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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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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.
Enhanced Out-of-Fold Stacking with Feature Grouping and Model-Specific Transformations for Diabetes Prediction Improvement Ari Nugroho Putro; Sidiq Noor Kharisma; Gea Destadia Al-Zahra; Much Aziz Muslim; Dwika Ananda Agustina Pertiwi
Journal of Student Research Exploration Vol. 4 No. 1 (2026): January 2026
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/josre.v4i1.674

Abstract

Diabetes mellitus is a chronic disease with serious implications for global health. Early detection is essential to reduce these risks, and machine learning methods are widely used in diabetes prediction. However, improving accuracy remains a major challenge in the development of predictive models. This study proposes a stacking-based ensemble learning approach with an out-of-fold (OOF) scheme to improve classification performance. The proposed method consists of several systematic steps, namely (1) data preprocessing via median imputation of invalid values and feature transformation according to model characteristics, (2) the creation of base learners comprising Logistic Regression, Gaussian Naïve Bayes, Support Vector Machine, Random Forest, and XGBoost, (3) model training using Stratified Cross Validation 5 Fold to generate OOF predictions, (4) combining all OOF predictions into a meta-feature matrix, and (5) training an XGBoost-based meta-model to generate the final prediction. This approach enables the meta-model to optimally learn the relationships among the outputs of the baseline models. Experimental results show that the proposed method achieves an accuracy of 91.15%, precision of 90.65%, recall of 83.21%, and an F1-score of 86.77%. These results indicate that stacking is effective in improving the accuracy of diabetes predictions.
Performance Analysis of VNC-Based Remote Monitoring for Flight Information Display Systems Using ANOVA and Regression Models Ceorido Ghalib Wibowo; Much Aziz Muslim
Scientific Journal of Informatics Vol. 13 No. 2: May 2026
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v13i2.47378

Abstract

Purpose: This study evaluates the performance of a Virtual Network Computing (VNC)-based remote monitoring system within a Flight Information Display System (FIDS) environment. The research aims to identify infrastructure factors affecting monitoring performance and to develop a data-driven framework for evaluating monitoring reliability in distributed airport systems. Methods: Correlation analysis, Analysis of Variance (ANOVA), and multiple linear regression were applied to analyze the relationship between system resource utilization, network characteristics, and monitoring performance. The dataset consisted of 1,000 observations collected under various simulated monitoring conditions representing variations in latency, throughput, CPU utilization, and memory usage. Residual analysis and model evaluation were also performed to validate the statistical model. Result: The results showed that most infrastructure variables had very weak correlations (−0.02 to 0.05), indicating minimal multicollinearity. ANOVA testing revealed no statistically significant latency differences across low, medium, and high CPU load categories (F = 0.1625, p = 0.8500), with average latency remaining stable at approximately 52.82 ms. Regression evaluation demonstrated stable residual distribution and acceptable model consistency. The findings indicate that monitoring performance is influenced more by network conditions, particularly latency and throughput variability, than by computational load. Novelty: This study proposes an integrated analytical framework combining correlation analysis, ANOVA, and regression modeling to evaluate VNC-based monitoring performance in distributed systems. The framework provides a practical and reproducible approach for monitoring performance evaluation and infrastructure optimization in airport monitoring environments.
Analysis and Visualization of Purchasing Pattern in Retail Product Transaction using Apriori Algorithm N. Nelis Febriani SM; Nuk Ghurroh Setyoningrum; Mae Lodana; Dwika Ananda Agustina Pertiwi; Much Aziz Muslim
Journal of Information System Exploration and Research Vol. 4 No. 1 (2026): January 2026
Publisher : shmpublisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joiser.v4i1.650

Abstract

The rapid growth of the retail industry generates large volumes of transaction data that can be analyzed to support data-driven business decision making. This study aims to analyze and visualize purchasing patterns in retail product transactions by applying data mining techniques using the Apriori algorithm and business intelligence visualization through Microsoft Power BI. The dataset consists of 1 million retail transactions collected from an open retail transaction repository. The research stages include data collection, transaction data preprocessing, implementation of the Apriori algorithm with a minimum support threshold of 0.002 and a minimum confidence of 0.5, and visualization of the analysis results through interactive dashboards using Power BI and a Python-based application developed with the Streamlit framework. The results indicate that the Apriori algorithm successfully identifies frequent product associations and generates 12 association rules that meet the criteria of strong association rules. Power BI visualizations provide comprehensive insights into transaction trends based on customer categories, store types, payment methods, seasons, and transaction regions. These findings are expected to assist retail companies in formulating marketing strategies, developing product recommendations, and optimizing inventory management in a more effective and data-driven manner. This study contributes by integrating large-scale association rule mining with interactive business intelligence visualization for retail decision support.
Comparative Analysis of Data Normalization Effects on RFMBased Customer Segmentation Using K-Means and DBSCAN Nabilah Zahra; Tyas Arum; Zerafica Patriawan; Dwika Ananda Agustina Pertiwi; Much Aziz Muslim; Yusuf Enril Fathurrohman
Journal of Information System Exploration and Research Vol. 4 No. 2: April 2026
Publisher : shmpublisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joiser.v4i2.86

Abstract

Customer segmentation is widely used to analyze customer transaction patterns and support effective business strategies. However, previous studies have reported inconsistent findings regarding the impact of data normalization on clustering quality across different datasets and algorithms. This study investigates the effect of data normalization on RFM-based customer segmentation using K-Means and DBSCAN. Two transaction datasets, Online Retail II and TransJakarta, were analyzed under three preprocessing scenarios: no normalization, Min-Max normalization, and Z-Score normalization. Clustering performance was evaluated using the Silhouette Score and Davies–Bouldin Index (DBI). For the Online Retail II dataset, K-Means achieved the best performance without normalization (Silhouette Score = 0.9845), while DBSCAN produced valid clusters only after Z-Score normalization. For the TransJakarta dataset, both algorithms performed best without normalization, whereas DBSCAN identified up to 20 clusters and noise points. These findings highlight that the effectiveness of normalization depends on dataset characteristics and the clustering algorithm used.
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