Klasifikasi Popularitas Menu Kafe Menggunakan Random Forest dengan Labeling Berbasis Quantile pada Data Transaksi Penjualan
Abstract
The rapid growth of the cafe industry requires a more accurate understanding of customer preferences, particularly in determining menu popularity. This study aims to develop a classification model for menu popularity levels using the Random Forest algorithm based on cafe sales transaction data. The dataset includes menu price attributes (Price Per Unit) and transaction time (Transaction Date), which are processed through a preprocessing stage and temporal feature extraction including Month, DayOfWeek, and IsWeekend. Popularity labels are determined based on the frequency of each item's occurrence in transaction data using the 60th percentile as the threshold value. The results show that the model achieves strong performance with 94.79% accuracy, 94.10% precision, 93.74% recall, and 93.92% F1-score. The model effectively identifies menu popularity patterns and can serve as a reference for data-driven customer preference analysis.
References
Artikel jurnal:
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Aryanti, Putri, A. T., Khairunisya, A., Pratama, I. Y., & Azizah, P. N. (2026). Klasifikasi Tempat Wisata Di Yogyakarta Berdasarkan Harga Dan Rating Menggunakan Algoritma Random Forest. 18(2), 192–204.
Berrettini, M., Hennig, C., & Viroli, C. (2024). The quantile-based classifier with variable-wise parameters The quantile classifier.
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Pudjihartono, N., Fadason, T., & Kempa-liehr, A. W. (2022). A Review of Feature Selection Methods for Machine Learning-Based Disease Risk Prediction. 2(June), 1–17. https://doi.org/10.3389/fbinf.2022.927312
Ramadhan, G., Faqih, A., Permana, F. E., Informatika, T., Informatika, M., & Cirebon, K. (2025). Analisis Tren Penjualan Menu Seafood Dengan Algoritma Random Forest Untuk Meningkatkan Strategi Pemasaran. 9(4), 5942–5949.
Rindiyani, Primadewi, A., Maimunah, & Purwantini, A. H. (2022). Klasifikasi Penjualan berdasarkan Platform pada UMKM Omah Branded Menggunakan Random Forest. 9(5), 1520–1529. https://doi.org/10.30865/jurikom.v9i5.4949
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Sathyanarayanan, S., & Tantri, B. R. (2024). Confusion Matrix-Based Performance Evaluation Metrics. 27(4).
Sch, L., Swift, A. J., Lu, H., & Member, S. (2022). Uncertainty Estimation for Heatmap-based Landmark Localization. XX(Xx), 1–14.
Solano-Barliza, A., Valls, A., Acosta-Coll, M., Moreno, A., Escorcia-Gutierrez, J., De-La-Hoz-Franco, E., & Arregoces-Julio, I. (2024). Enhancing Fair Tourism Opportunities in Emerging Destinations by Means of Multi-criteria Recommender Systems: The Case of Restaurants in Riohacha, Colombia. International Journal of Computational Intelligence Systems, 17(1). https://doi.org/10.1007/s44196-024-00700-8
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To, W. M., & Yu, B. T. W. (2025). Artificial Intelligence Research in Tourism and Hospitality Journals: Trends, Emerging Themes, and the Rise of Generative AI. Tourism and Hospitality, 6(2). https://doi.org/10.3390/tourhosp6020063
Buku :
Breiman, L. (2001). Random Forest. 1–33.
Arifuddin, N. A., Dahlan, A., Insani, C. N., Nur, N., Arifin, N., Setiawan, H., Sutanto, A., & Kristanto, T. (2025). Machine Learning.
Adityawirawan, S. S. K., & Kusuma, H. E. (2021). Cafe As Student ’ S Informal Learning Space : A Case Study In Bandung , Indonesia. 48(2), 109–119. https://doi.org/10.9744/dimensi.48.2.109-120
Akbar, M. S., & Ramadhan, I. (2026). Pemanfaatan Random Forest Classifier Untuk Sistem Rekomendasi Makanan Berbasis Preferensi Pengguna. 15.
Amelia, D., & R, R. K. (2025). Penerapan Algoritma Random Forest Untuk Prediksi Pejualan Dan Persedian Produk Pada Toko Frozen Food Anisa. 843–848.
Aryanti, Putri, A. T., Khairunisya, A., Pratama, I. Y., & Azizah, P. N. (2026). Klasifikasi Tempat Wisata Di Yogyakarta Berdasarkan Harga Dan Rating Menggunakan Algoritma Random Forest. 18(2), 192–204.
Berrettini, M., Hennig, C., & Viroli, C. (2024). The quantile-based classifier with variable-wise parameters The quantile classifier.
Gong, Y., Liu, G., Xue, Y., Li, R., & Meng, L. (2023). A survey on dataset quality in machine learning. Information and Software Technology, 162(September 2022), 107268. https://doi.org/10.1016/j.infsof.2023.107268
Hermawan, G. (2024). Memahami Peran Dataset dalam Penelitian Kecerdasan Buatan : Kualitas , Aksesibilitas , dan Tantangan. 1–9.
Ibrahim, M. (2022). Evolution of Random Forest from Decision Tree and Bagging : A Bias-Variance Perspective. 7(1), 66–71.
Mohammed, S., Budach, L., Feuerpfeil, M., Ihde, N., Nathansen, A., Noack, N., Patzlaff, H., Naumann, F., & Harmouch, H. (2025). The effects of data quality on machine learning performance on tabular data. Information Systems, 132(June 2024), 102549. https://doi.org/10.1016/j.is.2025.102549
Posch, K., Truden, C., Hungerländer, P., & Pilz, J. (2022). A Bayesian approach for predicting food and beverage sales in staff canteens and restaurants ✩. International Journal of Forecasting, 38(1), 321–338. https://doi.org/10.1016/j.ijforecast.2021.06.001
Pudjihartono, N., Fadason, T., & Kempa-liehr, A. W. (2022). A Review of Feature Selection Methods for Machine Learning-Based Disease Risk Prediction. 2(June), 1–17. https://doi.org/10.3389/fbinf.2022.927312
Ramadhan, G., Faqih, A., Permana, F. E., Informatika, T., Informatika, M., & Cirebon, K. (2025). Analisis Tren Penjualan Menu Seafood Dengan Algoritma Random Forest Untuk Meningkatkan Strategi Pemasaran. 9(4), 5942–5949.
Rindiyani, Primadewi, A., Maimunah, & Purwantini, A. H. (2022). Klasifikasi Penjualan berdasarkan Platform pada UMKM Omah Branded Menggunakan Random Forest. 9(5), 1520–1529. https://doi.org/10.30865/jurikom.v9i5.4949
Rodrigues, M., Miguéis, V., Freitas, S., & Machado, T. (2024). Machine learning models for short-term demand forecasting in food catering services: A solution to reduce food waste. Journal of Cleaner Production, 435(November 2023), 140265. https://doi.org/10.1016/j.jclepro.2023.140265
Sathyanarayanan, S., & Tantri, B. R. (2024). Confusion Matrix-Based Performance Evaluation Metrics. 27(4).
Sch, L., Swift, A. J., Lu, H., & Member, S. (2022). Uncertainty Estimation for Heatmap-based Landmark Localization. XX(Xx), 1–14.
Solano-Barliza, A., Valls, A., Acosta-Coll, M., Moreno, A., Escorcia-Gutierrez, J., De-La-Hoz-Franco, E., & Arregoces-Julio, I. (2024). Enhancing Fair Tourism Opportunities in Emerging Destinations by Means of Multi-criteria Recommender Systems: The Case of Restaurants in Riohacha, Colombia. International Journal of Computational Intelligence Systems, 17(1). https://doi.org/10.1007/s44196-024-00700-8
Strasser, S. (2024). Transparent Data Preprocessing for Machine Learning. https://doi.org/10.1145/3665939.3665960
To, W. M., & Yu, B. T. W. (2025). Artificial Intelligence Research in Tourism and Hospitality Journals: Trends, Emerging Themes, and the Rise of Generative AI. Tourism and Hospitality, 6(2). https://doi.org/10.3390/tourhosp6020063
Buku :
Breiman, L. (2001). Random Forest. 1–33.
Arifuddin, N. A., Dahlan, A., Insani, C. N., Nur, N., Arifin, N., Setiawan, H., Sutanto, A., & Kristanto, T. (2025). Machine Learning.
Published
2026-07-01
How to Cite
Aryanti, A., Nadra, N., Amanda, D., Riska, R., Akbar, M., & Adistra, F. (2026). Klasifikasi Popularitas Menu Kafe Menggunakan Random Forest dengan Labeling Berbasis Quantile pada Data Transaksi Penjualan. JIIFKOM (Jurnal Ilmiah Informatika Dan Komputer), 5(2), 1-6. https://doi.org/https://doi.org/10.51901/jiifkom.v5i02.724
Section
Articles
