Penerapan Metode Median Clustering Untuk Clusterisasi Peternakan di Provinsi Maluku
Keywords:
Animal Husbandr, Maluku, Median ClusteringAbstract
Livestock in Maluku Province is one of the sectors that is the main priority in the context of increasing people's welfare. The potential for livestock in Maluku Province is increasing every year. However, there needs to be integrated processing and identification of potential commodities in each region. One method that is a reliable statistical method is to use the median clustering method. Median clustering is a method of grouping based on the median value. The median clustering algorithm selects K cluster centers with the aim of minimizing the sum of the measurement distances between each point cluster and the closest cluster center. The data used in this study came from the Maluku Province Central Bureau of Statistics (BPS) in 2022. The results of this research were that there were 3 clusters formed in livestock clusterization in regencies and cities in Maluku Province. Clus ter 1 consists of Southwest Maluku Regency. Cluster 2 consists of the Regencies of Central Maluku, Buru and West Seram. Furthermore, Cluster 3 consists of the Tanimbar Islands, Southeast Maluku, Aru Islands, Eastern Seram, South Buru, Ambon and Tual City.
References
Siagian, H. (2011). Konstribusi Usaha Peternakan dalam Pengembangan Wilayah. Jurnal Wira Ekonomi Mikroskil, 1(1), 31-35.
Salakorry, M. (2022). Maluku Dominan Pulau Pulau Kecil, Permasalahannya, dan Solusi Peran Lulusan Geografer Universitas Pattimura. Geoforum, 1(1), 1-19.
Statistik, B. P. (2012). Maluku Dalam Angka Tahun 2012. BPS Provinsi Maluku.
Nugroho, S. (2008). Dasar Dasar Metode Statistika. Grasindo.
Nuraini, U. (2015). Perbandingan Metode Pengelompokan Terbaik Berdasarkan Algoritma K-Means Dan K-Medians Pada Data Yang Mengandung Pencilan (Doctoral dissertation, Universitas Brawijaya).
Kumar, M., & Patel, N. R. (2007). Clustering data with measurement errors. Computational Statistics & Data Analysis, 51(12), 6084-6101.
Bradley, P., Mangasarian, O., & Street, W. (1996). Clustering via concave minimization. Advances in neural information processing systems, 9.
Kaufman, L., & Rousseeuw, P. J. (2009). Finding groups in data: an introduction to cluster analysis. John Wiley & Sons.
Whelan, C., Harrell, G., & Wang, J. (2015). Understanding the k-medians problem. In Proceedings of the International Conference on Scientific Computing (CSC) (p. 219). The Steering Committee of The World Congress in Computer Science, Computer Engineering and Applied Computing (WorldComp).
Downloads
Published
Issue
Section
License
Copyright
It is the author's responsibility to ensure that his or her submitted work does not infringe any existing copyright. Authors should obtain permission to reproduce or adapt copyrighted material and provide evidence of approval upon submitting the final version of a manuscript.
