Performance Analysis of Naïve Bayes Classifier Algorithm with Chi-Square and Confix Stripping Stemmer Selection Features In Hadits Translation Classification System

• Published in: 2022 3rd International Conference on Big Data Analytics and Practices (IBDAP) pp. 74 - 78
• Main Authors: Masruroh, Siti Ummi, Dian Ramadhani, Nichyta, Wardhani, Luh Kesuma, Alivia Rizqy Vitalaya, Nanda
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.09.2022
• Subjects: Authentication; Big Data; Chi-Square; Confix Stripping Stemmer; Feature extraction; Hadith Classification; Naïve Bayes Classifier; System performance; Text categorization; Text Classification; Text mining; Training data
• DOI: 10.1109/IBDAP55587.2022.9907584

Hadith is the second source of Islamic law after the Qur'an. Hadith classification supports the authentication of a hadith. The hadith classification process is carried out by the computer research community, while hadith authentication is carried out by hadith scholars. Text classification is grouping a certain text into one of the classes given a category based on the content of the text. One of the text mining classification algorithms is Naïve Bayes Classifier. In this study, researchers simulated a combination of the Naïve Bayes Classifier algorithm with Confix Stripping Stemmer for stemming, and Chi-Square as a feature selection algorithm to classify the translation of Sahih Bukhari's hadith into three classes, namely suggestions, prohibitions, and information with 300 data training data, and 30 testing data. System performance testing uses a confusion matrix by calculating the value of accuracy, precision, recall, and f-measure. The results show the best performance is a combination of Naïve Bayes Classifier and Confix Stripping Stemmer.