Open AccessSpare part supplier selection model using decision tree classification techniques: J48 Algorithm
Author(s) -
Aulia Ishak,
Tommy Wijaya
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/801/1/012118
Subject(s) - c4.5 algorithm , spare part , selection (genetic algorithm) , computer science , operations research , decision tree , quality (philosophy) , business , operations management , data mining , economics , engineering , machine learning , philosophy , epistemology , support vector machine , naive bayes classifier
Spare parts are goods that consist of several components that form a single unit and have certain functions. To facilitate companies in selecting suppliers, a supplier selection model is needed to make it easier for companies to select suppliers and make it easier to see strategic directions to take several criteria from suppliers to achieve priorities. This research was conducted at a manufacturing company engaged in car spare parts with rubber raw materials. In this study the problems that occur are the difficulty of the company in selecting suppliers based on criteria that are in accordance with the company and the delay in the receipt of raw materials, and the lack of raw materials supplied from each supplier. This study aims to classify suppliers based on desired criteria by the company and to design a supplier selection model in the long term. The J48 algorithm is produced by a supplier selection model with 2 rule selection models to classify efficient suppliers and inefficient suppliers. The accuracy of the Decision Tree model is 90.8547%, the MAE error value is 0.1256. From the J48 algorithm the biggest gain is the criteria for quality, price, delivery, and warranty and complaint services.