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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)
Binti Sura, Suaini
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Computer Science & IT Engineering

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Improving the Accuracy of Tourism Recommendation System Based on Neural Collaborative Filtering Renita Astri; Lai Po Hung; Binti Sura, Suaini; Ahmad Kamal
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 4 (2025): August 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i4.6516

Abstract

This study proposes a Neural Collaborative Filtering (NCF) model for tourism recommendation systems by integrating user ratings and review data. This model was developed to overcome the limitations of conventional recommendation systems that rely solely on numerical data, by adding contextual information from user reviews to improve the accuracy of preference prediction. The development process includes data preprocessing, conversion of text reviews into numerical representations using embedding techniques, and the application of NCF models with various parameter configurations. Experimental results show that the NCF model that combines rating and review data produces the best performance with Root mean Square Error (RMSE) values of 0.892, Hit Ratio at 10( HR@10) of 0.735, and Normalized Discounted Cumulative Gain at 10 (NDCG@10) of 0.629, outperforming models that only use one type of data. These results demonstrate that combining numerical and textual information can improve the model's understanding of user preferences, resulting in more relevant tourist destination recommendations. These findings contribute to the development of artificial intelligence-based recommendation systems in the tourism sector.
Harnessing BERT for Semantic Understanding in Tourism Recommendation Engines Renita Astri; Po Hung, Lai; Binti Sura, Suaini; Kamal, Ahmad
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 4 (2025): August 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i4.6575

Abstract

It will be necessary for attraction managers within hotels to track guests' lifestyles to keep the business running. Such an understanding may be achieved, for example by analyzing reviews on attractions to capture the attitudes of the visitors towards the services and business within the tourism industry. The approach utilizes web scraping to gather user-generated reviews, using text preprocessing, data pre-processing, and further improvement of the model using labelled sentiment data divided into three sentiment classes: positive, negative, or neutral. The dataset consisting of 908 reviews were divided in 70:15:15 ratio for training, validation and testing. Model performance was measured in terms of accuracy, precision, recall and F1-score. In this study, the BERT deep learning model is used to classify sentiments of Indonesian tourist. Using the SmallBERT variant fine-tuned on 515k reviews for 5 epochs, the model achieved 91.40% accuracy, 90.51% precision, recall, and F1 score. The results indicate a dominance of positive sentiments, visualized using tableau. This research provides a robust foundation for developing intelligent sentiment-based recommendation systems in the tourism sector and suggests future exploration using other transformer-based models such as GPT, T5, or BART for comparative analysis.
Serendipitous Recommendations for Handicraft Store Discovery in Social Commerce Using a Genetic Algorithm with Adaptive Selection Kamal, Ahmad; Binti Sura, Suaini; Po Hung, Lai; Astri, Renita
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 1 (2026): February 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i1.7077

Abstract

In social commerce, particularly among small and medium-sized handicraft enterprises (SMEs), personalized recommender systems (RS) are crucial for enhancing store and product discovery. Conventional content-based filtering (CBF) often overemphasizes accuracy, leading to over-specialization and limiting exposure to novel or diverse items, an issue in the handicraft sector where uniqueness is valued. This study proposes a serendipitous recommendation approach using a Genetic Algorithm (GA) with adaptive selection strategies, Roulette Wheel Selection (RWS), Tournament Selection (TnS), and Rank-Based Selection (RBS), to balance relevance and unexpectedness. Handicraft store attributes, such as product types, materials, and services, are encoded in a 19-bit chromosome and evaluated via a hybrid fitness function. Tested on real data from West Sumatra SMEs, the model is assessed using Precision, Recall, Novelty, and Serendipity metrics. Results show that the GA-based adaptive selection approach outperforms baseline CBF in producing more diverse and surprising recommendations, fostering exploratory shopping experiences and supporting the discovery of unique local products in social commerce ecosystems.
Co-Authors ahmad kamal ahmad kamal, ahmad Lai Po Hung Po Hung, Lai Renita Astri Renita Astri