IMPLEMENTATION OF ARTIFICIAL NEURAL NETWORK WITH BACKPROPAGATION ALGORITHM FOR RATING CLASSIFICATION ON SALES OF BLACKMORES IN TOKOPEDIA

Dalfa Habibah Nurul  Aini; Dian Kurniasari; Aang  Nuryaman; Mustofa  Usman

doi:10.52436/1.jutif.2023.4.2.539

Dalfa Habibah Nurul Aini Matematika, Fakultas Matematika dan Ilmu Pengetahuan Alam, Universitas Lampung, Indonesia
Dian Kurniasari Matematika, Fakultas Matematika dan Ilmu Pengetahuan Alam, Universitas Lampung, Indonesia
Aang Nuryaman Matematika, Fakultas Matematika dan Ilmu Pengetahuan Alam, Universitas Lampung, Indonesia
Mustofa Usman Matematika, Fakultas Matematika dan Ilmu Pengetahuan Alam, Universitas Lampung, Indonesia

DOI: https://doi.org/10.52436/1.jutif.2023.4.2.539

Keywords: Artificial Neural Network, Backpropagation, Classification

Abstract

The rating assessment classification contains feedback from consumers, which is given in the form of stars which aims to assess a product. However, the amount of data in the classification process often have differences in each class or is called an imbalanced dataset. These problems can affect the classification results. An imbalanced dataset can be overcome by applying random oversampling. To classify the rating assessment, this study proposes the Neural network method, which has a good accuracy level with the backpropagation algorithm and applies random oversampling to overcome the unbalanced amount of data. The results indicate that the neural network method with the backpropagation algorithm can classify the available data with an accuracy level of 85%. The application of resampling data using random oversampling and determining the amount of distribution of training data, testing data, number of epochs and the correct number of batch sizes affect the results obtained.

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