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The Bintan MOS development: contribution of ideas to realize Nusantara marine observation network Dony Apdillah; Indra Jaya; Muhammad Iqbal; Ratih Deswati; Mahesa Glagah; Bili Kusumah; Agung Tri Nugroho; Imam Syafi’i
Depik Vol 10, No 1 (2021): April 2021
Publisher : Faculty of Marine and Fisheries, Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13170/depik.10.1.18181

Abstract

Several marine research have attempted to design a data observation system using Internet of Things (IoT). In the future, this system is predicted to become a necessity for marine researchers as a strategic technology to continuously access data. Therefore, this study provides information on the development and application of observation and data collection systems using IoT. The coastal weather and tidal data were obtained via the automatic coastal weather station and acoustic gauge respectively. Meanwhile, the various research activities commenced in July, 2018. In the first year, an operational observation system was developed, while comprehensive baseline data such as air temperature, humidity, wind speed, and sea surface level were also collected. This collection system is reliable, provides real-time data, and easily accessible with internet connection. Furthermore, the Bintan Marine Observation System (The Bintan MOS) is also suitable for other uses such as; monitoring water quality and marine mammals, as well as disaster mitigation. The adoption of this system by other sectors potentially conditions marine researchers for data exchange, to fully understand the fluctuations in coastal weather of Western and Eastern Indonesia as well as the tropical and sub-tropical marine weather. This study successfully developed the Bintan MOS, capable of providing reliable information both off-line and real-time.Keywords:Marine observation systemCoastal weatherTidalBintan
Analysis of Wind Characteristics and Sea Surface Elevation Dynamics in Coastal Waters of Mantang Island, Bintan Regency, Indonesia Yosepine Cinta Tyas Gusti Fa'u; Widodo Setiyo Pranowo; Mario Putra Suhana; Subekti Mujiasih; Rahaden Bagas Hatmaja; Herlina Ika Ratnawati; Dony Apdillah
Buletin Oseanografi Marina Vol 14, No 2 (2025): Buletin Oseanografi Marina
Publisher : Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/buloma.v14i2.70748

Abstract

Geographically, Mantang Island is situated between the Malacca Strait, Natuna Sea, and Karimata Strait, and is exposed to the open sea, which influences the oceanographic dynamics of the region. The island’s residents are heavily dependent on the sea for their livelihoods, making wind and sea tides crucial for meeting their daily needs. Consequently, this study aimed to measure wind data and sea surface elevation over a 30-day period, with the results visualized using a wind rose diagram. The specific objectives were: 1) to calculate harmonic constants using both the Least Squares and Admiralty methods to obtain FormZahl numbers, 2) to determine the characteristics of sea surface elevation based on each method, and 3) to analyze the relationship between sea surface elevation and wind speed. The findings revealed that the wind in the waters surrounding Mantang Island was primarily influenced by the monsoon, blowing from the west with maximum speeds ranging from 5.70 to 8.80 m/s. Each calculation method produced varying values for sea surface elevation, including Zo, HHWL, LLWL, MHWL, and MLWL, with respective values of 11.99 m, 15.9 m, 8 m, 17.9 m, and 6 m. FormZahl number calculations yielded values of 1.25 and 1.03 using the Least Squares and Admiralty methods, respectively. Despite the differences in the results, both methods indicated a mixed semi-diurnal tidal pattern. To examine the relationship between wind and sea surface elevation, a 6th-order polynomial regression analysis was performed. The analysis revealed a weak correlation, with a coefficient of determination (R²) of 0.21 and a Root Mean Square Error (RMSE) of 0.30. These values suggest that the model’s predictions were relatively close to actual field conditions.