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Abstract The current gap which appears in the Quran ontology population domain is stemming impact analysis on Indonesian Quran translation and their Tafsir to develop ontology instances. The existing studies of stemming effect analysis performed in various languages, dataset, stemming method, cases, and classifier. However, there is a lack of literature that studies about stemming influence on instances classification for Quran ontology with different dataset, classifier, Quran translation, and their Tafsir on Indonesian. Based on this problem, our study aims to investigate and analyze the stemming impact on instances classification results using Indonesian Quran translation and their Tafsir as datasets with multiple supervised classifiers. Our classification framework consists of text pre-processing, feature extraction, and text classification stage. Sastrawi stemmer was used to perform stemming operation in text pre-processing stage. Based on our experiment results, it was found that Support Vector Machine (SVM) with Term Frequency-Inverse Document Frequency (TF-IDF) and stemming operation owns the best classification performance, i.e., 70.75% for accuracy and 71.55% for precision in Indonesian Quran translation dataset on 20% test data size. While in 30% test data size, SVM and TF-IDF with stemming process own the best classification performance, i.e., 67.30% for accuracy and 68.10% for precision in Ministry of Religious Affairs Indonesia dataset. Furthermore, in this study, it was also discovered that the Backpropagation Neural Network has the most precision and accuracy reduction due to the negative impact of stemming operations.

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