Indra Jaya’s research while affiliated with IPB University and other places

Climate change and human activities significantly affect the dynamics of the marine environment, making accurate predictions essential for resource management and disaster mitigation. Deep learning models such as Long Short-Term Memory excel at capturing non-linear temporal patterns, while autoregressive models handle linear trends to improve prediction accuracy. This aim study predicts sea surface temperature, height, and salinity using deep learning compared to Moving Average and Autoregressive Integrated Moving Average methods. The research methods include spatial gap analysis, temporal variability modeling, and oceanographic parameter prediction. The relationship betweenparameters is analyzed using the Pearson Correlation method. The dataset is divided into 80% training and 20% test data, with prediction results compared between Long Short-Term Memory, Moving Average, and Autoregressive models. The results show that Long Short-Term Memory performs best with a Root Mean Squared Error of 0.1096 and a Mean Absolute Error of 0.0982 for salinity at 13 sample points. In contrast, Autoregressive models produce a Root Mean Squared Error of 0.193 for salinity, 0.055 for sea surface height, and 2.504 for sea surface temperature, with a correlation coefficient 0.6 between temperature and sea surface height. In conclusion, the Long Short Term Memory model excels in predicting salinity because it is able to capture complex non-linear patterns. Meanwhile, Autoregressive models are more suitable for linear data trends and explain the relationship between parameters, although their accuracy is lower in salinity prediction. This approach

Subjective and objective tests used to determine the fish deterioration process require specialized skills and time, making them inefficient for use by the general public in markets. The quality of fish products in markets is not always guaranteed, so consumers must determine their suitability. Deep learning can be used to analyze images and automatically and accurately detect the freshness of fish. This study aims to evaluate the efficiency of deep learning models in detecting fish freshness and implementing them into an Android application for public use. "Image datasets and pH tests were collected as references for the postmortem phase over a 24-hour period, with hourly checks on three fish species (Rachycentron canadum, Trachinotus blochi, and Lates calcarifer). Data were classified into three classes, pre-rigor/fresh, rigor mortis/semi-fresh, and post-rigor/not fresh. The dataset was divided using the 10-fold cross-validation method and analyzed using YOLOv5 and Faster R-CNN algorithms. The study results showed that YOLOv5 had higher average values for each metric compared to Faster R-CNN. Dataset 8 in YOLOv5 showed precision of 99.4%, recall of 98.1%, f1-score of 98.7%, accuracy of 99.3%, and mAP of 99.3%. The YOLOv5 model for dataset 8 was selected for implementation in the Android application due to its high metric values. This application effectively provides information on fish freshness detection and confidence scores.

This study underscores the critical role of accurate Chaetodontidae fish abundance observations, particularly in assessing coral reef health. By integrating deep learning algorithms (Faster R-CNN, SSD-MobileNet, and YOLOv5) into Autonomous Underwater Vehicles (AUVs), the research aims to expedite fish identification in aquatic environments. Evaluating the algorithms, YOLOv5 emerges with the highest accuracy, followed by Faster R-CNN and SSD-MobileNet. Despite this, SSD-MobileNet showcases superior computational speed with a mean average precision (mAP) of around 92.21% and a framerate of about 1.24 fps. Furthermore, employing the Coral USB Accelerator enhances computational speed on the Raspberry Pi 4, enabling real-time detection capabilities. This study incorporates centroid tracking, facilitating accurate counting by assigning unique IDs to identified objects per class. Ultimately, the real-time implementation of the system achieves 87.18% accuracy and 87.54% precision at 30 fps, empowering AUVs to conduct real-time fish detection and tracking, thereby significantly contributing to underwater research and conservation efforts.

Wind waves are natural phenomena primarily generated by the wind. Information about wave height and period is highly crucial in various marine fields such as coastal engineering, fisheries, and maritime transportation. However, accurately predicting wave height remains a challenge due to the stochastic nature of ocean waves themselves. Several approaches to predicting wave height have been developed, including numerical models and machine learning methods, such as the Long-Short Term Memory (LSTM) algorithm, which has currently garnered significant attention from researchers. The objective of this research is to develop a forecast model for wind wave height using the LSTM algorithm in Seibu Island Waters, DKI Jakarta. The ERA5 dataset comprises zonal and meridional wind components and significant wave height, along with wind measurement data using the Automatic Weather System (AWS) instrument, are used to train and test to train and test the LSTM model. The research results show that the LSTM model can predict significant wave height effectively. Predictions using the ERA5 significant height dataset are observed to be closer to field data, with RMSE, MAE, and MAPE values of 0.1535 m, 0.1181 m, and 37.11% respectively. Thus, the model evaluation results indicate good performance, with relatively low RMSE and MAE values, and a good MAPE value. The highest accuracy in significant wave height prediction is found for forecasts one week (7 days) ahead

Selayar slope is the confluence of the Indonesian Throughflow (ITF) from the Makassar Strait and seasonal Java-Flores current. The CTD data from Java-Makassar-Flores (JMF) Cruise where an intensive 24-h CTD “yoyo” measurement was conducted in Selayar Slope is used to determine the stratification and characteristic of water masses in the Selayar slope - southern Makassar Strait. The analyses were performed using TS Diagram processed with Matlab and Ocean Data View (ODV). The surface potential density of 24.25 sq with stratification of water masses is dominated by Makassar ITF. The water mass with higher salinity (34.6 psu) is North Pacific Subtropical water (NPSW) and lower salinity (34.44 psu) is North Pacific Intermediate Water (NPIW). However, water mass with density above 24.25 sq caused NPSW to be drastically extracted by less-saline water (34.15 psu) originated from Java Sea, where salinity profiles are more clearly observed between surface density of 22.0 sq and 23.50 sq.. Keyword: stratification and characteristic, water mass, Selayar Slope, JMF Cruise, TS Diagram