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All Journal International Journal of Electrical and Computer Engineering IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Jurnal Ilmu Komputer TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal Teknologi Informasi dan Ilmu Komputer Proceedings Konferensi Nasional Sistem dan Informatika (KNS&I) POSITIF KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal) JOIN (Jurnal Online Informatika) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Komputasi Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Journal of Electronics, Electromedical Engineering, and Medical Informatics Jurnal Mnemonic Jurnal Teknik Informatika (JUTIF) Journal of Data Science and Software Engineering Jurnal Informatika Polinema (JIP) Jurnal Pengabdian Masyarakat Tekno Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics
Radityo Adi Nugroho
Universitas Lambung Mangkurat
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Energy Engineering Health Professions Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology

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Optimizing Software Defect Prediction Models: Integrating Hybrid Grey Wolf and Particle Swarm Optimization for Enhanced Feature Selection with Popular Gradient Boosting Algorithm Angga Maulana Akbar; Herteno, Rudy; Saputro, Setyo Wahyu; Faisal, Mohammad Reza; Nugroho, Radityo Adi
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.v6i2.388

Abstract

Software defects, also referred to as software bugs, are anomalies or flaws in computer program that cause software to behave unexpectedly or produce incorrect results. These defects can manifest in various forms, including coding errors, design flaws, and logic mistakes, this defect have the potential to emerge at any stage of the software development lifecycle. Traditional prediction models usually have lower prediction performance. To address this issue, this paper proposes a novel prediction model using Hybrid Grey Wolf Optimizer and Particle Swarm Optimization (HGWOPSO). This research aims to determine whether the Hybrid Grey Wolf and Particle Swarm Optimization model could potentially improve the effectiveness of software defect prediction compared to base PSO and GWO algorithms without hybridization. Furthermore, this study aims to determine the effectiveness of different Gradient Boosting Algorithm classification algorithms when combined with HGWOPSO feature selection in predicting software defects. The study utilizes 13 NASA MDP dataset. These dataset are divided into testing and training data using 10-fold cross-validation. After data is divided, SMOTE technique is employed in training data. This technique generates synthetic samples to balance the dataset, ensuring better performance of the predictive model. Subsequently feature selection is conducted using HGWOPSO Algorithm. Each subset of the NASA MDP dataset will be processed by three boosting classification algorithms namely XGBoost, LightGBM, and CatBoost. Performance evaluation is based on the Area under the ROC Curve (AUC) value. Average AUC values yielded by HGWOPSO XGBoost, HGWOPSO LightGBM, and HGWOPSO CatBoost are 0.891, 0.881, and 0.894, respectively. Results of this study indicated that utilizing the HGWOPSO algorithm improved AUC performance compared to the base GWO and PSO algorithms. Specifically, HGWOPSO CatBoost achieved the highest AUC of 0.894. This represents a 6.5% increase in AUC with a significance value of 0.00552 compared to PSO CatBoost, and a 6.3% AUC increase with a significance value of 0.00148 compared to GWO CatBoost. This study demonstrated that HGWOPSO significantly improves the performance of software defect prediction. The implication of this research is to enhance software defect prediction models by incorporating hybrid optimization techniques and combining them with gradient boosting algorithms, which can potentially identify and address defects more accurately
A Comparative Analysis of Polynomial-fit-SMOTE Variations with Tree-Based Classifiers on Software Defect Prediction Nur Hidayatullah, Wildan; Herteno, Rudy; Reza Faisal, Mohammad; Adi Nugroho, Radityo; Wahyu Saputro, Setyo; Akhtar, Zarif Bin
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 6 No 3 (2024): July
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v6i3.455

Abstract

Software defects present a significant challenge to the reliability of software systems, often resulting in substantial economic losses. This study examines the efficacy of polynomial-fit SMOTE (pf-SMOTE) variants in combination with tree-based classifiers for software defect prediction, utilising the NASA Metrics Data Program (MDP) dataset. The research methodology involves partitioning the dataset into training and test subsets, applying pf-SMOTE oversampling, and evaluating classification performance using Decision Trees, Random Forests, and Extra Trees. Findings indicate that the combination of pf-SMOTE-star oversampling with Extra Tree classification achieves the highest average accuracy (90.91%) and AUC (95.67%) across 12 NASA MDP datasets. This demonstrates the potential of pf-SMOTE variants to enhance classification effectiveness. However, it is important to note that caution is warranted regarding potential biases introduced by synthetic data. These findings represent a significant advancement over previous research endeavors, underscoring the critical role of meticulous algorithm selection and dataset characteristics in optimizing classification outcomes. Noteworthy implications include advancements in software reliability and decision support for software project management. Future research may delve into synergies between pf-SMOTE variants and alternative classification methods, as well as explore the integration of hyperparameter tuning to further refine classification performance.
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.
The Comparison of Extreme Machine Learning and Hidden Markov Model Algorithm in Predicting The Recurrence Of Differentiated Thyroid Cancer Using SMOTE Aida, Nor; Saragih, Triando Hamonangan; Kartini, Dwi; Nugroho, Radityo Adi; Nugrahadi, Dodon Turianto
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.v6i4.467

Abstract

Differentiated thyroid cancer is the most common type of thyroid cancer; the types in this category are papillary, follicular, and hurthel cell carcinoma. Up to 20% of DTCs will experience recurrence, although this figure reduces to 5% in low-risk patients. There is still little research on thyroid cancer prediction using a machine learning approach, especially the prediction recurrence of DTCs. This research aims to compare the performance of the Extreme Learning Machine and the Hidden Markov Model using SMOTE in predicting the recurrence of DTCs. The dataset used in this research is differentiated thyroid cancer recurrence from Kaggle. This research methodology comprises preprocessing, data sharing, SMOTE, ELM and HMM modeling algorithms, and evaluation. ELM with SMOTE gets the best results at a ratio of 90:10 with 35 hidden neurons that get an accuracy value of 1.00, precision 1.00, recall 1.00, and AUC 1.00. ELM modeling gets the best results at a ratio of 90:10 with 45 hidden neurons that get an accuracy value of 1.00, precision 1.00, recall 1.00, and AUC 1.00. HMM modeling gets the best value at a ratio of 70:30 with two hidden states and two iterations, which get an accuracy value of 0.8696, precision 0.8696, recall 0.7944, and AUC 0.9575. Last, HMM modeling with SMOTE gets the best results at a ratio of 60:40 with two hidden states and two iterations, with an accuracy value of 0.8696, precision of 0.8832, recall of 0.7848, and AUC of 0.9174. Based on the results of this study, it can be concluded that ELM with SMOTE gets the best performance, followed by ELM without SMOTE, HMM without SMOTE, and finally, HMM with SMOTE. The implication is that ELM with SMOTE can produce high accuracy in predicting the recurrence of DTCs.
Comparative Evaluation of IndoBERT, IndoBERTweet, and mBERT for Multilabel Student Feedback Classification Indriani, Fatma; Nugroho, Radityo Adi; Faisal, Mohammad Reza; Kartini, Dwi
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 8 No 6 (2024): December 2024
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v8i6.6100

Abstract

Student feedback plays a crucial role in enhancing the quality of educational programs, yet analyzing this feedback, especially in informal contexts, remains challenging. In Indonesia, where student comments often include colloquial language and vary widely in content, effective multilabel classification is essential to accurately identify the aspects of courses being critiqued. Despite the development of several BERT-based models, the effectiveness of these models for classifying informal Indonesian text remains underexplored. Here we evaluate the performance of three BERT variants—IndoBERT, IndoBERTweet, and mBERT—on the task of multilabel classification of student feedback. Our experiments investigate the impact of different sequence lengths and truncation strategies on model performance. We find that IndoBERTweet, with a macro F1-score of 0.8462, outperforms IndoBERT (0.8243) and mBERT (0.8230) when using a sequence length of 64 tokens and truncation at the end. These findings suggest that IndoBERTweet is well-suited for handling the informal, abbreviated text common in Indonesian student feedback, providing a robust tool for educational institutions aiming for actionable insights from student comments.
Evaluation of the Impact of SMOTEENN on Monkeypox Case Classification Performance Using Boosting Algorithms Siena, Laifansan; Saragih, Triando Hamonangan; Nugroho, Radityo Adi; Kartini, Dwi; Muliadi; Caesarendra, Wahyu
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 7 No. 2 (2025): May
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v7i2.77

Abstract

Monkeypox is a zoonotic disease with increasing global prevalence, posing a significant challenge in healthcare. Its widespread transmission necessitates more accurate detection systems to assist medical professionals in diagnosing and managing cases effectively. One of the main challenges in developing monkeypox prediction models is class imbalance in datasets, which can cause models to favor the majority class and reduce predictive accuracy for rarer cases. To address this issue, this study evaluates the effectiveness of the SMOTEENN resampling technique in improving the classification performance of monkeypox cases. Three boosting algorithms Gradient Boosting, XGBoost, and LightGBM were applied to a monkeypox dataset consisting of 25,000 samples. The data preprocessing steps included handling missing values, feature encoding, and feature scaling. The dataset was then balanced using SMOTEENN, a hybrid technique combining the Synthetic Minority Over-sampling Technique (SMOTE) and Edited Nearest Neighbors (ENN). Additionally, hyperparameter tuning with GridSearchCV was performed to optimize model performance by systematically selecting the best parameter combinations. The results indicate that applying SMOTEENN significantly improved classification accuracy, achieving a maximum of 69%, with an F1-score of 67%. Compared to previous studies, the proposed approach demonstrated superior performance in handling class imbalance and enhancing classification robustness. These findings highlight the potential of SMOTEENN and boosting algorithms in medical data classification, particularly for infectious diseases with imbalanced datasets. This study contributes to the development of more reliable machine learning techniques for improving disease detection, classification accuracy, and overall model generalization. Future research should explore additional resampling techniques, deep learning architectures, and feature selection methods to further improve predictive performance in medical diagnostics.
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.
Implementation of the Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP) Method to Address Class Imbalance in Alzheimer’s Disease Magnetic Resonance Imaging (MRI) Datasets Alamudin, Muhammad Faiq; Mazdadi, Muhammad Itqan; Nugroho, Radityo Adi; Saragih, Triando Hamonangan; Muliadi, Muliadi; Athavale, Vijay Anant
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.v7i3.109

Abstract

Class imbalance in medical imaging datasets often leads to biased machine learning models, particularly in Alzheimer’s disease (AD) diagnosis using MRI. This study proposes the use of Wasserstein Generative Adversarial Networks with Gradient Penalty (WGAN-GP) to mitigate class imbalance in AD MRI datasets. Realistic MRI images were synthesized for underrepresented AD stages, and the quality of the generated data was quantitatively validatedusing the Fréchet Inception Distance (FID), with the lowest FID score recorded at 31.84, indicating a high degree of realism and diversity. The synthetic images were used to augment a dataset of 6,400 T1-weighted scans for training four Convolutional Neural Network (CNN) architectures: ResNet-50, AlexNet, VGG-16, and VGG-19. Results demonstrated statistically significant improvements in balanced accuracy across all models (p < 0.01 for all comparisons). The AlexNet + WGAN-GP combination achieved the highest accuracy of 98.54%, representing a mean improvement of 4.76% (95% CI: 2.45% to 6.98%) over its baseline. Significant gains were also observed for ResNet-50, VGG-16, and VGG-19. These enhancements were consistent across multiple evaluation metrics, including precision, recall, F1-score, and AUC. These findings confirm that WGAN-GP is a highly effective and statistically validated strategy for boosting the diagnostic accuracy of CNN models in Alzheimer's disease classification
Prediction of Life Expectancy of Lung Cancer Patients After Thoracic Surgery Using Decision Tree Algorithm and Adaptive Synthetic Sampling Erdi, Muhammad; Mazdadi, Muhammad Itqan; Nugroho, Radityo Adi; Farmadi, Andi; Saragih, Triando Hamonangan; Rozaq, Hasri Akbar Awal
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.5.4724

Abstract

This research focuses on predicting the life expectancy of lung cancer patients after undergoing thoracic surgery, using a decision tree classification algorithm (C4.5) combined with adaptive synthetic sampling to handle data imbalance. Data imbalance in the lung cancer patient dataset is a major obstacle in obtaining accurate prediction results, especially in identifying minority classes. Data imbalance in the lung cancer patient dataset is a major obstacle in obtaining accurate prediction results, especially in identifying minority classes. By applying ADASYN, the data distribution becomes more even, thus improving the performance of the C4.5 model. The results showed that combining these methods increased the prediction accuracy from 67% to 87%. In addition, the precision, recall, and f1-score for minority classes have significantly improved, which were previously difficult to identify by the model. Thus, combining the C4.5 algorithm and the ADASYN technique proved effective in dealing with the challenge of data imbalance and resulted in better prediction in the case of lung cancer. This study is expected to contribute to the field of medical classification and serve as a reference for further research on similar cases.
Game Development of Banjar Archive for Interactive Cultural Education Ultilizing Large Language Models Adi Mu'Ammar, Rifqi; Abadi, Friska; Budiman, Irwan; Adi Nugroho, Radityo; Turianto Nugrahadi, Dodon
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 10, No. 4, November 2025
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v10i4.2294

Abstract

The preservation of Banjar cultural heritage is threatened by globalization and the fading interest of younger generations. This study addressed these challenges by developing an interactive educational game using the Game Development Life Cycle (GDLC) framework and integrating Large Language Models (LLMs) for adaptive and immersive player interactions. The six stages of GDLC namely initiation, pre-production, production, testing, beta, and release were systematically applied, resulting in a game that blends dynamic narratives to engage players while educating them about Banjar culture. Black Box Testing verified 14 test scenarios that all passed successfully, ensuring system stability and reliability. Additionally, user experience evaluation using the Game Experience Questionnaire (GEQ) highlighted high levels of immersion (4.936), competence (4.448), flow (3.124) and positive affect (4.976) among players, with minimal reported tension (1), challenge (1.744) and negative affect (1.07). These results demonstrated that the game successfully balances educational goals with engaging gameplay, fostering meaningful connections to Banjar heritage. By leveraging LLM technology, the game enhances interactivity, offering an innovative approach to Banjar cultural preservation in the digital era. This research extends the existing body of knowledge on AI-driven gamification strategies in heritage conservation with a specific focus on Banjar culture.
Co-Authors Abdul Gafur Adi Mu'Ammar, Rifqi Adin Nofiyanto, Adin Ahmad Bahroini Ahmad Juhdi Ahmad Rusadi Aida, Nor Akhtar, Zarif Bin Alamudin, Muhammad Faiq Andi Farmadi Andi Farmadi Andi Farmadi Angga Maulana Akbar Arie Sapta Nugraha Arie Sapta Nugraha Aryanti, Agustia Kuspita Athavale, Vijay Anant Aylwin Al Rasyid Bayu Hadi Sudrajat Dendy Fadhel Adhipratama Dendy Deni Kurnia Dike Bayu Magfira, Dike Bayu Dodon Turianto Nugrahadi Dwi Kartini Dwi Kartini, Dwi Efendi Mohtar Emma Andini Erdi, Muhammad Faisal, Mohammad Reza Fatma Indriani Fauzan Luthfi, Achmad Fenny Winda Rahayu Fhadilla Muhammad Friska Abadi Friska Abadi Hanif Rahardian Irwan Budiman Irwan Budiman Itqan Mazdadi, Muhammad Ivan Sitohang Maya Yusida Muhammad Angga Wiratama Muhammad Azmi Adhani Muhammad Itqan Mazdadi Muhammad Latief Saputra Muhammad Noor Muhammad Reza Faisal, Muhammad Reza Muhammad Rizky Adriansyah Muhammad Rusli Muhammad Syahriani Noor Basya Basya Muhammad Zaien Muliadi Muliadi Muliadi Aziz Muliadi Muliadi Muliadi Muliadi Nur Hidayatullah, Wildan Nur Ridha Apriyanti Oni Soesanto Pratama, Muhammad Yoga Adha Putri, Nitami Lestari Rahayu, Fenny Winda Rahmat Ramadhani Raidra Zeniananto Reina Alya Rahma Reza Faisal, Mohammad Riadi, Putri Agustina Rinaldi Rizal, Muhammad Nur Rizky Ananda, Muhammad Rozaq, Hasri Akbar Awal Rudy Herteno Rudy Herteno Rudy Herteno Salsha Farahdiba Saputro, Setyo Wahyu Saragih, Triando Hamonangan Sarah Monika Nooralifa Septiadi Marwan Annahar Setyo Wahyu Saputro Siena, Laifansan Sri Redjeki Sri Redjeki Suci Permata Sari Suryadi, Mulia Kevin Sutan Takdir Alam Wahyu Caesarendra Wahyu Ramadansyah Wahyu Saputro, Setyo Zaini Abdan