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All Journal Journal of System and Computer Engineering Madani: Multidisciplinary Scientific Journal
Syam, Rahmat Fuady
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ARIMA Method Implementation for Electricity Demand Forecasting with MAPE Evaluation Wungo, Supriyadi La; Aziz, Firman; Jeffry, Jeffry; Mardewi, Mardewi; Syam, Rahmat Fuady; Nasruddin, Nasruddin
Journal of System and Computer Engineering Vol 6 No 1 (2025): JSCE: January 2025
Publisher : Universitas Pancasakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61628/jsce.v6i1.1666

Abstract

Electricity demand forecasting is critical for efficient energy management and planning. This study focuses on the development and implementation of the Autoregressive Integrated Moving Average (ARIMA) method for forecasting electricity demand in South Sulawesi's power system. The evaluation of forecasting accuracy was conducted using the Mean Absolute Percentage Error (MAPE), which measures the percentage error between predicted and actual values. Two experiments were conducted with different ARIMA models: ARIMA(5,1,0) and ARIMA(2,0,1). Results showed that the ARIMA(5,1,0) model achieved a MAPE of 2.15%, while the ARIMA(2,0,1) model performed slightly better with a MAPE of 1.91%, indicating highly accurate predictions. The findings highlight the effectiveness of the ARIMA method in forecasting electricity demand, providing a reliable tool for energy providers to optimize resource allocation and enhance operational efficiency. Future research may explore integrating ARIMA with other advanced methods to further improve forecasting performance.
Implementasi Algoritma Machine Learning untuk Forecasting Demand Pada Usaha Kerupuk Sehat Krusawi Wijaya, Neti Septi; Usman, Syahrul; Iskandar, Imran; Rimalia, Watty; Syam, Rahmat Fuady
Madani: Jurnal Ilmiah Multidisiplin Vol 4, No 1 (2026): February 2026
Publisher : Penerbit Yayasan Daarul Huda Kruengmane

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/zenodo.18358038

Abstract

The rapid development of information technology has encouraged business actors to utilize data analysis to improve efficiency and competitiveness, one of which is through demand forecasting. This study aims to implement machine learning algorithms to forecast product demand in the Krusawi Healthy Crackers business. The method employed is Prophet, which was selected due to its capability to handle time series data with nonlinear trends and seasonal patterns. The data used consist of historical daily sales data from April to July 2024, which were subsequently aggregated into weekly data. The research stages include data collection, data preprocessing (data aggregation, handling missing values, and Box-Cox transformation), Prophet model design with logistic growth and custom bi-monthly seasonality, model training, and performance evaluation. The results indicate that the Prophet model provides excellent forecasting performance, achieving a Mean Absolute Percentage Error (MAPE) of 6.57% or an accuracy level of 93.43%. The model successfully captures trend and seasonal patterns in Krusawi product sales. Therefore, the implementation of machine learning algorithms using the Prophet method proves to be a reliable solution for supporting production planning and inventory management in the Krusawi healthy crackers business, and has the potential to improve operational efficiency and business decision-making.
Co-Authors Firman Aziz Iskandar, Imran Jeffry Jeffry Mardewi, Mardewi Nasruddin Nasruddin Rimalia, Watty Syahrul Usman Wijaya, Neti Septi Wungo, Supriyadi La