Klasifikasi Pembibitan Udang Vanamey Yang Ideal Menggunakan Algoritma Naive Bayes - Hidayat Hidayat; Wardi; Andani Achmad; Yuyun (Bulletin of Information Technology (BIT), 2022)
Data mining is the process of finding information by looking for certain patterns or rules from large amounts of data. This study applies the Naïve Bayes algorithm to classify the yield of Vanamey shrimp into three classes, namely successful, less successful and failed from the harvest sample data owned. To facilitate the analysis, the data is divided into 2 categories, namely 90 training data and 10 for testing data. Nine parameters were used, namely the number of distributions, land area, type of disease, water color, soil conditions, season, feed, capital and yields. To validate the classification, we used a confusion matrix to test the accuracy of the algorithm. The test results show an accuracy of 54.4%, 100% precision, and 77.7% recall