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All Journal The Indonesian Journal of Computer Science
Bester Chimbo
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Computer Science & IT Electrical & Electronics Engineering Engineering

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Crime Link Prediction Across Geographical Location Through Multifaceted Analysis: A Classifier Chain Temporal Feature-Data Frame Joins Esan, Omobayo; Isaac Olusegun Osunmakinde; Bester Chimbo
The Indonesian Journal of Computer Science Vol. 14 No. 1 (2025): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v14i1.4627

Abstract

Crime link prediction across geographical locations is vital for law enforcement to uncover hidden connections between crime spanning different areas. Traditional methods often fails to capture the complexity and temporal dynamics of crime data, limiting g their predictive power. This research introduces a novel approach to enhance crime link prediction by leveraging multifaceted analysis that integrates multiple inputs and outputs. A classifier chain transformation is used for sequential multi-label classification, capturing interdependence between crime types across locations. The method facilitates a comphrensive understanding of crime patterns over time. Experiment conducted on South Africa Police Services (SAPS) crime dataset demonstrate the proposed model's superior performance compared to state-of-the-art methods, achieving precision, recall, F1-score, and accuracy of 0.98, 0.99,0.99, and 98.99%, respectively. This research aims to contribute to crime link prediction model's, offering a more nuanced and robust framework for forensic experts and law enforcement.
Systematic Literature Review on Crime Prediction using Machine Learning Techniques Esan, Omobayo; Bester Chimbo
The Indonesian Journal of Computer Science Vol. 14 No. 3 (2025): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v14i3.4881

Abstract

Abstract contains problem statement, approaches/problem solving method, objectives and resulTo lower the crime rate in the community, many governments around the world have made preventive security measures their top priority. Thus, a major and extensively studied field is the use of machine learning in crime prediction. To investigate crime prediction using machine learning approaches, this study carried out a systematic literature review. The review assesses performance evaluation criteria, forecast methods, present issues, and potential future directions. From 2018 to 2024, a total of 100 research papers covering machine learning techniques for crime prediction were reviewed. The supervised learning approach is the most often used crime prediction technology, according to the review. The evaluation and performance criteria, the tools used to construct the models, and the difficulties they face in predicting crime were also covered. Machine learning approaches for crime prediction are an interesting area of research, and academics have used a number of machine learning models.
Co-Authors Esan, Omobayo Isaac Olusegun Osunmakinde