Zharifa, Alifia Rifta Putri (2023) Pengembangan Sistem Prediksi dan Sistem Identifikasi IUU Transshipment Untuk Mengatasi Losses Data AIS Menggunakan Adaptive Neuro-Fuzzy Inference System (ANFIS) Dengan Gangguan Gelombang Laut. Other thesis, Institut Teknologi Sepuluh Nopember.
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Abstract
Salah satu ancaman yang dapat membahayakan keamanan maritim di Indonesia adalah terjadinya IUU fishing dan transshipment. Adanya AIS yang dipasang pada kapal dapat menjadi salah satu cara untuk mengatasi Illegal, Unreported, and Unregulated (IUU) Transshipment. Namun seringkali terjadi ketidakaktifan AIS pada kapal saat melakukan pelanggaran seperti IUU Transshipment sehingga menyebabkan terjadinya losses data AIS. Salah satu faktor cuaca yang dapat mempengaruhi IUU Transshipment adalah adanya gangguan gelombang laut. Penelitian Tugas Akhir ini melakukan pengembangan sistem prediksi dan sistem identifikasi IUU Transshipment. Sistem identifikasi terdiri dari tiga sub sistem, yaitu sub sistem identifikasi losses data AIS, subsistem prediktor yang berfungsi memprediksi data AIS yang hilang, dan subsistem identifikasi IUU Transshipment yang bertujuan untuk mengidentifikasi kegiatan IUU Transshipment. Sub sistem identifikasi losses data AIS yang hilang dengan menghitung selisih waktu dan hasil simulasi menunjukkan akurasi 100%. Sub sistem prediktor dirancang menggunakan RNN-GRU dan hasil simulasi menunjukkan hasil prediksi posisi (latitude dan longitude) dan heading memiliki akurasi yang tinggi sedangkan subsistem prediktor kecepatan memiliki akurasi yang baik. Sub sistem identifikasi IUU Transshipment dirancang menggunakan ANFIS dan memiliki akurasi 77,74%.
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One of the threats that can endanger maritime security in Indonesia is the occurrence of IUU fishing and transshipment. Having an AIS installed on a ship can be one way to deal with Illegal, Unreported, and Unregulated (IUU) Transshipment. However, AIS inactivity often occurs on ships when committing violations such as IUU Transshipment, causing AIS data losses. One of the weather factors that can affect IUU transshipment is sea wave disturbance. This final project research is developing a prediction system and identification system for IUU transshipment. The identification system consists of three sub-systems, namely the identification sub-system of AIS data losses, the predictor sub-system which functions to predict missing AIS data, and the IUU Transshipment identification sub-system which aims to identify IUU Transshipment activities. The missing AIS data loss identification system by calculating the time difference and the simulation results show 100% accuracy. The predictor subsystem was designed using the RNN-GRU and the simulation results show that the position (latitude and longitude) and heading predictions have high accuracy, while the speed predictor subsystem has good accuracy. The IUU Transshipment identification sub-system
was designed using ANFIS and has an accuracy of 77,74%.
| Item Type: | Thesis (Other) |
|---|---|
| Uncontrolled Keywords: | Adaptive Neuro-Fuzzy Inference System (ANFIS), IUU Transhipment, Losses Data AIS, Prediksi Data AIS, Reccurent Neural Network (RNN), IUU Transhipment |
| Subjects: | V Naval Science > VM Naval architecture. Shipbuilding. Marine engineering > VM293 Shipping--Indonesia--Safety measures |
| Divisions: | Faculty of Industrial Technology and Systems Engineering (INDSYS) > Physics Engineering > 30201-(S1) Undergraduate Thesis |
| Depositing User: | Alifia Rifta Putri Zharifa |
| Date Deposited: | 24 Jul 2023 16:04 |
| Last Modified: | 24 Jul 2023 16:04 |
| URI: | http://repository.its.ac.id/id/eprint/99243 |
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