Implementasi Algoritma Convolutional Neural Network (CNN) Untuk Klasifikasi Kerapihan Ruangan
Authors
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Irfan Andito Mahameru
Universitas Islam Negeri Sunan Ampel Surabaya
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Ravly Dwi Septian
Universitas Islam Negeri Sunan Ampel Surabaya
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Dwi Rolliawati
Universitas Islam Negeri Sunan Ampel Surabaya
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Mujib Ridwan
Universitas Islam Negeri Sunan Ampel Surabaya
DOI:
https://doi.org/10.62712/juktisi.v4i2.606Keywords:
CNN, Kerapihan Ruangan, Sigmoid, Data Augmentation, VGG16Abstract
This study implements a Convolutional NeurallNetwork (CNN) to classify room images into two categories: messy and clean. The model utilizes VGG16 as a feature extractor, followed by fully connected layers and a sigmoid activation function in the output layer. This approach is simpler compared to the softmax scheme, which is commonly used for multi-class classification. The dataset was augmented to enhance the model's generalization. Evaluation results show a validation accuracy of 98,63%, indicating the effectiveness of the model in binary classification tasks
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Copyright (c) 2025 Irfan Andito Mahameru; Ravly Dwi Septian, Dwi Rolliawati, Mujib Ridwan
This work is licensed under a Creative Commons Attribution 4.0 International License.
