Penerapan Metode Time Series Dalam Forecasting Penjualan Pada “Nasi Goreng Bacot”
DOI:
https://doi.org/10.61132/neptunus.v2i3.244Keywords:
Forecasting, Time Series, Simple Moving Average, Weighted Moving Average, Single Exponential Smoothing, Restaurant Sales.Abstract
The restaurant industry in Indonesia has experienced significant growth, driving the need for data-driven strategies to remain competitive. This study aims to apply and compare time series methods in forecasting sales at "Nasi Goreng Bacot" restaurant. The methods used are Simple Moving Average (SMA), Weighted Moving Average (WMA), and Single Exponential Smoothing (SES), with a focus on sales data from the year 2023.The research results indicate that SMA provides the most accurate predictions, with a Mean Absolute Error (MAE) value of 296.67, Mean Squared Error (MSE) of 129055.6, and Mean Absolute Percentage Error (MAPE) of 3.02%. WMA and SES, although useful in certain data conditions, show higher error rates in this case. This study confirms the effectiveness of SMA in the context of stable and less fluctuating restaurant sales data. With these results, restaurants can plan their inventory of raw materials and workforce more efficiently, reduce waste, and improve customer satisfaction.
References
Apriliani, A., Zainuddin, H., Agussalim, A., & Hasanuddin, Z. (2020). Peramalan Tren Penjualan Menu Restoran Menggunakan Metode Single Moving Average. Jurnal Teknologi Informasi dan Ilmu Komputer, 7(6).
Biri, R., Langi, Y. A. R., & Paendong, M. S. (2019). Penggunaan metode smoothing EKSPONENSIAL Dalam meramal pergerakan inflasi Kota Palu. JURNAL ILMIAH SAINS, 13(1), 68.
Frischa, L., Sari, R. F., & Husein, I. (2023). Comparison of ARIMA and Winters Methods on Sales Forecasting of Furniture Companies at UD Podomoro Asahan.
Heizer, J., & Render, B. (2020). Operations Management: Sustainability and Supply Chain Management. Edinburgh Gate: Pearson.
Lasek, A., Cercone, N., & Saunders, J. (2019). Sales and customer demand forecasting: Literature survey and categorization of methods. Social-Informatics and Telecommunications Engineering, LNICST, Lecture Notes of the Institute for Computer Sciences.
Liu, L.-M., Bhattacharyya, S., Sclove, S. L., Chen, R., & Lattyak, W. J. (2020). Data mining on time series: An illustration using fast-food restaurant franchise data. Computational Statistics & Data Analysis, 37(4).
Sri Radina Putri NWP, H. S. (2018). Usulan Penggunaan Metode Forecasting Untuk Permintaan Kopi Robusta Pada Pt. Xyz. Industrial Engineering Online.
Subagyo, Pangestu. (2020). Forecasting: Konsep dan Aplikasi. BPPE UGM.
Tanizaki, T., Hoshino, T., Shimmura, T., & Takenaka, T. (2020). Restaurants store management based on demand forecasting. Procedia CIRP, 88.
Tarsisius Handoko. (2018). Dasar-dasar Manajemen Produksi dan Operasi.
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