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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 Telematika 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 International Journal of Advances in Data and Information Systems 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 Education Electrical & Electronics Engineering Energy Engineering Health Professions Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology

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Seleksi Fitur dengan Particle Swarm Optimization pada Klasifikasi Penyakit Parkinson Menggunakan XGBoost Kurnia, Deni; Itqan Mazdadi, Muhammad; Kartini, Dwi; Adi Nugroho, Radityo; Abadi, Friska
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 10 No 5: Oktober 2023
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2023107252

Abstract

Penyakit Parkinson merupakan gangguan pada sistem saraf pusat yang mempengaruhi sistem motorik. Diagnosis penyakit ini cukup sulit dilakukan karena gejalanya yang serupa dengan penyakit lain. Saat ini diagnosa dapat dilakukan menggunakan machine learning dengan memanfaatkan rekaman suara pasien. Fitur yang dihasilkan dari ekstraksi rekaman suara tersebut relatif cukup banyak sehingga seleksi fitur perlu dilakukan untuk menghindari memburuknya kinerja sebuah model. Pada penelitian ini, Particle Swarm Optimization digunakan sebagai seleksi fitur, sedangkan XGBoost akan digunakan sebagai model klasifikasi. Selain itu model juga akan diterapkan SMOTE untuk mengatasi masalah ketidakseimbangan kelas data dan hyperparameter tuning pada XGBoost untuk mendapatkan hyperparameter yang optimal. Hasil pengujian menunjukkan bahwa nilai AUC pada model dengan seleksi fitur tanpa SMOTE dan hyperparameter tuning adalah 0,9325, sedangkan pada model tanpa seleksi fitur hanya mendapat nilai AUC sebesar 0,9250. Namun, ketika kedua teknik SMOTE dan hyperparameter tuning digunakan bersamaan, penggunaan seleksi fitur mampu memberikan peningkatan kinerja pada model. Model dengan seleksi fitur mendapat nilai AUC sebesar 0,9483, sedangkan model tanpa seleksi fitur hanya mendapat nilai AUC sebesar 0,9366.   Abstract   Parkinson's disease is a disorder of the central nervous system that affects the motor system. Diagnosis of this disease is quite difficult because the symptoms are similar to other diseases. Currently, diagnosis can be done using machine learning by utilizing patient voice recordings. The features generated from the extraction of voice recordings are relatively large, so feature selection needs to be done to avoid deteriorating the performance of a model. In this research, Particle Swarm Optimization is used as feature selection, while XGBoost will be used as a classification model. In addition, the model will also be applied SMOTE to overcome the problem of data class imbalance and hyperparameter tuning on XGBoost to get optimal hyperparameters. The test results show that the AUC value on the model with feature selection without SMOTE and hyperparameter tuning is 0.9325, while the model without feature selection only gets an AUC value of 0.9250. However, when both SMOTE and hyperparameter tuning techniques are used together, the use of feature selection is able to provide improved performance on the model. The model with feature selection gets an AUC value of 0.9483, while the model without feature selection only gets an AUC value of 0.9366.
Design of Application Framework for Vital Monitoring Mobile-Based System Rizky Ananda, Muhammad; Faisal, Mohammad Reza; Herteno, Rudy; Nugroho, Radityo Adi; Abadi, Friska
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.v10i2.28416

Abstract

In the realm of modern healthcare, continuous monitoring can leverage the affordable wearable devices available on the market to manage costs. However, these devices face several limitations, such as restricted access for other parties, including nurses and doctors, and the need for redevelopment to integrate new devices for data accessibility. This study addresses these challenges by establish an application framework tailored for mobile-based systems, by ensuring accessibility by external parties. The research contribution is encompassing two key aspects: the potential implementation of Feature-Oriented Domain Analysis (FODA) in the domain of mobile-based vital sign monitoring, particularly in the absence of prior studies addressing the same context, and the identification reusable (frozen spots) and adaptable components (hot spots), providing guidance for the development of mobile-based vital sign monitoring. FODA is utilized during the analysis activity. Design patterns are then implemented when creating class diagrams in the design activity. This study finding reveal 7 primary features and 18 sub-features essential that must be incorporated into the application framework. The framework includes 5 hot spots and 7 frozen spots, with the implementation of Strategy and Filter design patterns. In conclusion, the developed application framework represents a significant advancement in vital sign monitoring, particularly within mobile-based systems. This study emphasizing limitations in analysis and design phases. In future research, the focus will shift to the construction and stabilization phases, all crucial for refining the framework. Implementing framework in actual applications can aid in developing vital sign monitoring systems and potentially improving healthcare outcomes.
Improving with Hybrid Feature Selection in Software Defect Prediction Pratama, Muhammad Yoga Adha; Herteno, Rudy; Faisal, Mohammad Reza; Nugroho, Radityo Adi; Abadi, Friska
JOIN (Jurnal Online Informatika) Vol 9 No 1 (2024)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v9i1.1307

Abstract

Software defect prediction (SDP) is used to identify defects in software modules that can be a challenge in software development. This research focuses on the problems that occur in Particle Swarm Optimization (PSO), such as the problem of noisy attributes, high-dimensional data, and premature convergence. So this research focuses on improving PSO performance by using feature selection methods with hybrid techniques to overcome these problems. The feature selection techniques used are Filter and Wrapper. The methods used are Chi-Square (CS), Correlation-Based Feature Selection (CFS), and Forward Selection (FS) because feature selection methods have been proven to overcome data dimensionality problems and eliminate noisy attributes. Feature selection is often used by some researchers to overcome these problems, because these methods have an important function in the process of reducing data dimensions and eliminating uncorrelated attributes that can cause noisy. Naive Bayes algorithm is used to support the process of determining the most optimal class. Performance evaluation will use AUC with an alpha value of 0.050. This hybrid feature selection technique brings significant improvement to PSO performance with a much lower AUC value of 0.00342. Comparison of the significance of AUC with other combinations shows the value of FS PSO of 0.02535, CFS FS PSO of 0.00180, and CS FS PSO of 0.01186. The method in this study contributes to improving PSO in the SDP domain by significantly increasing the AUC value. Therefore, this study highlights the potential of feature selection with hybrid techniques to improve PSO performance in SDP.
Implementation of Copeland Method on Wrapper-Based Feature Selection Using Random Forest For Software Defect Prediction Aryanti, Agustia Kuspita; Herteno, Rudy; Indriani, Fatma; Nugroho, Radityo Adi; Muliadi, Muliadi
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 7 No. 1 (2025): February
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/2pgffc67

Abstract

Software Defect Prediction is crucial to ensure software quality. However, high-dimensional data presents significant challenges in predictive modelling, especially identifying the most relevant features to improve model performance. Therefore, efforts are needed to address these issues, and one is to apply feature selection methods. This study introduces a new approach by applying the Copeland ranking method, which aggregates feature weights from multi-wrapper methods, including Recursive Feature Elimination (RFE), Boruta, and Custom Grid Search, using 12 NASA MDP datasets. The study also applies Random Forest classification and evaluates the model using AUC and t-Test. In addition, this study also compares the accuracy and precision values produced by each method. The results consistently show that the Copeland ranking method produces superior results compared to other ranking methods. The average AUC value obtained from the Copeland ranking method is 0.7496, higher than the Majority ranking method with an average AUC of 0.7416 and the Optimal Rank ranking method with an average AUC of 0.7343. These findings confirm that applying the Copeland ranking method in wrapper-based feature selection can enhance classification performance in software defect prediction using Random Forest compared to other ranking methods. The strength of the Copeland method lies in its ability to integrate rankings from various feature selection approaches and identify relevant features. The findings of this research demonstrate the potential of the Copeland ranking method as a reliable tool for ranking features obtained from various wrapper-based feature selection techniques. The implementation of this approach contributes to improved software defect prediction and provides new insights for the development of ranking methods in the future
Enhancing Software Defect Prediction: HHO-Based Wrapper Feature Selection with Ensemble Methods Fauzan Luthfi, Achmad; Herteno, Rudy; Abadi, Friska; Adi Nugroho, Radityo; Itqan Mazdadi, Muhammad; Athavale, Vijay Anant
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/f2140043

Abstract

The growing complexity of data across domains highlights the need for effective classification models capable of addressing issues such as class imbalance and feature redundancy. The NASA MDP dataset poses such challenges due to its diverse characteristics and highly imbalanced classes, which can significantly affect model accuracy. This study proposes a robust classification framework integrating advanced preprocessing, optimization-based feature selection, and ensemble learning techniques to enhance predictive performance. The preprocessing phase involved z-score standardization and robust scaling to normalize data while reducing the impact of outliers. To address class imbalance, the ADASYN technique was employed. Feature selection was performed using Binary Harris Hawk Optimization (BHHO), with K-Nearest Neighbor (KNN) used as an evaluator to determine the most relevant features. Classification models including Random Forest (RF), Support Vector Machine (SVM), and Stacking were evaluated using performance metrics such as accuracy, AUC, precision, recall, and F1-measure. Experimental results indicated that the Stacking model achieved superior performance in several datasets, with the MC1 dataset yielding an accuracy of 0.998 and an AUC of 1.000. However, statistical significance testing revealed that not all observed improvements were meaningful; for example, Stacking significantly outperformed SVM but did not show a significant difference when compared to RF in terms of AUC. This underlines the importance of aligning model choice with dataset characteristics. In conclusion, the integration of advanced preprocessing and metaheuristic optimization contributes positively to software defect prediction. Future research should consider more diverse datasets, alternative optimization techniques, and explainable AI to further enhance model reliability and interpretability.
Multi-Criteria Decision Making dalam Seleksi Fitur Ensemble untuk Prediksi Cacat Perangkat Lunak Fikri, Muhammad; Herteno, Rudy; Adi Nugroho, Radityo; Wahyu Saputro, Setyo; Abadi, Friska
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 12 No 6: Desember 2025
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2025125

Abstract

Prediksi cacat perangkat lunak merupakan upaya strategis dalam meningkatkan kualitas produk melalui identifikasi dini modul yang berpotensi cacat. Kinerja prediksi dipengaruhi oleh pemilihan fitur, karena informasi yang berlebihan dan tidak relevan dapat mempengaruhi kualitas pembelajaran model. Seleksi fitur ensemble dinilai efektif dalam menyeleksi fitur yang relevan dengan menggabungkan beberapa metode seleksi fitur berbasis filter. Diperlukan mekanisme integrasi untuk menyatukan hasil dari empat teknik filter—Mutual Information, Fisher Score, Uncertainty dan Relief. Penelitian ini membandingkan empat metode Multi‑Criteria Decision Making—TOPSIS, VIKOR, EDAS, dan WASPAS—yang bekerja dengan merangking nilai relevansi fitur hasil seleksi filter tersebut. Sepuluh fitur teratas dari tiap metode kemudian dievaluasi menggunakan model Random Forest dengan metrik AUC melalui K‑Fold cross‑validation. Dari 12 dataset NASA MDP yang diuji, TOPSIS menunjukkan kinerja paling konsisten dan terbaik dengan nilai rata-rata AUC sebesar 0,8038. Temuan ini menegaskan pentingnya pemilihan metode integrasi yang tepat dalam meningkatkan akurasi prediksi cacat perangkat lunak dan memberikan panduan bagi pengembangan model yang lebih efektif.   Abstract Software defect prediction is a strategic effort to improve product quality through early identification of potentially defective modules. Prediction performance is influenced by feature selection, because redundant and irrelevant information can affect the quality of model learning. Ensemble feature selection is considered effective in selecting relevant features by combining several filter-based feature selection methods. An integration mechanism is needed to unify the results of four filter techniques—Mutual Information, Fisher Score, Uncertainty and Relief. This study compares four Multi-Criteria Decision Making methods—TOPSIS, VIKOR, EDAS, and WASPAS—which work by ranking the relevance values ​​of the filter-selected features. The top ten features from each method are then evaluated using the Random Forest model with the AUC metric through K-Fold cross-validation. Of the 12 NASA MDP datasets tested, TOPSIS showed the most consistent and best performance with an average AUC value of 0.8038. These findings emphasize the importance of choosing the right integration method in improving the accuracy of software defect prediction and provide guidance for the development of more effective models.
Analisis Sentimen Ulasan Media Sosial UMKM Kuliner dengan Pendekatan Lexicon-Based dan Kosakata Khusus Setyo Wahyu Saputro; Friska Abadi; Radityo Adi Nugroho
Jurnal Informatika Polinema Vol. 12 No. 2 (2026): Vol. 12 No. 2 (2026)
Publisher : UPT P2M State Polytechnic of Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33795/jip.v12i2.9302

Abstract

UMKM kuliner di Kalimantan Selatan memanfaatkan media sosial sebagai sarana utama untuk mengetahui opini pelanggan, namun jumlah komentar yang sangat besar menyulitkan pelaku usaha untuk menelaahnya secara manual. Kondisi ini menegaskan perlunya pendekatan analisis sentimen yang mampu mengolah data ulasan secara efisien serta sesuai dengan karakteristik bahasa lokal. Penelitian ini bertujuan mengembangkan metode analisis sentimen berbasis lexicon yang diperkaya dengan kosakata domain-spesifik kuliner dan bahasa Banjar agar hasil klasifikasi lebih akurat dan kontekstual. Data penelitian diperoleh dari 3.500 komentar publik di Instagram dan TikTok. Tahap preprocessing mencakup case folding, pembersihan karakter khusus, tokenisasi, stopword removal, normalisasi, dan stemming. Selanjutnya, InSet Lexicon disempurnakan melalui penyuntikan kosakata baru serta penyesuaian bobot kata sesuai konteks kuliner lokal. Hasil analisis menunjukkan distribusi sentimen terdiri dari 2.050 komentar positif (58,57%), 934 komentar netral (26,69%), dan 516 komentar negatif (14,74%). Evaluasi menunjukkan peningkatan akurasi signifikan setelah perluasan lexicon, yaitu 93,49% untuk sentimen negatif, 94,64% untuk netral, dan 96,94% untuk positif, dibandingkan akurasi awal yang berkisar antara 51–73%. Temuan ini membuktikan bahwa pengayaan lexicon menggunakan kosakata lokal dan domain-spesifik secara substansial meningkatkan performa analisis sentimen. Pendekatan ini memberikan solusi praktis dan terjangkau bagi UMKM untuk memahami opini pelanggan secara lebih representatif, serta dapat dimanfaatkan dalam pengambilan keputusan strategis dan perbaikan kualitas layanan maupun promosi produk kuliner.
Performance Analysis of the Fuzzing Method in Detecting API Vulnerabilities in Mobile Healthcare Application X Based on OWASP API Security Top 10 Hakim, Muhammad Ikhwanul; Nugroho, Radityo Adi; Nugrahadi, Dodon Turianto; Herteno, Rudy; Saputro, Setyo Wahyu
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.
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

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.
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 Hakim, Muhammad Ikhwanul Hanif Rahardian Herteno, Rudy Irwan Budiman Irwan Budiman Itqan Mazdadi, Muhammad Ivan Sitohang Maya Yusida Muhammad Angga Wiratama Muhammad Azmi Adhani Muhammad Fikri 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 Rahmat Ramadhani Raidra Zeniananto Reina Alya Rahma Reza Faisal, Mohammad Rezeki, Abdillah 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 Suci Permata Sari Suryadi, Mulia Kevin Sutan Takdir Alam Wahyu Caesarendra Wahyu Ramadansyah Wahyu Saputro, Setyo Zaini Abdan