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Review on measurement techniques and technologies to propose new rating scale measurement for shariah compliant hotel industry in Malaysia Hazlin Fardila Abdul Hasim; NorHapiza Mohd Ariffin; Mohd Zaki Zakaria
Indonesian Journal of Electrical Engineering and Computer Science Vol 16, No 2: November 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v16.i2.pp685-691

The tourism industry based on the Islamic approach is increasingly accepted by the tourists. This paper presents a review of measurement techniques and technologies’ scale measurement for Shariah Compliant Hotel Industry in Malaysia. Since the use of the rating system in Syariah Hotel Industry in Malaysia has yet to be implemented, it is difficult for the industry to determine the status level of a hotel. Furthermore, the consumers are unable to make the right choice based on their own needs. This study aims to propose new Rating Assessment for Scale Measurement system focusing on hotel industry based on Shariah Compliant standard known as Shariah Compliant Hotel Rating Measurement Scale System (SCHRMS). Scale measurement will be used to ensure all the Shariah Compliant Certificates are certified with certain level and follow the same standard. Multiple characteristics and categories must be clarified or considered when processing rating assessment for Shariah Compliance (SC) hotel. To solve the multiple characteristics’ issues, Fuzzy Analytic Hierarchy Process will be used. The process is accurate and suitable for selecting various types of information and can be used as a decision-making tool because it controls uncertain and inaccurate data. The raw data obtained from questionnaires as well as structured and unstructured interview will be analyzed using Rasch Analysis Method. Only this method is will be able to comply to the five main principles of analysis for a more meaningful and accurate conclusion of the research data obtained. Through this system that will developed, the problem related to rating measurement for SC hotel will be solved. The SC hotel rating system will be parallel to the conventional based hotel. The system is accepted for the convenience of Muslims as well as non-Muslims globally.

Real-Time Outlier Detection in Fast-Moving Data Streams Eka Puji Agustini; Mohd Zaki Zakaria
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

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.

Deep Learning Innovations in Fingerprint Recognition: A Comparative Study of Model Efficiencies Lusiana Efrizoni; Sheeba Armoogum; Mohd Zaki Zakaria
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.v1i1.294

Fingerprint recognition technology is integral to biometric security systems, providing secure and reliable identification through unique human fingerprint patterns. However, challenges such as low contrast, high intra-class variability, and partial fingerprints often compromise the efficiency and accuracy of traditional recognition systems. This research addresses these challenges by employing advanced deep learning techniques, specifically Convolutional Neural Networks (CNNs), to enhance fingerprint recognition performance. We propose a methodological approach that leverages state-of-the-art CNN architectures tailored to capture intricate fingerprint details. The study utilizes the Sokoto Coventry Fingerprint Dataset (SOCOFing), which includes diverse fingerprint types and synthetic alterations to evaluate model performance under realistic conditions. Through a comparative analysis of various CNN configurations, we assessed the models based on efficiency and accuracy, using metrics such as accuracy, precision, recall, and F1-score. Our experimental results demonstrate significant improvements in fingerprint recognition capabilities. The optimized CNN model achieved an accuracy of 98.61%, a precision of 97.12%, a recall of 97.46%, and an F1-score of 97.29%. These results validate the effectiveness of CNNs in handling complex biometric data and underscore their potential to enhance the reliability and security of fingerprint recognition systems. The study concludes that deep learning, through the use of CNNs, offers a powerful solution to the limitations of traditional fingerprint recognition techniques. This will pave the way for more sophisticated and accurate biometric security systems in practical applications. The research findings contribute to ongoing advancements in neural network architectures, enhancing their applicability in increasingly automated and data-driven security environments.

Scalability and Efficiency: A Comparative Study of Face Recognition Technologies Mohd Zaki Zakaria; Misinem Misinem; Nyimas Sopiah; Lusiana Efrizoni
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.v1i1.296

This article addresses the challenge of selecting the most effective machine learning algorithm for face recognition tasks, a common problem in academic research and practical applications. To tackle this issue, we conducted a comparative analysis of five widely used algorithms: Linear Discriminant Analysis (LDA), Logistic Regression, Naive Bayes, K-Nearest Neighbors (KNN), and Support Vector Machine (SVM). The study involved implementing each algorithm on a standardized dataset, followed by a rigorous evaluation of their performance based on accuracy metrics. The results revealed that LDA, Logistic Regression, and SVM significantly outperformed the other models, each achieving an impressive accuracy of 97%. This high accuracy indicates that these algorithms are well-suited for handling datasets with linearly separable classes. Naive Bayes also showed a strong performance with 90% accuracy, proving effective under the feature independence assumption. However, KNN lagged, with an accuracy of 70%, highlighting its sensitivity to data scale and local structure, which affects its applicability in larger datasets or real-time scenarios. The findings suggest that while LDA, Logistic Regression, and SVM are optimal for datasets with clear class distinctions, the choice of an algorithm should still be guided by specific data characteristics and computational constraints. This study underscores the necessity for carefully considering each algorithm’s strengths and limitations, ensuring that the selected model aligns with the unique demands of the application. Future work could explore ensemble methods and advanced parameter tuning further to enhance the performance and robustness of these models.

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

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.

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