Rekomendasi Klasifikasi Dan Desain Otomatis Menu Restoran Kopi XYZ Berbasis Web Menggunakan Metode Naïve Bayes
DOI:
https://doi.org/10.30865/jurikom.v12i6.9364Keywords:
Menu Classification, Naïve Bayes, Web System, MSMEs, Automatic Menu DesignAbstract
Abstract
The advancement of information technology has accelerated digital transformation in the culinary industry, particularly in menu management, which demands fast, structured, and error-minimized processes. In many MSMEs, menu classification and design activities are still performed manually, resulting in inefficiencies. This study developed a web-based system capable of automatically classifying menu categories using the Multinomial Naïve Bayes algorithm and generating menu designs automatically. The dataset consists of 148 menu items covering food, beverages, and snacks. The features used include text-based menu names processed using TF-IDF, as well as numerical price attributes. The data were split into 80% training and 20% testing portions. The results show that the Multinomial Naïve Bayes model achieved the best performance with an accuracy of 93.24%, a precision of 0.92, a recall of 0.93, and an F1-score of 0.92. These values demonstrate the model’s ability to consistently recognize word patterns representing menu categories. The system also successfully generated menu template designs automatically based on the classification results. This research contributes to the application of data mining in the culinary sector and supports MSMEs in improving the effectiveness of menu management.
References
[1] Sonia Wanda Mafriza and Armansyah, “Klasifikasi karir mahasiswa bidang web developer menggunakan algoritma naïve bayes,” INFOTECH J. Inform. Teknol., vol. 4, no. 2, pp. 270–280, 2023, doi: 10.37373/infotech.v4i2.907.
[2] Martiti and C. Juliane, “Implementation of Naive Bayes Algorithm on Sentiment Analysis Application,” Proc. 2nd Int. Semin. Sci. Appl. Technol. (ISSAT 2021), vol. 207, no. Issat, pp. 193–200, 2021, doi: 10.2991/aer.k.211106.030.
[3] S. Siswanto, Z. Mar’ah, A. S. D. Sabir, T. Hidayat, F. A. Adhel, and W. S. Amni, “The Sentiment Analysis Using Naïve Bayes with Lexicon-Based Feature on TikTok Application,” J. Varian, vol. 6, no. 1, pp. 89–96, 2022, doi: 10.30812/varian.v6i1.2205.
[4] B. Olana and D. Tolasa, “Adoption of Web Application Framework to Enhance Web Project Result,” Am. J. Comput. Sci. Technol., vol. 8, no. 2, pp. 102–120, 2025, doi: 10.11648/j.ajcst.20250802.15.
[5] T. Herlambang, V. Asy’ari, R. P. Rahayu, A. A. Firdaus, and N. Juniarta, “Comparison of Naïve Bayes and K-Nearest Neighbor Models for Identifying the Highest Prevalence of Stunting Cases in East Java,” Barekeng, vol. 18, no. 4, pp. 2153–2164, 2024, doi: 10.30598/barekengvol18iss4pp2153-2164.
[6] E. Septiani, T. M. Akhriza, and M. Husni, “Comparison of the Accuracy Between Naive Bayes Classifier and Support Vector Machine Algorithms for Sentiment Analysis in Mobile JKN Application Reviews,” Trans. Informatics Data Sci., vol. 1, no. 1, pp. 21–32, 2024, doi: 10.24090/tids.v1i1.12232.
[7] M. Qamal, I. Sahputra, N. Nurdin, M. Maryana, and M. Mukarramah, “Sistem Pendukung Keputusan Penentuan Penerimaan Bantuan PKH Menggunakan Metode Naïve Bayes,” TECHSI - J. Tek. Inform., vol. 14, no. 1, p. 21, 2023, doi: 10.29103/techsi.v14i1.6960.
[8] A. P. Permana, T. Chamidy, and C. Crysdian, “Klasifikasi Ulasan Fasilitas Publik Menggunakan Metode Naïve Bayes dengan Seleksi Fitur Chi-Square,” JISKA (Jurnal Inform. Sunan Kalijaga), vol. 8, no. 2, pp. 112–124, 2023, doi: 10.14421/jiska.2023.8.2.112-124.
[9] F. R. Imadudin, D. T. Murdiansyah, and Adiwijaya, “Implementation of Naïve Bayes and Gini Index for Spam Email Classification,” Indones. J. Comput., vol. 6, no. 1, pp. 85–94, 2021, doi: 10.34818/indojc.2021.6.1.452.
