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All Journal International Journal of Informatics, Information System and Computer Engineering (INJIISCOM)
Abdulnabi, Ghazwan
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Computer Science & IT Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mechanical Engineering

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Utilizing Machine Learning Algorithms and SMOTE for Analyzing and Predicting Homicides: AI Hayder, Israa M.; Abdulnabi, Ghazwan; Younis, Hussain A.
International Journal of Informatics, Information System and Computer Engineering (INJIISCOM) Vol. 7 No. 1 (2026): INJIISCOM: VOLUME 7, ISSUE 1, JUNE 2026 (ONLINE FIRST)
Publisher : Universitas Komputer Indonesia

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Abstract

This study analyzes homicide data in the United States from 1980 to 2014 using machine learning techniques to predict crime resolution and classify victim gender. The dataset, obtained from the FBI Supplementary Homicide Report, contains 638,454 records. Data preprocessing involved cleaning, converting categorical features to numerical values, and addressing class imbalance using SMOTE (Synthetic Minority Oversampling Technique).Various classification algorithms were applied, including Decision Tree and Naïve Bayes. The results showed that the Decision Tree model achieved 95% accuracy in predicting crime resolution and 85% accuracy in classifying victim gender, while Naïve Bayes reached 92% accuracy in crime resolution prediction. The findings highlight the effectiveness of machine learning in crime pattern analysis and prediction, aiding law enforcement in making more informed investigative decisions.
Co-Authors Hayder, Israa M. Younis, Hussain A.