A Classification Approach to Predicting Beef Knuckle Quality using the Decision Tree and Naïves Bayes Method: Case Study: Tiga Bersaudara Factory

摘要

Tiga Bersaudara factory is a small-scale traditional factory which is engaged in the food industry that produces beef knuckle. Business competition from year to year is getting tougher, every company is required to be able to participate in competition. According to data from the Central Agency on Statistics (CAS), business competition in beef commodities in Cianjur District is on an upward trend. With this competition forcing companies to look for various alternatives to excel in competition, one of the things that companies can do to compete is to improve the quality of their products. This research was conducted to predict the quality of the beef knuckle production at the Tiga Bersaudara factory. Data obtained manually by observation and interview methods; data obtained as many as 9 variables with a total of 110 data. The data obtained will be formed a prediction model using the Decision Tree and Naïve Bayes with the help of RapidMiner software. The results of data processing showed that the accuracy of the prediction model for Decision Tree reached 70% and Naïve Bayes reached 82%. Predictor variables that influence in determining the quality of beef knuckle are cooking water temperature, order time, second immersion water volume, and the third immersion water volume. This analysis will help beef knuckle producers to know important factors that need to be considered when producing good quality beef knuckle