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All Journal JURNAL ECONOMIA Jurnal Bisnis dan Ekonomi Media Statistika Jurnal Manajemen Bisnis JURNAL RELASI STIE MANDALA JEMBER Jurnal Manajemen Magister Darmajaya DERIVATIF IJEBD (International Journal Of Entrepreneurship And Business Development) FIDUSIA : JURNAL KEUANGAN DAN PERBANKAN PengabdianMu: Jurnal Ilmiah Pengabdian kepada Masyarakat Enrichment : Journal of Management Mabsya: Jurnal Manajemen Bisnis Syariah Ekombis Sains: Jurnal Ekonomi, Keuangan dan Bisnis IJECman (International Journal of Ecma Science) Relasi : Jurnal Ekonomi PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON DATA SCIENCE AND OFFICIAL STATISTICS SOCIRCLE : Journal Of Social Community Services
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Journal : Media Statistika

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FORECAST EVALUATION OF ARIMA AND ANFIS FOR INDONESIA'S MONTHLY EXPORT (2009-2024) Septiarini, Tri Wijayanti; Rofiqo, Azidni; Pariyanti, Eka; Abdulmana, Sahidan
MEDIA STATISTIKA Vol 18, No 1 (2025): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/medstat.18.1.93-104

Abstract

Indonesia’s export sector is a key driver of economic growth, contributing significantly to foreign exchange, employment, and industrial development. Accurate forecasting of export trends is crucial for policymakers, economists, and businesses in shaping strategies and reducing risks. This study applies the Autoregressive Integrated Moving Average (ARIMA) model to forecast Indonesia’s monthly export values from January 2014 to August 2024. Dataset has been divided into training (75%) and testing (25%) subsets, and the Box-Jenkins methodology was employed, including stationarity testing, identification via ACF and PACF plots, parameter estimation, and residual diagnostics. The optimal ARIMA(1,1,1) model achieved strong predictive performance in RMSE, MSE, and MAPE. To benchmark classical methods against modern approaches, ARIMA was compared with the Adaptive Neuro-Fuzzy Inference System (ANFIS). Results indicated that ARIMA delivered higher accuracy for this dataset, reaffirming the robustness of traditional models when data characteristics align with their assumptions. It has conducted prior research evaluation via 75%:25% holdout and rolling-actual back test. This research demonstrates that classical time-series models remain highly relevant in the era of artificial intelligence, emphasizing the importance of appropriate model selection in economic forecasting.
Co-Authors Abdulmana, Sahidan ALIYAH, HIMATUL Ardiansyah Japlani, Ardiansyah Buchori Buchori, Buchori Chauhan, Rahul desastra, Yolla zelika Diyah Putri Martinasari, Made Eliviana Ayu Asih Eman Sulaiman Febriyanto Febriyanto H, Dede Mahira, Putri MAULANA, YOGI Maya Puspitasari, Maya Muna, Siti Umamah Naili Najmidun, Najmidun Najmudin Najmudin Nasrulloh, Lisa Fahri Nurhamidah, Umi Rachmaniar Myrianda Dwiputri Rafika Zein Rinannik, Rinannik Rinnanik Rinnanik Rinnanik Rinnanik Rinnanik Rinnanik Rinnanik Rinnanik, Rinnanik Rinnanik1 Rinnanik Rofiqo, Azidni Sefia Febriyanti Sinta, Ega Dwi Sugiono, Sugiono Sulastri, Susi Syaiful Bakhri Syaiful Bakhri Tri Mardiono Tri Wijayanti Septiarini Tukasno Tukasno Vigory Gloriman Manalu Wahyu Hidayat Wijayanti Septiarini, Tri