Pradenta, May (2021) Pengembangan Sistem Pengambilan Keputusan Berbasis Data AIS Untuk Identifikasi IUU Transhipment Yang Mengakomodasi Kondisi Anomali Data Trayektori Dengan Menggunakan Jaringan Saraf Tiruan (JST). Undergraduate thesis, Institut Teknologi Sepuluh Nopember.
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Abstract
Laut Indonesia yang luas membuat pengawasan terhadap pelanggaran illegal, unreported and unregulated (IUU) Transhipment sulit dilakukan. Beberapa teknologi sudah digunakan untuk membantu mengawasi terjadinya IUU Transhipment, salah satunya adalah AIS (Automatic Identification System). Penelitian mengenai identifikasi terjadinya IUU Transhipment berbasis data AIS sudah banyak dilakukan. Penelitian ini dilakukan dengan merancang sistem identifikasi IUU Transhipment yang mampu memberikan keputusan pada saat trajektori kapal dikategorikan sebagai anomali, yaitu menyimpang dari jalur yang telah diinformasikan sebelumnya. Sistem identifikasi IUU transhipment ini terdiri dari tiga sub-sistem, yaitu: (i) sub-sistem anomali, (ii) sub-sistem selection, (iii) sub-sistem decision. Sub-sistem anomaly membantu untuk mengawasi kapal yang keluar dari jalur referensinya dan dicurigai melakukan IUU Transhipment. Sistem dirancang dengan menggunakan JST dengan software Jupyter Notebook. Arsitektur terbaik dari sistem identifikasi IUU adalah 1-10-1 untuk sub-sistem anomali, 2-25-1 untuk sub-sistem selection, 3-25-1 untuk sub-sistem decision. Model arsitektur tersebut menghasilkan nilai akurasi sebesar 100%.
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Indonesia's vast seas make it difficult to monitor illegal, unreported and unregulated (IUU) Transshipment violations. Several technologies have been used to help monitor the occurrence of IUU Transshipment, one of which is AIS (Automatic Identification System). Research on the identification of the occurrence of IUU Transshipment based on AIS data has been widely carried out. This research was conducted by designing an IUU Transshipment identification system that is able to provide a decision when the ship's trajectory is categorized as an anomaly, which deviates from the previously informed path. The IUU transshipment identification system consists of three sub-systems, namely: (i) anomaly sub-system, (ii) selection sub-system, (iii) decision sub-system. The anomaly sub�system helps to monitor vessels that deviate from their reference path and are suspected of IUU Transshipment. The system is designed using ANN with Jupyter Notebook software. The best architecture of the IUU identification system is 1-10-1 for the anomaly sub-system, 2-25-1 for the selection sub-system, 3-25-1 for the decision sub-system. The architectural model produces an accuracy value of 100%.
| Item Type: | Thesis (Undergraduate) |
|---|---|
| Uncontrolled Keywords: | AIS, Anomali, IUU Transhipment, Jaringan Saraf Tiruan, AIS, Anomalies, Artificial Neural Networks, IUU Transhipment. |
| Subjects: | Q Science > QA Mathematics > QA76.87 Neural networks (Computer Science) T Technology > T Technology (General) > T58.62 Decision support systems |
| Divisions: | Faculty of Industrial Technology and Systems Engineering (INDSYS) > Physics Engineering > 30201-(S1) Undergraduate Thesis |
| Depositing User: | May Pradenta M.P.N. |
| Date Deposited: | 23 Aug 2021 07:46 |
| Last Modified: | 23 Aug 2021 07:46 |
| URI: | http://repository.its.ac.id/id/eprint/88945 |
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