Implementation of the Double Exponential Smoothing Method in Predicting Palm Oil Harvest Yields - Muhammad Raihan Rangkuti; Taufiq; Hafizh Al Kautsar Aidilof (Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN), 2024)
Double Exponential Smoothing (DES) is a forecasting method that combines two main components: level and trend. This method is used for data that shows a trend pattern, meaning data that tends to increase or decrease over time. This study aims to implement the Double Exponential Smoothing method to predict oil palm yields at PT. Amal Tani. The data used in this study consists of historical oil palm yield data from 2019 to 2023. The prediction system designed is web-based, utilizing PHP programming language and MySQL database. The performance evaluation of the prediction model is conducted using the Mean Absolute Percentage Error (MAPE) and Mean Absolute Error (MAE) metrics. The study demonstrates that the Double Exponential Smoothing method can produce accurate and effective predictions. The implementation of this system facilitates data processing and the dissemination of information related to oil palm yields. The results indicate that this prediction model can assist the management of PT. Amal Tani in making more accurate yield forecasts, thereby increasing productivity and operational efficiency. The implementation of this method is also expected to ease the company’s decision-making process regarding production planning and seed planting. This study concludes that the Double Exponential Smoothing method is an effective and accurate tool for predicting oil palm yields and provides positive contributions to data management and decision-making processes at PT. Amal Tani. This study offers insights into the application of the Double Exponential Smoothing method in forecasting oil palm yields.