Analisis Ternak Menggunakan K-Means Clustering Dalam Business Intelligence
DOI:
https://doi.org/10.47065/bulletincsr.v6i3.1059Keywords:
K-Means Clustering; Livestock Population; Data Mining; Business IntelligenceAbstract
This study aims to analyze livestock population patterns and classify regions in Central Java using data from 2020–2023 covering cattle, goats, and chickens from official sources. The Knowledge Discovery in Database (KDD) framework and K-Means Clustering were applied, with the optimal number of clusters determined using the Elbow Method and Silhouette Score. The results show that the optimal number of clusters is two (K=2), with a Silhouette Score of 0.328, indicating a relatively weak clustering structure with potential overlap. Despite this limitation, the results reveal meaningful segmentation when combined with Business Intelligence analysis. Cluster 0 represents regions with lower population but higher growth, while Cluster 1 represents regions with higher population but lower or negative growth. Further analysis indicates that the relationship between population, growth, and production is not linear, where high production does not necessarily correspond to strong growth. These findings highlight the importance of distinguishing between current production capacity and future growth potential, providing more informative insights for data-driven decision-making in livestock sector management.
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References
R. Widyastuti, “Evaluasi Manajemen Kesehatan dan Kesejahteraan Hewan pada Peternakan Domba, Sapi Perah, Sapi Potong, dan Burung Puyuh di Wilayah Jatinangor, Jawa Barat, Indonesia,” Farmers J. community Serv., vol. 7, no. 1, pp. 114–118, 2026. https://doi.org/10.24198/fjcs.v7i1.69115
R. Ardhani, S. Marshelle, A. Fitrianto, and L. M. R. Dwi, “Perbandingan K-Medoids Dan Clara (Clustering Large Application),” J. Lebesgue J. Ilm. Pendidik. Mat. Mat. dan Stat., vol. 5, no. 3, pp. 1744–1763, 2024. https://doi.org/10.46306/lb.v5i3.764
P. Bisecting and K. D. A. N. Agglomerative, “Hierarchical Clustering Untuk Pengelompokan Daerah,” VARIANSI: Journal of Statistics and Its Application on Teaching and Research., vol. 8, no. 2, pp. 175–182, 2025. https://doi.org/10.35580/variansiunm38
L. Sumaryanti and D. Muchlis, “Mathematical Modelling of Engineering Problems Analysis of Regional Potential in Merauke Regency Based on Superior Livestock Population Using a Hybrid Algorithm,” Mathematical Modelling of Engineering Problems., vol. 11, no. 3, pp. 754–764, 2024. https://doi.org/10.18280/mmep.110320
Fajriana, “Analisis Algoritma K-Medoids pada Sistem Klasterisasi Produksi Perikanan Tangkap,” JEPIN (Jurnal Edukasi dan Penelitian Informatika)., vol. 7, no. 2, pp. 263–269, 2021. https://doi.org/10.26418/jp.v7i2.47795
B. Nikum and H. Yuliansyah, “Evaluation of K-Means Clustering Using Silhouette Score Method on Customer Segmentation,” jurnal.fikom., vol. 16, no. 3, pp. 330–342, 2025. https://doi.org/10.33096/ilkom.v16i3.2325.330-342
I. A. A. Iis Setiawan Mangku Negara1, Purwono2, “Analisa Cluster Data Transaksi Penjualan Minimarket Selama Pandemi Covid-19 dengan Algoritma K-means 1,3,” Journal of Information., vol. 3, no. 28, pp. 153–160, 2022. https://doi.org/10.31328/jointecs.v6i3.2693
D. Lasandi, R. Astuti, K. Anam, A. P. Putra, and B. Nurhakim, “Penerapan Algoritma K-Means Clustering Untuk Analisis Pola Populasi Ternak Di Kota,” Jurnal Informatika dan Teknik Elektro Terapan., vol. 14, no. 2. https://doi.org/10.23960/jitet.v14i2.9248
R. H. Bhahari, “Clustering Analysis of Socio-Economic Districts / Cities In East Java Province Using PCA And Hierarchical Clustering Methods,” Sinkron: jurnal dan penelitian teknik informatika., vol. 8, no. 4, pp. 2242–2251, 2024. https://doi.org/10.33395/sinkron.v8i4.14078
T. S. Irma Khoirunisa, Rini Astuti, “Penerapan Algoritma K-Medoids Dalam Menentukan Cluster Kabupaten Dan Kota Berdasarkan Populasi Peternakan Di Provinsi Jawa Barat,” JATI (Jurnal Mhs. Tek. Inform., vol. 7, no. 6, pp. 3753–3758, 2023. https://doi.org/10.36040/jati.v7i6.8202
M. T. J. Stefanus Arman, Kris Suryowati, “Pengelompokan Provinsi Di Indonesia Berdasarkan Indikator Peternakan Dengan Menggunakan Algoritma K-Means Dan K-Medoids,” Jurnal Statistika Industri dan Komputasi., vol. 09, no. 02, pp. 51–59, 2024. https://doi.org/10.34151/statistika.v9i2.4858
N. H. Cahyaningrum, N. Mas, and R. Yotenka, “Perbandingan Algoritma K-Means dan K-Medoids dalam Pengelompokan Peternakan Unggas di Daerah Istimewa Yogyakarta Tahun 2023,” Emerging Statistics and Data Science Journal.,vol. 3, no. 2, pp. 630–639, 2025. https://doi.org/10.20885/esds.vol3.iss.2.art16
R. Rafsanjani, S. M. Kristin, and A. Firman, “Peran Subsektor Peternakan dalam Perekonomian Jawa Tengah The Role of Livestock Subsector in Central Java Regional Economy,” Jurnal Peikiran Masyarakat Ilmiah Berwawasan Agribisnis., vol. 11, pp. 789–798, 2025. https://doi.org/10.25157/ma.v11i1.16477
Y. A. Wijaya et al., “K-Means Di Sekolah Menengah Kejuruan Wahidin Kota Cirebon,” Emerging Statistics and Data Science Journal., vol. 6, no. 2, pp. 552–559, 2022. https://doi.org/10.36040/jati.v6i2.5236
D. K. Zega, “Analisis Kelayakan Pengembangan Usaha Ternak Sapi Potong ( Beef Cattle ): Suatu Kajian Literatur,” Business and Enterpreneurship Journal (BEJ)., vol. 7, no. 1, pp. 1–9, 2026. https://doi.org/10.57084/bej.v7i1.2204
R. F. Miwan Kurniawan Hidayat, “Implementasi k-means dan k-medoids dalam pengelompokan wilayah potensial produksi daging ayam implementation of k-means and k-medoids in grouping potential areas of chicken meat production,” Journal of Agroindustrial Technology/Jurnal Teknologi Industri., vol. 32, no. 158, pp. 239–247, 2022. https://doi.org/10.24961/j.tek.ind.pert.2022.32.3.239
N. O. Syamsiah, I. Purwandani, M. Rosmiati, and S. Nurwahyuni, “Triangulation Approach Using K-Means , Hierarchical Clustering , and DBSCAN for Beef Production Analysis,” IJNMT (International Journal of New Media Technology)., vol. 12, no. 2, 2026. https://doi.org/10.31937/ijnmt.v12i2.4481
L. Trapanese, G. Bifulco, M. Santinello, N. Pasquino, G. Campanile, and A. Salzano, “Comparison of K-Means and Hierarchical Clustering Methods for Buffalo Milk Production Data,” Animals vol. 12, no. 2, pp. 1–14, 2025. https://doi.org/10.3390/ani15223246
F. Mukaromah and I. K. D. Nuryana, “Clustering of Goat Buyers in West Java with K-Means Algorithm,” Journal of Emerging Information Systems and Business Intelligence., vol. 6, no. 4, pp. 485–498, 2025. https://doi.org/10.26740/jeisbi.v6i4.72022
H. Rhomadhona, W. Kusrini, W. Aprianti, and J. Permadi, “Implementation of K - Means Clustering for Social Assistance Recipients with Silhouette Score Evaluation,” Brilliance: Research of Artificial Intelligence., vol. 5, no. 1, pp. 136–143, 2025. https://doi.org/10.47709/briliance.vxix.xxxx
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