PENERAPAN ALGORITMA K-MEANS CLUSTERING UNTUK ANALISIS POLA POPULASI TERNAK DI KOTA BANDUNG BERBASIS DATA MINING
Abstract
Perkembangan teknologi informatika memungkinkan pengolahan data besar untuk mendukung pengambilan keputusan berbasis bukti. Populasi ternak di Kota Bandung penting bagi pembangunan pertanian dan peternakan, namun data yang tersedia masih bersifat deskriptif. Penelitian ini bertujuan mengidentifikasi pola perubahan populasi ternak menggunakan algoritma K-Means Clustering dalam kerangka Knowledge Discovery in Databases (KDD). Data dikumpulkan dari BPS dan DKPP Kota Bandung periode 2021–2024, kemudian melalui preprocessing, normalisasi, dan transformasi ke format numerik. Analisis dilakukan dengan K-Means menggunakan Euclidean distance, K = 5, max runs = 10, dan max optimization steps = 100. Hasil klasterisasi membentuk lima cluster dengan distribusi tidak merata; satu cluster dominan, sementara cluster lain menunjukkan karakteristik wilayah spesifik. Temuan ini memberikan gambaran pola pertumbuhan populasi ternak di tingkat kecamatan dan menjadi dasar bagi pemerintah daerah dalam merumuskan kebijakan pengelolaan ternak yang lebih efektif dan berbasis data.
Downloads
References
A. Rezi and M. Allam, "Techniques in array processing by means of transformations, " in Control and Dynamic Systems, Vol. 69, Multidimensional Systems, C. T. Leondes, Ed. San Diego: Academic Press, 1995, pp. 133-180.
W.K. Chen, Linear Networks and Systems. Belmont, CA: Wadsworth, 1993, pp. 123-135
T. J. van Weert and R. K. Munro, Eds., Informatics and the Digital Society: Social, ethical and cognitive issues: IFIP TC3/WG3.1&3.2 Open Conf.e on Social, Ethical and Cognitive Issues of Informatics and ICT, July 22-26, 2002, Dortmund, Germany. Boston: Kluwer Academic, 2003.
L. Bass, P. Clements, and R. Kazman, Software Architecture in Practice, 2nd ed. Reading, MA: Addison Wesley, 2003. [Online] Available: Safari e-book.
M. W. Dixon, "Application of neural networks to solve the routing problem in communication networks," Ph.D. dissertation, Murdoch Univ., Murdoch, WA, Australia, 1999.
D. Duei Putri, G. F. Nama, and W. E. Sulistiono, “Analisis Sentimen Kinerja Dewan Perwakilan Rakyat (DPR) Pada Twitter Menggunakan Metode Naive Bayes Classifier”, JITET, vol. 10, no. 1, Jan. 2022.
S. Studi, K. Di, P. Jawa, and E. T. Setyasih, “TRANSFORMASI DIGITAL PEMERINTAH DAERAH DI ERA,” vol. 5, no. 3, pp. 59–66.
P. Gonnade and S. Ridhorkar, “INTELLIGENT SYSTEMS AND APPLICATIONS IN ENGINEERING Business Decision making through Big Data Analytics using Machine Learning Technique,” vol. 12, no. 4, pp. 3057–3063, 2025.
S. Pembibitan et al., “Analisis Potensi Pakan untuk Pengembangan Ternak Ruminansia di Provinsi Jawa Timur Analysis of Feed Potency for Development of Ruminant Livestock in East Java Province,” vol. 15, no. 3, pp. 251–258, 2020.
P. Ayu, W. Purnama, N. Pohan, and A. Restiady, “Predicting New Student Admissions with the SVM Regression Model in Data Mining,” vol. 8, no. 2, pp. 843–850, 2025.
D. Dari, P. Teknologi, and I. Dan, “Dampak dari perkembangan teknologi informasi dan komunikasi,” vol. 3, pp. 206–210, 2022.
I. I. N. Ernawati, “Jurnal Teknologi Informasi dan Pendidikan,” vol. 12, no. 2, 2019.
R. R. Marbun, C. Maisyarah, P. Studi, and S. Informasi, “No Title,” vol. 8, pp. 2084–2091, 2025.
K. B. Agbemadon et al., “Churn detection using machine learning in the retail industry To cite this version : HAL Id : hal-04257366 Churn detection using machine learning in the retail industry,” 2023.
W. B. Laksono, Y. Syahidin, and Y. Yunengsih, “Implementasi Data Mining Klasterisasi Data Pasien Rawat Inap dengan Algoritma K-Means Clustering,” vol. 7, no. 2, pp. 621–627, 2024, doi: 10.32493/jtsi.v7i2.39354.
B. Data, A. Dan, P. Di, and P. Dan, “Jurnal Ekonomi dan Bisnis Dharma Andalas,” vol. 25, no. 1, pp. 185–194, 2023.
D. Rizaldy, A. Fauzan, E. Rilvani, and U. P. Bangsa, “Analisa Dampak Kinerja Menteri ESDM terhadap Kesejahteraan Masyarakat : Studi Data Mining dengan Metode Clustering,” vol. 03, no. 04, pp. 2413–2426, 2025.
M. I. St Nur Rahmah, Muliani.S, Andi Nilwana, “Jurnal Ilmiah Pemerintahan,” vol. 12, no. Idm, pp. 27–38, 2024.
D. A. Setiady and H. Leong, “Implementation of K-Means Algorithm Elbow Method and Silhouette Coefficient for Rainfall Classification,” Proxies J. Inform., vol. 4, no. 1, pp. 18–25, 2024, doi: 10.24167/proxies.v4i1.12433.
D. A. Tarigan, “Optimization of the K-Means Clustering Algorithm Using Davies Bouldin Index in Iris Data Classification,” Media Online), vol. 4, no. 1, pp. 545–552, 2023, doi: 10.30865/klik.v4i1.964.
F. Juliawati, R. Buaton, R. Saragih, and S. Kaputama, “Pengelompokan Data Mining Penerimaan Bantuan Pangan Non Tunai (BPNT) Menggunakan Metode Clustering (Studi Kasus : Kantor Desa Payabakung Hamparan Perak),” J. Comput. Sci. Inf. Technol. E-ISSN, vol. 3, no. 2, p. 69, 2023.
A. Singh, S. Mittal, P. Malhotra, and Y. Srivastava, Clustering Evaluation by Davies-Bouldin Index(DBI) in Cereal data using K-Means. 2020. doi: 10.1109/ICCMC48092.2020.ICCMC-00057.
O. Gavrilenko, O. Khomenko, O. S. Zhurakovska, A. Piskun, and Y. Khavikova, “Establishing the grouping principle of public services based on the analysis of similarity coefficients,” Eastern-European J. Enterp. Technol., 2023, doi: 10.15587/1729-4061.2023.280218.
D. Shifman, I. Cohen, K. Huang, X. Xian, and G. Singer, “An adaptive machine learning algorithm for the resource-constrained classification problem,” Eng. Appl. Artif. Intell., 2023, doi: 10.1016/j.engappai.2022.105741.
W. Hu, F. Liu, and J. Peng, “An Efficient Data Classification Decision Based on Multimodel Deep Learning,” Comput. Intell. Neurosci., 2022, doi: 10.1155/2022/7636705.
C.-K. Bogor, “Implementasi Algoritma K-Means Clustering dengan Jarak Euclidean dalam Mengelompokkan Daerah Penyebaran,” vol. 2, no. 1, pp. 47–56, 2023.
A. Anas and A. J. Zebua, “Clustering Nilai Mahasiswa Menggunakan Algoritma K-Means,” vol. 19, no. 1, pp. 7–14, 2025.
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
