Article Per Year (5 Year)
p-Index From 2020 - 2025
1.704
P-Index
This Author published in this journals
All Journal JOURNAL OF APPLIED INFORMATICS AND COMPUTING Technologia: Jurnal Ilmiah Jurnal Pengabdian Masyarakat Asia Brilliance: Research of Artificial Intelligence Jurnal Pengabdian Kolaborasi dan Inovasi IPTEKS CARE: Jurnal Pengabdian Multi Disiplin SmartComp
Mahalisa, Galih
Unknown Affiliation
Agriculture, Biological Sciences & Forestry Arts Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Environmental Science Mathematics Social Sciences Other

Published : 10 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search
Journal : JOURNAL OF APPLIED INFORMATICS AND COMPUTING

 1 
Development Of A Collaborative Recommendation System Based on Singular Value Decomposition (SVD) on E-Commerce Data Mahalisa, Galih; Ratna, Silvia; Muflih, M.
Journal of Applied Informatics and Computing Vol. 9 No. 6 (2025): December 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i6.11688

Abstract

Recommendation systems (RS) are vital tools for mitigating information overload and data sparsity challenges in modern e-commerce platforms. This study focuses on developing and evaluating a Collaborative Filtering (CF) model utilizing Singular Value Decomposition (SVD) as a Matrix Factorization technique, applied to the publicly available E-commerce dataset. The dataset, comprising nine interconnected transactional tables, presents significant data sparsity due to limited explicit user ratings relative to the vast product catalog. The SVD model was implemented to decompose the highly sparse User-Item interaction matrix into lower-rank latent factor matrices, thereby capturing underlying purchasing patterns and user preferences. The model's performance was rigorously validated using k-fold cross-validation and assessed via standard accuracy metrics: Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). The experimental results demonstrated high predictive accuracy, achieving an RMSE of 1.25 and an MAE of 0.98. These findings indicate that the SVD model effectively overcomes the sparsity challenge inherent in large-scale e-commerce transactional data, providing robust prediction capabilities that surpass established industry benchmarks (e.g., RMSE » 1.31, MAE » 1.04 found in similar studies). The successful implementation validates SVD as a highly effective approach for generating personalized, high-quality product recommendations, offering substantial business implications for enhancing customer engagement and maximizing Average Order Value (AOV)
Co-Authors Agus, Rahmadi Alfah, Rina Arminarahmah, Nur Asegaff, Aulia Rizky Muhammad Hendrik Noor Basuki , Maulani, Jauhari Mayang Sari Muflih, M. Muhammad Amin Muhammad Iqbal Firdaus, Muhammad Iqbal Muhammad Rusdi Nadiya Hijriana, Nadiya Nurarminarahmah Ratna, Silvia Ridho, Ihda Innar Rizqi Elmuna Hidayah Setiawan, Kholik Syafarina, Gita Ayu Zaenuddin Zaenuddin, Zaenuddin