<![CDATA[This study aims to analyze the impact of varying stopword sets on the performance of Qur'anic text classification models in Indonesian translations, using two machine learning algorithms: Support Vector Machine (SVM) and Backpropagation Neural Network (BPNN). The research involved six stopword variants: Sastrawi, Damian Doyle, Fadillah Z. Tala, Natural Language Toolkit (NLTK) Indonesian, Yudi Wibisono, and a combination of all these lists. The preprocessing steps included cleaning, case folding, tokenization, stopword removal, and stemming, followed by TF-IDF (Term Frequency-Inverse Document Frequency) text representation. Feature selection was performed using the Chi-Square method to select the top 1,000 features. The evaluation results showed that SVM consistently outperformed BPNN across all metrics, including accuracy, precision, recall, and F1-score. The Sastrawi stopword variant delivered the best performance with an F1-score of 0.6697, followed by Fadillah Z. Tala and Damian Doyle. In contrast, BPNN showed lower performance, with the highest F1-score of 0.4607 achieved using the NLTK stopword variant. These findings highlight that selecting relevant, contextually appropriate stopwords is critical to classification Effectiveness. SVMs proved more reliable at handling high-dimensional text data while preserving the semantic meaning of Qur'anic verses.]]>
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