno Lemus, N. (2021). Neutrosophic K-means for the analysis of earthquake data in Ecuador. Neutrosophic Sets and Systems, 44(1), 29.
Fitri, E. M., Suryono, R. R., & Wantoro, A. (2023). Klasterisasi Data Penjualan Berdasarkan Wilayah Menggunakan Metode K-Means Pada Pt Xyz. Jurnal Komputasi, 11(2), 157-168. https://doi.org/10.23960/komputasi.v11i2.12582.
Gao, C. X., Dwyer, D., Zhu, Y., Smith, C. L., Du, L., Filia, K. M., ... & Cotton, S. M. (2023). An overview of clustering methods with guidelines for application in mental health research. Psychiatry Research, 327, 115265. https://doi.org/10.1016/j.psychres.2023.115265.
Ginting, B. S., & Fatmaira, Z. (2022). Clustering Peserta Kb Aktif Di Kota Binjai Menggunakan Metode K-Means (Study Kasus BKKBN Kota Binjai). Jurnal Sistem Informasi Kaputama (JSIK), 6(2), 208-220.
Hakim, M. L., Ulandari, S., & Hidayati, N. Implementasi Pembelajaran Berbasis Masalah dalam Meningkatkan Kompetensi Pedagogik Guru Sejarah Kebudayaan Islam. Jurnal Pendidikan Sultan Agung, 4(3), 223-234.
Haraty, R. A., Dimishkieh, M., & Masud, M. (2015). An enhanced k-means clustering algorithm for pattern discovery in healthcare data. International Journal of distributed sensor networks, 11(6), 615740. https://doi.org/10.1155/2015/615740.
Hutagalung, J. (2022). Pemetaan Siswa Kelas Unggulan Menggunakan Algoritma K-Means Clustering. JATISI (Jurnal Teknik Informatika dan Sistem Informasi), 9(1), 606-620. https://doi.org/10.35957/jatisi.v9i1.1516.
Hutagalung, J., & Sonata, F. (2021). Penerapan Metode K-Means Untuk Menganalisis Minat Nasabah. Jurnal Media Informatika Budidarma, 5(3), 1187-1194.
Lino, M., Jedo, A., & Adam, C. V. (2021). Identifikasi faktor-faktor yang mempengaruhi pengambilan keputusan pasangan usia subur dalam mengikuti program kb (studi kasus di desa leraboleng kecamatan titehena kabupaten flores timur). Jurnal Administrasi dan Demokrasi (Administration and Democracy Journal), 1(2), 100-123. https://doi.org/https://doi.org/10.35508/jad.v1i2.5599.
Maharani, S., & Yotenka, R. (2024). Pengelompokan Kecamatan di Daerah Istimewa Yogyakarta berdasarkan Jumlah Pengguna Alat Kontrasepsi Tahun 2022 dengan K-Medoids Cluster: Pengelompokan Kecamatan di Daerah Istimewa Yogyakarta berdasarkan Jumlah Pengguna Alat Kontrasepsi Tahun 2022 dengan K-Medoids Cluster. Emerging Statistics and Data Science Journal, 2(2), 222-237. https://doi.org/https://doi.org/10.20885.16.
Munawar, F., Utami, A. S. D., & Manurung, S. B. T. (2024). KLASTERISASI DAERAH PESERTA KB AKTIF DI KABUPATEN ASAHAN MENGGUNAKAN METODE K-MEANS. J-Com (Journal of Computer), 4(1), 58-67. https://doi.org/https://doi.org/10.33330/j-com.v4i1.3047.
Nafilah, M., Rahaningsih, N., & Dana, R. D. (2024). PERBANDINGAN ALGORITMA K-MEANS DAN K-MEDOIDS UNTUK PENGELOMPOKAN HASIL PERTANIAN DI KABUPATEN CIREBON. JATI (Jurnal Mahasiswa Teknik Informatika), 8(1), 223-229. https://doi.org/10.36040/jati.v8i1.8326.
Nithya, A., Appathurai, A., Venkatadri, N., Ramji, D. R., & Palagan, C. A. (2020). Kidney disease detection and segmentation using artificial neural network and multi-kernel k-means clustering for ultrasound images. Measurement, 149, 106952. https://doi.org/10.1016/j.measurement.2019.106952.
Nur, I. M. (2024). K-Means++ Algorithm for Health Services Clustering Based on Districts in West Java Province. EKSAKTA: Journal of Sciences and Data Analysis, 96-102.
Nurahman, N., & Susanto, J. (2023). Klasterisasi Data Penerima Bantuan Langsung Tunai Menggunakan Algoritma K-Means. JURIKOM (Jurnal Riset Komputer), 10(2), 461-470. https://doi.org/10.30865/jurikom.v10i2.5807.
Pratistha, R. N., & Kristianto, B. (2024). Implementasi Algoritma K-Means dalam Klasterisasi Kasus Stunting pada Balita di Desa Randudongkal. Jurnal Indonesia: Manajemen Informatika dan Komunikasi, 5(2), 1193-1205. https://doi.org/10.35870/jimik.v5i2.634.
Rahmawati, G., Sanmas, S. A., Nudyawati, E., Syaharani, N. D., Annas, M. R., & Fauzi, F. (2024, October). Studi Perbandingan Performa: Prediksi Status Stunting Pada Anak Berdasarkan Data Antropometri Menggunakan Algoritma Support Vector Machine (SVM) dan K-Nearest Neighbors (KNN). In PROSIDING SEMINAR NASIONAL SAINS DATA (Vol. 4, No. 1, pp. 782-790). https://doi.org/10.47065/bits.v6i1.5206.
Raihannabil, S. D., Ilyas, H. M. A., Shafira, H. N., Riani, M. A., Hastin, N. N., & Siregar, T. K. H. (2024, November). Perbandingan Agglomerative Nesting dan K-Means untuk Klasterisasi Ketimpangan Gender berdasarkan Dimensi Kesehatan Reproduksi. In Seminar Nasional Official Statistics (Vol. 2024, No. 1, pp. 459-470). https://doi.org/https://doi.org/10.34123/semnasoffstat.v2024i1.1977.
Sahputra, R. R., & Kurniawan, R. (2024). KLASTERISASI DATA BALITA STUNTING DI KECAMATAN WILAYAH KABUPATEN CIAMIS BERDASARKAN PREVALENSI DENGAN MENGGUNAKAN ALGORITMA K-MEANS. JATI (Jurnal Mahasiswa Teknik Informatika), 8(2), 2451-2461. https://doi.org/10.36040/jati.v8i2.9027.
Saputri, B. D., & Indriani, D. (2022). Pemetaan Cakupan Pengguna KB Aktif Dan Unmet Need Di Kabupaten Ponorogo Tahun 2021. J Statistika: Jurnal Ilmiah Teori dan Aplikasi Statistika, 15(1). https://doi.org/10.36456/jstat.vol15.no1.a5446.
Shahapure, K. R., & Nicholas, C. (2020, October). Cluster quality analysis using silhouette score. In 2020 IEEE 7th international conference on data science and advanced analytics (DSAA) (pp. 747-748). IEEE. https://doi.org/10.1109/DSAA49011.2020.00096.
Susilo, P. H. (2021). Klasterisasi Virus Covid-19 Di Wilayah Kabupaten Lamongan Dengan Metode K-Means Clustering. JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika), 6(2), 325-335. https://doi.org/10.29100/jipi.v6i2.1999.
Tifannii, W. F., Mayasari, M., & Rifai, M. (2020). Implementasi program keluarga berencana (kb) dalam upaya menekan pertumbuhan penduduk di kelurahan sumur batu kecamatan bantar gebang kota bekasi. Dinamika: Jurnal Ilmiah Ilmu Administrasi Negara, 7(3), 525-540. https://doi.org/http://dx.doi.org/10.25157/dinamika.v7i3.4348.