Implementation of Enhanced Confix Stripping Stemming and Chi-Squared Feature Selection on Classification UIN Walisongo Website with Naïve Bayes Classifier

Academic news classification on university websites remains a challenge due to the growing volume of content and lack of efficient categorization systems. At UIN Walisongo Semarang, this problem hinders students, faculty, and the public from easily accessing relevant information. This study aims to develop an automated academic news classification system to address this issue. We applied a Naïve Bayes Classifier model, enhanced with Term Frequency weighting, the Enhanced Confix Stripping Stemmer for Indonesian language preprocessing, and Chi-Squared feature selection to identify the most informative terms. The dataset consisted of 880 academic news articles from UIN Walisongo’s website, split into 704 training and 176 testing documents. The system achieved 95% accuracy on the test set. To evaluate generalizability, we used a separate evaluation set of 12 new articles, obtaining 83.3% accuracy. The preprocessing stage played a vital role in reducing morphological complexity, while Chi-Squared scoring improved the relevance of selected features. This research highlights the importance of robust text classification techniques in academic information systems, particularly in Indonesian language contexts where language morphology poses unique challenges. The proposed model demonstrates strong performance, scalability, and potential for integration into academic portals to improve information retrieval. This study contributes significantly to the field of Natural Language Processing and applied machine learning in academic settings, especially for Indonesian-language content. It provides an effective solution for automated academic content management in institutional information systems.