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All Journal JOIV : International Journal on Informatics Visualization TIERS Information Technology Journal International Journal of Industrial Engineering, Technology & Operations Management International Journal Research on Metaverse
Ahmad, Asmala
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Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Industrial & Manufacturing Engineering

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A Slab Multi-Fold Classification Technique on A Mixed Pixel Hyperspectral Image Purwadi, -; Abu, Nor Azman; Mohd, Othman; Kusuma, Bagus Adhi; Ahmad, Asmala
JOIV : International Journal on Informatics Visualization Vol 9, No 5 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.5.3432

Abstract

Hyperspectral imaging offers a significant edge over standard RGB and multispectral images for land classification. It captures a wider range of electromagnetic waves, producing more detailed images than previous methods. This allows objects to be identified and distinguished with high certainty due to hyperspectral capabilities. However, the large data volume makes reducing the computational workload challenging. Imbalanced data and suboptimal hyperparameter settings can reduce classification accuracy. Hyperspectral image classification is computationally demanding, especially with mixed-pixel issues in high-resolution images. This study uses EO-1 satellite imagery with a 30-meter resolution affected by mixed pixels. It introduces a new classification approach to effectively use hyperspectral remote sensing at this resolution. The process includes satellite image preprocessing—geometric correction, image enhancement with FLAASH, and geometric and atmospheric corrections. To lessen the computational burden, a slab approach partitions the 242 spectral bands into segments, extracting features from each, resulting in fewer total features. These features are then input into a support vector machine (SVM) for five-class classification. Parameters like polynomial order, kernel scale, and kernel type are tuned for optimal accuracy. A novel SLAB Multi-Fold technique is proposed. Results indicate that the slab method combined with SVM achieves a maximum accuracy of 51.39%. The best results came from slab 2, with a polynomial order of 8 and k=4, using both linear and Gaussian kernels. These findings offer valuable insights for future research on satellite image classification, especially when tuning multiple hyperparameters within this SLAB approach. Future work could compare these results with higher-resolution images and different datasets to better evaluate the technique's accuracy.
Conceptualizing the ICEBERG problem-solving tool (IPST): A case study of an authorized automobile dealer in Riyadh, Saudi Arabia Al-Homery, Hussein A.; Ashari, Hasbullah; Ahmad, Asmala
International Journal of Industrial Engineering, Technology & Operations Management Vol. 1 No. 1 (2023): June 2023
Publisher : Indonesia Academia Research Society

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62157/ijietom.v1i1.13

Abstract

This study offers the practical part of our academic, conceptual paper on the ICEBERG Problem Solving Tool (IPST) within the application of a case study for an authorized automobile dealer in Riyadh, Saudi Arabia. The research is designed to represent how to operationalize the ICEBERG model practically in business problem-solving by using the concept of the IPST. The research is a qualitative case study within action research. It is a three cycles of action research. The tool application identified five levels of analysis besides the cross-functional analysis, the root cause of the persistent operational events and clearly showed the leverage points in the whole sales retail system. The “IPST” five levels of analysis enable us to see the complete picture of the cause of the events, touching the leverage points for decision-makers or the organizers. It is a quick fix for pining issues of repeated business events for the dealer's high performance. With theoretical roots in system thinking, this paper contributes to applying the ICEBERG model through “IPST” as a practical problem-solving tool for the business complex environment, has puzzles (operational killing events) around the whole business process and needs to be solved for a better performance.
Scam Detection in Metaverse Platforms Through Advanced Machine Learning Techniques Prasetio, Agung Budi; Aboobaider, Burhanuddin bin Mohd; Ahmad, Asmala
International Journal Research on Metaverse Vol. 2 No. 1 (2025): Regular Issue March
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijrm.v2i1.19

Abstract

The rapid expansion of metaverse environments has introduced novel opportunities and challenges, particularly concerning user security and trust. This study investigates the application of machine learning techniques to detect scam activities within the metaverse by analyzing user behaviors and interaction patterns. Using a comprehensive dataset, we evaluated three machine learning models—Random Forest, Support Vector Machine (SVM), and Neural Network—for their effectiveness in identifying scams. The Neural Network model achieved the highest performance, with an accuracy of 91%, a recall of 92%, and an AUC of 95%, making it the most reliable solution for this task. Feature importance analysis revealed that attributes such as the number of transactions and average transaction value significantly contribute to scam detection. Hyperparameter optimization further improved model performance, demonstrating the potential of fine-tuned architectures in handling high-dimensional datasets. Despite the Neural Network’s superior performance, its computational complexity highlights the need for lightweight implementations for real-time applications. This research contributes to the growing field of metaverse security by providing a robust framework for scam detection using machine learning. Future work should focus on expanding datasets to capture multi-platform behaviors, incorporating explainable AI (XAI) for improved interpretability, and enhancing model efficiency. These advancements will ensure safer and more trustworthy metaverse ecosystems for users worldwide.
Hybrid Machine Learning Approach for Nutrient Deficiency Detection in Lettuce Zuriati, Zuriati; Widyawati, Dewi Kania; Arifin, Oki; Saputra, Kurniawan; Sriyanto, Sriyanto; Ahmad, Asmala
TIERS Information Technology Journal Vol. 6 No. 2 (2025)
Publisher : Universitas Pendidikan Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38043/tiers.v6i2.7143

Abstract

Early detection of nutrient deficiencies in lettuce is essential for precision agriculture. However, this task remains challenging due to limited data availability and class imbalance, which reduce model sensitivity toward minority classes and hinder generalization. This study introduces a hybrid machine learning approach integrating SMOTE, Optuna, and SVM to enhance the accuracy of nutrient deficiency classification using digital leaf image analysis. The dataset, obtained from Kaggle, includes four categories: Nitrogen Deficiency (-N), Phosphorus Deficiency (-P), Potassium Deficiency (-K), and Fully Nutritional (FN). Image features were extracted using MobileNetV2 pretrained on ImageNet and classified with a Support Vector Machine. Three scenarios were tested: (1) SVM before SMOTE, (2) SVM after SMOTE, and (3) Optuna-SVM after SMOTE, evaluated using accuracy, precision, recall, and f1-score. The hybrid model achieved the best performance with accuracy 0.929, precision 0.946, recall 0.835, and f1-score 0.869, outperforming the other scenarios. This hybrid framework effectively addressed class imbalance and improved classification margin stability through adaptive hyperparameter tuning using the Tree Structured Parzen Estimator within Optuna. The novelty of this study lies in combining MobileNetV2 based feature extraction with SMOTE and Optuna-SVM for small agricultural datasets. The proposed approach offers an efficient, accurate, and practical solution for automated nutrient deficiency diagnosis and contributes to the development of AI-driven smart agriculture systems.
Co-Authors Aboobaider, Burhanuddin bin Mohd Abu, Nor Azman Al-Homery, Hussein A. Arifin, Oki Ashari, Hasbullah Kurniawan Saputra Kusuma, Bagus Adhi Mohd, Othman Prasetio, Agung Budi Purwadi, - Sriyanto, Sriyanto Widyawati, Dewi Kania Zuriati, Zuriati