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All Journal PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Format : Jurnal Imiah Teknik Informatika Jurnal Abdimas BSI: Jurnal Pengabdian Kepada Masyarakat Bianglala Informatika : Jurnal Komputer dan Informatika Akademi Bina Sarana Informatika Yogyakarta Bina Insani ICT Journal Informatics for Educators and Professional : Journal of Informatics JITK (Jurnal Ilmu Pengetahuan dan Komputer) Jurnal Komtika (Komputasi dan Informatika) JSiI (Jurnal Sistem Informasi) JOURNAL INFORMATICS, SCIENCE & TECHNOLOGY INTI Nusa Mandiri JIKA (Jurnal Informatika) Jurnal Sistem Informasi Bulletin of Computer Science Research Journal of Students‘ Research in Computer Science (JSRCS) Jurnal Pengabdian Kepada Masyarakat PROSISKO : Jurnal Pengembangan Riset dan observasi Rekayasa Sistem Komputer Socius: Social Sciences Research Journal Jurnal Informatika dan Rekayasa Perangkat Lunak
Henny Leidiyana
Universitas Bina Sarana Informatika
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Journal : Jurnal Informatika dan Rekayasa Perangkat Lunak

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Soft Voting Based Optimized Ensemble for Migraine Type Classification Misriati, Titik; Aryanti, Riska; Leidiyana, Henny
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol. 6 No. 3 (2025): Volume 6 Number 3 September 2025
Publisher : Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33365/jatika.v6i3.861

Abstract

The accurate classification of migraine subtypes is a complex challenge in neurology, hindered by symptomatic similarities between types. This complexity necessitates advanced computational tools to support diagnostic precision. This study aims to develop and evaluate an optimized soft voting ensemble classifier to automate this multi-class classification task effectively. The methodology involved training eight base models—including Neural Network, Random Forest, and Gradient Boosting—on a publicly available migraine dataset, with an 80-20 train-test split. The top three performers were integrated into a soft voting ensemble, which aggregates their predicted probabilities to enhance decision robustness. Model performance was rigorously assessed using accuracy, precision, recall, F1-score, and AUC-ROC metrics. The results demonstrated that the proposed ensemble achieved superior performance, with an accuracy of 91.67% and an F1-score of 91.50%, outperforming all constituent models. Furthermore, the ensemble attained near-perfect AUC-ROC values across multiple classes, confirming its strong discriminatory capability. The study concludes that the soft voting ensemble is a highly effective and reliable approach for migraine subtype classification, offering significant potential as a decision-support tool in clinical environments. Future work will focus on hyperparameter optimization, explainability, and validation with larger multi-centric datasets to facilitate clinical adoption.
Co-Authors Agustiena Merdekawati, Agustiena Ahmad Yusuf Al Ghazali Ali Rachman, Ali Andrie Kurniawan Angga Aditya Permana Anugrah, Arya Deval Saladin Ridwan Dewi Ayu Nur Wulandari Fajar Santoso Farida Cinda R. S Simatupang Gustina, Rina Hariyanto, Risvan Dwi Hary Priantoro Imam Budiawan Iqbal Fadjarudin Luci Kanti Rahayu Meisyha, Rineke Muhammad Adil Muhammad Faisal Muhammad Ghudafa Taufiq Akbar Nandang Iriadi Nila Hardi Omar Pahlevi Purnamawati Purnamawati Rio Sudarsono Riska Aryanti Simanungkalit, Jumantho Simanungkalit, Jumantho Syifa Nur Rakhmah, Syifa Nur Titik Misriati Waryono Yopi Handrianto, Yopi