@article{(Open Science Index):https://publications.waset.org/pdf/10013208,
	  title     = {Optimizing Forecasting for Indonesia's Coal and Palm Oil Exports: A Comparative Analysis of ARIMA, ANN, and LSTM Methods},
	  author    = {Mochammad Dewo and  Sumarsono Sudarto},
	  country	= {},
	  institution	= {},
	  abstract     = {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.},
	    journal   = {International Journal of Industrial and Systems Engineering},
	  volume    = {17},
	  number    = {8},
	  year      = {2023},
	  pages     = {480 - 484},
	  ee        = {https://publications.waset.org/pdf/10013208},
	  url   	= {https://publications.waset.org/vol/200},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 200, 2023},
	}