PERBANDINGAN METODE FUZZY C-MEANS DAN K-MEANS UNTUK PEMETAAN DAERAH RAWAN KRIMINALITAS DI KOTA PALEMBANG
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Crime is one of the most important issues in regional areas, includ-ing Palembang. However, the Palembang City Police Headquarters has only recorded crime reports without visualizing them in spatial infor-mation. The clustering methods used are Fuzzy C-Means and K-Means. The Fuzzy C-Means method groups data based on membership degrees, while the K-Means method groups data based on the centroids of crimi-nal incidents. The vulnerability levels of the two methods produce dif-ferent results. The results of the Partition Coefficient Index test from the Fuzzy C-Means method were 0.818, while the results of the Silhouette Index test from the K-Means method were 0.569., PERBANDINGAN METODE FUZZY C-MEANS DAN K-MEANS UNTUK PEMETAAN DAERAH RAWAN KRIMINALITAS DI KOTA PALEMBANGAbstract
Kriminalitas adalah salah satu isu signifikan di wilayah, termasuk di Palembang. Namun, di Polrestabes Kota Palembang, selama ini hanya mencatat laporan mengenai kriminalitas tanpa memvisualisasikannya dalam bentuk informasi spasial. Metode pengelompokan yang diterapkan adalah Fuzzy C-Means dan K-Means. Metode Fuzzy C-Means adalah pengelompokan data yang ditentukan oleh derajat keanggotaan, sedangkan metode K-Means adalah pengelompokan data yang ditentukan oleh centroid kejadian kriminalitas. Daerah tingkat kerawanan dari kedua metode tersebut menunjukkan hasil yang berbeda. The Partition Coefficient Index obtained from the Fuzzy C-Means method is 0.818, while the Silhouette Index derived from the K-Means method is 0.569.
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