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All Journal Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi)
Hajijin Amri
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Computer Science & IT

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Augmentasi dan Fine-Tuning pada Deteksi Wajah Deepfake Cintia Putri Prasetia; Hajijin Amri; Yudhistira Arie Wijaya
Prosiding SISFOTEK Vol 9 No 1 (2025): SISFOTEK IX 2025
Publisher : Ikatan Ahli Informatika Indonesia

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Abstract

The rapid advancement of artificial intelligence, particularly in computer vision, has led to the proliferation of deepfake technology, which enables the creation of highly realistic synthetic facial images. This study proposes a deep learning-based approach for detecting real and fake faces using convolutional neural networks (CNN), specifically ResNet18, ResNet34, and ResNet50 architectures. The dataset used includes a public dataset from Kaggle (140K Real and Fake Faces) and a locally collected dataset to evaluate model generalization. Data preprocessing such as resizing, normalization, and augmentation were applied to improve robustness. Training employed transfer learning with fine-tuning over multiple epochs. Evaluation metrics included accuracy, precision, recall, F1-score, confusion matrix, and inference time. The results showed that ResNet50 achieved the highest validation accuracy of 94.1%, outperforming the other architectures. The integration of local datasets and data augmentation significantly improved classification performance. This model demonstrates strong potential for real-world deployment in digital security systems requiring deepfake detection.
Klasifikasi Wajah Mahasiswa Menggunakan Vertex AI AutoML untuk Sistem Absensi Berbasis TFLite Hajijin Amri; Cintia Putri Prasetia; Yudhistira Arie Wijaya
Prosiding SISFOTEK Vol 9 No 1 (2025): SISFOTEK IX 2025
Publisher : Ikatan Ahli Informatika Indonesia

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Abstract

This research focuses on the development of a student facial classification model for attendance verification using Google Vertex AI AutoML. A total of 401 facial images representing 20 student classes were utilized, undergoing preprocessing steps including resizing to 224×224 RGB resolution and conversion to 8-bit format. Data augmentation techniques such as horizontal flipping, ±15° rotation, and brightness modulation were applied to enhance dataset variability. After refinement, 367 images were retained and divided into training (80%), validation (10%), and testing (10%) sets. The model was trained using the Edge TPU – Best Prediction mode in Vertex AI AutoML, resulting in an excellent performance with an average precision of 0.999, precision of 100%, and recall of 89.2%. The confusion matrix indicated that most classes were accurately identified with minimal recall errors. The finalized model was converted to TensorFlow Lite (TFLite) format and tested on edge devices, demonstrating efficient inference and accurate recognition. The findings affirm the effectiveness of integrating AutoML and TFLite to implement lightweight, resource-efficient face recognition systems suitable for student attendance applications on constrained hardware platforms.
Co-Authors Cintia Putri Prasetia Yudhistira Arie Wijaya