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All Journal ARRUS Journal of Mathematics and Applied Science
Zulkifli Rais
Universitas Negeri Makassar
Biochemistry, Genetics & Molecular Biology Chemistry Decision Sciences, Operations Research & Management Mathematics Physics

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A Hybrid Neural Network Approach Using SOM and LVQ for Mapping Crime Clusters in Indonesia Zulkifli Rais; Sitti Masyitah Meliyana; Dinda Warfani Hasbullah
ARRUS Journal of Mathematics and Applied Science Vol. 5 No. 2 (2025)
Publisher : PT ARRUS Intelektual Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/mathscience4782

Abstract

Crime ratehigh crime rates in Indonesia are one of the important issues that need to be addressed with data-based strategies. This study aims to group provinces in Indonesia based on crime patterns using Self-Organizing Map (SOM) and classify the results using Learning Vector Quantization (LVQ). The results of the clustering analysis using SOM show that the optimal number of clusters is two, as supported by validation using Connectivity, Dunn Index, and Silhouette Score. Cluster 1 consists of 31 provinces with lower crime rates, while Cluster 2 includes 3 provinces with higher crime rates. To improve understanding of the clustering results, classification was carried out using the LVQ method, which produced an accuracy of 91.43%.
Classification of Family Welfare Card Recipients in Makassar City Using Decision Tree Algorithms Zulkifli Rais; Muhammad Fahmuddin S; Musfira Musfira
ARRUS Journal of Mathematics and Applied Science Vol. 5 No. 2 (2025)
Publisher : PT ARRUS Intelektual Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/mathscience4783

Abstract

This study aims to analyze the factors influencing the determination of recipients of the Family Welfare Card (KKS) program in Makassar City and evaluate the level of accuracy of the decision tree model in the classification process. The KKS program is a government effort to accelerate poverty alleviation, so it is important to ensure that the selection process for program recipients is carried out on target. The decision tree method is used in this study because of its ability to simplify the decision-making process through an easy-to-understand tree structure. This study utilizes KKS recipient data with various variables, such as income, number of dependents, employment status, asset ownership, and education level, to build a classification model. The results of the study indicate that the variable of the Head of Household's (KRT) Highest Education Level (X4) has the highest level of importance in determining KKS recipients, followed by the variable Number of Family Members (X1), and the variable Ownership of Residential Buildings (X5). The decision tree model that was built has an accuracy level of 84.21%, which states the model's ability to classify KKS recipients effectively. This study also provides insight into the description of factors influencing KKS receipts, which can be used as a basis for formulating more efficient and targeted policies.
Co-Authors Dinda Warfani Hasbullah Muhammad Fahmuddin Musfira Musfira Sitti Masyitah Meliyana R.