KLASIFIKASI JUDUL SKRIPSI MAHASISWA BERDASARKAN KONSENTRASI PROGRAM STUDI MENGGUNAKAN ALGORITMA NAIVE BAYES - Eva, Eva Yumami; Jannah, Miftahul; Putra, Ovella (Journal of Computer Science and Technology (JCS-TECH), 2025)
This study discusses the development of an automatic classification model for student thesis titles based on study program concentrations using the Naive Bayes algorithm. The background of this research is the increasing number of thesis titles produced each year, which complicates the classification and management process if done manually. The Naive Bayes algorithm was chosen for its simplicity, efficiency, and suitability for text classification tasks. The dataset comprises thesis titles from students of the D4 Software Engineering Program with six areas of concentration: Software Engineer, Mobile Developer, Full Stack Developer, UI/UX Designer, Software Quality Assurance Engineer, and Technopreneur. The data underwent several preprocessing stages including tokenization, stopword removal, and stemming. The model was trained and tested using a train-test split approach and evaluated using accuracy, precision, recall, and f1-score metrics. The results indicate that the Naive Bayes algorithm can classify thesis titles into their appropriate concentrations with an accuracy of 65%. This research contributes to improving the efficiency of academic administration management and serves as a foundation for developing AI-based classification systems in higher education