Prediction Model of Production Completion Delay to Improve Service Quality Using Decision Tree Versus Multilayer Perceptron Method

Abstract

Delays in the completion of pvd production can be caused by several factors. Including the actual experience in the production of the difficulty of each process and color type, even the difficulty of the product type can also be affected. In this study, the prediction of the delay in the completion of pvd production was carried out using the C4.5 decision tree and Multilayer Perceptron data mining method approach using Production Results data at PT. Surya Toto Indonesia, whose results are expected to provide information and input for the company in making production plans in the future. The data testing method was carried out with 5 (five) testing times with different amounts of data to determine the level of consistency of accuracy obtained. C4.5 gives the results of a decision tree where the root is the color type and as the leaf is the product category, type type and order period. The average value of accuracy generated in the C4.5 decision tree method is 87.15%. While the Multilayer Perceptron obtained an average accuracy of 87.98%, which is greater than the decision tree method with a difference of 0.83%.