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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi
Ramzan Pradana Maulsyid
Universitas Pendidikan Indonesia
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Engineering

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Penerapan Augmented Reality pada WebGIS Zona Penangkapan Ikan Tongkol di Pulau Panjang Vera Anggraini; Ramzan Pradana Maulsyid; Lukman Handyanto; Nuril Khairiyah; Haruni Najla Azizah; Bagus Firmansyah; Najwa Nur Hafazah
TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi Vol 5 No 2 (2025): TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/tamika.Vol5No2.pp170-174

Abstract

Mackerel Tuna (Euthynnus Affinis) is a commodity with a high production rate in Banten Bay. Panjang island, which is geographically located in the waters of Banten Bay, is a potential area for supporting sustainable Tuna production. Market demand for this fish continues to grow, yet the lack of precise fishing zone data hampers resource optimization. The WebGIS system was developed to provide potential information on fishing zones for tuna around Panjang Island. Oceanographic data such as sea surface temperature and chlorophyll-a concentration were utilized to predict high-probability areas. This system features GPS-based augmented reality to guide fishers in real time to their targeted locations. Interactive and accurate information presentation facilitates decision-making for fishers. The implementation of this technology enhances fisher productivity while promoting sustainable fishery management. The potential of WebGIS technology offers significant opportunities for advancing Indonesia’s modern fishery sector, particularly for the coastal communities of Panjang Island.
Weather and Marine Multi-output Prediction Using XGBoost on Automatic Weather Station Data Willdan Aprizal Arifin; Luthfi Anzani; M Ma'ruf; Anton Daud; Lukman Handyanto; Raisa Maulidia; Ramzan Pradana Maulsyid; Angga Fadzar
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 2 (2026): April 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i2.7031

Abstract

Climate change on a global scale has triggered an increase in sea levels and heightened the frequency of extreme weather events, especially in maritime countries such as Indonesia. These conditions necessitate the development of accurate and adaptive weather and marine prediction systems. This study proposes a multi-output prediction model using the eXtreme Gradient Boosting (XGBoost) algorithm based on BMKG's Automatic Weather Station (AWS) data from the BMKG. The data cover the period 2022-2025 with high temporal resolution and include five main parameters: wind speed, water level, water temperature, relative humidity, and wind direction. The hyperparameter tuning process led to the discovery of an optimal configuration capable of enhancing the model's accuracy. The evaluation results of the coefficient of determination (R²) and Root Mean Squared Error (RMSE) metrics show that the model can predict water temperature, water level, and relative humidity with very high accuracy, which is more than 85 percent. The model also performed well in predicting wind speed, although it still faced difficulties in handling wind direction due to its cyclical nature. Overall, the XGBoost approach proved effective in modeling weather and marine parameters simultaneously and has the potential to be integrated into environmental monitoring systems in Indonesia's coastal and archipelagic regions.
Co-Authors Angga Fadzar Anton Daud Anzani, Luthfi Bagus Firmansyah Haruni Najla Azizah Lukman Handyanto M Ma'ruf Najwa Nur Hafazah Nuril Khairiyah Raisa Maulidia Vera Anggraini Willdan Aprizal Arifin