Identification and Modeling of Illegal Fishing Violations in WPPNRI 715 Using Vessel Monitoring System Data
DOI:
https://doi.org/10.35800/jip.v14i1.67681Keywords:
fisheries surveillance; Getis-Ord Gi*; IUU fishing; MCDA; Vessel Monitoring System; WPPNRI 715.Abstract
This study aimed to analyze fishing activity characteristics based on Vessel Monitoring System (VMS) data, identify spatial patterns and hotspots of fishing violations in Fisheries Management Area of the Republic of Indonesia (WPPNRI) 715, and develop a VMS- and hotspot-based fisheries surveillance model. A quantitative descriptive method with a Geographic Information System-based spatial analysis approach was applied. The dataset consisted of fishing vessel VMS records and fishing violation data from 2022 to 2025 in WPPNRI 715. The analysis included data cleaning and validation, characterization by area, fishing gear, vessel size, and violation type, Kernel Density Estimation, Moran’s I spatial autocorrelation, Getis-Ord Gi* hotspot analysis, and risk-based surveillance modelling using Multi-Criteria Decision Analysis. The results identified 388 violation events concentrated mainly in Maluku, North Maluku, and North Sulawesi waters. The highest number of violations occurred in 2024, with 193 cases. Violations were dominated by small pelagic purse seine vessels with one boat (208 cases), while vessel size was dominated by 30-50 GT (149 cases) and 50-100 GT (123 cases). The most common violation types were fishing-lane violations (203 cases) and fishing-ground violations (156 cases). Moran’s I analysis produced a value of 0.1694, z-score of 2.3733, and pseudo p-value of 0.0237, indicating a statistically significant clustered pattern. Getis-Ord Gi* analysis identified significant hotspots in Maluku and North Maluku waters at the 95-99% confidence levels. The integration of hotspot analysis and MCDA classified Maluku and North Maluku waters as high-priority surveillance zones, North Sulawesi as a medium-priority zone, and West Papua as a low-priority zone. This study concludes that VMS data can support the identification of violation characteristics, hotspot mapping, and the development of risk-based fisheries surveillance in WPPNRI 715.
Keywords: fisheries surveillance; Getis-Ord Gi*; IUU fishing; MCDA; Vessel Monitoring System; WPPNRI 715.
Abstract. Penelitian ini bertujuan menganalisis karakteristik aktivitas penangkapan ikan berbasis data Vessel Monitoring System (VMS), mengidentifikasi pola spasial dan hotspot pelanggaran penangkapan ikan di WPPNRI 715, serta menyusun model pengelolaan pengawasan perikanan berbasis VMS dan hotspot pelanggaran. Penelitian menggunakan metode deskriptif kuantitatif dengan pendekatan analisis spasial berbasis Sistem Informasi Geografis. Data yang dianalisis meliputi data VMS kapal penangkap ikan dan data pelanggaran penangkapan ikan periode 2022-2025 di WPPNRI 715. Tahapan analisis mencakup pembersihan dan validasi data, analisis karakteristik aktivitas berdasarkan wilayah, alat tangkap, ukuran kapal, dan jenis pelanggaran, analisis kepadatan Kernel Density Estimation, autokorelasi spasial Moran’s I, analisis hotspot Getis-Ord Gi*, serta penyusunan model pengawasan berbasis risiko dengan pendekatan Multi-Criteria Decision Analysis. Hasil penelitian menunjukkan terdapat 388 kejadian pelanggaran yang terkonsentrasi terutama di Perairan Maluku, Maluku Utara, dan Sulawesi Utara. Pelanggaran tertinggi terjadi pada 2024 sebanyak 193 kasus. Berdasarkan alat tangkap, pelanggaran didominasi pukat cincin pelagis kecil dengan satu kapal sebanyak 208 kasus, sedangkan berdasarkan ukuran kapal didominasi kapal 30-50 GT sebanyak 149 kasus dan 50-100 GT sebanyak 123 kasus. Jenis pelanggaran terbanyak adalah pelanggaran jalur penangkapan sebanyak 203 kasus dan daerah penangkapan ikan sebanyak 156 kasus. Analisis Moran’s I menghasilkan nilai 0,1694, z-score 2,3733, dan pseudo p-value 0,0237, yang menunjukkan pola pelanggaran mengelompok secara signifikan. Analisis Getis-Ord Gi* mengidentifikasi hotspot signifikan pada Perairan Maluku dan Maluku Utara dengan tingkat kepercayaan 95-99%. Integrasi hotspot dan MCDA menghasilkan prioritas pengawasan tinggi pada Perairan Maluku dan Maluku Utara, sedang pada Perairan Sulawesi Utara, dan rendah pada Perairan Papua Barat. Penelitian ini menyimpulkan bahwa data VMS dapat digunakan sebagai dasar identifikasi karakteristik pelanggaran, pemetaan hotspot, dan penyusunan model pengawasan perikanan berbasis risiko di WPPNRI 715.
Kata kunci: Getis-Ord Gi*; IUU fishing; MCDA; pengawasan perikanan; Vessel Monitoring System; WPPNRI 715.
References
Anselin, L. (1995). Local indicators of spatial association-LISA. Geographical Analysis, 27(2), 93-115. https://doi.org/10.1111/j.1538-4632.1995.tb00338.x
Campbell, M. S., Stehfest, K. M., Votier, S. C., & Hall-Spencer, J. M. (2014). Mapping fisheries for marine spatial planning: Gear-specific vessel monitoring system data improve transparency in fishing activity. Marine Policy, 45, 293-300. https://doi.org/10.1016/j.marpol.2013.09.015
FAO. (2001). International plan of action to prevent, deter and eliminate illegal, unreported and unregulated fishing. Food and Agriculture Organization of the United Nations. https://www.fao.org/4/y1224e/y1224e.pdf
FAO. (2022). The state of world fisheries and aquaculture 2022: Towards blue transformation. Food and Agriculture Organization of the United Nations. https://doi.org/10.4060/cc0461en
FAO. (2024). The state of world fisheries and aquaculture 2024: Blue transformation in action. Food and Agriculture Organization of the United Nations. https://doi.org/10.4060/cd0683en
Fujii, I., Okochi, Y., Kawamura, H., & Inoue, J. (2021). Promoting cooperation of monitoring, control, and surveillance of IUU fishing in the Asia-Pacific. Sustainability, 13(18), 10231. https://doi.org/10.3390/su131810231
Henriques, N. S., Russo, T., Erzini, K., Gonçalves, J.M.S. (2025). Improving monitoring, control and surveillance efforts in fisheries using vessel tracking and fishery-dependent data. Ocean & Coastal Management, Vol. 269. https://doi.org/10.1016/j.ocecoaman.2025.107789.
Hintzen, N. T., Bastardie, F., Beare, D., Piet, G. J., Ulrich, C., Deporte, N., Egekvist, J., & Degel, H. (2012). VMStools: Open-source software for the processing, analysis and visualisation of fisheries logbook and VMS data. Fisheries Research, 115-116, 31-43. https://doi.org/10.1016/j.fishres.2011.11.007
Kroodsma, D. A., Mayorga, J., Hochberg, T., Miller, N. A., Boerder, K., Ferretti, F., Wilson, A., Bergman, B., White, T. D., Block, B. A., Woods, P., Sullivan, B., Costello, C., & Worm, B. (2018). Tracking the global footprint of fisheries. Science, 359(6378), 904-908. https://doi.org/10.1126/science.aao5646
Laia, D. Y. W., Siregar, V. P., & Agus, S. B. (2023). Hot spots analysis of fishing vessels in Anambas Islands 2014-2020. Indonesian Fisheries Research Journal, 29(1), 13-24.
Lee, J., South, A. B., & Jennings, S. (2010). Developing reliable, repeatable, and accessible methods to provide high-resolution estimates of fishing-effort distributions from vessel monitoring system data. ICES Journal of Marine Science, 67(6), 1260-1271. https://doi.org/10.1093/icesjms/fsq010
Russo, T., D’Andrea, L., Parisi, A., & Cataudella, S. (2014). VMSbase: An R-package for VMS and logbook data management and analysis in fisheries ecology. PLoS ONE, 9(6), e100195. https://doi.org/10.1371/journal.pone.0100195
Yan, Z., He, R., Ruan, X., & Yang, H. (2022). Footprints of fishing vessels in Chinese waters based on automatic identification system data. Journal of Sea Research, Vol. 187. https://doi.org/10.1016/j.seares.2022.102255
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Teddy Feky Paulus, Lefrand Manoppo, Patrice Nelson I Kalangi, Deiske A. Sumilat, Joshian Nicolas William Schaduw, Johnny Budiman, Juliet Merry Eva Mamahit
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
COPYRIGHT
Authors who publish with this journal agree to the following terms:
Authors hold their copyright and grant this journal the privilege of first publication, with the work simultaneously licensed under a Creative Commons Attribution License that permits others to impart the work with an acknowledgment of the work's origin and initial publication by this journal.
Authors can enter into separate or additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (for example, post it to an institutional repository or publish it in a book), with an acknowledgment of its underlying publication in this journal.
Authors are permitted and encouraged to post their work online (for example, in institutional repositories or on their website) as it can lead to productive exchanges, as well as earlier and greater citation of the published work (See The Effect of Open Access).
