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All Journal MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Journal of Machine Learning and Soft Computing Jurnal Keuangan dan Akuntansi Terapan (KUAT) Jurnal Dharmabakti Nagri International Journal of Information Technology and Computer Science Applications (IJITCSA) Jurnal Informatika: Jurnal Pengembangan IT
Tb Ai Munandar, Tb Ai
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Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Social Sciences Other

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Agglomerative Spatial Clustering Analysis for Mapping Crime Risk Zone Clusters Munandar, Tb Ai; Ramdhania, Khairunnisa Fadhilla
International Journal of Information Technology and Computer Science Applications Vol. 3 No. 2 (2025): May - August 2025
Publisher : Jejaring Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58776/ijitcsa.v3i2.197

Abstract

Public safety and order are crucial aspects of social and economic life, especially in densely populated urban areas. High crime rates can undermine the sense of security and quality of life within society. Therefore, a deep understanding of crime distribution patterns is essential for designing effective prevention strategies. This study aims to map crime risk zones in Indonesia using the Agglomerative Clustering method, by integrating socio-economic and demographic variables. This method was chosen for its ability to group data based on similarity of characteristics, making it easier to identify areas with high-risk levels. The results show the formation of four main clusters that reflect crime risk distribution in Indonesia. The first cluster includes several provinces with similar crime patterns, while the other clusters reflect significant differences in crime patterns, particularly in Jakarta, which has very distinct criminal characteristics. This mapping provides valuable insights for the planning of more efficient, data-driven crime prevention policies. The research is expected to provide a strong foundation for policymakers and law enforcement agencies to formulate more targeted strategies to combat crime in Indonesia.
Regional Clustering Based on Types of Non-Communicable Diseases Using k-Means Algorithm Munandar, Tb Ai; Yunizar Yusuf Pratama, Ajif
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 23 No. 2 (2024)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v23i2.3352

Abstract

Noncommunicable diseases (NCDs) have become a global threat to public health, necessitating a comprehensive understanding of their geographic and epidemiological distribution in order to devise appropriate interventions. The objective of this study is to clustering areas of Banten Province based on NCDS profiles using the unsupervised learning technique. The method used in this study is the k-means algorithm for grouping types of non-communicable diseases based on region. The processing and normalisation of NCDS prevalence data from various health sources preceded cluster analysis using the k-means clustering algorithm. This research is categorised into two scenarios: the first involves the clustering of data obtained from outlier analysis, while the second scenario excludes any outliers. The objective is to observe disparities in regional clustering outcomes by categorising non-communicable diseases according to these two scenarios. The silhouette index is used to determine the validity of cluster results. These findings are analysed in depth to determine the geographic and socioeconomic patterns associated with each cluster's NCDS profile. Based on the mean silhouette index value of 0.812, the results indicate that the sum of k = 2 in the k-means algorithm is the optimal cluster result in this case. Five non-communicable diseases, namely diabetes, hypertension, obesity, stroke, and cataracts, necessitate significant focus in the first cluster (C1), where 202 regions were grouped. Six regions belong to the second cluster (C2), which includes areas that are not only susceptible to the five non-communicable diseases in cluster C1 but also to breast cancer, cervical cancer, heart disease, chronic obstructive pulmonary disease (COPD), and congenital deafness.
Co-Authors Dwi Budi Srisulistiowati Dwipa Handayani Fathurrazi, Ahmad Kapriadi, Engkap Karyaningsih, Dentik Khairunnisa Fadhilla Ramdhania Kristian Vieri, Jhon Muhammad Fairuzabadi Noe’man,, Achmad No’eman, Achmad Priatna , Wowon Ramdhania, Khairunnisa Fadhilla Retno Wulandari Rizki Surya Pratama, Daffa Sani, Ardila Sri Lestari, Tyastuti Suhendar, Akip Yunizar Yusuf Pratama, Ajif