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All Journal InComTech: Jurnal Telekomunikasi dan Komputer JOURNAL OF APPLIED INFORMATICS AND COMPUTING
Rizaqi, Hanif
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Computer Science & IT Electrical & Electronics Engineering Engineering

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Comparative Analysis of VGG16 and ResNet50 Model Performence in Cardiac ECG Image Classification Rizaqi, Hanif; Tahyudin, Imam
Journal of Applied Informatics and Computing Vol. 9 No. 3 (2025): June 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i3.9350

Abstract

This study systematically evaluates and compares the effectiveness of two deep learning architectures, VGG16 and ResNet50, in automating electrocardiogram (ECG) image classification for cardiac condition diagnosis. The dataset was obtained from a public source and consists of 2,898 color ECG images converted from raw signals, categorized into four classes: Abnormal Heartbeat, Myocardial Infarction, Normal Individual, and History of Heart Attack. The data underwent preprocessing steps including resizing to 224×224 pixels, pixel normalization to a 0–1 range, label encoding, one-hot encoding, and an 80:20 split for training and testing. Transfer learning was applied using feature representations from the VGG16 and ResNet50 models, employing the Adam optimizer and categorical cross-entropy loss function. To enhance training efficiency and prevent overfitting, early stopping was implemented based on validation loss performance. Model performance was evaluated using accuracy, precision, recall, and F1-score metrics. The results showed that VGG16 achieved 95% accuracy with a loss of 0.1522, precision of 95%, recall of 94%, and F1-score of 94%. In contrast, ResNet50 attained 81% accuracy with a loss of 0.5730, precision of 82%, recall of 79%, and F1-score of 80%. These findings indicate that, within the context of this study, VGG16 consistently outperformed ResNet50 across all evaluation metrics in the ECG image classification task. Therefore, the application of transfer learning using the VGG16 model demonstrates strong potential as an effective approach for AI-based ECG image classification systems.
Analisis Performa Model Deep Learning dalam Deteksi Penyakit Mata: Studi CNN, RNN, dan FNN Rizaqi, Hanif; Tahyudin, Imam
InComTech : Jurnal Telekomunikasi dan Komputer Vol 15, No 2 (2025)
Publisher : Department of Electrical Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/incomtech.v15i2.31261

Abstract

Penyakit mata menjadi salah satu penyebab utama gangguan penglihatan yang dapat mengarah pada kebutaan permanen jika tidak ditangani dengan baik. Beberapa penyakit mata yang sering terjadi antara lain katarak, glaukoma, dan retinopati diabetik. Penelitian ini bertujuan untuk mengembangkan model klasifikasi penyakit mata berbasis citra menggunakan tiga metode deep learning, yaitu Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), dan Feedforward Neural Network (FNN). Dataset yang digunakan dalam penelitian ini terdiri dari 4.217 citra, yang diproses melalui tahapan preprocessing yang mencakup pembagian data menjadi tiga kelompok: pelatihan (training), validasi, dan pengujian (testing). Selanjutnya, citra tersebut melalui tahap resizing dan normalisasi untuk meningkatkan kualitas data yang digunakan dalam pelatihan model. Model-model yang dikembangkan kemudian dilatih menggunakan teknik Early Stopping untuk mencegah overfitting dan mengoptimalkan performa. Berdasarkan hasil evaluasi, model CNN menunjukkan performa terbaik dengan akurasi uji sebesar 85% dan nilai loss 0, 3841, mengungguli RNN yang memiliki akurasi 83% dan FNN dengan akurasi 78%. Selain itu, analisis menggunakan Confusion Matrix menunjukkan bahwa CNN lebih unggul dalam menangkap fitur spasial pada citra dibandingkan model lainnya. Implementasi model dilakukan dalam aplikasi web berbasis Flask untuk memprediksi penyakit mata berdasarkan citra yang diunggah pengguna. Penelitian lanjutan dapat meningkatkan akurasi model dengan menerapkan transfer learning menggunakan model pretrained yang lebih kompleks.
Co-Authors Imam Tahyudin