Machine Learning-Based Classification of Quranic Verses Based on Islamic Pillars
DOI:
https://doi.org/10.47709/brilliance.v6i4.9760Keywords:
FastText, Islamic Pillars, Naive Bayes, Quranic Verses, Support Vector Machine, TF-IDFAbstract
Background: Text classification is a machine learning process used to categorize data based on specific characteristics. This study focuses on classifying the content of Islamic Pillars in Quranic verses into six categories: Shahada, Salah, Zakat, Fasting, Hajj, and Non-Islamic Pillars.
Objective: This study seeks to develop a classification application while evaluating Naive Bayes and Support Vector Machine (SVM) algorithms with TF-IDF and FastText as feature representation methods.
Methods: The dataset consisted of 800 Quranic verses selected using purposive sampling and divided into 80% training data and 20% testing data. The research stages included data preparation, text preprocessing, feature representation, model training, testing, evaluation, and implementation of the best-performing model in an application. Model performance was evaluated using accuracy, macro precision, macro recall, and macro F1-score.
Results: The results showed that the SVM model using TF-IDF achieved the best performance, with an accuracy of 81.88%, macro precision of 92.55%, macro recall of 75.63%, and macro F1-score of 81.98%. Based on these results, SVM with TF-IDF was selected as the final classification model and implemented in the application to classify Quranic verses according to the content of the Islamic Pillars.
Conclusion: The results demonstrate that the combination of SVM and TF-IDF can be applied to classify Quranic verses into the defined Islamic Pillars categories.
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