%0 Journal Article
	%A Mochammad Dewo and  Sumarsono Sudarto
	%D 2023
	%J International Journal of Industrial and Systems Engineering
	%B World Academy of Science, Engineering and Technology
	%I Open Science Index 200, 2023
	%T Optimizing Forecasting for Indonesia's Coal and Palm Oil Exports: A Comparative Analysis of ARIMA, ANN, and LSTM Methods
	%U https://publications.waset.org/pdf/10013208
	%V 200
	%X The Exponential Triple Smoothing Algorithm approach nowadays, which is used to anticipate the export value of Indonesia's two major commodities, coal and palm oil, has a Mean Percentage Absolute Error (MAPE) value of 30-50%, which may be considered as a "reasonable" forecasting mistake. Forecasting errors of more than 30% shall have a domino effect on industrial output, as extra production adds to raw material, manufacturing and storage expenses. Whereas, reaching an "excellent" classification with an error value of less than 10% will provide new investors and exporters with confidence in the commercial development of related sectors. Industrial growth will bring out a positive impact on economic development. It can be applied for other commodities if the forecast error is less than 10%. The purpose of this project is to create a forecasting technique that can produce precise forecasting results with an error of less than 10%. This research analyzes forecasting methods such as ARIMA (Autoregressive Integrated Moving Average), ANN (Artificial Neural Network) and LSTM (Long-Short Term Memory). By providing a MAPE of 1%, this study reveals that ANN is the most successful strategy for forecasting coal and palm oil commodities in Indonesia.
	%P 480 - 484