No abstract

Exploitation of maritime natural resources in Indonesia is still widespread. Efforts to monitor illegal fishing and transshipment practices are still less than optimal due to the limited ability of monitoring instruments. The loss of automatic identification system (AIS) data has an impact on weakness in the ship’s motion monitoring system. The weakness of the system in the previous research, without regard to data losses so that in real identification of illegal fishing and transshipment, it becomes less accurate and valid. Losses data it means as missing of some the data in along ship trajectory. This research designs system integration with predictor to identify the occurrence of illegal fishing and transshipment in the presence of missing AIS data. Predictor are designed using recurrent neural networks (RNN) and system integration is designed using artificial neural networks (ANN). Predictor and system integration are simulated, tested and validated using data of real ship that committed illegal fishing and transshipment. Data achieved from the marinetraffic.com and NASDEC-ITS data centers. The validation results show results from the predictor can be used as input for system integration and system integration, and it has a high accuracy.

This paper proposes a simulation-based method to estimate collision risk for a ship operating in a two-lane canal. According to rule 9 of the Colreg-72 navigation rules, in a narrow canal, a vessel shall keep as near to the wall that lies on its starboard side. However, a busy harbor entered through a narrow canal still presents impact hazards. Certain conditions in a two-lane canal, such as a head-on situation in the straight part of the canal during an overtaking maneuver and large curvature of a turning maneuver in the bend part of the canal, could lead to accidents. In the first condition, the ship alters its own course to the port side to overtake another ship in the same lane but the course altered is too large and hits the wall of the canal. In the second condition, the target ship may take an excessively large turn on the bend part of the canal, causing collision with the ship on the opposite lane. Collision risk is represented as the risk of damage to the ship structure and includes the probability of impact accident and severity of structural damage. Predictions of collision probabilities in a two-lane canal have been developed based on a simulation of ship maneuvering using a mathematical maneuvering group (MMG) model and automatic identification system (AIS) data. First, the propeller revolution and rudder angle of the subject ship are simulated to determine safe trajectories in both parts of the canal. Second, impact accidents are simulated for both conditions. The ship’s speed, and current and wind velocity are randomly simulated based on the distribution of the AIS and environment data for the research area. The structural consequences of the impact accident are measured as collision energy losses, based on the external dynamics of ship collision. The research area of the two-lane canal is located at the Madura Strait between the Java and Madura islands in East Java of Indonesia, as shown by the red line in Figure 1. A project for developing a new port and dredging a new two-lane canal to facilitate an increase in the number of ship calls is currently underway in the research area. Figure 1 shows the ships’ trajectories plotted using the AIS data as on January 1, 2011. The trajectories are mostly seen to be coming out of the canal, confirming that it is shallow and needs to be dredged.

No abstract

Kendali manuver kapal untuk menghindari tabrakan menjadi masalah penting pada sebagian besar sistem transportasi kapal. Banyak kajian yang dilakukan agar kapal dapat terhindar dari tabrakan. Tetapi semuanya menggunakan pendekatan numerik. Pendekatan numerik memiliki kelemahan yaitu nilai yang diperoleh adalah nilai hampiran dan bukan nilai exact. Oleh karena itu perlu dilakukan kajian kendali manuver kapal menggunakan pendekatan pengujian skala model. Pada paper ini akan dibahas perancangan kendali manuver yang akan digunakan pada model kapal untuk menghindarkan kapal dari tabrakan. Dimana didalam sistem kendali tersebut terdapat beberapa sensor, antara lain sensor ultrasonic, sensor gyro dan kamera Qualysis. Pengujian dilakukan di kolam Manuvering Ocean Basin milik Balai Teknologi Hidrodinamika (d/h LHI), BPP. Teknologi. Model kapal yang digunakan adalah kapal patroli cepat (fast patrol boat). Halangan yang dipasang berjumlah 1 dan dalam posisi diam. Dari pengujian model kapal yang dilakukan, perancangan kendali manuver terbukti mampu menghindarkan kapal dari terjadinya tabrakan. Jarak minimum kapal dan halangan pada jarak tabrakan 250 m sebesar 88,25 m, pada jarak tabrakan 200 m sebesar 47,33 m sedangkan pada jarak tabrakan 150 m sebesar 12,75 m.

The identification system for illegal, unreported, and unregulated (IUU) fishing and transshipment of incidents is one effort as early warning and it can be adopted by the Indonesian ministry of fisheries and marine affairs. The system has been developed using several methods. One of them is the artificial neural networks (ANN) have been used, but it is several weaknesses when AIS data is lost. The system able to identify in missing data in a short duration of time only. The development of a system for IUU identification proposed using a fuzzy logic system (FLS). The IUU identification system consists of subsystems: (i) missing data prediction, (ii) anomaly trajectory decision, (iii) IUU selection, and (iv) IUU decision. The performance of FLS is compared between fuzzy type 1 and type 2. The system tested using data of IUU occurrences waters in high frequency. Both SLF type 1 and type 2 are able to give result in very good accuracy for IUU fishing and transshipment identification.

Perkembangan pendidikan tingkat menengah di Indonesia saat ini sedang difokuskan untuk meningkatkan kuantitas maupun kualitas lulusan Sekolah Menengah Kejuruan (SMK) yang mampu memenuhi kebutuhan industri. Salah satu teknologi yang saat ini banyak digunakan di industri darat maupun di bidang maritim adalah teknologi otomatis berbasis Programmable Logic Controller (PLC). PLC merupakan salah satu mode kontrol otomatis yang memerlukan logika desain untuk menjalankan aturan dan langkah-langkah kontrolnya, sehingga diperlukan pengetahuan dan kemampuan untuk mendesain PLC. Namun demikian, dengan situasi pandemi Covid-19 menjadikan pembelajaran PLC menjadi lebih sulit dilakukan. Untuk mempelajari cara memprogram PLC, siswa maupun guru harus memasangkan piranti lunak yang membutuhkan kapasitas besar dan membebani komputer. Solusi dari permasalahan tersebut adalah dengan mempelajari pemrograman PLC melalui media daring, yaitu melalui PLC Fiddle. Media PLC Fiddle sendiri tergolong baru dan belum banyak diketahui oleh pengajar di Indonesia. Dengan dilakukan pelatihan dasar PLC melalui media daring kepada guru-guru SMK di Surabaya, diharapkan guru-guru SMK dapat tetap mengajarkan kemampuan untuk mendesain PLC kepada siswa sesuai kebutuhan industri walaupun dunia masih dilanda pandemic Covid-19. Selanjutnya, diharapkan bahwa para guru tersebut dapat mengaplikasikan ilmu dalam mendesain PLC dalam proses belajar mengajar, sehingga lulusan para lulusan SMK dapat memiliki kemampuan untuk mendesain PLC dengan baik.

Nowadays, the development of the of Automatic Identification System (AIS) device has continuously increased. It was initially used to send information on the whereabouts of ships to avoid collisions, but with stored data, it is used for monitoring waters. Therefore, this study was carried out using AIS data to classify ships in Indonesian waters. Based on features such as length, width, and weight, it classified them into 9 types of vessels. The data mining process was used to characterize each type with the ensemble method. Furthermore, data processing was carried out to determine the ship’s trajectory pattern. In this study, 80% of training data was used while the rest were testing data. The results showed that an accuracy value of 99.8% was obtained with a Root Mean Square Error (RMSE) value of 0.12.