AN INTEGRATED MODEL OF NATURAL LANGUAGE PROCESSING TECHNIQUE AND CASE-BASED REASONING FOR SUPPORTING STUDY PROGRAM ACCREDITATION

Academic quality assurance is an important issue for higher education. This study presents an integrated model of Natural Language Processing (NLP) and Case-Based Reasoning (CBR) to support accreditation. The model uses NLP techniques to extract information from accreditation documents. Feature extraction uses Latent Dirichlet Allocation (LDA) to determine the topic model. The result of feature extraction is as input to the CBR system. CBR provides recommendations based on previous similar cases. The Jensen-Shannon Divergence algorithm is used to measure the similarity of cases with a mean similarity of 81.57%. The results of this study demonstrate the potential of NLP and CBR to increase the effectiveness of the accreditation process and provide insights for future research in this area. ICIC International