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All Journal Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer
Wicky Prabowo Juliastoro
Fakultas Ilmu Komputer, Universitas Brawijaya
Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering

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Sistem Prediksi Hasil Produksi Udang Vaname menggunakan Algoritma Multiple Linear Regression (MLR) Kombinasi Gradient Descent (GD) dengan Apache Spark Wicky Prabowo Juliastoro; Imam Cholissodin; Fitra Abdurrachman Bachtiar
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 5 No 11 (2021): November 2021
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Vannamei shrimp is a shrimp in the tiger prawn pond culture favored by the Indonesian people because it has an economical price and has the expected prospects and benefits. The cultivation of vaname shrimp is quite tricky compared to tiger prawns. Vannamei shrimp is vulnerable to diseases such as White Faces Syndrome (WFS) and White Spot Syndrome (WSS) which results in death and a decrease in the amount of production produced. The decrease in production results is also caused by poor air quality management such as poor air circulation, irregular ponds, and providing excessive feed which causes poor air quality. Therefore, an algorithm is needed that can predict the production of vannamei shrimp based on the quality of the water used so that cultivators can take preventive actions against problems that occur. In this study, prediction is carried out into several processes, namely, pre-processing, data normalization, prediction, and calculation of the error value. In the prediction process, the algorithm used is Multiple Linear Regression (MLR) a combination of Gradient Descent (GD) with Apache Spark and in the process of calculating the error value using the Root Means Square Error (RMSE) method. The results of this study are based on the testing process using water quality data for 1 harvest of ponds owned by FPIK UB partners in. Lamongan which consists of 18 parameters in 4 different pools, the RMSE value is 210,634 and the Adjusted R-Squared value is 0.8341 with a training and testing data percentage of 70%:30%, the number of features is 18, the alpha value is 0.33, the error value is 0. .01 and the number of nodes is 6.
Co-Authors Fitra Abdurrachman Bachtiar Imam Cholissodin