Article Per Year (5 Year)
p-Index From 2021 - 2026
2.879
P-Index
This Author published in this journals
All Journal Jurnal Masyarakat Informatika Jurnal Simetris Rekursif: Jurnal Informatika Jurnal Pseudocode Jurnal SOLMA Jurnal Eksplora Informatika Indonesian Journal of Computer Science and Engineering
Desi Andreswari
University of Bengkulu
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Other

Published : 21 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 21 Documents
Search

 1   2   3 
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 Aan Erlansari Alfareza, Muhammad Yusril Azaharah, Diyah Ishita Balqis, Nabilatul Coastera, Funny Faraday Dwi Jayanto, Paksi Dwi Safitri, Dania Egisthi, Vonny Afriyona Endina Putri Purwandari Epana Sari, Renti Ernawati Ernawati Ernawati Funny Farady Coastera Funny Farady Coastera, Funny Farady Ginting , Sempurna br. Ginting, Sempurna br Hasian Lumbanraja, Joi Pebrianty Julia Purnama Sari Julia Purnama Sari Juwita, Fatma Manurung, Wahyu Ozorah Mochammad Yusa, Mochammad Monica, Exca Wella Oktoeberza, Widhia KZ Rima Yanti Nasution, Nofa Rusdi Efendi Safitri, Hanifah Nur Sarwono, Sarwit Siregar, Michael Exaudi Syafitri, Arinda Tatik Suteky Tiara Sella, Miranda Wahyudi, Rahmat Fikri Wijanarko, Andang Winda P, Desi Ade Yudi Setiawan