PENAMBANGAN POLA RUANG WAKTU PADA PETA PRAKIRAAN DAERAH PENANGKAPAN IKAN DI PERAIRAN WILAYAH PENGELOLAAN PERIKANAN (WPP) 712, 713, DAN 573

Nur Mohammad Farda, Dinarika Jatisworo

Abstract


Peta PDPI (Prakiran Daerah Penangkapan Ikan) Nasional sejak tahun 2000-an telah diproduksi secara kontinyu oleh Kementrian Kelautan dan Perikanan melalui Balai Riset dan Observasi Laut (BROL) setiap dua hingga tiga hari sekali sehingga merupakan sebuah basis data spasial yang besar. Teknologi penambangan data berkembang tidak hanya pada penambangan data yang bersifat spasial namun juga pada data spasial dan temporal. Basis data spasial besar dari kumpulan peta PDPI multitemporal sangat potensial, namun sampai saat ini belum dimanfaatkan secara optimal untuk melihat pola prakiraan penangkapan ikan pada periode waktu tertentu (mingguan, bulanan, dan tahunan). Agregasi data tersebut dalam kurun waktu tertentu, misalnya dalam bentuk kalender tetap, bisa dimanfaatkan nelayan dalam merencanakan penangkapan ikan. Penelitian ini bertujuan untuk mendapatkan pola hot spot dengan intensitas yang tinggi dari kumpulan titik-titik prakiran daerah penangkapan ikan (PDPI) dalam kurun waktu tahun 2012 hingga 2017. Metode yang digunakan meliputi space time cube untuk menghasilkan basis data multitemporal, Getis-Ord Gi* statistic (Hot Spot Analysis) untuk menghasilkan hot dan cold spot trends, dan selanjutnya trends tersebut dievaluasi menggunakan Mann-Kendall trend test. Hasil akhir dari penelitan ini adalah berupa peta potensi prakiraan daerah penangkapan ikan tahunan di perairan Wilayah Pengelolaan Perikanan (WPP) 712, 713, dan 573.

Keywords


Penambangan Pola Ruang Waktu; PDPI; Daerah Penangkapan Ikan

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References


Aggarwal, C. C. (2013). Outlier Analysis. Berlin, Germany: Springer.

Ardianto, R., Setiawan, A., Hidayat, J. J., & Zaky, A. R. (2017). Development of an Automated Processing System for Potential Fishing Zone Forecast. In LISAT - IOP Conference Series: Earth and Environmental Science (p. 54). IOP.

Carlstein, T. (1982). Time resources, society, and ecology: on the capacity for human interaction in space and time. London, Boston: Allen & Unwin. ISBN 978-0043000823.OCLC 7946554.

Celik, M., Shekhar, S., Rogers, J.P., Shine, J.A., & Yoo, J.S. (2006). Mixed-drove spatio-temporal cooccurence pattern mining: A summary of results. In Proceedings of the Sixth International Conference on Data Mining. Washington, DC, USA.

Dharmawan, R.D., Suharyadi, & Farda, N.M. (2018). Geovisualization using hexagonal tessellation for spatiotemporal earthquake data analysis in Indonesia. In 3rd International Conference on Soft Computing in Data Science - Communications in Computer and Information Science (p. 177). Springer Nature.

Ester, M., Kriegel, H.P., Sander, J. (1997). Spatial Data Mining: A Database Approach. In Advances in Spatial Databases, Proceedings of the 5th International Symposium (SSD ’97) 47–66. Berlin, Germany: Springer.

Fitrianah, D., Fahmi, H., Hidayanto, A. N., & Arymurthy, A. M. (2016). A Data Mining Based Approach for Determining the Potential Fishing Zones. International Journal of Information and Education Technology, 6(3).

Fitrianah, D., Hidayanto, A. N., Gaol, J. L., Fahmi, H., & Arymurthy, A. M. (2016). A Spatio-Temporal DataMining Approach for Identification of Potential Fishing Zones Based on Oceanographic Characteristics in the Eastern Indian Ocean. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 9(8).

Getis, A., & Ord, J. K. (1992). The Analysis of Spatial Association by Use of Distance Statistics. Geographical Analysis, 3, 24.

Hägerstrand, T. (1970). What about people in regional science? Regional Science Association, 24(1), 6– 21.

Hamed, K. H. (2009). Exact Distribution of The MannKendall Trend Test Statistic for Persistent Data. Journal of Hydrology, 86–94.

Jatisworo, D. (2017). Kajian Spasial dan Temporal Sebaran Front di Selat Makassar dan Laut Banda terkait Variasi Musim. Universitas Gadjah Mada. J

Jatisworo, D., & Murdimanto, A. (2012). Peranan Teknologi Penginderaan Jauh bagi Penangkapan Ikan di Indonesia (Studi Kasus Kabupaten Indramayu). In K. Wikantika & L. Fajri (Eds.), Bunga Rampai Penginderaan Jauh Indonesia. Pusat Penginderaan Jauh ITB.

Kisilevich, S., Mansmann, F., Nanni, M., & Rinzivillo, S. (2010). Spatio-Temporal Clustering. Berlin, Germany: Springer.

Koperski, K., Adhikary, J., Han, J. (1996). Spatial data mining: Progress and challenges survey paper. In Proceedings of the ACM SIGMOD Workshop on Research Issues on Data Mining and Knowledge Discovery. Montreal, QC, Canada.

Little, B., Schucking, M., Gartrell, B., Chen, B., Ross, K., & McKellip, R. (2008). High granularity remote sensing and crop production over space and time: NDVI over the growing season and prediction of cotton yields at the farm field level in Texas. In Proceedings of the IEEE International Conference on Data Mining Workshops (ICDMW ’08) 426– 435. Pisa, Italy.

Miller, H. J., & Han, J. (2009). Geographic Data Mining and Knowledge Discovery. (H. J. Miller & J. Han, Eds.) (Second). Boca Raton, FL: Taylor & Francis Group, LLC.

Ord, J. K., & Getis, A. (1995). Local Spatial Autocorrelation Statistics: Distributional Issues and an Application. Geographical Analysis, 4, 27.

Pei, Y., Zaıane, O.R., & Gao, Y. (2006). An efficient reference-based approach to outlier detection in large Datasets. In Proceedings of the Sixth International Conference on Data Mining (ICDM ’06) (p. 478–487). Hong Kong, China.

Roddick, J. F., & Spiliopoulou, M. (1999). A Bibliography of Temporal, Spatial and Spatiotemporal Data Mining Research. SIGKDD Explor., 1, 34–38.

Shekhar, S., Evans, M. R., Kang, J. M., & Pradeep, M. (2011). Identifying Patterns in Spatial Information: a Survey of Methods. WIREs Data Mining and Knowledge Discovery, 1, 193–214.

Shekhar, S., Jiang, Z., Ali, R. Y., Eftelioglu, E., Tang, X., Gunturi, V. M. V., & Zhou, X. (2015). Spatiotemporal Data Mining: A Computational Perspective. ISPRS International Journal of GeoInformation, 4, 2306–2338.

Silverman, B.W. (1986). Density Estimation for Statistics and Data Analysis. Chapman & Hall, London. doi: 10.1007/978-1-4899-3324-9.

Su, F., Zhou, C., Lyne, V., Du, Y., & Shi, W. (2004). A data-mining approach to determine the spatiotemporal relationship between environmental factors and fish distribution. Ecological Modelling, 174, 421–431.

Suniada, K. I., Susilo, E., & Hastuti, A. W. (2015). Validasi Peta Prakiraan Daerah Penangkapan Ikan (PPDPI) di Perairan Laut Jawa (WPP-RI 712). In Prosiding Forum Nasional Sains dan Teknologi Kelautan dan Perikanan.

Thrift, N. & Pred, A. (1981). Time-geography: a new beginning. Progress in Human Geography, 5 (2), 277–286. doi:10.1177/030913258100500209.

Zhang, P., Huang, Y., Shekhar, S., & Kumar, V. (2003). Exploiting spatial autocorrelation to efficiently process correlation-based similarity queries. In Proceedings of the 8th International Symposium on Advances in Spatial and Temporal Databases (SSTD 2003) 449–468.

Santorini Island, Greece. Zhou, X., Shekhar, S., & Ali, R. Y. (2014). Spatiotemporal Change Footprint Pattern Discovery: An Interdisciplinary Survey. Wiley Interdiscip. Rev. Data Min. Knowl. Discov., 4, 1–23.




DOI: http://dx.doi.org/10.24895/MIG.2019.21-2.956

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