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All Journal Jurnal Matematika BIMASTER GRADIEN Jurnal Geocelebes BAREKENG: Jurnal Ilmu Matematika dan Terapan Jurnal Pengabdian Masyarakat Bumi Raflesia Dharma Raflesia : Jurnal Ilmiah Pengembangan dan Penerapan IPTEKS Jurnal Ilmiah Matematika International Journal of Research in Community Services Jurnal Eurekamatika Journal of Mathematics UNP Jurnal Ilmiah Ilmu Terapan Universitas Jambi Indonesian Journal of Community Empowerment and Service Aceh International Journal of Science and Technology Proceeding International Conference on Mathematics and Learning Research Indonesian Journal of Applied Mathematics and Statistics
Jose Rizal
Department Of Mathematics, Faculty Of Mathematics And Natural Sciences, Universitas Bengkulu, Indonesia
Agriculture, Biological Sciences & Forestry Humanities Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Energy Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mathematics Mechanical Engineering Physics Public Health Social Sciences Transportation Other

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ANN-ENHANCED ARIMA MODELS FOR SST-BASED SEASONAL FISH-CATCH FORECASTING AND DECISION-SUPPORT IN BENGKULU WATERS, INDONESIA Rizal, Jose; Afandi, Nur; Rahman, Refpo; Astuti, Mulia; Mayasari, Zulfia Memi; Faisal, Fachri; Yosmar, Siska
Jurnal Ilmiah Ilmu Terapan Universitas Jambi Vol. 10 No. 4 (2026): Volume 10, Nomor 4, August 2026
Publisher : LPPM Universitas Jambi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22437/jiituj.v10i4.55311

Abstract

This study presents a comparative evaluation of hybrid ARIMA-family forecasting models combined with Artificial Neural Networks (ANNs) for seasonal fish-catch prediction in Bengkulu waters, Indonesia, while assessing the role of sea surface temperature (SST) as an environmental predictor. Monthly SST data from NASA’s Giovanni portal and pelagic fish-catch records collected between January 2017 and June 2025 were used to develop ARIMA, ARIMAX, SARIMA, and SARIMAX models, whose residuals were subsequently modeled using Feedforward Neural Networks (FFNN) and Long Short-Term Memory (LSTM) networks to capture nonlinear temporal dependencies. Among the evaluated models, the hybrid ARIMA–LSTM achieved the highest forecasting accuracy on the available dataset, with an RMSE of 76.779 and a MAPE of 19.223%, whereas hybrid models that explicitly incorporate SST as a linear exogenous predictor showed lower predictive performance. These findings suggest that although SST remains an ecologically important environmental driver of pelagic fisheries, its predictive contribution may be better captured by nonlinear, lag-dependent relationships embedded in historical fish-catch observations rather than by contemporaneous linear exogenous modeling. Overall, this study provides empirical evidence for selecting appropriate hybrid forecasting models under practical fisheries data conditions and highlights their potential application as analytical components within fisheries Decision Support Systems (DSS) for adaptive fisheries management.
Co-Authors , Debora Acnesya, Vivin Adi Irwanto Agwil, Winalia Amelliana Melasarrah Baki Swita Cinta Rizki Oktarina Claudia Cantika Bella Dwi Kumala, Siska Dzakirah, Qanitahudz Etis Sunandi Fachri Faisal Faisal, Fahri Firdaus Firdaus Hermalia, Hermalia Idhia Sriliana Lestari, Wina Ayu Mulia Astuti Natisharevi, Raditya Janaloka Nur Afandi Nur Afandi Nurul Hidayati Pepi Novianti Pratiwi, Stevy Cahya Rafflesia, Ulfa Ramya Rachmawati Ratna Widayati Refpo Rahman Riki Wahyudi Riri Sesiati Septa, Oon Septri Damayanti Setiawan, Aisyah Nooravieta Sigit Nugroho Sigit Nugroho Siska Yosmar Sunandi, Etis Syahada, Sri Syahrul Akbar Tasyah, Qhiky Lioni Ulfasari Rafflesia Winalia Agwil Yulian Fauzi Zulfia Memi Mayasari