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Exploitation of maritime natural resources in Indonesia is still widespread. Efforts to monitor illegal fishing and transshipment practices are still less than optimal due to the limited ability of monitoring instruments. The loss of automatic identification system (AIS) data has an impact on weakness in the ship’s motion monitoring system. The weakness of the system in the previous research, without regard to data losses so that in real identification of illegal fishing and transshipment, it becomes less accurate and valid. Losses data it means as missing of some the data in along ship trajectory. This research designs system integration with predictor to identify the occurrence of illegal fishing and transshipment in the presence of missing AIS data. Predictor are designed using recurrent neural networks (RNN) and system integration is designed using artificial neural networks (ANN). Predictor and system integration are simulated, tested and validated using data of real ship that committed illegal fishing and transshipment. Data achieved from the marinetraffic.com and NASDEC-ITS data centers. The validation results show results from the predictor can be used as input for system integration and system integration, and it has a high accuracy.

IUU fishing and transhipment identification with the miss of AIS data using Neural Networks