A Study of Prediction Model for Capture Fisheries Production in Indonesian Sea Waters Using Machine Learning

Ganjar Adi Pradana

Abstract


The potential for capture fisheries in Indonesia is a priceless wealth. This wealth has not been explored optimally. Fisheries resources are included in the category of renewable resources whose sustainability needs to be considered. This is important in maintaining food security which will increase over time, due to population growth. Capture Fisheries Production Prediction Model is needed to find out what determining variables affect capture fisheries production. There are many methods for predicting, the method that is widely used today is using machine learning since it ability to handle complex jobs with large input data. This research is a literature study, which aims to: (1) identify and analyze machine learning methods that are suitable for predicting capture fisheries production, and (2) identify variables that can affect capture fisheries production. The results of the study show that the Neural Network method is most widely used as a predictive model. In addition, the Random Forest and Linear Logistics methods provide better accuracy results. The results of the study also succeeded in finding 12 determining variables for the capture fisheries production prediction model.


Keywords


Capture Fisheries Production; Machine Learning; Prediction Models

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References


Sofiyanti, N., Suartini, S. (2016). Pengaruh Jumlah Kapal Perikanan dan Jumlah Nelayan terhadap Hasil Produksi Perikanan di Indonesia. Journal of Accounting and Finance, 1 (1), 49-61. DOI: https://doi.org/10.35706/acc.v1i01.442

Nababan, B.O., Sari, Y.D.,Hermawan, M. (2017). Analisis Keberlanjutan Perikanan Tangkap Skala Kecil di Kabupaten Tegal, Jawa Tengah. Jurnal Bijak dan Riset Sosek KP, 2 (2), 137-158. DOI: https://doi.org/10.15578/jsekp.v2i2.5868

Kusdiantoro, Fahrudin, A., Wisudo, S. H., dan Juanda, B. (2019). Perikanan Tangkap di Indonesia: Potret dan Tantangan Keberlanjutannya. Jurnal Sosek KP, 14 (2), 145-162. DOI: http://dx.doi.org/10.15578/jsekp.v14i2.8056

Andriani, A. (2015). Prediksi Kenaikan Rata-Rata Volume Perikanan Tangkap Dengan Teknik Data Mining. Seminar Nasional Teknologi Informasi dan Komunikasi Terapan, 117-121. Source: http://conference.dinus.ac.id/index.php/SEMANTIK/SEMANTIK2015/ paper/view/79

Bahri, S., Simbolon, D., dan Mustaruddin, M. (2017). Analisis Daerah Penangkapan Ikan Madidihang (Thunnus albacares) Berdasarkan Suhu Permukaan Laut dan Sebaran Klorofil-a di Perairan Provinsi Aceh. Jurnal Teknologi Perikanan Dan Kelautan, 8 (1), 95-104. DOI: https://doi.org/10.24319/jtpk.8.95-104

Bukhari, B., Adi, W., Kurniawan, K. (2017). Pendugaan Daerah Penangkapan Ikan Tenggiri Berdasarkan Distribusi Suhu Permukaan Laut dan Klorofil-a di Perairan Bangka. Jurnal Perikanan Tangkap, 1 (3), 1-22. Source: https://ejournal2.undip.ac.id/index.php/ juperta/article/view/1871

Damayanti, H.O. (2018). Faktor-Faktor yang Mempengaruhi Produksi Ikan pada Nelayan Kecil (Studi di Desa Pecangaan Kabupaten Pati). Jurnal Litbang, 8 (2), 83-92. DOI: https://doi.org/10.33658/jl.v12i2.39

Hastomo, W., Karno, A.S.B., Kabuana, N., Bisfiani, E., Lussiana. (2021). Optimasi Deep Learning untuk Prediksi Saham di Masa Pandemi Covid-19. Jurnal Edukasi dan Penelitian Informatika, 7 (2), 133-140. DOI: https://doi.org/10.26418/jp.v7i2.47411

Ikhsan, A.N., Arsi, P., Suhaman, J. (2021). Komparasi Model Prediksi Kurs Pada Masa Pandemi Covid-19 Menggunakan Neural Network Berbasis Genetic Algorithm dan Particle Swarm Optimization. Jurnal Infotekmesin, 13 (01), 74-79. DOI: https://doi.org/10.35970/infotekmesin.v13i1.938

Mursyidin. (2019). Prediksi Zona Tangkapan Ikan Menggunakan Citra Klorofil-a dan Citra Suhu Permukaan Laut Satelit Aqua MODIS Di Perairan Aceh Jaya. Jurnal Ilmiah Pendidikan Teknik Elektro, 3 (1), 11-18. DOI: http://dx.doi.org/10.22373/crc.v3i1.3657

Noor, A. (2017). Prediksi Siswa Lulus Tidak Tepat Waktu Menggunakan Backpropagation Neural Network. Jurnal Humaniora Teknologi, 1 (3), 8-15. DOI: https://doi.org/10.34128/ jht.v3i1.28

Pambudi, H.K., Kusuma, P.G.A., Yulianti, F.,Julian, K.A. (2020). Prediksi Status Pengiriman Barang Meggunakan Metode Machine Learning. Jurnal Ilmiah Teknologi Informasi Terapan, 6 (2), 100-109. DOI: https://doi.org/10.33197/ jitter.vol6.iss2.2020.396

Pitriyani, D., Permanasari, Y. (2022). Prediksi Jumlah Penumpang Pesawat dengan Backpropagation Neural Network. Jurnal Riset Matematika, 2 (2), 129-136. DOI : https://doi.org/10.29313/jrm.v2i2.1327

Pradana, R.S. (2019). Faktor Penentu Produksi Perikanan Laut Tangkap di Kabupaten Aceh Jaya. Jurnal Ekonomi dan Pembangunan, 10 (1), 51-62. DOI: https://doi.org/10.22373/jep.v10i1.48

Pramuntadi, A. (2017). Model Prediksi Rentet Waktu Neural Network berbasis Particle Swarm Optimazion untuk Prediksi Saham. Jurnal Informatika dan Teknologi Informasi, 14 (2), 100-106. DOI: https://doi.org/10.31315/telematika.v14i2.2097.g1834

Putra, H., Walmi, N.U. (2020). Penerapan Prediksi Produksi Padi Menggunakan Artificial Neural Network Algoritma Backpropagation. Jurnal Nasional Teknologi dan Sistem Informasi, 6 (2), p. 100-107. DOI: https://doi.org/10.25077/ TEKNOSI.v6i2.2020.100-107

Putra, M.S.T., Azhar, Y. (2021). Prediksi Model Logistic Regression dan Artificial Neural Network pada Prediksi Pembatalan Hotel. Jurnal Informasi Sunan Kalijaga, 6 (1), 29-37. DOI: https://doi.org/10.14421/jiska.2021.61-04

Saiful, A., Andryana, S., Gunaryati, A. (2021). Prediksi Harga Rumah Menggunakan Web Scrapping dan Machine Learning Dengan Algoritma Linear Regression. Jurnal Teknik Informatika dan Sistem Informasi, 8 (1), 41-50. DOI: https://doi.org/10.35957/jatisi.v8i1.701

Sari, M., Wiyono, E.S., Zulkarnain. (2021). Pengaruh Cuaca tehadap Pola Musim Penangkapan Ikan Pelagis di Perairan Teluk Lampung. ALBACORE Jurnal Penelitian Perikanan Laut, 5 (3), 277-289. DOI: https://doi.org/10.29244/core.5.3.277-289

Solihin, I., Wisodo, S.H., Haluan, J., Martianto, D. (2011). Pengembangan Produksi Perikanan Tangkap di Wilayah Perbatasan (Kasus Kabupaten Nunukan Kalimantan Timur). Buletin PSP, 19 (2), 9-18. Source: https://journal.ipb.ac.id/index.php/ bulpsp/article/view/4180

Suyudi, M.A.D., Djamal, E.C., Maspupah, A. (2019). Prediksi Harga Saham Menggunakan Metode Recurrent Neural Network. Seminar Nasional Aplikasi Teknologi Informasi, A33-A38. Source: https://journal.uii.ac.id/Snati/article/view/13398

Kubat, M. (2017). An Introduction to Machine Learning Second Edition. Cham: Springer International Publishing.




DOI: https://doi.org/10.31326/jisa.v6i1.1553

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