STEMMING IMPACT ANALYSIS ON INDONESIAN QURAN TRANSLATION AND THEIR EXEGESIS CLASSIFICATION FOR ONTOLOGY INSTANCES

The current gap that appears in the Quran ontology population domain is stemming impact analysis on Indonesian Quran translation and its exegesis (Tafsir) to develop ontology instances. The existing studies of stemming effect analysis were performed in various languages, datasets, stemming methods, cases, and classifiers. However, there is a lack of literature that studies the stemming influence on instance classification for Quran ontology with different datasets, classifiers, Quran translations, and their exegesis in Indonesian. Based on this problem, our study aims to investigate and analyse the stemming impact on instance classification results using Indonesian Quran translation and their exegesis 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 the 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 average accuracy and 71.55% for average 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 average accuracy and 68.10% for average 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.