Classification of large ruminant carriers as an effort in selecting the feasibility of livestock drivers with the Naive Bayes Algorithm. Issue 1 (1st April 2022) - Jaman, JH; Buono, A; Astuti, D A; S H, Wijaya; Burhanuddin,; Rahmi, H
Abstract: Livestock is one of the leading commodities in Indonesia. Transportation costs and the quality of the produced are the problems that lead to the lack of competitiveness of these commodities. The main focus of this paper, is the transportation factor with the driver component as the object, the Knowledge Data Discovery we use as a methodology, and Naïve Bayes as one of the algorithms that can classify the driver and provide knowledge to the transportation managers, K-Means, and PCA we use as a supporting model. The results showed that the use of Naïve Bayes with a small number of datasets resulted in accuracy values of 0.80, the precision of 83%, recall 80%, and 81% of F1score, this value indicates that the resulting classification is in a good category, and this can be the basis of the manager's decision in the selection of drivers.