Prediction of Successful Harvest of Vaname Shrimp Pond at PT FEI With Machine Learning Approach
Abstract
The demand for shrimp from Indonesia continues to increase every year, thus creating greater interest in the shrimp farming industry. Although shrimp is relatively easy to farm, many variables affect the success of the harvest. The harvest in shrimp farming is calculated using % SR (Survival Rate). In our research, we used machine learning approaches, namely decision tree (DT) and k-Nearest Neighbor (KNN). DT and KNN will be used to predict whether we will have a successful harvest. From these predictions, we also provide suggestions for business improvements to utilize data. The expected result of such advice is that the business can improve its performance and get more consistent results.
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Ayesha Jasmin, S., Ramesh, Pradeep, & Tanveer, Mohammad. (2022). An intelligent framework for prediction and forecasting of dissolved oxygen level and biofloc amount in a shrimp culture system using machine learning techniques. Expert Systems with Applications, 199(August 2021), 117160. https://doi.org/10.1016/j.eswa.2022.117160
Fuady, Muhammad Faiz, Haeruddin, -, & Nitisupardjo, Mustofa. (2013). Pengaruh Pengeololaan Kualitas Air Terhadap Tingkat Kelulushidupan dan Laju Pertumbuhan Udang Vaname di PT Indokor Bangun Desa, Yogyakarta. Management of Aquatic Resources Journal, 2(4), 155–162. https://doi.org/10.14710/marj.v2i4.4279
Gao, Huan, Kong, Jie, Li, Zhanjun, Xiao, Guangxia, & Meng, Xianhong. (2011). Quantitative analysis of temperature, salinity and pH on WSSV proliferation in Chinese shrimp Fenneropenaeus chinensis by real-time PCR. Aquaculture, 312(1–4), 26–31. https://doi.org/10.1016/j.aquaculture.2010.12.022
Gladju, J., Kamalam, Biju Sam, & Kanagaraj, A. (2022). Applications of data mining and machine learning framework in aquaculture and fisheries: A review. Smart Agricultural Technology, 2(April), 100061. https://doi.org/10.1016/j.atech.2022.100061
Heizer, Jay; Render, Barry. (2017). Operation Management. In Pearson (Vol. 12).
Kustanti, Eni. (2020). Big data implementation for agriculture commodity knowledge management. Jurnal Pustakawan Indoesia, 20.
Lawrence, Tim. (2020, May). Exploiting Big Data and Analytics to Improve Productivity in Manufacturing. Manufaturing Digital.
Mahendra, Arya Bima. (2022). Jadi Daerah dengan Tambak Udang Vaname Terluas di Bangka Belitung, Sayang Belum Bisa Sumbangkan PAD - Bangkapos.com.
Prastianti, Atikah Indri. (2021). Faktor-Faktor yang Mempengaruhi Produksi Udang Vannamei (Litopeneaus Vannamei) di Desa Pantai Bahagia, Kecamatan Muara Gembong. Universitas Islam Negeri Syarif Hidayatullah.
Rahman, Ashfaqur, Arnold, Stuart, & Dabrowski, Joel Janek. (2019). Identification of variables affecting production outcome in prawn ponds: A machine learning approach. Computers and Electronics in Agriculture, 156, 618–626. https://doi.org/10.1016/j.compag.2018.12.015
Rahman, Ashfaqur, Xi, Mingze, Dabrowski, Joel Janek, McCulloch, John, Arnold, Stuart, Rana, Mashud, George, Andrew, & Adcock, Matt. (2021). An integrated framework of sensing, machine learning, and augmented reality for aquaculture prawn farm management. Aquacultural Engineering, 95(July), 102192. https://doi.org/10.1016/j.aquaeng.2021.102192
Whiting, David G., Tolley, H. Dennis, & Fellingham, Gilbert W. (2000). An empirical Bayes procedure for adaptive forecasting of shrimp yield. In Aquaculture (Vol. 182).
DOI: https://doi.org/10.21107/pamator.v16i2.19794
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