IMPLEMENTATION OF CONVOLUTIONAL NEURAL NETWORK USING MOBILENETV2 TO DISTINGUISH HUMAN AND ARTIFICIAL INTELLIGENCE PAINTING

Dwi Bagia  Santosa; Agung  Wahana; Wisnu Uriawan

doi:10.52436/1.jutif.2025.6.1.3827

Dwi Bagia Santosa Faculty of Science and Technology, Informatics Engineering, UIN Sunan Gunung Djati Bandung, Indonesia
Agung Wahana Faculty of Science and Technology, Informatics Engineering, UIN Sunan Gunung Djati Bandung, Indonesia
Wisnu Uriawan Faculty of Science and Technology, Informatics Engineering, UIN Sunan Gunung Djati Bandung, Indonesia

DOI: https://doi.org/10.52436/1.jutif.2025.6.1.3827

Keywords: Art Painting, Convolutional Neural Network, CRISP-DM, MobileNetV2, TensorFlow

Abstract

The advancement of artificial intelligence technology has had a significant impact on various fields, including painting. Artificial intelligence is now able to create works of art that resemble paintings produced by humans with a high level of detail and complexity. However, this progress has also created new problems in the world of painting, namely the difficulty in distinguishing between works produced by humans and those created by artificial intelligence. This problem has an impact on the originality of the artwork and has implications for aspects of ethics and creativity. This study aims to develop a deep learning model that can classify human and artificial intelligence paintings, and overcome the challenges in distinguishing between the two. The methodology used is the Cross Industry Standard Process for Data Mining (CRISP-DM), with a dataset consisting of 1,000 painting images. The architecture used is MobileNetV2, implemented using TensorFlow to build a Convolutional Neural Network (CNN). Techniques such as data preparation, data labeling, data splitting, resizing, and data augmentation are applied to improve model performance. Six test scenarios were carried out with variations in the learning rate, number of epochs, and freeze or unfreeze configurations on the base model. The results showed that the best model with a learning rate of 0.0001, base model unfreeze, and 5 epochs managed to achieve an accuracy of 97%, without any indication of overfitting or underfitting. This model was then implemented on an Android application in TFLite format, which can predict image classes with a confidence level of 89.98%.

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