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Fahrur Rozzi Iskak
Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro

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IMPLEMENTASI ALGORITMA K-MEDOIDS DAN K-ERROR UNTUK PENGELOMPOKAN KABUPATEN/KOTA DI PROVINSI JAWA TENGAH BERDASARKAN JUMLAH PRODUKSI PETERNAKAN TAHUN 2020 Fahrur Rozzi Iskak; Iut Tri Utami; Triastuti Wuryandari
Jurnal Gaussian Vol 11, No 3 (2022): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/j.gauss.11.3.366-376

Abstract

The livestock sub-sector is one of the sub-sectors that contribute to the national economy and can significantly absorb labour so that it can be relied upon in efforts to improve the national economy. One of the steps used to increase livestock production in each region in Central Java Province is regional mapping. Cluster analysis is one of the regional mapping methods that can increase livestock production by grouping regencies/cities with characteristics of the same level of livestock production based on the type of livestock production. The k-error and k-medoids method is a non-hierarchical cluster analysis method, where the k-error is a method developed to overcome the problem of data measurement errors in classical cluster analysis, while the k-medoids is a method used to overcome the problem of outliers contained in the data. The validity test of the standard deviation ratio and the WB Index was used to determine the quality of the clustering results. The small validity value of the standard deviation ratio and the WB Index shows the best results of clustering and selecting method. Based on the results of the clustering, the optimal cluster was obtained at k=7 using the k-medoids algorithm, where the validation value of the standard deviation ratio=0.773 and WB Index=0.531.