Abstract : Multiple methods for classifying rice seeds were developed because of the time and effort involved in manually observing rice seeds. In light with this, machine learning has been found to be an effective method for seed classification. The goal of this paper is to determine a better classifier using WEKA Machine Learning Algorithms for rice seed classification in terms of rate of accuracy and training time. To do this, data mining technique was done since agriculture has been a clear focus of big data efforts. The tool used for data mining is WEKA, a collection of machine learning algorithms for data mining tasks. Data preparation, classification, regression, clustering, mining of association rules, and visualization are all included in this package. Among the 23 classifiers used to classify rice seeds, the Random Tree Classifier achieved a 100% accuracy and training time of 0.02 seconds, hence the mentioned machine learning algorithm was proven effective in providing a perfect accuracy rate and shorter period of training time.