RANCANG BANGUN SISTEM INFORMASI PEMBELAJARAN ADAPTIF MENGGUNAKAN MACHINE LEARNING UNTUK PENDIDIKAN TINGGI

TASYA NOVELIA BR SITORUS, NPM 2209400174 (2026) RANCANG BANGUN SISTEM INFORMASI PEMBELAJARAN ADAPTIF MENGGUNAKAN MACHINE LEARNING UNTUK PENDIDIKAN TINGGI. Tugas_Akhir (Artikel) : Jurnal Sistem Informasi, Teknik Komputer Dan Teknologi Pendidikan (JUSTIKPEN) Sinta 5, 5 (2). pp. 15-19. ISSN 2828-7921 (e-ISSN)

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Abstract

Penelitian ini bertujuan untuk merancang dan mengembangkan Sistem Informasi Pembelajaran Adaptif (SIPA) di lingkungan pendidikan tinggi yang mampu menyesuaikan konten, jalur, dan kecepatan belajar berdasarkan karakteristik unik setiap mahasiswa. Metode yang digunakan adalah Research and Development (R&D) dengan pendekatan System Development Life Cycle (SDLC) dan berfokus pada integrasi algoritma Machine Learning (ML), khususnya Model Collaborative Filtering. Tujuan utama ML adalah mempersonalisasi rekomendasi materi ajar dan menilai tingkat penguasaan konsep secara dinamis. Hasil rancang bangun menunjukkan bahwa SIPA berhasil diimplementasikan, menyediakan fitur analisis kinerja mahasiswa real-time dan kemampuan untuk memprediksi risiko kegagalan akademis. Temuan penting mencakup tingginya akurasi model Collaborative Filtering (mencapai 89%) dalam merekomendasikan materi tambahan yang relevan, serta peningkatan signifikan pada tingkat keterlibatan mahasiswa. Sebagai simpulan, SIPA berbasis ML ini menawarkan platform yang efektif dan efisien untuk mempersonalisasi pengalaman belajar, yang krusial untuk meningkatkan kualitas dan hasil pembelajaran di pendidikan tinggi. Kata Kunci : Pembelajaran Adaptif, Sistem Informasi Pembelajaran, Machine Learning, Pendidikan Tinggi, Collaborative Filtering, Personalisasi ================================================================================================= This study aims to design and develop an Adaptive Learning Information System (ALIS) in a higher education environment capable of adjusting content, pathways, and pace of learning based on the unique characteristics of each student. The method used is Research and Development (R&D) with a System Development Life Cycle (SDLC) approach, focusing on the integration of Machine Learning (ML) algorithms, specifically the Collaborative Filtering Model. The primary objective of ML is to personalize the recommendation of teaching materials and dynamically assess the level of concept mastery. The design results show that ALIS was successfully implemented, providing features for real-time student performance analysis and the ability to predict academic failure risk. Key findings include the high accuracy of the Collaborative Filtering model (achieving 89%) in recommending relevant supplementary materials, and a significant increase in student engagement levels. In conclusion, this ML based ALIS offers an effective and efficient platform for personalizing the learning experience, which is crucial for improving the quality and outcomes of higher education. Keywords : Adaptive Learning, Learning Information System, Machine Learning, Higher Education, Collaborative Filtering, Personalization

Item Type:	Article
Uncontrolled Keywords:	Pembelajaran Adaptif, Sistem Informasi Pembelajaran, Machine Learning, Pendidikan Tinggi, Collaborative Filtering, Personalisasi ====================================== Adaptive Learning, Learning Information System, Machine Learning, Higher Education, Collaborative Filtering, Personalization
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software T Technology > T Technology (General)
Divisions:	Fakultas Sains Dan Teknologi > Sistem Informasi
Depositing User:	Unnamed user with email repository@ulb.ac.id
Date Deposited:	12 May 2026 04:27
Last Modified:	12 May 2026 04:28
URI:	http://repository.ulb.ac.id/id/eprint/2287

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