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All Journal Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Journal of Systems Engineering and Information Technology Jurnal Elektronika dan Teknik Informatika Terapan
Putrama Alkhairi
STIKOM Tunas Bangsa
Computer Science & IT Electrical & Electronics Engineering Engineering

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Classification Analysis of Back propagation-Optimized CNN Performance in Image Processing Putrama Alkhairi; Agus Perdana Windarto
Journal of Systems Engineering and Information Technology (JOSEIT) Vol 2 No 1 (2023)
Publisher : Ikatan Ahli Informatika Indonesia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/joseit.v2i1.5015

Abstract

This study aims to optimize the performance of the Convolutional Neural Network (CNN) in the image classification task by applying data augmentation and fine-tuning techniques to a case study of mammal classification. In this study, we took a fairly complex image classification dataset and used the CNN model as a basis for training and evaluating the performance of the model compared to Back propagation. From this study, the CNN VGG16 architecture optimized with ADAM optimization has been compared with the Back propagation optimization of SGD. We also conducted a literature review on several related studies and basic concepts in CNN, such as convolution, pooling, and fully connected layers. The research methodology involves creating datasets using data augmentation techniques, model training using fine-tuning techniques, and testing model performance using a number of evaluation metrics, including accuracy, precision, and recall. The results of this study indicate that the techniques used have succeeded in improving the performance of the CNN model in complex image classification tasks with accuracy in identifying and monitoring animal species more accurately, with an accuracy of 91.18% for the best model. Model accuracy increased by 2% after applying data augmentation and fine-tuning techniques to the CNN model. These results indicate that the techniques applied in this study can be a good alternative in improving the performance of the CNN model in the image classification task.
Reducing Overfitting in Neural Networks for Text Classification Using Kaggle's IMDB Movie Reviews Dataset Poningsih Poningsih; Agus Perdana Windarto; Putrama Alkhairi
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 10 No. 3 (2024): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v10i3.29509

Abstract

Overfitting presents a significant challenge in developing text classification models using neural networks, as it occurs when models learn too much from the training data, including noise and specific details, resulting in poor performance on new, unseen data. This study addresses this issue by exploring overfitting reduction techniques to enhance the generalization of neural networks in text classification tasks using the IMDB movie review dataset from Kaggle. The research aims to provide insights into effective methods to reduce overfitting, thereby improving the performance and reliability of text classification models in practical applications. The methodology involves developing two LSTM neural network models: a standard model without overfitting reduction techniques and an enhanced model incorporating dropout and early stopping. The IMDB dataset is preprocessed to convert reviews into sequences suitable for input into the LSTM models. Both models are trained, and their performances are compared using various metrics. The model without overfitting reduction techniques shows a test loss of 0.4724 and a test accuracy of 86.81%. Its precision, recall, and F1-score for classifying negative reviews are 0.91, 0.82, and 0.86, respectively, and for positive reviews are 0.84, 0.92, and 0.87. The enhanced model, incorporating dropout and early stopping, demonstrates improved performance with a lower test loss of 0.2807 and a higher test accuracy of 88.61%. For negative reviews, its precision, recall, and F1-score are 0.92, 0.84, and 0.88, and for positive reviews are 0.86, 0.93, and 0.89. Overall, the enhanced model achieves better metrics, with an accuracy of 89%, and macro and weighted averages for precision, recall, and F1-score all at 0.89. The applying overfitting reduction techniques significantly enhances the model's performance.
Analysis of the Impact of Backpropagation Hyperparameter Optimization on Heart Disease Prediction Models Nita Syahputri; Putrama Alkhairi; Enok Tuti Alawiah
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 4 (2025): August 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i4.6473

Abstract

Heart disease is a major global health issue, highlighting the need for early and accurate prediction to reduce complications and improve patient outcomes. The Backpropagation Neural Network (BPNN) is a widely used method for heart disease prediction, but its performance relies heavily on proper hyperparameter selection, including neuron count, activation function, optimizer, and batch size. This study analyzed the impact of hyperparameter optimization on BPNN performance. A standard BPNN model was compared with an optimized version, where key hyperparameters were fine-tuned to enhance predictive accuracy and stability. Both models were trained and tested on the same dataset, and their performance was evaluated using Accuracy, Precision, Recall, Mean Squared Error (MSE), and Mean Absolute Error (MAE). The results show that the optimized model achieves a slightly better accuracy (99.11% vs. 99.09%) and lower error rates (MSE and MAE of 0.0089 vs. 0.0091). It also demonstrates higher precision, reflecting an improved capability in correctly identifying heart disease cases. Although the performance gap was small, the optimized model showed a more balanced and consistent outcome. These findings highlight the importance of hyperparameter tuning for improving neural network models for medical prediction. This study contributes to the development of more accurate and reliable AI tools for the early diagnosis of heart disease. Future studies may apply advanced optimization techniques, such as Bayesian Optimization or Genetic Algorithms, and use larger and more diverse datasets to enhance model generalization.
Deteksi Penyakit Daun Padi Berbasis Deep Learning untuk Pertanian Presisi: Studi Komparatif ResNet-18 dan Arsitektur CNN Putrama Alkhairi; Harly Okprana; Andri Nofiar. Am
Jurnal Elektronika dan Teknik Informatika Terapan ( JENTIK ) Vol. 3 No. 4 (2025): Desember: Jurnal Elektronika dan Teknik Informatika Terapan (JENTIK)
Publisher : Politeknik Kampar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59061/jentik.v3i4.1532

Abstract

Rice plant diseases can reduce agricultural productivity and yield quality, highlighting the need for rapid and accurate identification methods to support precision agriculture. This study evaluates four deep learning architectures—ResNet-18, VGG-16, MobileNetV2, and Inception V3—for rice leaf disease classification using digital images. The dataset underwent preprocessing and augmentation before training, while transfer learning with ImageNet pre-trained weights was applied. Models were trained using CrossEntropyLoss and Adam optimizer with a learning rate of 0.0001 for 10 epochs. Performance was evaluated using accuracy, precision, recall, F1-score, AUC, and confusion matrix. Results demonstrate that the proposed ResNet-18 achieved the best overall performance, obtaining 96.94% accuracy, 100% precision, 95.45% recall, 96.18% F1-score, and 1.00 AUC. Inception V3 and MobileNetV2 showed competitive performance, whereas VGG-16 achieved relatively lower recall. These findings indicate that ResNet-18 provides effective feature representation and stable learning, demonstrating strong potential for rice leaf disease classification and future integration into mobile, field-camera, and IoT-based precision agriculture systems.
Co-Authors Agus Perdana Windarto Andri Nofiar. Am Enok Tuti Alawiah Nita Syahputri Okprana, Harly Poningsih Poningsih