Penerapan Data Mining Untuk Klasifikasi Kualitas Pipa Pvc Menggunakan Metode Algoritma C4.5 Studi Kasus Pt Cipta Aneka Agung - Asep Muhidin; Ading Bagus Saputra (Teknik Informatika, Universitas Pelita Bangsa, 2019)
The research was aimed to determine the results of the prediction of PVC pipe quality test by looking at the accuracy of the C4.5 Algorithm so as to facilitate QC in determining the quality of PVC pipes. The research was carried out on the PVC pipe quality test data, from the data was carried out the distribution of training data and testing data. Data mining extracts data to find information and patterns in determining the quality of PVC pipes. Classification method is carried out on training data to find a rule that can be applied to classify PVC pipe quality test categories in new data. The learning process using the C.45 decision tree technique transforms facts into decision trees that represent rules that are easier to understand. Variables used in this method are: Flattening Test, Tensile Strength Test, Elongation Test, Hydrostatic Pressure Test, and Impact Test. Based on the classification results using C4.5 algorithm shows that the accuracy reached 92.92%, which shows that the C4.5 algorithm is suitable for measuring the quality of PVC pipe tests. Keyworad: Quality, C4.5 Algorithm, Data mining, Decision Tree