Abstract :

Illegal fishing in Indonesian waters poses a serious challenge that requires innovative solutions. This research offers an advanced technological approach by applying the Hidden Markov Model (HMM) in Machine Learning to address this issue. Data from the Vessel Monitoring System (VMS) is utilized to efficiently identify fishing vessel activities. By involving a dataset that encompasses various vessel activities, this model can detect suspicious fishing practices in real-time. The research findings demonstrate that this model consistently identifies fishing vessel activities with a high level of accuracy. This study makes a significant contribution to efforts in preventing Illegal, Unreported, and Unregulated (IUU) Fishing and supports marine resource sustainability initiatives.

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Keywords :

Fishing Vessel Activities, Hidden Markov Model, IUU Fishing, Machine learning, Vessel Monitoring System.

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References :

1. Franzese, M., & Luliano, G. (2018). Hidden Markov Model for Sequential Data Analysis. Journal of Machine Learning Applications, 23(4), 112-125.
2. Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. Annals of Statistics, 29(5), 1189-1232
3. Joo, R., Bertrand, S., Tam, J., Fablet, R., & Chavance, P. (2011). Hidden Markov models: The best models for foraging movements in the case of the Peruvian anchovy (Engraulis ringens). Progress in Oceanography, 91(4), 504-519.
4. Kuflik, T., Shoval, P., & Minkov, E. (2010). Enhancing decision-making processes using support vector machines. Decision Support Systems, 48(3), 560-570.
5. Mazzarella, F., Vespe, M., Tarchi, D., & Battistello, G. (2014). Data mining techniques for AIS-based maritime anomaly detection. Proceedings of the 2014 Maritime Big Data Conference, 22-29.
6. Nurholis, N., Handayani, T., & Puspitasari, D. (2017). Challenges in Monitoring Small-Scale Fisheries in Indonesia. Journal of Fisheries Science, 45(2), 101-115. in Oceanography, 91(4), 504-519.
7. Pallotta, G., Vespe, M., & Bryan, K. (2013). Vessel pattern knowledge discovery from AIS data: A framework for anomaly detection and route prediction. Entropy, 15(6), 2218-2245.
8. Pallotta, G., Vespe, M., & Bryan, K. (2013). Vessel pattern knowledge discovery from AIS data: A framework for anomaly detection and route prediction. Entropy, 15(6), 2218-2245.
9. Rish, I. (2001). An empirical study of the naive Bayes classifier. Proceedings of the 2001 IJCAI Workshop on Empirical Methods in AI, 41-46.
10. Shearer, C. (2000). The CRISP-DM model: The new blueprint for data mining. Journal of Data Warehousing, 5(4), 13-22.
11. Wijayanto, D. P., Santoso, W., & Sukandar, E. Y. (2019). Machine learning applications for identifying fishing activities in Indonesian waters. Journal of Marine Science and Technology, 14(3), 230-239.