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dc.contributor.advisorSutarman
dc.contributor.authorSyahri, Muhammad Alfi
dc.date.accessioned2024-09-12T04:23:36Z
dc.date.available2024-09-12T04:23:36Z
dc.date.issued2024
dc.identifier.urihttps://repositori.usu.ac.id/handle/123456789/97182
dc.description.abstractThe high crime rate in North Sumatra province in 2022 made North Sumatra the second province with the highest crime rate in Indonesia. This study aims to apply the k-means clustering algorithm to crime data in regencies/cities in North Sumatra in 2022 by identifying the patterns and characteristics of the formed crime. The research data consists of crime data that occurred in North Sumatra province in 2022, collected from the publication of the Central Statistics Agency (BPS) of North Sumatra. The variables used in this study include theft, murder, rape, domestic violence (KDRT), fraud, and embezzlement. The k-means clustering method is a non-hierarchical data clustering method that divides data into groups. The results show that the clustering of regencies/cities in North Sumatra based on crime data that occurred in 2022 using the k-means clustering method produced 4 optimal iterations. With 3 clusters, the results show that cluster 1 contains one regency/city with a high crime rate, cluster 2 contains 5 regencies/cities with a moderate crime rate, and cluster 3 contains 23 regencies/cities with a low crime rate. An ANOVA test was also conducted, and the significance values for each variable were less than 0.05, indicating that the three clusters have significant differences.en_US
dc.language.isoiden_US
dc.publisherUniversitas Sumatera Utaraen_US
dc.subjectclusteringen_US
dc.subjectk-meansen_US
dc.subjectcrimeen_US
dc.subjectSDGsen_US
dc.titlePenerapan Algoritma K-Means Clustering pada Pengelompokkan Data Kriminal Menurut Kabupaten/Kota di Sumatera Utara Tahun 2022en_US
dc.title.alternativeApplication of K-Means Clustering Algorithm on Crime Data Clustering by Regency/City in North Sumatra in 2022en_US
dc.typeThesisen_US
dc.identifier.nimNIM212407030
dc.identifier.nidnNIDN0026106305
dc.identifier.kodeprodiKODEPRODI49401#Statistika
dc.description.pages56 Pagesen_US
dc.description.typeKertas Karya Diplomaen_US


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