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All Journal Jurnal Masyarakat Informatika JSI: Jurnal Sistem Informasi (E-Journal) Jurnal Informatika dan Teknik Elektro Terapan IT JOURNAL RESEARCH AND DEVELOPMENT JMKSP (Jurnal Manajemen, Kepemimpinan, dan Supervisi Pendidikan) JSAI (Journal Scientific and Applied Informatics) Indonesian Journal of Business Intelligence (IJUBI) Teknomatika (Jurnal Teknologi dan Informatika) Jurnal Generic Jurnal Altifani Penelitian dan Pengabdian kepada Masyarakat Jurnal Pendidikan Sains dan Komputer Jurnal Puan Indonesia HISTORIA: Jurnal Program Studi Pendidikan Sejarah Journal of Social Work and Science Education Indonesian Journal of Computer Science and Engineering Jurnal Teknik Informatika dan Desain Komunikasi Visual
Rusdi Efendi
Department of Informatics Engineering Faculty of Computer Science Universitas Sriwijaya
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Journal : Jurnal Masyarakat Informatika

 1 
Comparative Analysis of Machine Learning for Stroke Classification Using YOLOv11 Detection and a Radiomics-Based Two-Stage Model Manurung, Wahyu Ozorah; Ernawati, Ernawati; Oktoeberza, Widhia KZ; Andreswari, Desi; Purwandari, Endina Putri; Efendi, Rusdi
Jurnal Masyarakat Informatika Vol 17, No 1 (2026): May 2026 (Ongoing)
Publisher : Department of Informatics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jmasif.17.1.78464

Abstract

Stroke is a leading cause of disability and death worldwide, including in Indonesia. Rapid and accurate diagnosis is crucial, especially during the golden period (3–4.5 hours). CT scans are the primary imaging modality, but manual interpretation is often limited by time, subjectivity, and radiologist availability. This study proposes a two-stage model integrating YOLOv11 for lesion detection and machine learning for classification, using radiomics for feature extraction. In the first stage, YOLOv11 detects lesions and generates bounding boxes, which serve as Regions of Interest (ROIs). In the second stage, radiomics features are extracted and classified using Naïve Bayes, Support Vector Machine (SVM), and Random Forest. Results show YOLOv11 achieved an overall mAP@50 of 0.732, with the highest performance in hemorrhagic stroke (0.741). Radiomics-based classification further improves stability, achieving accuracies of 0.97–0.99 and precision, recall, and F1 scores≥0.94. Among classifiers, SVM performed best, with a test accuracy of 0.97, a false positive rate of 1.23%, total error 0.0218, generalization gap -0.0117, variance 0.0002, standard deviation 0.003635, confidence interval 0.9708 (+/-0.0073), and consistent fold accuracy between 96.5–97.5%, indicating stability without overfitting. These findings confirm that the combination of the YOLOv11 two-stage model, radiomics, and SVM provides a robust approach to support stroke diagnosis.
Co-Authors -, Abdurahman - -, Isbatudinia - -, Sukemi - . Apriansyah AA Sudharmawan, AA Afif, Hasnan Akbari, Hayqal Nur Ali Ibrahim Aliya, Moza Gina Andini, Vania Raissa Anggariani Rahayu , Devi Apriansyah Putra Bayu Wijaya Putra Boko Susilo Buchari, M. Ali Daud Yahya Dedy Hamdani Desi Andreswari Eko Risdianto Endang Lestari Ruskan Endina Putri Purwandari Erlanshari, Aan Ernawati Ernawati Farady Coastera , Funny Florensia, Yesinta Gibran, M. Aqeel Hallatu, Stefani Tasya Hamidah, Wanda Happy Fitria, Happy Hargiani, Fransisca Xaveria Irwan Fathurrochman, Irwan Iwan Setiawan Jambak, Muhammad Ihsan Junia Kurniati, Junia Kirom, Miftahul Kurniawati, Junia Manurung, Wahyu Ozorah Marjusalinah, Anna D. Masito, Fitri MAsykur, Yahya Miranti Miranti Mochammad Yusa, Mochammad Muhammad Akbar Nabir, Nazwa Abelia Netty Herawati Oktoeberza, Widhia KZ Prananosa, Ahmad Gawdy Prasetyo, Yoga Adi Prawira, Wahyu Purwita Sari, Purwita Rahman, Fadhil Raihan, Muhammad Ramadhan, Niki Rita Erlina, Rita Rizky, Raden Ayu Hikmah Ronaldo, M. Sagita, Rani Sanjaya, M. Rudi Seprina, Iin Siti Latifah Utami, Dara Wulandari, Dinda Yudi Setiawan Yunita Yunita