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All Journal International Journal of Informatics, Information System and Computer Engineering (INJIISCOM) International Journal of Education Research and Development
Hayder, Israa M.
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Computer Science & IT Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mathematics Mechanical Engineering

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Enhancing Academic Knowledge Dissemination: A Comprehensive Guide to Crafting Survey, Review, and Systematic Literature Review Articles Younis, Hussain A.; Hayder, Israa M.; Salisu, Sani; Muthmainnah, Muthmainnah; Shahid, Misbah; Younis, Hameed Abdulkareem
International Journal of Education Research and Development Vol. 4 No. 1 (2024): March
Publisher : Yayasan Corolla Education Centre

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52760/ijerd.v4i1.54

Abstract

This article provides a comprehensive and concise guide to the distinct steps involved in writing survey articles, review articles, and systematic literature review articles. These three types of articles play pivotal roles in summarizing, analyzing, and synthesizing existing research, thereby contributing to the dissemination of knowledge in various academic fields. For survey articles, the key steps encompass topic selection, extensive literature review, logical organization, and critical analysis. In contrast, review articles focus on selecting, summarizing, and evaluating pertinent research, while providing context and addressing controversies. Systematic literature review articles demand a methodical approach, beginning with a well-defined research question, protocol development, comprehensive literature search, stringent inclusion criteria, data extraction, quality assessment, data synthesis, and transparent reporting. This guide aims to assist researchers, scholars, and students in navigating the intricate process of producing high-quality articles in these genres, facilitating knowledge dissemination, and contributing to academic advancement. By elucidating the unique requirements and methodologies associated with each type of article, this comprehensive guide empowers writers to effectively communicate existing knowledge and enhance scholarly conversation in their respective fields.
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 Abdulnabi, Ghazwan Muthmainnah Muthmainnah Salisu, Sani Shahid, Misbah Younis, Hameed Abdulkareem Younis, Hussain A.