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All Journal Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal Teknologi Informasi dan Ilmu Komputer Journal of Electronics, Electromedical Engineering, and Medical Informatics Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics
Setyo Wahyu Saputro
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Health Professions Materials Science & Nanotechnology

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Impact of a Synthetic Data Vault for Imbalanced Class in Cross-Project Defect Prediction Putri Nabella; Rudy Herteno; Setyo Wahyu Saputro; Mohammad Reza Faisal; Friska Abadi
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 6 No 2 (2024): April
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v6i2.409

Abstract

Software Defect Prediction (SDP) is crucial for ensuring software quality. However, class imbalance (CI) poses a significant challenge in predictive modeling. This study delves into the effectiveness of the Synthetic Data Vault (SDV) in mitigating CI within Cross-Project Defect Prediction (CPDP). Methodologically, the study addresses CI across ReLink, MDP, and PROMISE datasets by leveraging SDV to augment minority classes. Classification utilizing Decision Tree (DT), Logistic Regression (LR), K-Nearest Neighbors (KNN), Naive Bayes (NB), and Random Forest (RF), also model performance is evaluated using AUC and t-Test. The results consistently show that SDV performs better than SMOTE and other techniques in various projects. This superiority is evident through statistically significant improvements. KNN dominance in average AUC results, with values 0.695, 0.704, and 0.750. On ReLink, KNN show 16.06% improvement over the imbalanced and 12.84% over SMOTE. Similarly, on MDP, KNN 20.71% improvement over the imbalanced and a 10.16% over SMOTE. Moreover, on PROMISE, KNN 13.55% improvement over the imbalanced and 7.01% over SMOTE. RF displays moderate performance, closely followed by LR and DT, while NB lags behind. The statistical significance of these findings is confirmed by t-Test, all below the 0.05 threshold. These findings underscore SDV's potential in enhancing CPDP outcomes and tackling CI challenges in SDV. With KNN as the best classification algorithm. Adoption of SDV could prove to be a promising tool for enhancing defect detection and CI mitigation
Optimization of Backward Elimination for Software Defect Prediction with Correlation Coefficient Filter Method Muhammad Noor; Radityo Adi Nugroho; Setyo Wahyu Saputro; Rudy Herteno; Friska Abadi
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 6 No 4 (2024): October
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v6i4.466

Abstract

Detecting software defects is a crucial step for software development not only to reduce cost and save time, but also to mitigate more costly losses. Backward Elimination is one method for detecting software defects. Notably Backward Elimination may remove features that may later become significant to the outcome affecting the performance of Backward Elimination. The aim of this study is to improve Backward Elimination performance. In this study, several features were selected based on their correlation coefficient, with the selected feature applied to improve Backward Elimination final model performance. The final model was validated using cross validation with Naïve Bayes as the classification method on the NASA MDP dataset to determine the accuracy and Area Under the Curve (AUC) of the final model. Using top 10 correlation feature and Backward Elimination achieve an average result of 86.6% accuracy and 0.797 AUC, while using top 20 correlation feature and Backward Elimination achieved an average result of 84% accuracy and 0.812 AUC. Compare to using Backward Elimination and Naïve Bayes respectively the improvement using top 10 correlation feature as follows: AUC:1.52%, 13.53% and Accuracy: 13%, 12.4% while the improvement using top 20 correlation feature as follows: AUC:3.43%, 15.66% and Accuracy: 10.4%, 9.8%. Results showed that selecting the top 10 and top 20 feature based on its correlation before using Backward Elimination have better result than only using Backward Elimination. This result shows that combining Backward Elimination with correlation coefficient feature selection does improve Backward Elimination’s final model and yielding good results for detecting software defects.
Pengembangan Sistem Manajemen Sarana Dan Prasarana, IT, Serta Laboratorium Di SMK Telekomunikasi Putri Nabella; Rudy Herteno; Setyo Wahyu Saputro; Friska Abadi; Muhammad Itqan Mazdadi; Nabella, Putri
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 12 No 1: Februari 2025
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2025128649

Abstract

Bidang Sarana dan Prasarana, IT, serta Laboratorium di SMK Telekomunikasi menghadapi tantangan dalam pengelolaan data yang tersebar di berbagai file Microsoft Excel, menyebabkan kesulitan dalam pengumpulan laporan untuk audit dan sertifikasi. Penelitian ini bertujuan mengembangkan sistem manajemen terpadu menggunakan framework CodeIgniter 4, PHP, dan MySQL dengan metode Rational Unified Process (RUP) dan desain Unified Modelling Language (UML). Sistem ini dirancang untuk menyelaraskan pengelolaan data dan memfasilitasi penyajian informasi yang efisien. Hasil pengujian black box menunjukkan tingkat keberhasilan 100%, sementara user acceptance testing memperoleh skor 92% dengan predikat sangat baik. Implementasi sistem ini diharapkan meningkatkan efisiensi dan efektivitas manajemen sarana, prasarana, IT, dan laboratorium di SMK Telekomunikasi, memberikan kontribusi signifikan terhadap peningkatan kualitas pengelolaan dan kepuasan pengguna.   Abstract. The Facilities and Infrastructure, IT, and Laboratory Department at SMK Telekomunikasi faces challenges in managing data scattered across various Microsoft Excel files, resulting in difficulties in compiling reports for audits and certifications. This research aims to develop an integrated management system using the CodeIgniter 4 framework, PHP, and MySQL, employing the Rational Unified Process (RUP) methodology and Unified Modelling Language (UML) design. This system is designed to streamline data management and facilitate efficient information presentation. The results of the black box testing showed a success rate of 100%, while the user acceptance testing scored 92% with an excellent rating. The implementation of this system is expected to enhance the efficiency and effectiveness of managing facilities, infrastructure, IT, and laboratories at SMK Telekomunikasi, significantly contributing to improved management quality and user satisfaction.
Newspaper Ad Submission and Payment Website Measurement Analysis Using McCall and PIECES Muhammad Nazar Gunawan; Friska Abadi; Dodon Turianto Nugrahadi; Irwan Budiman; Setyo Wahyu Saputro
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 11 No. 2 (2025): June
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v11i2.30355

Abstract

The transition to digital platforms in the media industry requires robust systems to ensure efficiency and user satisfaction. As with Digital Iklan Radar Banjarmasin, the Newspaper ad submission and payment website, there is a need for evaluation to comprehensively ensure software feasibility and quality. This research evaluates the quality of the Newspaper ad submission and payment website using the McCall and PIECES frameworks, comparing their strengths and identifying areas for improvement. This research contributes to determining the most suitable evaluation methods for such types of websites while offering actionable insights for developers to improve the quality of systems and services. Data collection involved online surveys with 106 respondents and 38 Likert-scale questions mapped to McCall and PIECES frameworks. Statistical tests, including validity, reliability, and an independent t-test, were applied to compare results. McCall's evaluation rated the system at 68% (Good), with low scores in Usability (38.5%), Reliability (36.77%), and Efficiency (38.15%), indicating areas needing significant improvement. PIECES evaluation scored 80.4% (Good), with Performance (81%) and Service (82.39%) rated Very Good, though Control and Security (78.55%) required enhancement. Statistical analysis with independent t-test confirmed significant differences between the two methods, indicating that both methods measure aspects of software quality from different perspectives, thus providing complementary insights for evaluation. The study highlights the complementary nature of McCall and PIECES in software quality evaluation. Recommendations include improving usability, system stability, and security for better user experiences. Future research should involve broader demographic samples and different system types to validate findings and enhance generalizability.
An Empirical Study of Cross-Project and Within-Project Performance in Software Defect Prediction Models Using Tree-Based and Boosting Classifiers Raidra Zeniananto; Herteno, Rudy; Radityo Adi Nugroho; Andi Farmadi; Setyo Wahyu Saputro
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 7 No. 3 (2025): August
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v7i3.95

Abstract

Software Defect Prediction (SDP) is a vital process in modern software engineering aimed at identifying faulty components in the early stages of development. In this study, we conducted a comprehensive evaluation of two widely employed SDP approaches, Within-Project Software Defect Prediction (WP-SDP) and Cross-Project Software Defect Prediction (CP-SDP), using identical preprocessing steps to ensure an objective comparison. We utilized the NASA MDP dataset, where each project was split into 70% training and 30% testing data, and applied three distinct resampling strategies—no sampling, oversampling, and undersampling—to address the challenge of class imbalance. Five classification algorithms were examined, including Support Vector Machine (SVM), Random Forest (RF), Gradient Boosting (GB), XGBoost (XGB), and LightGBM (LGBM). Performance was measured primarily using Accuracy and Area Under the Curve (AUC) metrics, resulting in 360 experimental outcomes. Our findings revealed that WP-SDP, combined with oversampling and Random Forest, demonstrated superior predictive capability on most projects, achieving an Accuracy of 89.92% and an AUC of 0.931 on PC4. Nonetheless, CP-SDP excelled in certain small-scale projects (e.g., MW1), underscoring its potential when local historical data is scarce but inter-project characteristics remain sufficiently similar. This study’s results underscore the importance of selecting a prediction scheme tailored to specific project attributes, class imbalance levels, and available historical data. By establishing a standardized methodological framework, our work contributes to a clearer understanding of the strengths and limitations of WP-SDP and CP-SDP, paving the way for more effective defect detection strategies and improved software quality.
Co-Authors Andi Farmadi Dodon Turianto Nugrahadi Friska Abadi Herteno, Rudy Irwan Budiman Muhammad Itqan Mazdadi Muhammad Nazar Gunawan Muhammad Noor Muhammad Reza Faisal, Muhammad Reza Nabella, Putri Putri Nabella Radityo Adi Nugroho Raidra Zeniananto Rudy Herteno