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All Journal Jurnal Teknologi dan Manajemen Informatika
Rijal, Khaidar Ahsanur
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Computer Science & IT Decision Sciences, Operations Research & Management Engineering

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Forecasting Model of Indonesia's Oil & Gas and Non-Oil & Gas Export Value using Var and LSTM Methods Rijal, Khaidar Ahsanur; Vitianingsih, Anik Vega; Kristyawan, Yudi; Maukar, Anastasia Lidya; Wati, Seftin Fitri Ana
Jurnal Teknologi dan Manajemen Informatika Vol. 10 No. 1 (2024): Juni 2024
Publisher : Universitas Merdeka Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26905/jtmi.v10i1.13127

Abstract

As a country with abundant natural resources in the form of mineral and non-mineral products, Indonesia is characterized by its ability to fulfill domestic and foreign needs through export activities categorized into two commodities: oil and gas and non-oil and gas. Export activities are an indicator of the country's economic growth that often fluctuates in value, and these conditions are fundamentally caused by a decrease in production quantity and the instability of the global economic climate. The strategy to overcome these problems is to create a forecasting model. This research aims to develop a forecasting model using time series analysis methods, including vector autoregressive (VAR) and long short-term memory (LSTM) methods based on oil and non-oil and gas value parameters. The results of the Granger causality test stated that the values of oil and gas and non-oil and gas affect each other. The VAR model with the optimum lag produced by the Akaike Information Criterion (AIC) test obtained an accuracy value of MAPE oil & gas and non-oil and gas of 18.4% and 32.1%, respectively. LSTM generates the best model with a MAPE value of 6,23% for oil & gas and 8,18% for non-oil and gas.
Co-Authors Anastasia Lidya Maukar Anik Vega Vitianingsih KRISTIAWAN KRISTIAWAN Wati, Seftin Fitri Ana