MUZDALIFAH, AN NISAA (2022) Analisis Daerah Kerawanan Kriminalitas di Wilayah Hukum Polres Lebak menggunakan Algoritma K – Means Clustering. S1 thesis, Universitas Mercu Buana.
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Abstract
Criminality or crime is an act that violates the laws, laws, norms, and values that apply in society, criminality is a common problem that often occurs in everyday life, including in the Lebak area. To find out the area of criminality vulnerability, in this study using the k-means algorithm which aims to cluster the crime vulnerability area in the Lebak Police Jurisdiction using kaggle tools. Based on the results of research, the k- means clustering algorithm has shown that in cluster 0 (Prone), cluster 1 (Moderately Vulnerable), and Cluster 2 (Very Vulnerable). The data mining technique used in this study is the k-means method by optimizing the determination of the number of clusters using the Elbow method. From the results of the analysis, it can be concluded to the community in the Lebak Region where the places that affect the occurr ence of crime in cluster 0 have 491 crime data, Cluster 1 has 323 crime data, and Cluster 2 has 204 crime data. From the results of the analysis, it can be concluded that the vulnerability of criminality in the jurisdiction of the Lebak Police, so it is hoped that the Lebak Police will further improve security and also the community can increase awareness of the importance of safety in the Lebak area. Key words: Criminality, Kaggle, Data Mining, K-Means, Clustering Kriminalitas atau tindak kejahatan merupakan suatu tindakan yang melanggar hukum, undang - undang, norma, dan nilai yang berlaku dalam masyarakat, kriminalitas adalah masalah umum yang sering terjadi di kehidupan sehari – hari, termasuk di wilayah Lebak. Untuk mengetahui daerah kerawanan kriminalitas maka dalam penelitian ini menggunakan algoritma k – means yang bertujuan untuk mengclustering daerah kerawanan kriminalitas di Wilayah Hukum Polres Lebak menggunakan tools kaggle. Berdasarkan hasil penelitian algoritma k – means clustering telah menunjukan bahwa dalam cluster 0 (Rawan), cluster 1 (Cukup Rawan), dan Cluster 2 (Sangat Rawan). Teknik data mining yang digunakan pada penelitian ini adalah metode k – means dengan optimasi penentuan jumlah cluster menggunakan metode Elbow. Dari hasil analisis dapat disimpulkan kepada masyarakat di Wilayah Lebak dimana tempat- tempat yang mempengaruhi terjadinya kriminalitas pada cluster 0 memiliki 491 Data kriminalitas, Cluster 1 memiliki 323 Data kriminalitas, dan Cluster 2 memiliki 204 Data kriminalitas. Dari hasil analisis dapat disimpulkan bahwa kerawanan kriminalitas di wilayah hukum Polres Lebak, sehingga diharapkan kepada Polres Lebak lebih meningkatkan keamanan dan juga masyarakat dapat memingkatkan kesadaran pentingnya keselamatan diwilayah Lebak. Kata kunci: Kriminalitas, Kaggle, Data Mining, K-Means, Clustering
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