Fusion: Practice and Applications FPA 2692-4048 2770-0070 10.54216/FPA https://www.americaspg.com/journals/show/3919 2018 2018 Department of Information System, Faculty of Science and Technology, Universitas Terbuka, Tangerang Selatan, Banten 15437, Indonesia Lintang Lintang Faculty of Informatics and Computing, Universiti Sultan Zainal Abidin, Campus Besut, 22200 Terengganu, Malaysia Zahratul Amani Zakaria This study aims to forecast the export volumes of oil and gas and non-oil and gas sectors in Indonesia, as export volumes reflect the economic condition of a country. The research utilizes data from BPS, spanning from January 2018 to December 2023, and employs the Fuzzy Time Series (FTS) methodology. Six different methods are applied: First-Order FTS Chen, First-Order FTS Cheng, Second-Order FTS Chen, Second-Order FTS Cheng, Markov Chain FTS, and Time-Invariant FTS. FTS is a predictive technique based on fundamental logic and various concepts and rules within fuzzy sets. The prediction accuracy is evaluated using the Mean Absolute Percentage Error (MAPE). The MAPE values for these six methods are compared to determine the most suitable method for this case study. The findings reveal that First-Order FTS Chen achieves an accuracy of 4.07%, First-Order FTS Cheng 4%, Second-Order FTS Chen 1.61%, Second-Order FTS Cheng 1.58%, Markov Chain 3.96%, and Time-Invariant 8.88%. The results indicate that Second-Order FTS Cheng provides the highest accuracy and is effective for predicting the export volumes of oil and gas and non-oil and gas sectors in Indonesia.     2026 2026 27 44 10.54216/FPA.210103 https://www.americaspg.com/articleinfo/3/show/3919