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All Journal Bulletin of Electrical Engineering and Informatics Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal Teknologi Informasi dan Ilmu Komputer Telematika Bianglala Informatika : Jurnal Komputer dan Informatika Akademi Bina Sarana Informatika Yogyakarta Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Journal of Electronics, Electromedical Engineering, and Medical Informatics International Journal of Advances in Data and Information Systems Jurnal Teknik Informatika (JUTIF) International Journal of Electronics and Communications Systems Journal of Computing Theories and Applications Jurnal Informatika Polinema (JIP) Scientific Journal of Informatics Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics
Setyo Wahyu Saputro
Lambung Mangkurat University
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Health Professions Library & Information Science Materials Science & Nanotechnology

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Empirical Performance of E2E Frameworks in React-Vue SPAs Using DIA Rezeki, Abdillah; Saputro, Setyo Wahyu; Saragih, Triando Hamonangan; Nugroho, Radityo Adi; Abadi, Friska
International Journal of Advances in Data and Information Systems Vol. 7 No. 1 (2026): April 2026 - International Journal of Advances in Data and Information Systems
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/ijadis.v7i1.1528

Abstract

Modern web applications increasingly adopt Single-Page Application (SPA) architectures to enhance the user experience through client-side rendering and dynamic content loading. However, these characteristics introduce significant challenges for automated end-to-end (E2E) testing, including asynchronous DOM manipulation, complex state management, and timing synchronization issues. This study presents a comprehensive empirical comparison of three prominent E2E testing frameworks—Selenium WebDriver, Cypress, and Playwright—across React and Vue-based SPAs. Using a quantitative experimental approach, 25 standardized test cases were executed 15 times each across Chrome, Firefox, and Edge, for a total of 270 testing sessions. Performance evaluation focused on four key metrics: execution time, success rate, CPU usage, and memory consumption. Results demonstrate that Playwright achieved the fastest execution time (56.25 seconds on React-Chrome), while Selenium exhibited superior resource efficiency with the lowest memory consumption (196.59 MB on Vue-Chrome). The Distance to Ideal Alternative (DIA) multi-criteria decision analysis method identified Playwright-Chrome as optimal for React applications (DIA score: 0.886715) and Selenium-Chrome for Vue applications (DIA score: 0.908237), indicating that framework selection should be context-dependent based on application characteristics and deployment requirements. This research supports the conclusion that no universal "best" testing framework exists, underscoring the importance of evidence-based, application-specific tool selection in software quality assurance.
Performance Analysis of the Fuzzing Method in Detecting API Vulnerabilities in Mobile Healthcare Application X Based on OWASP API Security Top 10 Muhammad Ikhwanul Hakim; Radityo Adi Nugroho; Dodon Turianto Nugrahadi; Rudy Herteno; Setyo Wahyu Saputro
Telematika Vol 19, No 1: February (2026)
Publisher : Universitas Amikom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35671/telematika.v19i1.3149

Abstract

Traditional perimeter security measures, such as Web Application Firewalls (WAFs) and static analysis, often fail to detect logic-based vulnerabilities in healthcare Application Programming Interfaces (APIs), creating significant risks for patient data confidentiality. Addressing the scarcity of empirical performance evaluations in this domain, this study employs a grey-box controlled experimental design to assess the effectiveness of automated HTTP fuzzing against a production-grade mobile health application ("Application X"). Using the FFUF tool configured with sequential identifier injection, status-code filtering, and hidden-field probing, the experiment tested 33 endpoints against the OWASP API Security Top 10 2023 benchmarks. To ensure data reliability, a rigorous multi-step validation protocol including replay testing and environmental noise elimination was applied to filter false positives. The results identified 88 distinct vulnerabilities distributed across six categories, with a critical dominance of Security Misconfiguration (API8) and Broken Object Property Level Authorization (API3). Analytically, the high prevalence of API3 reveals a systemic failure in backend serialization, where sensitive fields  including password hashes and internal administrative flags were exposed due to the absence of Data Transfer Objects (DTOs), contradicting the assumption of secure client-side filtering. Limitations of this study include the restriction to a single patient-role perspective and the exclusion of third-party integrations. The study concludes that automated fuzzing is superior to static analysis in detecting runtime data leakage and recommends mandatory Server-Side Output Filtering through explicit DTOs as a critical standard for secure health API development and data privacy compliance.
Android Malware Detection with Hybrid Feature Selection and Bayesian Optimization Fadhillah, Muhammad Alif; Saputro, Setyo Wahyu; Muliadi, Muliadi; Faisal, Mohammad Reza; Nugroho, Radityo Adi
International Journal of Advances in Data and Information Systems Vol. 7 No. 1 (2026): April 2026 - International Journal of Advances in Data and Information Systems
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/ijadis.v7i1.1526

Abstract

The increasing dimensionality of Android application features poses significant challenges for accurate and efficient malware detection. This study proposes a hybrid feature selection framework that combines Minimum Redundancy Maximum Relevance (mRMR) and correlation filtering to optimize classification performance on the Drebin-215 dataset. A selected configuration of 175 features with a correlation threshold of 0.7 was evaluated using five classifiers: LSTM, Support Vector Machine (SVM), Random Forest, K-Nearest Neighbors (KNN), and XGBoost. The experimental results show that dimensionality reduction improves classification stability and overall predictive performance. SVM exhibits the most notable improvement, with accuracy increasing from 63.05% without feature selection to 98.57% after applying the proposed framework. LSTM achieves 98.57% accuracy with an AUC of 99.86%, while Random Forest, KNN, and XGBoost consistently achieve accuracy above 97%. In addition to performance enhancement, the hybrid feature selection approach substantially improves computational efficiency. SVM training time decreases from 770.75 seconds to 155.88 seconds, and testing time is reduced from 15.581 seconds to 0.3824 seconds. KNN testing time also decreases from 1.623 seconds to 0.4595 seconds..
Enhancing Software Defect Prediction through Hybrid Multi-Filter Feature Selection and Imbalance Handling Muhammad Khalid Maulana; Setyo Wahyu Saputro; Mohammad Reza Faisal; Radityo Adi Nugroho; As’ary Ramadhan
Journal of Computing Theories and Applications Vol. 3 No. 4 (2026): JCTA 3(4) 2026
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jcta.15943

Abstract

Software Defect Prediction (SDP) aims to identify defective modules early in the software development lifecycle to improve software quality and reduce maintenance costs. However, SDP datasets commonly suffer from high dimensionality, feature redundancy, and class imbalance, which can degrade model performance and stability. This study proposes a hybrid feature selection framework to address these challenges and enhance prediction performance. The proposed approach integrates Combined Correlation and Mutual Information (CONMI), which combines the Pearson Correlation Coefficient (PCC) and Mutual Information (MI) to capture both linear and nonlinear feature relevance. The selected features are further refined through Top-K selection, correlation-based filtering to reduce multicollinearity, and Backward Elimination (BE) to obtain an optimal feature subset. To address class imbalance, SMOTE-Tomek is applied by combining over-sampling and data cleaning techniques. Experiments are conducted on twelve NASA MDP datasets using Logistic Regression (LR) and Naïve Bayes (NB) classifiers. The results show that the proposed framework consistently achieves the best performance, with Logistic Regression combined with SMOTE-Tomek obtaining the highest average AUC of 0.7923 ± 0.0714, while NB achieves 0.7554 ± 0.0580. Statistical analysis using a paired t-test indicates that the proposed method significantly outperforms MI+SMOTE-Tomek and BE+SMOTE-Tomek for Logistic Regression, whereas no significant differences are observed for NB. In addition to improving overall classification performance (AUC), the proposed approach also enhances minority class detection, as reflected in improved Recall and F1-score. Overall, the proposed hybrid framework provides an effective and reliable solution for software defect prediction, particularly for high-dimensional and imbalanced datasets.
Quantifying the Impact of Text Preprocessing on IndoBERT Fine-Tuning for Indonesian Informal Culinary Sentiment Analysis Rahmat Budianoor; Setyo Wahyu Saputro; Friska Abadi; Radityo Adi Nugroho; Andi Farmadi
Journal of Computing Theories and Applications Vol. 3 No. 4 (2026): JCTA 3(4) 2026
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jcta.15980

Abstract

Indonesian culinary comments on social media platforms such as Instagram are characterized by informal spelling, regional language mixing, slang expressions, and emojis, posing substantial challenges for automated sentiment classification. While IndoBERT has demonstrated strong performance across Indonesian natural language processing tasks, the contribution of individual preprocessing components to fine-tuning performance on informal text remains underexplored, particularly in the culinary domain. This study addresses this gap by conducting a systematic preprocessing ablation study on IndoBERT-Base fine-tuning for Indonesian culinary sentiment classification, accompanied by a comparative evaluation against Naive Bayes with TF-IDF, SVM with TF-IDF, and BiLSTM as representative baselines. A dataset of 3,500 manually labeled Instagram culinary comments across three sentiment classes was used, with a stratified 80/10/10 split. Six preprocessing variants were evaluated under identical experimental conditions to isolate the contribution of each component. The results show that slang normalization is the most impactful single preprocessing step, yielding a macro F1-score gain of +0.0609 over the no-preprocessing baseline, while the full pipeline achieves an accuracy of 0.8800 and a macro F1-score of 0.8465. IndoBERT-Base with the full pipeline outperforms all baselines across all evaluation metrics. Per-class analysis reveals that the negative class achieves the lowest F1-score of 0.7600, with sarcastic expressions and Banjar regional vocabulary identified as primary sources of misclassification. These findings indicate that preprocessing decisions have a measurable and non-uniform effect on IndoBERT fine-tuning performance. In this study, slang normalization provides the most substantial individual contribution in bridging the vocabulary gap between informal user-generated text and the model’s pre-training distribution.
Evaluasi Usability Sistem Informasi Manajemen Kepegawaian Kalimantan Selatan Berdasarkan ISO 9241-11 Maulana, Syarif; Saputro, Setyo Wahyu; Abadi, Friska; Turianto Nugrahadi, Dodon; Reza Faisal, Mohammad
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 13 No 3: Juni 2026
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2026133

Abstract

Penelitian ini menyajikan evaluasi usability secara komprehensif terhadap Sistem Informasi Manajemen Kepegawaian (SIMPEG) Provinsi Kalimantan Selatan berdasarkan kerangka ISO 9241-11. Evaluasi dilakukan dengan mengintegrasikan Performance Measurement, System Usability Scale (SUS), dan Retrospective Think-Aloud (RTA) untuk menilai efektivitas, efisiensi, dan kepuasan pengguna. Sebanyak 25 partisipan menyelesaikan tujuh skenario tugas yang merepresentasikan fungsi utama sistem. Hasil pengujian menunjukkan tingkat Task Completion Rate sebesar 96% dan Overall Relative Efficiency sebesar 91%, yang mengindikasikan efektivitas dan efisiensi sistem yang tinggi. Skor rata-rata SUS sebesar 79,7 menempatkan sistem pada kategori “baik” dengan tingkat akseptabilitas “dapat diterima”. Analisis inferensial menggunakan uji korelasi Spearman menun-jukkan tidak terdapat hubungan signifikan antara efisiensi objektif dan kepuasan subjektif (r = 0,129; p = 0,538), yang mengindikasikan bahwa dimensi usability dapat bersifat independen dalam konteks sistem pemerintahan. Uji Friedman menunjukkan adanya perbedaan signifikan waktu penyelesaian antar skenario tugas (χ²(6) = 141,071; p < 0,001), yang mengidentifikasi adanya bottleneck usability pada modul tertentu. Temuan kualitatif dari RTA memperkuat hasil kuantitatif dengan mengungkap kendala pada aspek kemudahan belajar, konsistensi navigasi, dan integrasi fitur. Penelitian ini memberikan kontribusi melalui integrasi analisis deskriptif dan inferensial dalam evaluasi usability sistem pemerintahan sebagai baseline pengembangan sistem selanjutnya.   Abstract   This study presents a comprehensive usability evaluation of the Civil Service Management Information System (SIMPEG) of South Kalimantan Province, based on the ISO 9241-11 framework. The evaluation integrates Performance Measurement, the System Usability Scale (SUS), and the Retrospective Think-Aloud (RTA) protocol to assess the system's effectiveness, efficiency, and user satisfaction. A total of 25 participants completed seven task scenarios representing the system’s core functions. The testing results showed a Task Completion Rate of 96% and an Overall Relative Efficiency of 91%, indicating high levels of effectiveness and efficiency. The average SUS score of 79.7 places the system in the “Good” category with an “Acceptable” level of usability. Inferential analysis using Spearman’s correlation test revealed no significant relationship between objective efficiency and subjective satisfaction (r = 0.129; p = 0.538), suggesting that usability dimensions may be independent in the context of government systems. The Friedman test indicated a significant difference in task completion times across scenarios (χ²(6) = 141.071; p < 0.001), identifying specific modules as usability bottlenecks. Qualitative findings from the RTA supported the quantitative results by uncovering challenges related to learnability, navigation consistency, and feature integration. This study contributes by integrating descriptive and inferential analysis in the usability evaluation of government systems, serving as a baseline for future system development.
Effect of SMOTE Variants on Software Defect Prediction Classification Based on Boosting Algorithm Rahmina Ulfah Aflaha; Rudy Herteno; Mohammad Reza Faisal; Friska Abadi; Setyo Wahyu Saputro
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 10 No. 2 (2024): June
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v10i2.28521

Abstract

Detecting software defects early on is critical for avoiding significant financial losses. However, building accurate software defect prediction models can be challenging due to class imbalance, where the data for defective modules is much less than for standard modules. This research addresses this issue using the imbalanced dataset NASA MDP. To address this issue, researchers have proposed new methods that combine data level balancing approaches with 14 variations of the SMOTE algorithm to increase the amount of defective module data. An algorithm-level approach with three boosting algorithms, Catboost, LightGBM, and Gradient Boosting, is applied to classify modules as defective or non-defective. These methods aim to improve the accuracy of software defect prediction. The results show that this new method can produce a more accurate classification than previous studies. The DSMOTE and Gradient Boosting pair with 0.9161 has the highest average accuracy (0.9161). The DSMOTE and Catboost model achieved the highest average AUC value (0.9637). The ADASYN kernel and Catboost showed the best ability to perform the average G-mean value (0.9154). The research contribution to software defect prediction involves developing new techniques and evaluating their effectiveness in addressing class imbalance.
Functional Evaluation of the Logia Dashboard Using Boundary Value Testing and Cause-Effect Graph Techniques Muhammad Rizky Aulia Ramadhan; Friska Abadi; Dodon Turianto Nugrahadi; Setyo Wahyu Saputro; Rudy Herteno
Telematika Vol 18, No 2: August (2025)
Publisher : Universitas Amikom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35671/telematika.v18i2.3121

Abstract

The Logia Dashboard is a web-based information system used to manage rehabilitation plant data on post-mining land. As an alpha-stage system, Logia requires thorough functional and performance evaluation to ensure that all input validations, logical processes, and system responses operate correctly before wider implementation. This study aims to evaluate the functional reliability and performance of the Logia Dashboard by applying a combined approach of Boundary Value Testing (BVT) and Cause-Effect Graph (CEG) techniques, supported by performance testing using Google Lighthouse. The research design adopts a black-box testing approach. BVT is applied to validate input boundaries on critical features, including login, data editing, QR code generation, and account creation. Meanwhile, CEG is used to model logical relationships between input conditions and system outputs to generate systematic test cases. A total of 39 optimized functional test cases were executed in a controlled local environment. Performance testing was conducted using Lighthouse by measuring key metrics such as First Contentful Paint (FCP), Largest Contentful Paint (LCP), Total Blocking Time (TBT), and Cumulative Layout Shift (CLS). The functional testing results show that 37 out of 39 test cases passed, yielding a success rate of 94.87%. Two failed cases were identified in the login feature, indicating weaknesses in input validation feedback. Performance testing produced an average Lighthouse score of 97, demonstrating that the system has excellent load speed and interface stability, although minor layout instability was detected on certain pages. These results indicate that the combined application of BVT and CEG is effective for detecting boundary-related and logical input errors in alpha-stage web systems. The findings also provide concrete recommendations for improving login validation and interface stability, supporting further development of the Logia Dashboard toward a more reliable and robust system for post-mining land management.
Comparative Analysis of Distance Metrics in KNN and SMOTE Algorithms for Software Defect Prediction Khusnul Rahmi Maulidha; Mohammad Reza Faisal; Setyo Wahyu Saputro; Friska Abadi; Dodon Turianto Nugrahadi; Puput Dani Prasetyo Adi; Hariyady Hariyady
Telematika Vol 18, No 1: February (2025)
Publisher : Universitas Amikom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35671/telematika.v18i1.3008

Abstract

As the complexity and scale of projects increase, new challenges arise related to handling software defects. One solution uses machine learning-based software defect prediction techniques, such as the K-Nearest Neighbors (KNN) algorithm. However, KNN’s performance can be hindered by the majority vote mechanism and the distance/similarity metric choice, especially when applied to imbalanced datasets. This research compares the effectiveness of Euclidean, Hamming, Cosine, and Canberra distance metrics on KNN performance, both before and after the application of SMOTE (Synthetic Minority Over-sampling Technique). Results show significant improvements in the AUC and F-1 measure values across various datasets after the SMOTE application. Following the SMOTE application, Euclidean distance produced an AUC of 0.7752 and an F1 of 0.7311 for the EQ dataset. With Canberra distance and SMOTE, the JDT dataset produced an AUC of 0.7707 and an F-1 of 0.6342. The LC dataset improved to 0.6752 and 0.3733 in tandem with the ML dataset, which climbed to 0.6845 and 0.4261 with Canberra distance. Lastly, after using SMOTE, the PDE dataset improved to 0.6580 and 0.3957 with Canberra distance. The findings confirm that SMOTE, combined with suitable distance metrics, significantly boosts KNN’s prediction accuracy, with a P-value of 0.0001.
Detecting respiratory diseases using spectrogram-based deep features and machine learning algorithms Elvina Nur Hana; Mohammad Reza Faisal; Dwi Kartini; Muhammad Itqan Mazdadi; Setyo Wahyu Saputro; Fatma Indriani; Kenji Satou
Bulletin of Electrical Engineering and Informatics Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i2.10585

Abstract

Early diagnosis of respiratory diseases is difficult as lung sound analysis requires the skills of medical professionals. Respiratory diseases are one of the leading causes of death in the world, so early detection is critical. Automatic identification is made possible by artificial intelligence. However, lung sound data is unstructured, while artificial intelligence often requires structured data. Therefore, feature extraction is required to structure the voice data. Traditional techniques such as mel-frequency cepstral coefficients (MFCC) often produce fewer features and information. This research uses a deep feature approach, which produces more features, as a solution. This research applies three convolutional neural network (CNN) architectures as deep features, namely VGG-16, DenseNet-121, and ResNet50, with machine learning classifications, namely random forest, support vector machine (SVM), Naïve Bayes, and K-nearest neighbors (KNN). This research will identify the optimal combination of methods. The results of this study show that respiratory disease classification can be effectively achieved by combining deep features and machine learning classification. The results of 10-fold cross-validation show that the three CNN architectures perform best on SVM with a linear kernel. The accuracy of VGG-16 is 70.63%, ResNet-50 is 64.93%, and DenseNet-121 is 73.58%.
Co-Authors Akhmad Zakirin Akhtar, Zarif Bin Andi Farmadi Andi Farmadi Angga Maulana Akbar Anshory, Muhammad Naufal As’ary Ramadhan Badali, Rahmat Amin Deni Sutaji Dodon Turianto Nugrahadi Dwi Kartini, Dwi Elvina Nur Hana Fadhillah, Muhammad Alif Fatma Indriani Fatma Indriani Febrian, Muhamad Michael Friska Abadi Hanafi, Muhammad Bashir Hariyady Hariyady Herteno, Rudy Hertono, Rudy Irwan Budiman Irwan Budiman Kamil, Hawariul Kenji Satou Khusnul Rahmi Maulidha Martalisa, Asri Mbeledogu, Njideka Nkemdilim Muhammad Fikri Muhammad Ikhwanul Hakim Muhammad Itqan Mazdadi Muhammad Khalid Maulana Muhammad Nazar Gunawan Muhammad Noor Muhammad Reza Faisal, Muhammad Reza Muhammad Rizky Aulia Ramadhan Muliadi Nabella, Putri Nur Hidayatullah, Wildan Puput Dani Prasetyo Adi Putri Nabella Radityo Adi Nugroho Rahmat Budianoor Rahmina Ulfah Aflaha Raidra Zeniananto Reza Faisal, Mohammad Rezeki, Abdillah Rozaq, Hasri Akbar Awal Rudy Herteno Rudy Herteno Rudy Herteno Saragih, Triando Hamonangan Suryadi, Mulia Kevin Syahputra, Muhammad Reza Syarif Maulana, Syarif Ujang Ruyandi Wahyudi Ariannor Wardana, Muhammad Difha YILDIZ, Oktay Yulia Yudihartanti