Sistem Rekomendasi Personalisasi Pembelajaran Mahasiswa untuk Prediksi Karir dan Sertifikasi Kompetensi yang Tepat - Safrizal Safrizal; Chaerul Anwar; Augury El Rayeb; Yohana Citra Simamora; Acce Venio Hasugian; Javier Alvino Alfian (Pusat Riset dan Inovasi Nasional, 2025)
In the era of digital and globalization, the need for graduates who have competencies in accordance with industry demands is becoming increasingly important. Students often face difficulties in determining the right direction of learning, both for career development and achieving competency certification. This study aims to develop a personalized recommendation system for student learning that is able to predict appropriate career paths and recommend relevant certifications. This system utilizes a data-driven approach using data mining and machine learning techniques, by processing academic data, interests, expertise, and current industry trends. The recommendation system algorithm used includes a content-based and collaborative approach, which are combined to produce more accurate and adaptive results. This system is designed to provide learning suggestions in the form of courses, additional training, and external certifications that support students' career goals. Initial test results show that the system is able to improve students' understanding of their potential and career prospects. Thus, this system is expected to be an innovative solution in supporting the personalization of future-oriented higher education.