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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) JOURNAL OF APPLIED INFORMATICS AND COMPUTING Journal of Information Systems and Informatics
Trisna, I Nyoman Prayana
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Computer Science & IT Control & Systems Engineering

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Journal : JOURNAL OF APPLIED INFORMATICS AND COMPUTING

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Web-Based Makeup Recommendation System Using Hybrid Filtering Utami, Putu Mia Setya; Trisna, I Nyoman Prayana; Vihikan, Wayan Oger
Journal of Applied Informatics and Computing Vol. 9 No. 3 (2025): June 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i3.9339

Abstract

The increasing use of makeup products in the modern era, driven by evolving beauty trends and e-commerce accessibility, presents challenges in selecting products suited to individual skin types and conditions. A recommendation system addresses this issue by enhancing selection efficiency. This study explores the implementation of Content-Based Filtering (CBF) using TF-IDF and Cosine Similarity, Collaborative Filtering (CF) with Singular Value Decomposition (SVD), and a Hybrid Filtering approach integrating both methods through Weighted Hybrid techniques. The system's performance is evaluated across two user scenarios: new users (without prior ratings) and old users (with rating history). The evaluation method includes Precision, Normalized Discounted Cumulative Gain (NDCG), and accumulation of the best scenario based on user opinion. Results show that Hybrid Filtering outperforms CBF and CF, with notable differences between user groups. For new users, 32% prefer Scenario 1, which emphasizes CBF, achieving 80.8% Precision and 89.73% NDCG. For old users, 23% favor Scenario 2, attaining 83.4% Precision and 90.31% NDCG.
Fine-Tuned Transformer Models for Keyword Extraction in Skincare Recommendation Systems Ni Putu Adnya Puspita Dewi; Putri, Desy Purnami Singgih; Trisna, I Nyoman Prayana
Journal of Applied Informatics and Computing Vol. 9 No. 3 (2025): June 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i3.9687

Abstract

The skincare industry in Indonesia is experiencing rapid growth, with projected revenues reaching nearly 40 billion rupiah by 2024 and expected to continue to increase. The large number of products in circulation makes it difficult for consumers to find products that suit their needs. In this context, a text-based recommendation system that utilizes advances in Natural Language Processing (NLP) technology is a promising solution. This research aims to develop a skincare product recommendation system based on user needs by applying the DistilBERT model, which is specifically fine-tuned with text in the skincare recommendation domain to perform keyword extraction. The resulting keywords are then used as parameters to provide recommendations by using co-occurrence as well as using a modification of Jaccard Similarity to assess the suitability between the content and benefits of the product and user preferences. The trained extraction model achieved the best performance with a micro F1-score of 0.96 at the token level and an exact match rate of 74.25% at the entity level. The evaluation of the recommendation system showed excellent results, with an nDCG value of 0.96 and a user satisfaction rate (CSAT) of 91.9%.
Co-Authors I Made Agus Dwi Suarjaya Ni Putu Adnya Puspita Dewi Patni, Ni Putu Sri Ratih Dia Putra, I Made Wiraharja Jaya Putri, Desy Purnami Singgih Putri, I Gusti Agung Intan Utami Rusjayanthi, Ni Kadek Dwi Setiawati, Putu Ayulia Susila, Anak Agung Ngurah Hary Utami, Putu Mia Setya Wayan Oger Vihikan, Wayan Oger