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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) JURNAL SISTEM INFORMASI BISNIS
Wijanarko, Bambang Dwi
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Computer Science & IT Economics, Econometrics & Finance Engineering

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Evaluasi Pengalaman Pengguna Pada Learning Management System Menggunakan Metode User Experience Questionnaire Wijanarko, Bambang Dwi; Leandros, Riyan; Murad, Dina Ftiria
Jurnal Sistem Informasi Bisnis Vol 14, No 4 (2024): Volume 14 Nomor 4 Tahun 2024
Publisher : Diponegoro University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21456/vol14iss4pp385-391

Abstract

The purpose of this study was to conduct a survey to assess the performance of the Learning Management System (LMS). This is done related to the need to evaluate its use by users. This study used the User Experience Questionnaire (UEQ) method regarding (1) attractiveness, (2) clarity, (3) efficiency, (4) reliability, (5) stimulation, and (6) novelty. The survey was conducted online via a Google form and had 164 participants. The benchmark results show that all criteria are above the average, but the UEQ scale for novelty is the lowest at 0.87, while attractiveness is 1.44, pragmatic quality is 1.37, and hedonic quality is 1.05. Therefore, it is suggested that the learning management system be improved through interactive learning styles, such as live discussions in LMS, integration with other technologies, updating artificial intelligence and big data-based technologies, and adapting to the metaverse, while maintaining the attractiveness and identity of the platform through design. rework color schemes, content, including engaging videos and gamification, and implement live chat with lecturers to overcome time constraints during video conferencing.
Solving sparsity and scalability problems for book recommendations on e-commerce Ichsanudin, Muhammad; Handari, Bevina Desjwiandra; Wijanarko, Bambang Dwi; Hertono, Gatot Fatwanto
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i6.pp4865-4877

Abstract

This study proposed a hierarchical density-based spatial clustering of applications with noise (HDBSCAN) and randomized singular value decomposition (RSVD) collaborative filtering (CF) method to overcome sparsity and scalability problems for book recommendations on e-commerce. CF is an information retrieval system that assumes a user has the same interest in an object as other users have in the past. When handling large volumes of data, sparsity problems can arise, where finding a similarity relation of user preferences results from a small assessment of an object by users. The scalability is the increased computation of an algorithm caused by increased users or objects, which makes recommendations take longer to form, therefore making them less accurate. HDBSCAN is a density-based clustering method that simplifies the hierarchical arrangement of the most significant clusters for extraction to group users in the same cluster. RSVD is a linear dimension reduction method that breaks a matrix into three sub matrices by reconstructing the size of that matrix without removing its dominant part, especially for cluster result matrices. The HDBSCAN RSVD-CF model reduced the root mean squared error (RMSE) by 21.83%, being 3793.73 seconds faster than the CF model. It also performed very well compared to both RSVD-CF and HDBSCAN-CF.
Co-Authors Bevina D Handari Hertono, Gatot Fatwanto Ichsanudin, Muhammad Leandros, Riyan Murad, Dina Ftiria