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

Khasanah, Maidatul (2022) "Pengembangan Sistem Identifikasi IUU Transshipment Ketika Terjadi Losses Data AIS dengan Mengakomodasi Cuaca Menggunakan Adaptive Neuro-Fuzzy Inference System (ANFIS)". Other thesis, Institut Teknologi Sepuluh Nopember.

Text 02311840000043-Undergraduate_Thesis.pdf - Accepted Version Download (5MB)

Abstract

Salah satu cara untuk mengatasi Illegal, Unreported, and Unregulated (IUU) transshipment yaitu dengan memasang AIS (Automatic Identification System) di kapal. Beberapa kapal menunjukkan ketidakaktifan data AIS saat melakukan pelanggaran, sehingga terjadi losses data AIS selama beberapa waktu. Terjadinya losses data AIS dan faktor kondisi meteorologi seperti kecepatan angin mempengaruhi IUU transshipment. Penelitian Tugas Akhir ini melakukan pengembangan sistem identifikasi IUU transshipment. Sistem identifikasi terdiri dari tiga sub sistem, yaitu sub sistem identifikasi losses data AIS yang berfungsi untuk mengidentifikasi terjadinya losses data AIS, sub sistem prediktor yang berfungsi untuk memprediksi data AIS yang hilang, serta sub sistem identifikasi IUU transshipment yang bertujuan untuk mengidentifikasi kegiatan IUU transshipment. Sub sistem identifikasi losses data AIS bekerja dengan menghitung selisih waktu dan hasil simulasi menunjukkan akurasi 100%. Sub sistem prediktor dirancang menggunakan metode Recurrent Neural Network (RNN) dan hasil simulasi menunjukkan hasil prediksi posisi (latitude dan longitude), heading, dan kecepatan memiliki akurasi yang tinggi. Sub sistem identifikasi IUU transshipment dirancang menggunakan metode Adaptive Neuro-Fuzzy Inference System (ANFIS) dan memiliki akurasi 83.6%.
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One way to overcome Illegal, Unreported, and Unregulated (IUU) transshipment is to install AIS (Automatic Identification System) on ships. Some ships show AIS data inactivity when they violate, resulting in AIS data loss for some time. The occurrence of AIS data losses and meteorological conditions such as wind speed affect IUU transshipment. The goal of this final project research is to develop an IUU transshipment identification system. The identification system consists of three sub-systems, namely the AIS data loss identification sub-system, which functions to identify the occurrence of AIS data losses; the predictor sub-system, which functions to predict the missing AIS data; and the IUU transshipment identification sub-system, which aims to identify IUU transshipment activities. The AIS data loss identification sub system works by calculating the time difference, and the simulation results show 100% accuracy. The predictor sub-system was designed using the Recurrent Neural Network (RNN) method and the simulation results show that the prediction results for position (latitude and longitude), heading, and speed have high accuracy. The IUU transshipment identification sub system was designed using the Adaptive Neuro-Fuzzy Inference System (ANFIS) method and has an accuracy of 83.6%.

Item Type:	Thesis (Other)
Additional Information:	RSF 629.89 Kha p-1 • 2022
Uncontrolled Keywords:	Adaptive Neuro-Fuzzy Inference System (ANFIS), IUU Transshipment, Losses Data AIS, Prediksi Data AIS, Recurrent Neural Network (RNN)
Subjects:	Q Science > QA Mathematics > QA76.87 Neural networks (Computer Science) Q Science > QA Mathematics > QA9.64 Fuzzy logic
Divisions:	Faculty of Industrial Technology and Systems Engineering (INDSYS) > Physics Engineering > 30201-(S1) Undergraduate Thesis
Depositing User:	- Davi Wah
Date Deposited:	15 Oct 2024 08:39
Last Modified:	15 Oct 2024 08:39
URI:	http://repository.its.ac.id/id/eprint/115725

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