SISTEM REKOMENDASI PEMILIHAN DOSEN PEMBIMBING SKRIPSI BERDASARKAN CONTENT BASED FILTERING PADA PROGRAM STUDI SISTEM INFORMASI UNIVERSITAS BINA DARMA - Riska Oktapiana; Mohamad Farozi (Journal of Innovation Research and Knowledge, 2025)
This Final Project is entitled "A Recommendation System for Selecting Thesis Supervisors Based on Content-Based Filtering in the Information Systems Study Program at Bina Darma University." This research aims to develop a recommendation system to assist students in selecting thesis supervisors based on the alignment between the student's proposed thesis topic and the lecturer's expertise. The method used is Content-Based Filtering with the Term Frequency-Inverse Document Frequency (TF-IDF) algorithm for weighting keywords and Cosine Similarity to calculate the similarity level between the student's thesis title and the lecturer's journal publications. The system is built as a web-based application using PHP and MySQL. Testing results based on 30 data samples show that the system provides accurate recommendations with a Precision of 86.7%, a Recall of 80.0%, and an F1-Score of 83.2%. The highest recommendation score achieved was 98.69 for a lecturer whose journal closely matched the proposed thesis title. The implementation of this system is expected to streamline the thesis supervisor selection process, ensuring a more objective and appropriate match between students and supervisors based on their research fields.