Ahmad, I. S., Bakar, A. A., Yaakub, M. R., & Muhammad, S. H. (2020). A survey on machine learning techniques
in movie revenue prediction. SN Computer Science, 1(4), 1–14. https://doi.org/10.1007/S42979-020-00249-1
Ariffin, F. F. (2020). Zara tak putus asa, buka restoran baharu - Berita Harian.
https://www.bharian.com.my/hiburan/selebriti/2020/06/704960/zara-tak-putus-asa-buka-restoran-baharu
Bera, S. (2021). An application of operational analytics: For predicting sales revenue of restaurant. In Studies in
Computational Intelligence, 907, 209–235. https://doi.org/10.1007/978-3-030-50641-4_13
Chang, X. & Li, J. (2019). Business performance prediction in location-based social commerce. Expert Systems with
Applications, 126, 112–123. https://doi.org/10.1016/j.eswa.2019.01.086
Chen, C.-Y., Lee, W.-I., Kuo, H.-M., Chen, C.-W., & Chen, K.-H. (2010). The study of a forecasting sales model for
fresh food. Expert Systems with Applications, 37(12), 7696–7702. https://doi.org/10.1016/j.eswa.2010.04.072
Demšar, J., Zupan, B., Leban, G., & Curk, T. (2004). Orange: From Experimental Machine Learning to Interactive
Data Mining. In European conference on principles of data mining and knowledge discovery (pp. 537-539). Springer,
Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30116-5_58
Department of Statistic Malaysia. (2020).
https://www.dosm.gov.my/v1/index.php?r=column/coneandmenu_id=V3R2ZnB6dE0xU1NDRXNKSHUvdm
hkQT09
Gogolev, S., & Ozhegov, E. M. (2019). Comparison of machine learning algorithms in restaurant revenue
prediction. Communications in Computer and Information Science, 1086CCIS, 27–36.
Hájek, P. & Olej, V. (2010). Municipal revenue prediction by ensembles of neural networks and support vector
machines. WSEAS Transactions on Computers, 9(11), 1255–1264.
Hu, C., Chen, M., & McCain, S.-L. C. (2004). Forecasting in short-term planning and management for a casino
buffet restaurant. Journal of Travel and Tourism Marketing, 16(2–3), 79–98.
https://doi.org/10.1300/J073v16n02_07
Kolkova, A. (2020). The application of forecasting sales of services to increase business competitiveness. Journal of
Competitiveness, 12(2), 90–105. https://doi.org/10.7441/joc.2020.02.06
Kotsiantis, S. B., Zaharakis, I., & Pintelas, P. (2007). Supervised machine learning: A review of classification
techniques. Emerging Artificial Intelligence Applications in Computer Engineering, 160(1), 3-24.
Mackinlay, J., Hanrahan, P., & Stolte, C. (2007). Show me: Automatic presentation for visual analysis. IEEE
Transactions on Visualization and Computer Graphics, 13(6), 1137–1144. https://doi.org/10.1109/TVCG.2007.70594
McDonald’s: number of restaurants worldwide. Statista. (2021).
https://www.statista.com/statistics/219454/mcdonalds-restaurants-worldwide/
Mohamed, A. (2017). Comparative study of four supervised machine learning techniques for classification.
Academia.Edu, 7(2). https://www.academia.edu/download/54482697/2.pdf
National Restaurant Association. (2019). Restaurant industry added nearly 14K locations in 2018. NRA. (n.d.).
Retrieved from https://restaurant.org/education-and-resources/resource-library/restaurant-industry-addednearly-
14k-locations-in-2018/
Othman, K. (2019). Hanya bertahan 3 bulan, Ning Baizura tutup restoran – Hiburan. mStar.
https://www.mstar.com.my/spotlight/hiburan/2019/03/30/ning-tutup-kedai
Posch, K., Truden, C., Hungerländer, P., & Pilz, J. (2021). A Bayesian approach for predicting food and beverage
sales in staff canteens and restaurants. International Journal of Forecasting.
https://doi.org/10.1016/j.ijforecast.2021.06.001
Restaurant Revenue Prediction. Kaggle. (2015). https://www.kaggle.com/c/restaurant-revenue-prediction
Reynolds, D., Rahman, I., & Balinbin, W. (2013). Econometric modeling of the U.S. restaurant industry.
International Journal of Hospitality Management, 34(1), 317–323. https://doi.org/10.1016/J.IJHM.2013.04.003
Tanizaki, T., Hoshino, T., Shimmura, T., & Takenaka, T. (2019). Demand forecasting in restaurants using machine
learning and statistical analysis. Procedia CIRP, 79(2), 679–683. https://doi.org/10.1016/j.procir.2019.02.042