APPLICATION OF DATA MINING FOR PREDICTION OF FISHERY CULTIVATION HARVEST RESULTS FROM HARVEST PARTNERS USING THE SUPPORT VECTOR REGRESSION ALGORITHM
Case Study: PT. Adma Digital Solutions
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Claudia Millennia Suprapto(1)
Universitas Pembangunan Nasional "Veteran" Jawa Timur
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Wahyu Syaifullah Jauharis Saputra(2)
Universitas Pembangunan Nasioanal “Veteran” Jawa Timur
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Firza Prima Aditiawan(3*)
Universitas Pembangunan Nasioanal “Veteran” Jawa Timur
(*) Corresponding Author
Abstract
PT. Adma Digital Solusi is a company that serves as a harvest partner for cultivators in the fields of agriculture, animal husbandry and fisheries which is used for planning and controlling supply chain results. Planning and controlling PT fishery supply chain results. Adma Digital Sousi in the digital era needs to utilize various technologies and information systems. This aims to ensure that planning and controlling fish resources fulfill aspects of effectiveness and efficiency in decision making. In this research, a machine learning method will be implemented using the Support Vector Regression (SVR) algorithm to predict the harvest results of PT's fishery cultivation partners. Adma Digital Solutions. The SVR algorithm is a theory used to solve a regression classification problem using a Support Vector Machine (SVM). The SVR forecasting process uses the SVR() model by filling in the parameters, namely the kernel using polynomials, C is filled with the value 100, gamma is filled with auto, degree is filled with the value three, epsilon is filled with the value 0.1, and finally coef0 is filled with the value one. Then, using the fit function to train the model using x train and y train data to produce a MAPE error rate value of 0.12865018182566176 and an R2 value of 0.9998831470091238 with very good and accurate prediction capabilities. By knowing the estimated harvest results of aquaculture, the benefits obtained by harvest partners are adjusting production and marketing strategies to maximize profits. And can help harvest partners in managing risks, because they can prepare themselves well for situations where harvest results do not match estimates.
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