EVALUATION OF CLASSIFICATION ALGORITHMS WITH SOLUTION TO CLASS IMBALANCE PROBLEM ON ZAKAT DISTRIBUTION DATASET

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Class imbalance is one of the most serious and significant issues in data mining classification studies. In recent years, there has been a lot of interest in the problem of imbalance in various real-world applications, such as fraud detection, medical diagnosis, and bank marketing datasets. Similarly, the Zakat distribution UiTM Perlis dataset was utilized to assess the imbalance in the dataset classes. The study aims to evaluate the performance of the resampling technique, Synthetic Minority Over-sampling Technique (SMOTE), and to identify the best classifiers for class-imbalanced Zakat datasets by comparing the classifier's performance. A resampling-based approach is proposed in this study to solve the imbalance dataset. This approach aims to enhance the true positive or the detection of the minority class, which in this case is the not eligible class. The evaluation is based on various metrics, which include accuracy, precision, recall, F-measure, and the ROC area. The algorithms considered in the study include Logistic Regression, Decision Tree (C.4.5), and Random Forest. The results indicate that Random Forest consistently performs well across all evaluation metrics after implementing SMOTE. It attains the highest accuracy, precision, recall, F-measure, and ROC area levels. In conclusion, this research highlights the effectiveness of SMOTE in resolving class imbalance in the Zakat distributions dataset. The evaluation of various classification algorithms will be conducted using WEKA.