Penerapan Algoritma Klasifikasi dalam Data Mining pada Berbagai Studi Kasus: Literature Review - Hibatullah Naufal Ramadhan; Amran, Hasanatul Fu'adah (Universitas Muhammadiyah Riau, 2026)
Data mining is one of the most widely used approaches in the field of Information Technology to extract knowledge from large data sets. One of the main techniques in data mining is the classification method, which aims to predict a particular class or category based on available attributes. Various classification algorithms such as Naïve Bayes, Decision Tree (C4.5), Random Forest, Support Vector Machine, and Artificial Neural Network have been applied in various research domains, including health, education, government, agriculture, and cybersecurity. Differences in data characteristics and methods used cause variations in performance in each study. Therefore, this study aims to conduct a literature review on the application of data mining with classification methods in various data prediction and classification cases. The research method used is a literature review by examining eleven scientific articles from accredited national journals. The results of the study show that the Naïve Bayes and Decision Tree algorithms are the most frequently used methods due to their ease of implementation and interpretation, while Random Forest and Support Vector Machine tend to provide more stable performance on data with high complexity.