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All Journal Jurnal Ilmu dan Teknologi Kelautan Tropis ILMU KELAUTAN: Indonesian Journal of Marine Sciences JUITA : Jurnal Informatika Indonesian Journal on Computing (Indo-JC) IJoICT (International Journal on Information and Communication Technology) JOIN (Jurnal Online Informatika) JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Sisfokom (Sistem Informasi dan Komputer) Jiko (Jurnal Informatika dan komputer) JURNAL SUMBER DAYA AIR Charity : Jurnal Pengabdian Masyarakat Indonesian Community Journal eProceedings of Engineering LOGIC: Jurnal Penelitian Informatika
Didit Adytia
Telkom University
Agriculture, Biological Sciences & Forestry Arts Humanities Biochemistry, Genetics & Molecular Biology Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Library & Information Science Public Health Social Sciences Other

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Deep Learning-Based Sea Level Forecasting Using Informer in Cilacap, Indonesia Reva Rivandi Salim; Didit Adytia
JUITA: Jurnal Informatika JUITA Vol. 13 Issue 3, November 2025
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v13i3.27176

Abstract

Sea level forecasting is very important for coastal risk management and operational planning, especially in regions vulnerable to frequent tidal flooding events. Tidal Harmonic Analysis (THA) and other traditional methods can effectively reconstruct tidal components but typically overlook non-tidal influences such as meteorological variability and ocean swell. This study mitigates these limitations by proposing the Informer model, a Transformer-based deep learning architecture for long-range sequence forecasting, to predict sea levels using 11 months of hourly observational data (December 2023 – October 2024) from Cilacap, a tropical coastal region in Indonesia. A new preprocessing pipeline is introduced, integrating THA-based tidal reconstruction with interpolation techniques to handle missing data. Forecasting performance is evaluated across multiple prediction horizons (1, 3, 5, 7, and 14 days) and compared against XGBoost, LSTM, and the standard Transformer. The results show that Informer does better than the other models, especially over longer horizons. It has the lowest RMSE (0.091), the lowest MAPE (2.14%), and the highest correlation coefficient (0.98) on the 14-day forecast. In this study, we focused on the Informer’s capability for long horizon from sea level data for providing a reliable solution for sea level prediction. This results show that the model is applicable for integration into early warning systems.
Co-Authors Aditya Firman Ihsan Adiwijaya Adiwijaya, - Alif Rizal Yonanta Andonowati Andonowati Aniq Atiqi Rohmawati Annas Wahyu Ramadhan Arionmaro Asi Simaremare Arkan Priya Anggana Hadna Arnida Lailatul Latifah Azka Khoirunnisa Cornelius Stephanus Alfredo Dede Tarwidi Deni Saepudin Derry Destian Dio Navialdy E. van Groesen Egi Shidqi Rabbani Faishal Rasyid Fakhrurroja, Hanif Fhira Nhita Four Saputra BM Hadi, Risyad Faisal Huda Bachtiar I Putu Samskerta Indira Citra Widya Indra A Aditya Isman Kurniawan Kemas Muslim Lhaksmana Leo Sembiring Lukman Hardian Mahmud Dwi Sulistiyo Muhammad Adzhar Amrullah Muhammad Rafiuddin Nabila Wardah Zamani Naufal Abdurrahman Burhani Navialdy, Dio Nopendri Nopendri Novalianda Jeriano Nugrahinggil Subasita Nurfadhlina Mohd Sharef Putu Harry Gunawan R. Fajrika hadnis Putra Radika Rafif Gibran Raihani Mohamed Rasyadan Farouq Reva Rivandi Salim Riam Badriana sa'adah, siti Semeidi Husrin Semeidi Husrin Seno Adi Putra Setyorini Setyorini Tatang Mulyana Tien Fabrianti Kusumasari Widianto Soekarnen Yuninda, Alifa Puspa ZK Abdurahman Baizal