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All Journal Jurnal Informatika Dan Tekonologi Komputer Proceeding of The International Conference on Electrical Engineering and Informatics
Safrizal Safrizal
Universitas Pembangunan Jaya
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Other

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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
Jurnal Informatika Dan Tekonologi Komputer (JITEK) Vol. 5 No. 2 (2025): Juli : Jurnal Informatika dan Tekonologi Komputer
Publisher : Pusat Riset dan Inovasi Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jitek.v5i2.5514

Abstract

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.
An Artificial Intelligence Based Recommendation Model for Personalizing Students' Learning Interest Paths at Universities Safrizal Safrizal; Chaerul Anwar; Augury El Rayeb
Proceeding of the International Conference on Electrical Engineering and Informatics Vol. 1 No. 2 (2024): July : Proceeding of the International Conference on Electrical Engineering and
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/iceei.v1i2.28

Abstract

This study explores the integration of artificial intelligence (AI) in education, particularly in supporting personalized learning. AI presents new opportunities through adaptive learning platforms, virtual tutors, and intelligent assessment systems that have the potential to revolutionize teaching and learning methods. By conducting in-depth data analysis, AI can identify student performance patterns and provide tailored recommendations, enabling educators to deliver more targeted interventions. Furthermore, personalized learning plays a crucial role in enhancing student motivation and engagement by customizing learning experiences to meet individual needs and learning styles. This study aims to implement personalized learning strategies in educational settings and offers insights into best practices for their integration. It also examines their impact on student engagement and academic achievement. The findings highlight the importance of personalized learning in fostering an inclusive and effective educational environment. By leveraging AI, educators can optimize learning, empower students, and address achievement gaps. This study provides practical recommendations for educators and policymakers to implement AI-based learning strategies effectively.
Co-Authors Acce Venio Hasugian Augury El Rayeb Chaerul Anwar Javier Alvino Alfian Yohana Citra Simamora