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All Journal International Journal of Advances in Artificial Intelligence and Machine Learning
Dhidhi Pambudi
Universitas Sebelas Maret, Surakarta
Computer Science & IT

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The Eye's Signature: Innovative Approaches to Iris Detection Dhidhi Pambudi; Fadly Fadly; Muhammad Hafiz Kurniawan; Haryanto Haryanto
International Journal of Advances in Artificial Intelligence and Machine Learning Vol. 2 No. 1 (2025): International Journal of Advances in Artificial Intelligence and Machine Learni
Publisher : CV Media Inti Teknologi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58723/ijaaiml.v2i1.379

Abstract

This research aims to develop and evaluate a deep learning-based iris detection system using a specialized Convolutional Neural Network (CNN) architecture. The research methodology includes data set preprocessing, CNN model design, training using Adam optimization, as well as evaluation using accuracy, precision, recall, and F1 score metrics. The dataset used was obtained from Kaggle and preprocessed before being divided into training, validation, and testing sets. The CNN model consists of three convolutional layers with increasing filter sizes (32, 64, and 128), ReLU activation, batch normalization, and MaxPooling layers for efficient feature extraction, as well as dropout regularization to reduce overfitting. Experimental results show that the proposed model achieves a high classification accuracy of 97.33%, with robust performance against variations and noise in iris images. Comparative analysis with traditional iris recognition methods confirms the superiority of deep learning in handling challenges such as lighting changes and occlusions. Although the results are promising, challenges such as data bias and computational demands are still a concern. Future research will explore more advanced architectures as well as additional pre-processing techniques to improve the generalizability and effectiveness of the system in real-world applications.
3D Box Packing with Heuristics and Metric Analytics Mashal Kasem Alqudah; Dhidhi Pambudi; Mohd Zaki Zakaria
International Journal of Advances in Artificial Intelligence and Machine Learning Vol. 2 No. 2 (2025): International Journal of Advances in Artificial Intelligence and Machine Learni
Publisher : CV Media Inti Teknologi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58723/ijaaiml.v2i2.409

Abstract

Background of Study: The 3D Bin Packing Problem (3D-BPP) is an NP-hard problem crucial for logistics and supply chain optimization, aiming to efficiently pack boxes into containers while maximizing space and maintaining stability. Traditional heuristics like First Fit and Best Fit are fast but lack optimality and adaptability in dynamic environments. Metaheuristic approaches, such as Genetic Algorithms (GA), offer better solutions but with higher computational costs.Aims and Scope of Paper: This study presents a comparative analysis of First Fit, Best Fit, and a custom Genetic Algorithm as packing strategies for 3D-BPP. It evaluates these methods against multiple performance metrics to understand their trade-offs and proposes future research directions.Methods: The study uses a dataset of 5,000 cargo records from an Indonesian logistics company, including item dimensions and weights, preprocessed for normalization and filtering. A 3D simulation environment built with PyBullet visualizes the packing process. Performance metrics include space utilization, total packed weight, packing time, access efficiency, stability score, and placement success rate. A Wall-Building heuristic acts as a fallback for unplaced items.Result: First Fit provides fast, lightweight solutions suitable for real-time applications. Best Fit shows marginally better space utilization but lacks robustness. The Genetic Algorithm outperforms both heuristics in packing quality, accessibility, and load stability, though with significantly higher computation time. No single algorithm dominates across all metrics.Conclusion: The choice of packing method should align with specific operational constraints: speed, compactness, or quality. A hybrid model combining heuristic initialization with GA refinement is a promising direction for future research to develop more intelligent, context-aware packing systems.
Co-Authors Fadly Fadly Haryanto Haryanto Mashal Kasem Alqudah Mohd Zaki Zakaria Muhammad Hafiz Kurniawan