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All Journal JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Informatika: Jurnal Pengembangan IT
Putri, Desy Purnami Singgih
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Computer Science & IT

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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%.
Comparison of IndoBERT and Bi-LSTM Models for Indonesian Law Violation Text Classification Pramana, Made Wahyu Adwitya; Putri, Desy Purnami Singgih; Purnawan, I Ketut Adi
Jurnal Informatika: Jurnal Pengembangan IT Vol 10, No 4 (2025)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v10i4.8795

Abstract

Legal violations in Indonesia, particularly those under the Criminal Code (KUHP) and the Information and Electronic Transactions Law (UU ITE), are often difficult for the general public to interpret due to the complexity of legal language and article structures. This research aims to build a multilabel classification model that can automatically identify relevant legal articles from user-provided case descriptions. Two models were developed and compared: Bidirectional Long Short-Term Memory (Bi-LSTM) and IndoBERT. Using a manually labeled dataset, both models were evaluated through accuracy, F1-score, and Hamming Loss metrics, as well as 5-fold cross-validation. The results showed that IndoBERT outperformed Bi-LSTM with an average accuracy of 97% and a Hamming Loss of 0.027. However, t-test analysis revealed no statistically significant difference in F1-scores, indicating that both models have comparable effectiveness in capturing multiple labels. A confusion matrix analysis further identified patterns of misclassification in semantically similar articles. This study demonstrates the potential of NLP and deep learning to support legal awareness and provide the public with easier access to legal information.
Co-Authors I Ketut Adi Purnawan Ni Putu Adnya Puspita Dewi Pramana, Made Wahyu Adwitya Trisna, I Nyoman Prayana