Development of the “Scentify” Application as a Web and Mobile-Based Perfume Recommendation System Using Content-Based Filtering Based on User Preferences
DOI:
https://doi.org/10.32664/j-intech.v13i02.2143Keywords:
Perfume Recommendation, Content-Based Filtering, Flutter, Flask, Mobile ApplicationAbstract
This study presents the development of Scentify, a web and mobile-based perfume recommendation system employing the Content-Based Filtering (CBF) algorithm. The research aims to assist users in identifying perfumes aligned with their personal preferences such as fragrance type, concentration, gender, age, and price range. The development methodology includes literature review, user preference data collection via questionnaires, system design, and implementation using Flutter for mobile and Flask for backend API. User preference data were integrated with curated perfume datasets obtained from reliable online sources to form the recommendation base. The results demonstrate that Scentify can produce personalized recommendations with relevant accuracy. It features login, registration, perfume questionnaire, favorites, and admin dashboard modules. The system was validated using black-box testing, proving its reliability and user-friendliness. This work confirms that Content-Based Filtering is effective in building personalized digital recommendation systems and contributes to digital innovation in the perfume and e-commerce industries.
References
[1] A. Sulami and V. Atina, “STRING (Satuan Tulisan Riset dan Inovasi Teknologi) PENERAPAN METODE CONTENT BASED FILTERING DALAM SISTEM REKOMENDASI PEMILIHAN PRODUK SKINCARE.”
[2] N. Azizah and A. Rozi, “Sistem Rekomendasi Produk Somethinc Menggunakan Metode Content-based Filtering,” Jurnal Teknologi Dan Sistem Informasi Bisnis, vol. 6, no. 3, Jul. 2024, doi: 10.47233/jteksis.v6i3.1411.
[3] D. Debashis, S. Laxman, and D. Sujoy, “A Survey on Recommendation System,” Int J Comput Appl, vol. 160, no. 7, pp. 6–10, 2017, doi: 10.5120/ijca2017913081.
[4] M. B. Bukhori, “Perfume Recommendation System Using Content-Based Filtering,” Universiti Teknologi MARA, Malaysia, 2025.
[5] B. C. L. Rodrigues, V. V Santana, L. P. Queiroz, C. M. Rebello, and I. B․ R․ Nogueira, “Harnessing graph neural networks to craft fragrances based on consumer feedback,” Comput Chem Eng, vol. 185, p. 108674, 2024, doi: https://doi.org/10.1016/j.compchemeng.2024.108674.
[6] A. Ziqri and N. Ghaniaviyanto Ramadhan, “JIP (Jurnal Informatika Polinema) SISTEM REKOMENDASI PEMILIHAN SOFTWARE BERBASIS CONTENT-BASED FILTERTING (STUDI KASUS: PT.XYZ)”, [Online]. Available: https://s.id/1usED
[7] L. Lovrić, M. Fischer, N. Röderer, and A. Wünsch, “Evaluation of the Cross-Platform Framework Flutter Using the Example of a Cancer Counselling App,” in International Conference on Information and Communication Technologies for Ageing Well and e-Health, ICT4AWE - Proceedings, Science and Technology Publications, Lda, 2023, pp. 135–142. doi: 10.5220/0011824500003476.
[8] N. Chauhan, M. Singh, A. Verma, A. Parasher, and G. Budhiraja, “Implementation of database using python flask framework,” International Journal of Engineering and Computer Science, vol. 8, pp. 24894–24899, 2019, [Online]. Available: https://api.semanticscholar.org/CorpusID:212930456
[9] A. Y. Timur, “SISTEM REKOMENDASI LAGU INDONESIA MENGGUNAKAN METODE CONTENT-BASED FILTERING DAN COSINE SIMILARITY,” Jurnal Informatika dan Teknik Elektro Terapan, vol. 13, no. 1, Jan. 2025, doi: 10.23960/jitet.v13i1.5949.
[10] R. Ardiansyah, M. Ari Bianto, and B. D. Saputra, “Sistem Rekomendasi Buku Perpustakaan Sekolah menggunakan Metode Content-Based Filtering,” Jurnal CoSciTech (Computer Science and Information Technology), vol. 4, no. 2, pp. 510–518, Oct. 2023, doi: 10.37859/coscitech.v4i2.5131.
[11] P. Vedhavyas, V. Srikar, N. Ashwarda, and R. Begum, “Movie recommendation app with cosine similarity and flask,” MATEC Web of Conferences, vol. 392, p. 01071, 2024, doi: 10.1051/matecconf/202439201071.
[12] A. A. Zuhri, R. Kusumawati, M. A. Yaqin, A. F. Anwar, A. Fahreza, and A. Pahlevi, “Sinta 4 Ministry of Higher Education, Science and Technology Republic of Indonesia 1,2,3,4,5 Universitas Islam Negeri Maulana Malik Ibrahim Malang, Magister Informatika, Fakultas Sains dan Teknologi,” JL. Gajayana No, vol. 50.
[13] L. Putu Ary Sri Tjahyanti, G. Rai Sutama, and P. Korespondensi, “PERAN ANALISIS KEBUTUHAN DALAM MENCIPTAKAN SISTEM INFORMASI YANG RESPONSIF DAN BERKELANJUTAN,” Jurnal Komputer dan Teknologi Sains (KOMTEKS), vol. 3, no. 2, pp. 1–11, 2024.
[14] E.-R. Luţan and C. Bădică, “Personalized Quiz-Based Perfume Recommender System Using Social Data,” in Advances in Computational Collective Intelligence, N. T. Nguyen, J. Botzheim, L. Gulyás, M. Nunez, J. Treur, G. Vossen, and A. Kozierkiewicz, Eds., Cham: Springer Nature Switzerland, 2023, pp. 30–43.
[15] L. M. de Campos, J. M. Fernández-Luna, and J. F. Huete, “Use of topical and temporal profiles and their hybridisation for content-based recommendation,” Jan. 2024, [Online]. Available: http://arxiv.org/abs/2401.10607
[16] M. J. Pazzani and D. Billsus, “0 Content-based Recommendation Systems.”
[17] O. M. A. AL-atraqchi, “A Proposed Model for Build a Secure Restful API to Connect between Server Side and Mobile Application Using Laravel Framework with Flutter Toolkits,” Cihan University-Erbil Scientific Journal, vol. 6, no. 2, pp. 28–35, Aug. 2022, doi: 10.24086/cuesj.v6n2y2022.pp28-35.
[18] S. Suhada, S. Bahri, S. B. Nugraha, T. Hidayatulloh, and D. Wintana, “Sistem Rekomendasi Produk Menggunakan Metode User-Based Collaborative Filtering Pada Digital Marketing”.
[19] A. I. Nurfauzi and A. T. Wibowo, “Evaluating Non-Negative Matrix Factorization and Singular Value Decomposition for Skincare Recommendation Systems,” Journal on Computing, vol. 9, no. 3, pp. 220–231, 2024, doi: 10.34818/indojc.2024.9.3.983.
[20] F. E. Aurora and Z. K. A. Baizal, “Perfume Product Selection Recommender System Using Content-based Filtering Approach,” in 2025 International Conference on Data Science and Its Applications (ICoDSA), 2025, pp. 86–90. doi: 10.1109/ICoDSA67155.2025.11157374.
Downloads
Published
Issue
Section
License
Copyright (c) 2025 J-INTECH
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
