Peningkatan Performa Prediksi Daerah Potensi Penangkapan Ikan Dengan Metode Threshold Adaptif

  • Ridla Kumara Hadi Departemen Teknik Elektro dan Teknologi Informasi, Universitas Gadjah Mada, Yogyakarta, Indonesia
  • Rudy Hartanto Departemen Teknik Elektro dan Teknologi Informasi, Universitas Gadjah Mada, Yogyakarta, Indonesia
  • Silmi Fauziati Departemen Teknik Elektro dan Teknologi Informasi, Universitas Gadjah Mada, Yogyakarta, Indonesia
Keywords: Analisis Performa, Deteksi Tepi, Prediksi Daerah Potensi Penangkapan Ikan, Threshold Adaptif

Abstract

Metode yang digunakan untuk penentuan thermal fronts adalah algoritme Single Image Edge Detection dengan threshold statis 0,5 yang didapatkan dari penelitian terdahulu. Kekurangan dari metode threshold statis adalah tingginya bias akurasi hasil deteksi dikarenakan lebih banyaknya hasil deteksi negatif tervalidasi dibandingkan deteksi front murni yang tervalidasi. Penelitian yang diusulkan bertujuan untuk meningkatkan performa metode deteksi daerah potensi ikan. Peningkatan performa deteksi thermal front dapat dilakukan dengan mencari nilai threshold optimal yang sesuai untuk masing-masing citra. Threshold adaptif didapatkan dari hasil analisis histogram pada setiap citra greyscale yang diproses. Untuk mendapatkan nilai threshold optimal dipilih Algoritme Otsu dengan pertimbangan proses cepat dan ketepatan hasil menengah. Penyesuaian metode dibutuhkan karena sifat dasar data SST yang dikonversi menjadi raster. Modifikasi metode Otsu dilakukan pada perhitungan nilai threshold optimal dengan rentang intensitas greyscale 1-254. Pemurnian front menggunakan pendekatan Geodesic Buffering dengan jarak maksimal 10 kilometer untuk mengatasi pergeseran front akibat noise suppression. Penelitian telah dilakukan dan menghasilkan metode deteksi daerah potensi ikan dengan performa recall yang lebih tinggi 25,42% dibandingkan metode threshold statis. Nilai recall lebih tinggi membuktikan bahwa metode yang diusulkan mampu menghasilkan lebih banyak hasil deteksi front murni yang lokasinya tervalidasi dengan data aktual penangkapan ikan.

Downloads

Download data is not yet available.

References

Adnan, “Analisis Suhu Permukaan Laut dan Klorofil-A Data Inderaja Hubungannya dengan Hasil Tangkapan Ikan Tongkol (Euthynnus Affinis) di Perairan Kalimantan Timur,” Amanisal, vol. 1, no. 1, hal. 1–12, 2010.
R. Hamzah, T. Prayogo, dan S. Marpaung, “Metode Penentuan Titik Koordinat Zona Potensi Termal Front Suhu Permukaan Laut ( Method of Determination Points Coordinate for Potential Fishing Zone Based on Detection of Thermal Front Sea Surface Temperature ),” J. Penginderaan Jauh, vol. 13, no. 2, hal. 97–108, 2016.
D. Jatisworo, A. Murdimanto, dan K. Wikantika, “Peranan Teknologi Penginderaan Jauh Bagi Penangkapan Ikan di Indonesia (Studi Kasus Kabupaten Indramayu),” in Bunga Rampai Penginderaan Jauh Indonesia, 2012, hal. 123–137.
D. Setiapermana, S. H. Santoso, dan Riyono, “Chlorofil Content In Relation to Physical Structure in East Indian Ocean,” Oseanologi Indones. LIPI, vol. 25, hal. 13–29, 1992.
N. Hendiarti et al., “Seasonal Variation of Pelagic Fish Catch Around Java,” Oceanogr. Soc., vol. 18, no. 4, hal. 112–123, 2005.
M. Firdaus, “Profil Perikanan Tuna dan Cakalang di Indonesia,” MARINA, vol. 4, no. 1, hal. 23–32, 2018.
R. J. Beamish, G. A. McFarlane, dan J. R. King, “Migratory patterns of pelagic fishes and possible linkages between open ocean and coastal ecosystems off the Pacific coast of North America,” Deep. Res. Part II Top. Stud. Oceanogr., vol. 52, no. 5, hal. 739–755, 2005.
A. S. Genisa, “Pengenalan Jenis-jenis Ikan Laut Ekonomi Penting di Indonesia,” Oseana, vol. xxiv, no. 1, hal. 17–38, 1999.
Indrayani, A. Mallawa, dan M. Zainuddin, “Penentuan Karakteristik Habitan Daerah Potensial Ikan Pelagis Kecil dengan PendekatanSpasial di Perairan Sinjai,” e-Journal Progr. Pascasarj. Univ. Hasanuddin, vol. 12, no. 1, hal. 1–10, 2012.
M. Zainuddin, “Skipjack Tuna in Relation To Sea Surface Temperature and Chlorophyll-a Concentration of Bone Bay Using Remotely Sensed Satellite Data,” J. Ilmu dan Teknol. Kelaut. Trop., vol. 3, no. 1, 2011.
M. Zainuddin, M. B. Selamat, A. Farhum, dan S. Hidayat, “Prediction of Potential Fishing Zones for Skipjack Tuna During the Northwest Monsoon Using Remotely Sensed Satellite Data,” Ilmu Kelaut., vol. 22, no. 2, hal. 59–66, 2017.
W. E. Rintaka dan E. Susilo, “Validation of potential fishing zone forecast using experimental fishing method in Tolo Bay, Central Sulawesi Province,” IOP Conf. Ser. Earth Environ. Sci., vol. 137, no. 1, 2018.
T. M. Lillesand, R. W. Kiefer, dan J. W. Chipman, “Remote sensing and image interpretation Wiley,” New York, hal. 1–59, 1994.
C. C. Wall, F. E. Muller-Karger, M. A. Roffer, C. Hu, W. Yao, dan M. E. Luther, “Satellite remote sensing of surface oceanic fronts in coastal waters off west-central Florida,” Remote Sens. Environ., vol. 112, no. 6, hal. 2963–2976, 2008.
J.-F. Cayula dan P. Cornillon, “Edge Detection Algorithm for SST Images,” J. Atmos. Ocean. Technol., vol. 9, no. 1, hal. 67–80, 1992.
B. Hasyim, Pengembangan dan Penerapan Informasi Spasial dan Temporal Zona Potensi Penangkapan Ikan Berdasarkan Data Penginderaan Jauh. Bogor: Crespent Press, 2015.
D. Jatisworo dan A. Murdimanto, “Identifikasi thermal front di Selat Makassar dan Laut Banda,” in Simposium Nasional Sains Geoinformasi III, 2013, hal. 226–232.
E. R. Davies, Computer and Machine Vision: Theory, Algorithms, Practices, 4 ed. London: Elsevier, 2012.
J. Kittler, J. Illingworth, dan J. Föglein, “Threshold Selection Based on a Simple Image Statistic,” Comput. Vision, Graph. Image Process., vol. 30, no. 2, hal. 125–147, 1985.
Y. Wu, Y. He, dan H. Cai, “Optimal threshold selection algorithm in edge detection based on wavelet transform,” Image Vis. Comput., vol. 23, no. 13, hal. 1159–1169, 2005.
J. Marcello, F. Eugenio, S. Estrada-Allis, dan P. Sangrà, “Segmentation and tracking of anticyclonic eddies during a submarine volcanic eruption using ocean colour imagery,” Sensors (Switzerland), vol. 15, no. 4, hal. 8732–8748, 2015.
L. Roa-Pascuali, H. Demarcq, dan A. E. Nieblas, “Detection of mesoscale thermal fronts from 4km data using smoothing techniques: Gradient-based fronts classification and basin scale application,” Remote Sens. Environ., vol. 164, no. July, hal. 225–237, 2015.
Y. Yang, J. Dong, X. Sun, R. Lguensat, M. Jian, dan X. Wang, “Ocean Front Detection from Instant Remote Sensing SST Images,” IEEE Geosci. Remote Sens. Lett., vol. 13, no. 12, hal. 1960–1964, 2016.
X. Sun, C. Wang, J. Dong, E. Lima, dan Y. Yang, “A Multiscale Deep Framework for Ocean Fronts Detection and Fine-Grained Location,” IEEE Geosci. Remote Sens. Lett., vol. 16, no. 2, hal. 178–182, 2019.
John Canny, “A Computational Approach To Edge Detection,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 8, no. 6, hal. 679–714, 1986.
G. Kirches, M. Paperin, H. Klein, C. Brockmann, dan K. Stelzer, “GRADHIST - A method for detection and analysis of oceanic fronts from remote sensing data,” Remote Sens. Environ., vol. 181, hal. 264–280, 2016.
V. Oerder, J. P. Bento, C. E. Morales, S. Hormazabal, dan O. Pizarro, “Coastal upwelling front detection off Central Chile (36.5-37°S) and spatio-temporal variability of Frontal characteristics,” MDPI Remote Sens., vol. 10, no. 690, hal. 1–24, 2018.
NASA, “PO.DAAC MODIS Level 3 Data User Guide,” no. September 23. California Institute of Technology, hal. 1–52, 2015.
P. J. Minnett, R. H. Evans, E. J. Kearns, dan O. B. Brown, “Sea-surface temperature measured by the Moderate Resolution Imaging Spectroradiometer (MODIS),” Int. Geosci. Remote Sens. Symp., vol. 2, no. July, hal. 1177–1179, 2002.
W. E. Esaias et al., “An overview of MODIS capabilities for ocean science observations,” IEEE Trans. Geosci. Remote Sens., vol. 36, no. 4, hal. 1250–1265, 1998.
NASA, “MODIS Design.” [Daring]. Tersedia pada: https://modis.gsfc.nasa.gov/about/design.php. [Diakses: 26-Agu-2019].
G. C. Feldman dan NASA, “Long-Wave Sea Surface Temperature (SST).” [Daring]. Tersedia pada: https://oceancolor.gsfc.nasa.gov/atbd/sst/. [Diakses: 19-Sep-2019].
O. B. Brown dan P. J. Minnett, “MODIS Infrared Sea Surface Temperature Algorithm Algorithm Theoretical Basis Document,” 1999.
G. C. Feldman dan NASA, “Chlorophyll a (chlor_a).” [Daring]. Tersedia pada: https://oceancolor.gsfc.nasa.gov/atbd/chlor_a/. [Diakses: 22-Sep-2019].
C. Proctor, “A. Manufacturer calibration and coefficients,” hal. 1–4, 2012.
PERATURAN MENTERI KELAUTAN DAN PERIKANAN REPUBLIK INDONESIA NOMOR 48/PERMEN-KP/2014 TENTANG LOG BOOK PENANGKAPAN IKAN. 2014.
A. F. Torres-Monsalve dan J. Velasco-Medina, “Hardware implementation of ISODATA and Otsu thresholding algorithms,” in 2016 21st Symposium on Signal Processing, Images and Artificial Vision, STSIVA 2016, 2016.
S. Guiming dan S. Jidong, “Remote sensing image edge-detection based on improved Canny operator,” in Proceedings of 2016 8th IEEE International Conference on Communication Software and Networks, ICCSN 2016, 2016, hal. 652–656.
S. I. Syafi’i, R. T. Wahyuningrum, dan A. Muntasa, “Segmentasi Obyek Pada Citra Digital Menggunakan Metode Otsu Thresholding,” J. Inform., vol. 13, no. 1, 2016.
N. Otsu, “A Threshold Selection Method from Gray-Level Histograms,” IEEE Trans. Syst. Man. Cybern., vol. 9, no. 1, hal. 62–66, 1979.
D. Putra, “Binerisasi citra tangan dengan metode otsu,” Teknol. Elektro, vol. 3, no. 2, hal. 11–13, 2004.
Y. Chang dan P. Cornillon, “A comparison of satellite-derived sea surface temperature fronts using two edge detection algorithms,” Deep. Res. Part II Top. Stud. Oceanogr., vol. 119, hal. 40–47, 2015.
J. Kittler dan J. Illingworth, “Minimum Error Thresholding,” Pattern Recognit., vol. 19, no. 1, hal. 41–47, 1986.
J. Kittler dan D. Pairman, “Contextual Pattern Recognition Applied to Cloud Detection and Identification,” IEEE Trans. Geosci. Remote Sens., vol. GE-23, no. 6, hal. 855–863, 1985.
S. Cho, R. Haralick, dan S. Yi, “Improvement of kittler and illingworth’s minimum error thresholding,” Pattern Recognition, vol. 22, no. 5. hal. 609–617, 1989.
D. Flater, “Understanding Geodesic Buffering Correctly use the Buffer tool in ArcGIS,” ArcUser, hal. 33–37, 2011.
T. Saito dan M. Rehmsmeier, “The Precision-Recall Plot Is More Informative than the ROC Plot When Evaluating Binary Classifiers on Imbalanced Datasets,” PLoS One, no. March, hal. 1–21, 2015.
D. M. Powers, “Evaluation : From precision , recall and F-measure to ROC , informedness , markedness & correlation,” J. Mach. Learn. Technol., vol. 2, no. 1, hal. 37–63, 2015.
B. Özdemir, S. Aksoy, S. Eckert, M. Pesaresi, dan D. Ehrlich, “Performance measures for object detection evaluation,” Pattern Recognit. Lett., vol. 31, no. 10, hal. 1128–1137, 2010.
Published
2020-06-17
Section
Articles
Abstract viewed = 37 times
PDF downloaded = 43 times