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All Journal Jurnal Pepadun
M. Iqbal Parabi
Department of Computer Science, Universitas Lampung
Computer Science & IT

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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.
Implementation of ResNet-50 for Solid Waste Image Classification Alfa Rizki Fadlillah; Favorisen Rosyking Lumbanraja; Rico Andrian; M. Iqbal Parabi
Jurnal Pepadun Vol. 7 No. 2 (2026): August
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.v7i2.286

Abstract

Operational efficiency of municipal solid waste management is critically hampered by manual sorting, a process that is slow, labor-intensive, and poses occupational health risks. This study investigates the application of deep learning to automate this task, specifically by developing and accessing a classification model using Residual Network (ResNet-50) architecture. Utilizing a public dataset of 24,705 images of organic and recyclable waste, we implemented a transfer learning protocol. The pre-trained ResNet-50 model, with weights initialized from ImageNet, was fine-tuned for this binary classification problem. To ensure model robustness and prevent overfitting on the specialized dataset, a comprehensive data augmentation strategy was applied, expanding the training set to 207,522 images. Upon evaluation with a dedicated test set of 6,177 that was previously unseen, the model demonstrated high evaluation metrics, achieving an all-around classification accuracy of 95%. Another key evaluation metrics, like precision, recall (sensitivity), specificity, and F1-score, corroborated the result with a weighted average of 0.95. These findings establish that a transfer-learned ResNet-50 model provides a highly effective and reliable solution for automated solid waste classification, offering a technologically robust foundation for next-generation recycling and waste management systems.
Co-Authors Akmal Junaidi Alfa Rizki Fadlillah Didik Kurniawan Lumbanraja, Favorisen R Rico Andrian Sinta Nurhalifah