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All Journal JOURNAL OF APPLIED INFORMATICS AND COMPUTING Advance Sustainable Science, Engineering and Technology (ASSET) Advance Sustainable Science, Engineering and Technology (ASSET)
Rabei Raad Ali
Northern Technical University
Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Industrial & Manufacturing Engineering Materials Science & Nanotechnology

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Milkfish Freshness Classification Using Convolutional Neural Networks Based on Resnet50 Architecture Maulana Malik Ibrahim Al-Ghiffary; Christy Atika Sari; Eko Hari Rachmawanto; Noorayisahbe Mohd Yacoob; Nur Ryan Dwi Cahyo; Rabei Raad Ali
Advance Sustainable Science Engineering and Technology Vol 5, No 3 (2023): August-October
Publisher : Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v5i3.17017

Abstract

Milkfish (Chanos chanos) had become the main commodity in three major cities in Indonesia, contributed at least 77 thousand tons of aquaculture production in 2021. The quality of fish is determined based on the level of freshness carried out in the sorting process, the sorting process is generally done by evaluating physical characteristics of the fish. However, this method is still considered less efficient and economical because the ability to classify the freshness level of fish can vary for each individual. In this study, by utilizing deep learning, a classification method for milkfish freshness level classification with ResNet50 architecture is proposed, the proposed method is purposed to overcome the previously stated problems, thus creating an efficient and economical system. By creating an efficient system, milkfish sorting process can be carried out quicker and more accurately. Using personal dataset divided into four different classes, the proposed method produces excellent result
Image Classification using DenseNet-121 Based on MediaPipe Face Mesh for Real-Time Drowsiness Detection Raihan Ramadhan Hamzah; Christy Atika Sari; Eko Hari Rachmawanto; Rabei Raad Ali
Journal of Applied Informatics and Computing Vol. 10 No. 4 (2026): August 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i4.13586

Abstract

The high rate of traffic accidents caused by driver drowsiness and microsleep highlights the urgent need for reliable driver monitoring systems. However, conventional Eye Aspect Ratio (EAR) methods often fail due to their high sensitivity to changes in head poses and ambient lighting conditions, while standard Convolutional Neural Network (CNN) models impose heavy computational loads on hardware. This study aims to implement and evaluate a real-time drowsiness detection system by integrating the DenseNet-121 architecture with MediaPipe Face Mesh. The proposed method utilizes MediaPipe Face Mesh to isolate the left and right eye Regions of Interest (ROI) independently, using a proportional padding of 35%, which are then classified using a DenseNet-121 transfer learning model fine-tuned in two stages across its last 30 layers. Evaluation was conducted using a custom dataset of 2,000 source images from five subjects, yielding 3,926 eye-region samples after extraction and quality filtering, assessed using a Subject-Independent Leave-One-Subject-Out (LOSO) cross-validation protocol. Across five folds, the model achieved a mean accuracy of 83.22% (standard deviation 13.22 percentage points) and a mean AUC of 0.879 (standard deviation 0.131), with performance variation across subjects found to correlate with inter-subject differences in eye-closure expressiveness, where the two lowest performing subjects also exhibited the lowest AUC values (0.707 and 0.769). The system achieved an average total latency of 198.00 ms per frame, equivalent to 5.1 FPS. These findings indicate that the integration of MediaPipe Face Mesh and DenseNet-121 shows meaningful potential for real-time drowsiness monitoring, while also highlighting the importance of subject-independent evaluation and cross-domain generalization for reliable real-world deployment.
Dynamic Chaotic–Adversarial Framework for High-Capacity and Imperceptible Image Steganography Wellia Shinta Sari; Christy Atika Sari; Safira Hasna Setiyani; Agus Triyono; Rabei Raad Ali
Advance Sustainable Science Engineering and Technology Vol. 8 No. 4 (2026): August-October
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v8i4.3060

Abstract

The rapid growth of digital communication has intensified concerns regarding data confidentiality as sensitive information transmitted through multimedia images is increasingly vulnerable to interception and unauthorized analysis. Conventional image steganography methods often struggle to simultaneously achieve high embedding capacity, strong imperceptibility, and resistance to modern steganalysis. To address this challenge, this study proposes a steganographic framework that integrates dynamic logistic chaotic encryption with an adversarial feature-level embedding network. The chaotic sequence is generated using a time-varying logistic map within a highly unstable region, where the control parameter is adaptively derived from a hash-modulated process to produce unpredictable keystreams and strengthen payload security. The encrypted secret image is then embedded through a GAN-based generator guided by a discriminator to preserve natural image characteristics, while a dedicated extractor ensures accurate recovery. Experimental results on multiple standard test images with resolutions of 256 × 256 and 512 × 512 demonstrate high visual fidelity, achieving PSNR values above 58 dB and SSIM values above 0.995, supported by nearly identical histogram distributions between cover and stego images. These findings indicate that the proposed framework provides a promising solution for secure multimedia communication by enabling visually imperceptible and reliably recoverable hidden transmission in digital images.
Co-Authors Agus Triyono Christy Atika Sari Eko Hari Rachmawanto Maulana Malik Ibrahim Al-Ghiffary Noorayisahbe Mohd Yacoob Nur Ryan Dwi Cahyo Raihan Ramadhan Hamzah Safira Hasna Setiyani Wellia Shinta Sari