SMART RECOMMENDATION SYSTEM MODELING FOR BATIK USING THE CONTENT BASED RECOMMENDATION METHOD
DOI:
https://doi.org/10.47701/f0vwwz52Keywords:
batik, content based recommendation, modeling, prototyping, smart recommendation systemAbstract
Batik was an intangible cultural heritage recognized by UNESCO, with unique variations of motifs, colors, and philosophies in each region, both in Indonesia and Malaysia. The development of the fashion industry and e-commerce brought both opportunities and challenges, since users often had difficulties finding batik that matched their preferences, occasions, or symbolic needs. This research aimed to develop a smart recommendation system model for batik using the content-based recommendation method. The dataset consisted of batik data from Indonesia and Malaysia with attributes such as region of origin, dominant color, main motif, category, and usage. The system development method applied was Prototyping, which included the stages of requirement identification, quick design, and prototype construction. The results showed that the system was able to provide relevant recommendations according to user preferences. For example, when the user selected batik preferences with green color, leaf motif, and casual usage, the system recommended Batik Priangan from Indonesia with the highest similarity value of 0.75. These findings proved that the content-based approach successfully connected batik attributes with user needs. This research was expected not only to simplify the search for batik products in the digital era but also to contribute to the preservation of batik culture through the utilization of information technology.
References
Ali, S. K., Aydam, Z. M., & Rashed, B. M. (2020). Similarity metrics for classification: A Review. IOP Conference Series: Materials Science and Engineering, 928(3), 032052. https://doi.org/10.1088/1757-899X/928/3/032052
Aqthar, A. Z. A., & Prapanca, A. (2025). Sistem Rekomendasi Motif Batik Sesuai dengan Kebutuhan Acara Pernikahan Pengguna Menggunakan Metode. 06.
Biasio, A. D., Navarin, N., & Jannach, D. (2024). Economic Recommender Systems—A Systematic Review. Electronic Commerce Research and Applications, 63, 101352. https://doi.org/10.1016/j.elerap.2023.101352
De Campos, L. M., Fernández-Luna, J. M., & Huete, J. F. (2024). An explainable content-based approach for recommender systems: A case study in journal recommendation for paper submission. User Modeling and User-Adapted Interaction, 34(4), 1431–1465. https://doi.org/10.1007/s11257-024-09400-6
Levy, A., Shalom, B. R., & Chalamish, M. (2025). A guide to similarity measures and their data science applications. Journal of Big Data, 12(1), 188. https://doi.org/10.1186/s40537-025-01227-1
Roy, D., & Dutta, M. (2022). A systematic review and research perspective on recommender systems. Journal of Big Data, 9(1), 59. https://doi.org/10.1186/s40537-022-00592-5
Sari, L., Arif Setiawan, & R. Rhoedy Setiawan. (2025). Transformasi Digital Pendidikan Rancang Bangun Sistem Informasi Akademik Berbasis Web Di SD 2 Mlati Kidul. JEKIN - Jurnal Teknik Informatika, 5(1), 261–275. https://doi.org/10.58794/jekin.v5i1.1294
Senyurek, E., & Kevric, J. (2024). Effects of Binary Similarity Metrics for the Relevance Component of Serendipity-Oriented Clustering Method in Collaborative Filtering. IFAC-PapersOnLine, 58(9), 172–177. https://doi.org/10.1016/j.ifacol.2024.07.391
Sonali, K., Reddy, S. V. G., Reddy, K. T., & Kumari, V. V. (2023). Analysing the performance of Recommendation System using different similarity metrics. 10(04).
Sulami, A., Atina, V., & Nurmalitasari, N. (2024). Penerapan Metode Content Based Filtering dalam Sistem Rekomendasi Pemilihan Produk Skincare. STRING (Satuan Tulisan Riset dan Inovasi Teknologi), 9(2), 172. https://doi.org/10.30998/string.v9i2.24066
Susanto, A. (2019). System Development Method with The Prototype Method. 8(07).
Wahed, W. J. E., Amin, H., Mohamad Bohari, A. A., Pindah, C., & Azmi, S. (2022). Malaysian Batik, Our Pride: A Systematic Literature Review. International Journal of Academic Research in Business and Social Sciences, 12(10), Pages 942-955. https://doi.org/10.6007/IJARBSS/v12-i10/14950
