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All Journal Jurnas Nasional Teknologi dan Sistem Informasi Jurnal Pepadun
Akmal Junaidi
Department of Computer Science, Universitas Lampung
Computer Science & IT Decision Sciences, Operations Research & Management

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Application of MobileNetV1 and DenseNet-121 CNN Architectures for Eye-Based Gender Classification Sinta Nurhalifah; Akmal Junaidi; Didik Kurniawan; M. Iqbal Parabi
Jurnal Pepadun Vol. 7 No. 1 (2026): April
Publisher : Department of Computer Science, Faculty of Mathematics and Natural Sciences, University of Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/pepadun.v7i1.296

Abstract

Gender classification is an important field in biometric identification systems that plays a vital role in security, forensics, and human–computer interaction. Human eye images are a promising visual object for gender classification because they contain distinct anatomical features that differ between males and females. This study aims to implement and evaluate two Convolutional Neural Network (CNN) architectures, namely MobileNetV1 and DenseNet-121, for gender classification based on human eye images. The dataset used was obtained from the Kaggle platform, consisting of 11,525 eye images, with 6,323 male and 5,202 female samples. The research process involved several stages, including pre-processing, data splitting, augmentation using Affine transformations (rotation and translation), as well as model training and evaluation. The evaluation was conducted using accuracy, precision, recall, and F1-score metrics. The results showed that both architectures were capable of performing gender classification effectively, although differences in performance were observed. The best accuracy was achieved by MobileNetV1 with a rotation scenario of 92.49%, while DenseNet-121 obtained 86.84% with a combined rotation and translation scenario. This research is expected to contribute to the development of efficient and accurate eye image–based gender classification systems using deep learning approaches.
Detection of Hate Speech in TikTok Comment Sections Using the Naïve Bayes Algorithm with Smoothing Implementation Roy Rafles Matorang Pasaribu; Didik Kurniawan; Muhaqiqin Muhaqiqin; Akmal Junaidi
Jurnal Pepadun Vol. 6 No. 3 (2025): December
Publisher : Department of Computer Science, Faculty of Mathematics and Natural Sciences, University of Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/pepadun.v6i3.268

Abstract

Hate speech is a biased, antagonistic, and discriminatory expression that commonly appears on social media platforms, including TikTok. The high volume of comments and varied language styles make manual detection challenging. This research proposes a hate speech detection model using the Multinomial Naïve Bayes algorithm with smoothing to address zero-probability issues and enhance prediction performance. The dataset is split into 80% training and 20% testing portions. The model achieves an accuracy of 88.41%, with precision, recall, and F1-score showing balanced performance. A user evaluation involving 35 participants and 7,415 TikTok comments records a detection accuracy of 68.6%. The model is further implemented into a Google Chrome extension capable of real-time hate speech detection, displaying prediction probabilities and allowing user validation. This study aims to support healthier digital interactions by improving automated hate speech detection on social media.
Comparison of FA-SSD: Performance of Feature Extractors VGG-19, MobileNetV3, and ResNet-152 for Human Body Temperature Prediction Ridho Sholehurrohman; Akmal Junaidi; Hamzah Hanif; Favorisen Rosyking Lumbanraja; Muhammad Reza Habibi
Jurnal Nasional Teknologi dan Sistem Informasi Vol 12 No 2 (2026): Agustus 2026
Publisher : Departemen Sistem Informasi, Fakultas Teknologi Informasi, Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/TEKNOSI.v12i2.2026.300-311

Abstract

Body temperature is a critical health indicator, and non-contact thermal imaging-based prediction systems are essential for early detection of infectious diseases. This study compares the performance of three FA-SSD (Feature Fusion and Spatial Attention-Based Single Shot Detector) models with different feature extractors—VGG-19, MobileNetV3, and ResNet-152—for face detection and body temperature prediction using thermal images. A subset of 520 thermal facial images from the Comprehensive Facial Thermal Dataset was used, with 80% for training, 10% for validation, and 10% for testing. Model performance was evaluated using Generalized Intersection over Union (GIoU) for detection accuracy, and Mean Absolute Error (MAE) with Mean Absolute Percentage Error (MAPE) for temperature prediction accuracy. The results showed that no single model excelled in all metrics. ResNet-152 achieved the highest average GIoU, indicating superior object detection performance. VGG-19 delivered the lowest average MAE of 0.451°C and MAPE of 1.278%, making it the best for temperature prediction. MobileNetV3 achieved the lowest minimum MAE of 0.000050°C but showed higher average errors and validation fluctuations. In conclusion, VGG-19 is recommended for clinical temperature accuracy, ResNet-152 for robust detection, and MobileNetV3 for edge deployment.
Co-Authors Didik Kurniawan Hamzah Hanif Lumbanraja, Favorisen R M. Iqbal Parabi Muhammad Reza Habibi Muhaqiqin Muhaqiqin Ridho Sholehurrohman Roy Rafles Matorang Pasaribu Sinta Nurhalifah