Implementasi Algoritma Convolutional Neural Networks Untuk Klasifikasi Jenis Cat Tembok Menggunakan Arsitektur MobileNet - Carlos, Daniel; Herwindiati, Dyah Erny; Lubis, Chairisni (Forum Kerjasama Pendidikan Tinggi, 2024)
The development of image recognition technology has made significant advancements, particularly with the emergence of Convolutional Neural Networks (CNN) algorithms. One of the CNN architectures that is efficient and effective for mobile devices is MobileNet. This study aims to implement the CNN algorithm using the MobileNet architecture for classifying types of wall paint. The main problem addressed is the accurate identification of wall paint types based on images, requiring a model that performs well even on devices with limited resources. MobileNet was chosen as the solution due to its ability to reduce computational complexity without sacrificing performance. The methodology used in this research involves two approaches: classification with feature extraction using GLCM and histogram, and classification without feature extraction directly using MobileNet. The training and testing process was conducted using the early stopping technique to prevent overfitting, with the model trained for 50 epochs. The final results show that classification without feature extraction using MobileNet yields excellent results. The model achieved a training accuracy of 89.68% and a testing accuracy of 88.86%, with low loss values (0.0111 for training and 0.0117 for testing). These results indicate that MobileNet is effective in recognizing and classifying types of wall paint and can operate efficiently on devices with limited resources. Therefore, this research demonstrates that using the MobileNet architecture for classifying wall paint types is an effective and efficient solution, opening opportunities for similar applications on various mobile devices in the future.