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All Journal Algoritme Jurnal Mahasiswa Teknik Informatika
Buleuw, Zulaeva Rajiah
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Computer Science & IT

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Rekomendasi Produk Fashion Berdasarkan Rating Menggunakan Metode Singular Value Decomposition Buleuw, Zulaeva Rajiah; Agustining Tyas, Windi; Aryanto, Aryanto
Algoritme Jurnal Mahasiswa Teknik Informatika Vol 6 No 1 (2025): Oktober 2025 || Algoritme Jurnal Mahasiswa Teknik Informatika
Publisher : Program Studi Teknik Informatika Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/algoritme.v6i1.12154

Abstract

The growth of e-commerce in the fashion industry has significantly increased the number of digital products offered to consumers. This situation leads to an information overload problem, where users find it difficult to choose products that match their preferences. To address this issue, a recommendation system is needed to filter information and provide relevant product suggestions. This study aims to develop a fashion product recommendation system based on user ratings using the Singular Value Decomposition (SVD) method. The data used is secondary data sourced from the Kaggle platform, containing user interactions with various fashion products. The research process includes data collection, preprocessing, rating matrix construction, data decomposition, and model performance evaluation. The evaluation results show a Mean Absolute Error of 0.47 and a Root Mean Squared Error of 0.67, indicating a relatively low prediction error. The system also successfully recommends items with high predicted ratings, such as a rating of 4.30, demonstrating strong relevance to user preferences. These findings confirm that the applied method is effective in building an accurate and relevant recommendation system that assists users in making fashion product purchase decisions more efficiently.
Co-Authors Agustining Tyas, Windi Aryanto Aryanto