Comparative Analysis of SARIMA, FFNN, and Hybrid Models for Sea Surface Temperature Prediction at Enggano Island (2018–2024)
DOI:
https://doi.org/10.70561/geocelebes.v9i2.46445Keywords:
Enggano Island, FFNN, Hybrid SARIMA-FFNN, SARIMA, Sea Surface TemperatureAbstract
Sea Surface Temperature (SST) is a key oceanographic variable that influences fish distribution and the livelihoods of coastal communities. On Enggano Island, where most residents rely on fishing, SST is critical for identifying optimal fishing grounds due to limited accessibility and high operational costs. Accurate modeling and forecasting of SST are therefore essential for effective fisheries management and sustainable resource use. This study analyzes and predicts monthly SST patterns in Enggano Island using Seasonal Autoregressive Integrated Moving Average (SARIMA), Feed Forward Neural Network (FFNN), and Hybrid SARIMA-FFNN models. SARIMA effectively captures linear trends and seasonal variations but struggles with nonlinear dynamics and requires statistical assumptions. Conversely, FFNN models nonlinear relationships without such assumptions but is less efficient in representing linear and seasonal structures. The hybrid SARIMA-FFNN combines the strengths of both approaches, integrating linear-seasonal accuracy with nonlinear adaptability. Monthly SST data from January 2018 to December 2024, covering northern, eastern, southern, and western regions of Enggano Island, were analyzed. Results show that all models achieved high predictive accuracy, with MAPE values below 10%. Based on RMSE, FFNN outperformed the other models across all regions (north: 1.173, east: 0.999, south: 1.245, west: 1.049), confirming FFNN as the most accurate model for SST prediction. Predicted SST values across the four regions exhibited only minor differences, offering fishermen flexibility in selecting fishing grounds. Sustainable fishing strategies should also consider species-specific temperature preferences and other ecological factors influencing fish distribution.
References
Alrowais, R., Alwushayh, B., Bashir, M. T., Nasef, B. M., Ghazy, A., & Elkamhawy, E. (2023). Modeling and Analysis of Cutoff Wall Performance Beneath Water Structures by Feed-Forward Neural Network (FFNN). Water, 15(21), 3870. https://doi.org/10.3390/w15213870
Aminy, L. N., & Walid, W. (2022). Perbandingan Model Feed Forward Neural Network dan Arima untuk Meramalkan Perkembangan Covid-19 di Indonesia. MATHunesa Jurnal Ilmiah Matematika, 12(1), 1–10. https://doi.org/10.26740/mathunesa.v12n1.p1-10
Azmi, B. N., Hermawan, A., & Avianto, D. (2023). Analisis Pengaruh Komposisi Data Training dan Data Testing pada Penggunaan PCA dan Algoritma Decision Tree untuk Klasifikasi Penderita Penyakit Liver. JTIM : Jurnal Teknologi Informasi dan Multimedia, 4(4), 281–290. https://doi.org/10.35746/jtim.v4i4.298
Baharudin, A., Tangke, U., & Titaheluw, S. S. (2022). Distribusi Parameter Oseanografi Dengan Hasil Tangkapan Ikan Pelagis Kecil Untuk Pemetaan Distribusi Daerah Potensial Penangkapan Di Perairan Teluk Weda. Jurnal Biosainstek, 4(1), 32–41. https://jurnal.ummu.ac.id/index.php/biosaintek/article/view/719
Chicco, D., Warrens, M. J., & Jurman, G. (2021). The Coefficient of Determination R-squared is More Informative than SMAPE, MAE, MAPE, MSE and RMSE in Regression Analysis Evaluation. PeerJ Computer Science, 7, e623. https://doi.org/10.7717/peerj-cs.623
Crayer, J. D. & Chan, K.-S. (2008). Time Series Analysis with Applications in R. Springer Science+Business Media. 2 ed. New York. https://doi.org/10.1007/978-0-387-75959-3
Esra, G., Herliany, N. E., & Adriani, A. (2023). Analisis Spasial Dna Temporal Hasil Tangkapan Ikan Pelagis Dan Thermal Front pada Musim Peralihan di Kabupaten Toli-Toli Sulawesi Tengah. Prosiding Seminar Nasional Hasil Penelitian Kelautan Dan Perikanan, 1(1), 147–152. https://semnas.bpfp-unib.com/index.php/semnaskel/article/view/115
Fadhlia, S., Hendri, E. P., & Cahyaningtyas A, D. D. (2024). Model Peramalan Nilai Tukar Rupiah Terhadap Dollar Singapura Menggunakan Metode Hybrid ARIMA-ANN. Jurnal Ilmiah Pendidikan Matematika, Matematika Dan Statistika, 5(3), 1513–1523. https://doi.org/10.46306/lb.v5i3.720
Glorot, X., & Bengio, Y. (2010). Understanding the Difficulty of Training Deep Feedforward Neural Networks. Journal of Machine Learning Research, 9, 249–256. https://proceedings.mlr.press/v9/glorot10a.html
Hastuti, R. P. W., Widianingsih, W., & Nuraini, R. A. T. (2024). Kondisi Meteorologi Laut Terhadap Suhu Permukaan Laut Di Perairan Kota Surabaya Tahun 2019 – 2021. Journal of Marine Research, 13(4), 701–712. https://doi.org/10.14710/jmr.v13i4.43227
Hisyam, M., Sumardi, S. R., Kainama, T. L. J., & Haryati, K. (2025). Prediksi Suhu Permukaan Laut di Perairan Kota Jayapura Tahun 2021-2025 Menggunakan Model SARIMA. ACROPORA: Jurnal Ilmu Kelautan Dan Perikanan Papua, 8(1), 37–43. https://doi.org/10.31957/acr.v8i1.4605
Ichwan, M., & Alfarisyi, S. F. (2024). Pemodelan Ritme Kalori Terbakar Setiap Waktu Selama Bersepeda dengan Feedforward Neural Network. ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika, 12(1), 13–22. https://doi.org/10.26760/elkomika.v12i1.13
Izonin, I., Tkachenko, R., Shakhovska, N., Ilchyshyn, B., & Singh, K. K. (2022). A Two-Step Data Normalization Approach for Improving Classification Accuracy in the Medical Diagnosis Domain. Mathematics, 10(11), 1942. https://doi.org/10.3390/math10111942
Kambey, E. M., Jasin, M. I., & Mamoto, J. D. (2023). Studi Hidro Oseanografi Di Pantai Ranowulu. Tekno, 21(83), 319–326. https://ejournal.unsrat.ac.id/v3/index.php/tekno/article/view/47332
Kurniasari, D., Mahyunis, R. V., Warsono, W., & Nuryaman, A. (2023). Implementation of Artificial Neural Network (ANN) Using Backpropagation Algorithm By Comparing Four Activation Functions in Predicting Gold Prices. Kumpulan Jurnal Ilmu Komputer (KLIK), 10(1), 93–105. https://klik.ulm.ac.id/index.php/klik/article/view/587
Li, M. (2024). Comprehensive Review of Backpropagation Neural Networks. Academic Journal of Science and Technology, 9(1), 150–154. https://doi.org/10.54097/51y16r47
NASA Goddard Earth Sciences Data and Information Services Center (GES DISC). (2024). Giovanni Interactive Visualization and Analysis. NASA GES DISC. https://giovanni.gsfc.nasa.gov/giovanni
Pangruruk, F. A., & Barus, S. P. (2022). Predicting the Number of People Exposed to Covid 19 with the Newton Gregory Maju Polynomial Interpolation Method. Formosa Journal of Science and Technology, 1(8), 1275–1290. https://doi.org/10.55927/fjst.v1i8.2185
Permana, I., & Salisah, F. N. (2022). The Effect of Data Normalization on the Performance of the Classification Results of the Backpropagation Algorithm Pengaruh Normalisasi Data Terhadap Performa Hasil Klasifikasi Algoritma Backpropagation. Indonesian Journal of Informatic Research and Software Engineering, 2(1), 67–72. https://doi.org/10.57152/ijirse.v2i1.311
Puteri, D. I. (2023). Implementasi Long Short Term Memory (LSTM) dan Bidirectional Long Short Term Memory (BiLSTM) Dalam Prediksi Harga Saham Syariah. Euler : Jurnal Ilmiah Matematika, Sains Dan Teknologi, 11(1), 35–43. https://doi.org/10.34312/euler.v11i1.19791
Rifai, S. N. (2023). Analisis Oseanografi Lautan Menggunakan Model Jaringan Syaraf Tiruan. Prosiding Seminar Nasional Pemanfaatan Sains Dan Teknologi Informasi, 1(1), 1–10. https://epublikasi.digitallinnovation.com/index.php/sempatin/article/view/35
Ryan, O., Haslbeck, J. M. B., & Waldorp, L. J. (2025). Non-Stationarity in Time-Series Analysis : Modeling Stochastic and Deterministic Trends. Multivariate Behavioral Research, 60(3), 556–588. https://doi.org/10.1080/00273171.2024.2436413
Sari, I. P. (2020). Agama, Etnisitas dan Perdamaian di Pulau Enggano Provinsi Bengkulu. Hanifiya: Jurnal Studi Agama-Agama, 3(2), 77–86. https://doi.org/10.15575/hanifiya.v3i2.9987
Septiana, D., & Bangun, M. B. (2023). Perbandingan Antara Feed Forward Neural Network (FFNN) Dan Seasonal Autoregressive Integrated Moving Average (Sarima) Untuk Peramalan Data Runtun Waktu Terapan. Jurnal Deli Sains Informatika, 2(2). https://jurnal.unds.ac.id/index.php/dsi/article/view/218
Shabrina, N. N., Sunarto, S., & Hamdani, H. (2017). Penentuan Daerah Penangkapan Ikan Tongkol Berdasarkan Pendekatan Distribusi Suhu Permukaan Laut dan Hasil Tangkapan Ikan di Perairan Utara Indramayu Jawa Barat. Jurnal Perikanan Dan Kelautan, 8(1), 139–145. https://jurnal.unpad.ac.id/jpk/article/view/13901
Silsia, D., Yahya, R., & Lumbanbatu, U. M. (2019). Rendemen dan Karakteristik Minyak Bintangur dari Pulau Enggano Sebagai Bahan Baku Pembuatan Biodiesel. Jurnal Agroindustri, 9(1), 1–7. https://doi.org/10.25077/greentech.v1i1.10
Susila, M. R., Jamil, M., & Santoso, B. H. (2023). Akurasi Model Hybrid ARIMA-Artificial Neural Network dengan Model Non Hybrid pada Peramalan Peredaran Uang Elektronik di Indonesia. Jambura Journal of Mathematics, 5(1), 46–58. https://doi.org/10.34312/jjom.v5i1.14889
Syahrin, K. A., Disera, T. E., Youwea, A. S. G. N., Merdeka, J., Saputra, A. H., Norman, Y., & Nugraheni, I. R. (2024). Prakiraan Perubahan Suhu Permukaan Laut dengan Neuralprophet di Taman Laut Bunaken. Jurnal Laut Pulau, 3(2), 42–50. https://doi.org/10.30598/jlpvol3iss2pp42-50
Wei, W. W. S. (2006). Time Series Analysis Univariate and Multivariate Methods (2nd ed.), Pearson. https://civil.colorado.edu/~balajir/CVEN6833/lectures/wwts-book.pdf
Wulandari, S., & Novita, D. (2024). Implementasi Feed Forward Neural Network (FFNN) dalam Memprediksi Penyakit Diabetes. Jurnal MIPA Sains Terapan, 3(1), 1–11. https://journal.unindra.ac.id/index.php/sainsmath/article/view/3048
Zhang, G. P. (2003). Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing, 50, 159–175. https://doi.org/10.1016/S0925-2312(01)00702-0
Zhang, Y., & Meng, G. (2023). Simulation of an Adaptive Model Based on AIC and BIC ARIMA Predictions. Journal of Physics: Conference Series, 2449(1), 012027. https://doi.org/10.1088/1742-6596/2449/1/012027
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 JURNAL GEOCELEBES
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
