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All Journal International Journal of Advances in Applied Sciences Jurnal Transformatika Jurnal Informatika dan Teknik Elektro Terapan Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Jurnal Abdimas BSI: Jurnal Pengabdian Kepada Masyarakat KOMPUTIKA - Jurnal Sistem Komputer Abdimasku : Jurnal Pengabdian Masyarakat Computer Science and Information Technologies Patria : Jurnal Pengabdian Kepada Masyarakat Jurnal Teknologi Informasi dan Komunikasi Journal of Computer and Information Systems Ampera Advance Sustainable Science, Engineering and Technology (ASSET)
Alauddin Maulana Hirzan
Universitas Semarang
Aerospace Engineering Chemistry Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Public Health Social Sciences Other

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Journal : Advance Sustainable Science, Engineering and Technology (ASSET)

 1 
Hybrid Filtering for Student Major Recommendation: A Comparative Study Nurtriana Hidayati; Titin Winarti; Alauddin Maulana Hirzan
Advance Sustainable Science Engineering and Technology Vol. 7 No. 1 (2025): November-January
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v7i1.1250

Abstract

Choosing the right university major is an important decision for students, as delays or incorrect choices can harm their future careers and cause problems for academic departments. High dropout rates, which are frequently the result of poorly informed decisions, can be a considerable burden on faculty. This project aims to address these challenges by creating a recommendation system that provides individualized counsel to students based on their psychological profiles. A quantitative method was used, with questionnaires distributed to a large number of students. To verify the data's authenticity, replies were sought from students who were pleased with their selected majors rather than those who regretted their choices. The collected data formed the basis for a hybrid recommendation system that integrated Content-based Filtering and Collaborative Filtering methods. The system was then compared against standalone implementations of each filtering method to determine its usefulness in increasing suggestion accuracy. The results showed that the Hybrid Filtering strategy obtained a recommendation accuracy of 84.29%, outperforming Content-based  Filtering at 81.43% and Collaborative Filtering at 78.57%. The proposed model is easy to implement in a school or a university, as long as the required data is available. Thus, the model can help a school or university to reduce dropout rates and boost academic outcomes.
Hybrid Filtering for Student Major Recommendation: A Comparative Study Hidayati, Nurtriana; Winarti, Titin; Hirzan, Alauddin Maulana
Advance Sustainable Science Engineering and Technology Vol. 7 No. 1 (2025): November-January
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v7i1.1250

Abstract

Choosing the right university major is an important decision for students, as delays or incorrect choices can harm their future careers and cause problems for academic departments. High dropout rates, which are frequently the result of poorly informed decisions, can be a considerable burden on faculty. This project aims to address these challenges by creating a recommendation system that provides individualized counsel to students based on their psychological profiles. A quantitative method was used, with questionnaires distributed to a large number of students. To verify the data's authenticity, replies were sought from students who were pleased with their selected majors rather than those who regretted their choices. The collected data formed the basis for a hybrid recommendation system that integrated Content-based Filtering and Collaborative Filtering methods. The system was then compared against standalone implementations of each filtering method to determine its usefulness in increasing suggestion accuracy. The results showed that the Hybrid Filtering strategy obtained a recommendation accuracy of 84.29%, outperforming Content-based  Filtering at 81.43% and Collaborative Filtering at 78.57%. The proposed model is easy to implement in a school or a university, as long as the required data is available. Thus, the model can help a school or university to reduce dropout rates and boost academic outcomes.
Co-Authors Agnes Saputri Agus Hartanto April Firman Daru Bahaman, Nazrulazhar Febrian Wahyu Christanto Galih Setiarso Handayani, Sri Hanif, Mohammad Burhan Huizen, Lenny Margaretta Nurtriana Hidayati Rastri Prathivi Saputri, Agnes Setiarso, Galih Suprayogi, Muhammad Sani Surono Surono Surono Surono Susanto Susanto Titin Winarti Whisnumurti Adhiwibowo Wicaksana, Dinar Anggit