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All Journal ComEngApp : Computer Engineering and Applications Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) JURNAL SISTEM INFORMASI BISNIS Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal Teknologi Informasi dan Ilmu Komputer Telematika KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal) JOIN (Jurnal Online Informatika) Jurnal Teknologi dan Sistem Komputer Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Komputasi Sebatik Journal of Electronics, Electromedical Engineering, and Medical Informatics Jurnal Informatika dan Rekayasa Elektronik Jurnal Mnemonic Bubungan Tinggi: Jurnal Pengabdian Masyarakat Computer Science and Information Technologies Jurnal Teknik Informatika (JUTIF) Journal of Data Science and Software Engineering Jurnal Informatika Polinema (JIP) Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Jurnal Komputasi
Rudy Herteno
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Library & Information Science Materials Science & Nanotechnology Social Sciences Other

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The Effect of Smote-Tomek on the Classification of Chronic Diseases Based on Health and Lifestyle Data Muhammad Adika Riswanda; Friska Abadi; Muhammad Itqan Mazdadi; Mohammad Reza Faisal; Rudy Herteno
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 8 No. 1 (2026): February
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v8i1.324

Abstract

Machine learning models for chronic disease prediction are often trained on imbalanced healthcare datasets, where non-disease cases dominate. This condition can lead to misleadingly high accuracy while failing to identify patients with chronic diseases, limiting clinical usefulness. This study aims to analyze the impact of class imbalance on model performance and to evaluate the effectiveness of the SMOTE–Tomek resampling technique in improving chronic disease prediction. This research provides empirical evidence that accuracy alone is insufficient for evaluating healthcare models and demonstrates that imbalance-aware preprocessing is essential for valid and reliable chronic disease detection. Five classification models, such as Support Vector Machine, Random Forest, K-Nearest Neighbors, Gradient Boosting, and XGBoost, were evaluated on a lifestyle-based chronic disease dataset under two conditions: without resampling and with SMOTE–Tomek. Model performance was assessed using accuracy, precision, recall, F1-score, and AUC. Without SMOTE–Tomek, all models failed to detect chronic disease cases, producing near-zero recall and F1-scores despite accuracy exceeding 80%. After applying SMOTE–Tomek, substantial improvements were observed across all models, particularly in recall and AUC. Support Vector Machine achieved the best overall performance, with an accuracy of 92.9%, a precision of 92%, a recall of 93.9%, an F1-score of 0.93, and an AUC of 0.98. The findings confirm that handling class imbalance is a prerequisite for meaningful chronic disease prediction. The consistent increase in recall and AUC across all evaluated models confirms that the improvement stems from enhanced class separability rather than metric inflation. The proposed approach supports more reliable early screening and decision-support systems in preventive healthcare
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.
Metrics Based Feature Selection for Software Defect Prediction Radityo Adi Nugroho; Friska Abadi; M. Reza Faisal; Rudy Herteno; Rahmat Ramadhani
Jurnal Komputasi Vol. 8 No. 2 (2020)
Publisher : Jurusan Ilmu Komputer Fakultas MIPA Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/komputasi.v8i2.2670

Abstract

Nowadays, software is very influential on various sectors of life, both to solve business needs, as well as personal needs. To have a Software with high quality, testing is needed to avoid software defect. Research on software defects involving Machine Learning is currently being carried out by many researchers. This method contains one important step, which is called feature selection. In this study, researchers conducted a feature selection based on the software metric category to determine the level of accuracy of the prediction of software defects by utilizing 13 (thirteen) datasets from NASA MDP namely CM1, JM1, KC1, KC3, KC4, MC1, MC2, MW1, PC1, PC2, PC3, PC4, and PC5. To classify, the researchers involved 5 (five) classifiers, namely Naive Bayes, Decision Trees, Random Forests, K-Nearest Neighbor, and Support Vector Machines. The research result shows that each attribure on software metric categories has effect on each dataset. Naive Bayes Algorithm and Random Forest Algorithm can give better performance than other algorithm in classifieng software defect with feature selection based on metrics. On the other hand, the best metrics category on each classifier algorithm is metric Misc. From average AUC value, it can be concluded that metrics category which can give best performance is metric LoC, followed by metric Misc. Both categories have achieved highest AUC value in Random Forest classifier.
Co-Authors Abdullayev, Vugar Achmad Zainudin Nur Adawiyah, Laila Adela Putri Ariyanti Aflaha, Rahmina Ulfah Ahmad Juhdi Ahmad Rusadi Akhtar, Zarif Bin Al Ghifari, Muhammad Akmal Al Habesyah, Noor Zalekha Alfando, Muhammad Alvin Andi - Farmadi Andi Farmadi Andi Farmadi Andi Farmadi Angga Maulana Akbar Antoh, Soterio Arifin Hidayat Aryanti, Agustia Kuspita Athavale, Vijay Anant Azizah, Azkiya Nur Azizah, Siti Roziana Bahriddin Abapihi Dendy Fadhel Adhipratama Dendy Dodon Turianto Nugrahadi Dwi Kartini Dwi Kartini, Dwi Emma Andini Faisal, M. Reza Fatma Indriani Fauzan Luthfi, Achmad Fayyadh, Muhammad Naufaldi Febrian, Muhamad Michael Friska Abadi Ghinaya, Helma Hermiati, Arya Syifa Huynh, Phuoc-Hai Irwan Budiman Irwan Budiman Itqan Mazdadi, Muhammad Junaidi, Ridha Fahmi Lilies Handayani Lisnawati Lumbanraja, Favorisen R M Kevin Warendra Mariana Dewi Miftahul Muhaemen Muflih Ihza Rifatama Muhammad Adika Riswanda Muhammad Alkaff Muhammad Anshari Muhammad Azmi Adhani Muhammad Denny Ersyadi Rahman Muhammad Ikhwanul Hakim Muhammad Itqan Mazdadi Muhammad Noor Muhammad Reza Faisal, Muhammad Reza Muhammad Rizky Mubarok Muhammad Sholih Afif Muhammad Syahriani Noor Basya Basya Muliadi Muliadi MULIADI -, MULIADI Muliadi Muliadi Muliadi Muliadi Muliadi Muliadi Nabella, Putri Nafis Satul Khasanah Ngo, Luu Duc Noor Hidayah Noryasminda Nur Hidayatullah, Wildan Nurdiansyah Nurdiansyah Nursyifa Azizah Oni Soesanto Pratama, Muhammad Yoga Adha Putri Nabella Putri, Nitami Lestari Radityo Adi Nugroho Rahmad Ubaidillah Rahmat Ramadhani Raidra Zeniananto Ramadhan, As`'ary Reza Faisal, Mohammad Rizky Ananda, Muhammad Rozaq, Hasri Akbar Awal Saragih, Triando Hamonangan Setyo Wahyu Saputro Siti Aisyah Solechah Suci Permata Sari Suryadi, Mulia Kevin Tri Mulyani Ulya, Azizatul Vina Maulida, Vina Wahyu Ramadansyah Zaini Abdan Zamzam, Yra Fatria