KLASIFIKASI TINGKAT KEBERHASILAN SURVIVAL RATE (SR) PADA PRODUKSI UDANG VANAME MENGGUNAKAN ALGORITMA NAÏVE BAYES

Abstract

Data mining is the process of collecting and processing data with the aim of extracting important information from the data. This process can be done using software that uses mathematical calculations, statistics, or AI. Naive Bayes is the most common classification technique and has a high level of accuracy. Many studies on classification have used the Naive Bayes algorithm. Naive Bayes is a simple probability classification technique used to assume that the explanatory variables are independent. The focus of learning this algorithm is probability estimation. One of the advantages of the naive Bayes algorithm is that the resulting error rate is lower. In addition, this algorithm has a higher level of accuracy and speed when used on larger datasets. This research uses the Naïve Bayes algorithm to classify the Survival Rate (SR) of Vaname shrimp into three classes, namely high, medium and low. The number of sample data used was 200 data which was divided into 2 categories, namely 170 training data and 30 testing data. The variables used in this research are temperature, PH, DO (dissolved oxygen) and salinity. The classification was validated using a confusion matrix and produced an accuracy of 70.4%, precision of 98%, and recall of 79.7%.