Perancangan Sistem Identifikasi IUU Transshipment Ketika Terjadi Losses Data AIS dengan Mengakomodasi Cuaca Menggunakan Metode Adaptive Neuro-Fuzzy Inference System (ANFIS)

Ramadhan, Mochammad Akbar (2024) Perancangan Sistem Identifikasi IUU Transshipment Ketika Terjadi Losses Data AIS dengan Mengakomodasi Cuaca Menggunakan Metode Adaptive Neuro-Fuzzy Inference System (ANFIS). Other thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Salah satu ancaman yang dapat membahayakan keamanan maritim di Indonesia adalah terjadinya penangkapan ikan Illegal, Unreported, and Unregulated (IUU fishing) serta transshipment. Pemasangan AIS pada kapal dapat menjadi salah satu cara untuk mengatasi IUU Transshipment. Namun, sering kali terjadi ketidakaktifan AIS pada kapal saat melakukan pelanggaran seperti IUU Transshipment sehingga menyebabkan hilangnya data AIS. Salah satu faktor cuaca yang dapat mempengaruhi IUU Transshipment adalah kecepatan angin. Penelitian Tugas Akhir ini mengembangkan sistem prediksi dan sistem identifikasi IUU Transshipment. Sistem identifikasi terdiri dari dua subsistem, yaitu subsistem prediktor yang berfungsi memprediksi data AIS yang hilang dan subsistem identifikasi IUU Transshipment yang bertujuan untuk mengidentifikasi kegiatan IUU Transshipment. Subsistem prediktor dirancang menggunakan metode Recurrent Neural Networks (RNN) dan hasil simulasi menunjukkan bahwa prediksi posisi (latitude dan longitude) serta heading memiliki akurasi yang tinggi, sedangkan prediksi kecepatan memiliki akurasi yang baik. Subsistem identifikasi IUU Transshipment dirancang menggunakan ANFIS dan memiliki akurasi sebesar 77.08%.
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One of the threats that can endanger maritime security in Indonesia is the occurrence of Illegal, Unreported and Unregulated fishing (IUU fishing) and transshipment. Installing AIS on ships can be one way to overcome IUU Transshipment. However, AIS is often inactive on ships when violations such as IUU Transshipment are committed, causing loss of AIS data. One of the weather factors that can influence IUU Transshipment is wind speed. This final project research develops a prediction system and identification system for IUU Transshipment. The identification system consists of two subsystems, namely the predictor subsystem which functions to predict missing AIS data and the IUU Transshipment identification subsystem which aims to identify IUU Transshipment activities. The predictor subsystem was designed using the Recurrent Neural Networks (RNN) method and the simulation results show that position (latitude and longitude) and heading predictions have high accuracy, while speed predictions have good accuracy. The IUU Transshipment identification subsystem was designed using ANFIS and has an accuracy of 77.08%.

Item Type:	Thesis (Other)
Uncontrolled Keywords:	IUU Transshipment, Losses Data AIS, ANFIS, Prediction Data AIS, RNN, Prediksi Data AIS
Subjects:	H Social Sciences > HA Statistics > HA30.3 Time-series analysis H Social Sciences > HE Transportation and Communications > HE566.F7 Freighters. Cargo ships H Social Sciences > HE Transportation and Communications > HE566.T3 Tankers
Divisions:	Faculty of Industrial Technology and Systems Engineering (INDSYS) > Physics Engineering > 30201-(S1) Undergraduate Thesis
Depositing User:	Mochammad Akbar Ramadhan
Date Deposited:	19 Aug 2024 06:14
Last Modified:	19 Aug 2024 06:14
URI:	http://repository.its.ac.id/id/eprint/110037

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