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All Journal International Journal of Advances in Artificial Intelligence and Machine Learning
Fadly Fadly
Politeknik Kesehatan Kemenkes Palembang, Palembang
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.
AI and the Optimization of Product Placement: Enhancing Sales through Strategic Positioning Shahreen kasim; Mohd Zaki Zakaria; Lusiana Efrizoni; Fadly Fadly
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.v2i1.381

Abstract

This study aims to analyze the impact of strategic product placement and promotion strategies using the Customer's Purchase Behavior Dataset. The study utilized a controlled experimental design, wherein trial stores were matched with control stores based on pre-trial performance metrics, including total sales and customer demographics. A detailed exploratory data analysis (EDA) was conducted to segment customers based on life-stage and purchasing behaviour. Additionally, a t-Test was performed to determine whether price sensitivity and purchasing patterns differed significantly between mainstream, budget, and premium customer segments. The results indicate that trial stores implementing strategic initiatives experienced a measurable uplift in sales compared to their control counterparts. Young and mid-age singles and couples in the mainstream category were found to be more willing to pay a premium for chips, whereas families tended to purchase in bulk. The t-test confirmed statistically significant differences in purchasing behaviour across customer segments. The findings suggest that a data-driven, segment-specific marketing approach can optimise retail performance by aligning promotions and pricing with the behavioural tendencies of different consumer groups. This study demonstrates that well-targeted strategic retail initiatives can significantly improve sales performance. The insights derived from this research provide retailers with actionable strategies for tailoring product placement and promotions to maximise customer engagement. Future work should incorporate machine learning techniques to refine predictive models for real-time decision-making in retail marketing.
Co-Authors Dhidhi Pambudi Haryanto Haryanto Lusiana Efrizoni Mohd Zaki Zakaria Muhammad Hafiz Kurniawan Shahreen kasim