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All Journal JOIV : International Journal on Informatics Visualization Journal of Applied Data Sciences International Journal of Advances in Artificial Intelligence and Machine Learning
Zakaria, Mohd Zaki
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management

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Real-Time Outlier Detection in Fast-Moving Data Streams Eka, Eka Puji Agustini; Zakaria, Mohd Zaki
International Journal of Advances in Artificial Intelligence and Machine Learning Vol. 1 No. 1 (2024): 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.v1i1.287

Abstract

Anomaly detection is a critical task in various fields such as finance, healthcare, network monitoring, and sensor data analysis, where identifying unusual patterns or outliers in data streams is essential for timely decision-making. Two commonly used techniques for anomaly detection are the Moving Average (MA) and Exponential Moving Average (EMA) methods. Despite their widespread use, selecting the appropriate method depends on the nature of the data and the requirements of the system. This paper presents a comparative analysis of MA and EMA for anomaly detection, focusing on critical factors such as speed of detection, stability, precision and recall, false positive rate, and computational efficiency. This research addresses the problem of determining which method, MA or EMA, is better suited for specific types of data, particularly in streaming environments with varying trends and anomalies. The results of our comparison indicate that EMA performs better in dynamic environments where rapid identification of anomalies is critical, such as financial markets or network traffic analysis. It quickly detects sudden deviations but may flag minor fluctuations as false positives due to its sensitivity. MA, on the other hand, is more stable and computationally efficient, with a lower false positive rate, making it more suitable for applications where long-term trend monitoring is required, and stability is prioritized over speed. This research highlights the strengths and weaknesses of both methods, demonstrating that the choice between MA and EMA should be based on the specific needs of the anomaly detection system. For real-time, high-speed environments, EMA offers a more responsive solution, while MA provides better stability and efficiency in long-term monitoring. A hybrid approach combining both methods could offer a more robust solution, adapting to different types of data and detection requirements.
AI and the Optimization of Product Placement: Enhancing Sales through Strategic Positioning kasim, Shahreen; Zakaria, Mohd Zaki; Efrizoni, Lusiana; 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.
3D Box Packing with Heuristics and Metric Analytics Kasem Alqudah, Mashal; Pambudi, Dhidhi; Zakaria, Mohd Zaki
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 - Kurniawan, - Alqudah, Mashal Kasem Alqudah, Musab Kasim Andri Andri Armoogum, Sheeba Azmi, Nurhafifi Binti Bujang, Nurul Shaira Binti Dewi, Deshinta Arrova Dhidhi Pambudi Efrizoni, Lusiana Eka, Eka Puji Agustini Fadly Fadly Fatoni, Fatoni Firosha, Ardian Hanan, Nur Syuhana binti Abd Henderi . Hisham, Putri Aisha Athira binti Irwansyah Irwansyah Ismail, Abdul Azim Bin Kasem Alqudah, Mashal Kurniawan, Tri Basuki M Said Hasibuan Misinem, Misinem Muhammad Nasir Muhayeddin, Abdul Muniif Mohd Nazmi, Che Mohd Alif Saringat, Zainuri Shahreen Kasim, Shahreen Udariansyah, Devi