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All Journal TEKNIK INFORMATIKA JOURNAL OF ELECTRICAL AND SYSTEM CONTROL ENGINEERING Jurnal Minfo Polgan (JMP)
Gea, Muhammad Nasri
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Computer Science & IT Electrical & Electronics Engineering Library & Information Science Mathematics Social Sciences

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Journal : TEKNIK INFORMATIKA

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Impact of Hyperparameter Tuning on CNN-Based Algorithm for MRI Brain Tumor Classification Gea, Muhammad Nasri; Wanayumini, Wanayumini; Rosnelly, Rika
JURNAL TEKNIK INFORMATIKA Vol. 18 No. 1: JURNAL TEKNIK INFORMATIKA
Publisher : Department of Informatics, Universitas Islam Negeri Syarif Hidayatullah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/jti.v18i1.44147

Abstract

This study examines the impact of hyperparameter tuning on the performance of Convolutional Neural Networks (CNN) in classifying brain tumors using MRI images. The dataset, sourced from Kaggle, underwent preprocessing techniques such as normalization, augmentation, and resizing to enhance consistency and diversity. The study evaluates five hyperparameter configurations, analyzing their effects on classification accuracy, precision, recall, and F1-score. The optimal configuration (batch size: 16, epochs: 10, learning rate: 0.001) achieved an accuracy of 86%, precision of 81%, recall of 85%, and an F1-score of 0.83. Other configurations showed trade-offs, where larger batch sizes increased recall but reduced precision. These findings emphasize the importance of careful hyperparameter tuning to optimize medical imaging classification performance.
Co-Authors Habib Satria Hanani Hutabarat, Jamina Ismail, Juni Prasetya, Hardi Rika Rosnelly, Rika Sihombing, Rotua Tammamah Lubis, Hartati Tembusai, Zoelkarnain Rinanda Wanayumini