Pengembangan Sistem Prediksi dan Identifikasi IUU Fishing dan Transshipment dengan Sistem Logika Fuzzy Tipe 2 saat Losses Data

Nabilah, Putri Nurfiana (2022) Pengembangan Sistem Prediksi dan Identifikasi IUU Fishing dan Transshipment dengan Sistem Logika Fuzzy Tipe 2 saat Losses Data. Other thesis, Institut Teknologi Sepuluh Nopember.

Text 02311840000030-Undergraduate_Thesis.pdf - Accepted Version Download (6MB)

Abstract

Dari total wilayah Indonesia, 74,26% merupakan perairan yang menimbulkan banyak permasalahan, seperti tindakan Illegal Unreported Unregulated fishing dan transshipment. Kapal pelaku seringkali mematikan Automatic Identification System (AIS) dalam rentang waktu tertentu yang menyebabkan adanya losses data Maka penelitian ini dilakukan dengan merancang sistem identifikasi IUU yang terdiri dari 5 sub-sistem yaitu sub-sistem identifikasi losses data, prediktor losses data, sistem selektor, sistem decision IUU transshipment, dan sistem decision IUU fishing. Sistem identifikasi losses data dengan metode If Statement menghasilkan RMSE 0. Jika terdapat losses data, maka sistem prediktor akan dengan menggunakan metode Recurrent Neural Network (RNN) dengan variasi jumlah unit neuron, learning rate, dan batch size. Hasil prediktor menunjukkan nilai RMSE pada prediksi kapal transshipment dan fishing sebesar 0.4133 dan 0.0339. Sementara sistem selektor dan decision dirancang dengan sistem logika fuzzy tipe 2. Sistem selektor dirancang dengan 24 rules, sistem decision IUU transshipment dengan 162 rules, dan sistem decision IUU fishing dengan 243 rules. Ketiga sistem tersebut memberikan RMSE sebesar 0. Nilai RMSE sistem yang terintegrasi untuk kasus transshipment dan fishing sebesar 0.1033 dan 0.0084.
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From total of Indonesia's territory, 74.26% is water which could causes many problems, such as IUU fishing and transshipment. The ship's perpetrators often turn off the AIS within a period of time that causes losses data.. This research was conducted by an IUU identification system which consists of 5 sub-systems, the data losses identification sub-system, data loss predictor, selector, IUU transshipment decision, and IUU fishing decision. The data loss identification system using the If Statement methods produces 100% accuracy. If there is data loss, the prediktor system will use the RNN method with combinations of number of neuron units, learning rate, and batch size. The predictor results show the RMSE of transshipment and fishing vessels of 0.4133 and 0.0339. The selector and decision systems designed with a type 2 fuzzy logic system with 0 RMSE value. The selector system was designed with 24 rules, the IUU transshipment and fishing decision with 162 and 243 rules. RMSE value for integrated system in transshipment and fishing case is 0.1033 and 0.0084.

Item Type:	Thesis (Other)
Additional Information:	RSF 629.89 Nab p-1 2022
Uncontrolled Keywords:	AIS, IUU fishing and transshipment, Losses data, RNN, SLF Type-2
Subjects:	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:	26 Sep 2024 06:12
Last Modified:	26 Sep 2024 06:12
URI:	http://repository.its.ac.id/id/eprint/115679

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