Algorithm Decission Tree C4.5 and Backpropagation Neural Network for Smarthpone Price Classification - Muhammad Ridho Al Fathan; M Fadhil Arfa; Habibah Br. Lumbantobing; Rahmaddeni Rahmaddeni (Universitas Islam Negeri Sultan Syarif Kasim Riau, 2022)
Smartphones are a necessity in this technological age. In fact, everyone has at least one smartphone, this is because of its role that can help daily activities. There are data smartphone prices from major companies from Kaggle. The data is divided into 2000 training data and 1000 test data, the price range of smartphones based on the features provided. The analysis needed is the relationship between the features of smartphone and the selling price. To get this information, data mining techniques can be used. This study uses the Decission Tree C4.5 algorithms and the Backpropagaition Neural Network algorithm for classification problems. The technique used will be compared to a better algorithm in carrying out the classification process. The classification method consists of predictor variables and one target variable. The software used to process the data is Rapid Miner software. The results of the study get the accuracy of the Backpropagation Neural Network algorithm 96.65% and the same data is also applied to the C4.5 algorithms with an accuracy of 83.75%. From the research results, it can be concluded that the backpropagation neural network algorithm is the best algorithm for smartphone price classification with accuracy 96.65%.