Samudya, Muhammad Arif (2021) Pengembangan Sistem Identifikasi IUU Transhipment dengan Sistem Logika Fuzzy Tipe 2 saat Terjadi Losses Data Automatic Identification System (AIS). Undergraduate thesis, Institut Teknologi Sepuluh Nopember.
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Abstract
Kapal pelaku illegal, unreported and unregulated (IUU) transhipment sengaja untuk tidak mengaktifkan Automatic Identification System (AIS) untuk menghindari pemantauan sehingga terjadi hilangnya data (losses data) AIS pada rentang waktu tertentu. Terjadinya losses data AIS menyebabkan pergerakan kapal tidak bisa diawasi oleh pihak yang berwenang. Penelitian pada tugas akhir ini melakukan perancangan sistem identifikasi IUU transhipment. Sistem identifikasi IUU transhipment terdiri dari 2 sub-sistem yaitu sub-sistem prediktor untuk memprediksi data AIS yang hilang dan sub-sistem identifikasi untuk mengidentifikasi praktik IUU transhipment. Sub-sistem prediktor untuk prediksi longitude, latitude dan heading kapal dirancang menggunakan recurrent neural network (RNN) dan prediktor untuk prediksi kecepatan dirancang menggunakan metode perbandingan senilai. Sub-sistem identifikasi dirancang menggunakan sistem logika fuzzy tipe 2 dengan algoritma reduksi tipe Karnik-Mendel (KM) dan proses defuzzifikasi dengan metode centroid. Sub-sistem identifikasi memiliki 3 fungsi keanggotaan pada input dan 1 fungsi keanggotaan pada output dengan 18 rule base. Sub-sistem prediktor dan sub-sistem identifikasi disimulasikan dan diuji menggunakan beberapa skenario data AIS praktik IUU transhipment di Perairan Indonesia. Hasil simulasi dan pengujian menunjukkan bahwa keluaran dari sub-sistem prediktor memiliki akurasi yang baik sehingga bisa digunakan sebagai masukan sub-sistem identifikasi. Sedangkan, hasil simulasi dan pengujian sistem identifikasi IUU transhipment memiliki akurasi sebesar 99.9949%.
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Ships carrying out illegal, unreported and unregulated (IUU) transhipments deliberately do not activate the Automatic Identification System (AIS) to avoid monitoring so that AIS data loss occurs at a certain time. The occurrence of AIS data losses causes the movement of the ship to not be monitored by the authorities. The research in this final project is to design an IUU transshipment identification system. The IUU transshipment identification system consists of 2 sub-systems, namely the predictor sub-system to predict missing AIS data and the identification sub-system to identify IUU transhipment practices. Predictor sub-system for longitude, latitude and ship heading prediction is designed using recurrent neural network (RNN) and predictor for speed prediction is designed using value comparison method. The identification sub-system is designed using a type 2 fuzzy logic system with a Karnik-Mendel (KM) type reduction algorithm and the defuzzification process using the centroid method. The identification sub-system has 3 membership functions at the input and 1 membership function at the output with 18 rule bases. The predictor sub-system and identification sub-system were simulated and tested using several AIS data scenarios for IUU transshipment practices in Indonesian waters. The simulation and test results show that the output of the predictor sub-system has good accuracy so that it can be used as input for the identification sub-system. Meanwhile, both of the IUU transshipment identification system simulation and test has an accuracy of 99.9949%.
| Item Type: | Thesis (Undergraduate) |
|---|---|
| Uncontrolled Keywords: | IUU transhipment, logika fuzzy tipe 2, losses data AIS, RNN, sistem identifikasi, sistem prediktor, AIS data losses, identification system, predictor system, type 2 fuzzy logic |
| Subjects: | T Technology > T Technology (General) > T174 Technological forecasting T Technology > T Technology (General) > T58.62 Decision support systems V Naval Science > V Naval Science (General) |
| Divisions: | Faculty of Industrial Technology and Systems Engineering (INDSYS) > Physics Engineering > 30201-(S1) Undergraduate Thesis |
| Depositing User: | Muhammad Arif Samudya |
| Date Deposited: | 19 Aug 2021 05:52 |
| Last Modified: | 19 Aug 2021 05:52 |
| URI: | http://repository.its.ac.id/id/eprint/87733 |
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