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All Journal JOIV : International Journal on Informatics Visualization Asean International Journal of Business
Mohd, Othman
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Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance

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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.
Modeling the civil servant discipline in Indonesia: partial least square-structural equation modeling approach Soelaiman, Nur Fauzi; Ahmad, Sharifah Sakinah Syed; Mohd, Othman; Al Hakim, Rosyid Ridlo; Hidayah, Hexa Apriliana
Asean International Journal of Business Vol. 1 No. 1 (2022)
Publisher : Asosiasi Dosen Peneliti Ilmu Ekonomi dan Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1471.965 KB) | DOI: 10.54099/aijb.v1i1.72

Abstract

Purpose – This paper seeks to discover the factors that influence the supervisor to give the punishment level to civil servant staff—the data being used is a questionnaire to several civil servants in public academic institutions. Methodology/approach – This research used computational tools to classify transgressions into punishment categories (light, medium, or severe) with the model using the data science technique based on the partial least square-structural equation modeling (PLS-SEM) approach. Findings – It was found that the model of civil servant discipline in Indonesia is based on 14 hypotheses from bootstrapping technique and by using data science technique to support the result analysis of PLS-SEM. Novelty/value – This research contributed to providing civil servant supervisors to understand factors that influence the discipline of their staff, so it can be used to determine the punishment categorization.
Co-Authors Abu, Nor Azman Ahmad, Asmala Ahmad, Sharifah Sakinah Syed Hexa Apriliana Hidayah Kusuma, Bagus Adhi Nur Fauzi Soelaiman Purwadi, - Rosyid Ridlo Al-Hakim