Abstract
Food products are an essential source of human life, so they have a very important place, and it will be important to monitor and determine their quality in a short time. Our study deals with a very important and indispensable food product, which is the milk product, which is considered the main and important element in people’s lives, especially children, because it is the main source of their growth, building their bones, and strengthening. Because it is a highly perishable product, it must be monitored and its specifications must be monitored, because any gram of milk that is of low or poor quality may cause tons of milk to spoil, and also cause major financial losses. Therefore, a study was conducted to determine the quality of dairy product through machine learning algorithms (ML), which are support vector machine (SVM) algorithm, nearest neighbors (KNN) algorithm, decision tree (DT) algorithm and Bagging algorithm using milk dataset taken from data warehouse Kaggle. This data consists of 1059 samples and seven features. The proposed models were trained and tested with the aim of finding the best and most accurate model for detecting milk quality and were evaluated using the evaluation metrics: accuracy, precision, recall, f1_score and confusion matrix. According to the evaluation results three models: SVM, KNN, and DT outperformed Bagging algorithm, as they obtained the highest level for all metrics 100%. The SVM algorithm was the most efficient because its execution time was 0.146 seconds, which was less than the other models.
