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This Author published in this journals
All Journal Journal of Student Research Exploration Journal of Information System Exploration and Research Scientific Journal of Informatics Journal of Soft Computing Exploration Journal of Information System Exploration and Research
Much Aziz Muslim
Department of Computer Science, Universitas Negeri Semarang, Indonesia
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering

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Journal : journal of soft computing exploration

 1 
Image recognition based on combination of RGB–HSV color and shape features using PCA and K-nearest neighbor Hadiq Hadiq; Solehatin Solehatin; Much Aziz Muslim; Dwika Ananda Agustina Pertiwi
Journal of Soft Computing Exploration Vol. 7 No. 2 (2026): June 2026
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v7i2.111

Abstract

This study aims to accurately separate flower objects from complex backgrounds in artificial intelligence (AI)-based plant management systems. Previous studies have shown limitations in preprocessing techniques and often did not explicitly report classification accuracy. To address these issues, the proposed framework consists of eight stages. The process begins with image size standardization to 150 × 150 pixels, followed by low-pass filtering and image sharpening to enhance object boundaries. Segmentation is then performed sequentially using RGB and HSV color models to achieve more precise object separation. Subsequently, Sobel edge detection and thinning are applied to extract geometric features, such as distances between petal tips and flower perimeter measurements. The extracted features are optimized using Principal Component Analysis (PCA), which reduces the original ten attributes to four principal components, thereby eliminating data redundancy before classification using the Euclidean distance-based K-Nearest Neighbor (KNN) algorithm. Experimental results show that PCA preserves 86.7% of the original data variance, while the proposed system achieves an overall average classification accuracy of 88% at k = 5. Specifically, the recognition accuracies obtained for the four flower categories were 93.33% for Flower A, 86.67% for Flower B, 90.00% for Flower C, and 80.00% for Flower D. The main contribution of this research is the integration of intensive preprocessing techniques, the combination of RGB-HSV color features with geometric shape features, and PCA-based feature optimization, which collectively improve the stability and computational efficiency of KNN classification.
Comparative performance of MLP, CNN, TabNet, and FT-Transformer for credit risk prediction with TPOT  hyperparameter optimization Much Aziz Muslim; Dwika Ananda Agustina Pertiwi; Jumanto Unjung; Sidiq Noor Kharisma; Gea Destadia Al-Zahra; Shahrul Nizam Bin Salahudin
Journal of Soft Computing Exploration Vol. 7 No. 3 (2026): September 2026
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v7i3.250

Abstract

Credit risk prediction is important for supporting accurate lending decisions and reducing the potential risk of loan default. However, deep learning performance on tabular credit data can vary according to model architecture and TPOT hyperparameter configuration. Therefore, this study aims to comparatively evaluate Multilayer Perceptron, Convolutional Neural Network, TabNet, and FT-Transformer for credit risk prediction and examine the effect of TPOT hyperparameter optimization. The study uses the Lending Club dataset from 2007–2020, with 2,000 records from 2019–2020 sampled for analysis. The data were preprocessed through feature selection, missing-value handling, outlier treatment, categorical encoding, and feature scaling, while Synthetic Minority Over-sampling Technique was applied to address class imbalance. A stratified 70:30 train-test split was used, and the models were evaluated using accuracy, precision, recall, and F1-score before and after optimization. The results show that all four models improved after optimization, although the magnitude varied. Convolutional Neural Network achieved the highest optimized accuracy of 95.50%, while TabNet achieved the highest recall of 86.05% and F1-score of 86.29%. Multilayer Perceptron obtained the highest precision of 91.02%, whereas TabNet achieved the largest F1-score improvement of 6.84 percentage points. Overall, TPOT hyperparameter optimization improved model performance, with TabNet providing the most favorable overall balance for credit risk prediction.
Co-Authors Ari Nugroho Putro Ceorido Ghalib Wibowo Dwika Ananda Agustina Pertiwi Dwika Ananda Agustina Pertiwi Dwika Ananda Agustina Pertiwi Dwika Ananda Agustina Pertiwi Gea Destadia Al-Zahra Hadiq Hadiq Jumanto Unjung Mae Lodana N. Nelis Febriani SM Nabilah Zahra Nuk Ghurroh Setyoningrum Shahrul Nizam Bin Salahudin Sidiq Noor Kharisma Solehatin Solehatin Tyas Arum Yusuf Enril Fathurrohman Zerafica Patriawan