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All Journal INOVTEK Polbeng - Seri Informatika
Hafiz Irsyad
Multi Data Palembang University
Computer Science & IT

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Convolutional Block Attention Module Integration into YOLO11 Architecture for MRI Image-based Brain Tumor Detection Jendraja Husin Kotan; Yohannes; Hafiz Irsyad
Journal of Innovation and Technology Polbeng Series on Informatics (INOVTEK Polbeng - Seri Informatika) Vol. 11 No. 2 (2026): May
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/n4nrvj87

Abstract

Brain tumor is one of the deadly diseases in the world that can affect anyone, this disease is characterized by the growth of abnormal cells or tissues in the brain, medically it can be life-threatening if not treated properly. Most tumor detection tasks are done by manual assessment by radiologists or pathologists where this work is time-consuming, so accurate and reliable detection is needed in the medical field in diagnosing brain tumors. The purpose of this study is to integrate CBAM on the YOLO11 architecture in detecting brain tumors and determine the performance of the brain tumor detection model using the YOLO11 architecture with CBAM integration. The method used to detect brain tumors is the YOLO11 architecture with CBAM integration. The dataset used is an image in the form of brain MRI. The results of this study indicate that the precision is 86.9%, recall is 86.2%, mAP50 is 91%, and mAP50-95 is 64% in the validation data and precision is 89.1%, recall is 92%, mAP50 is 79%, mAP50-95 is 51.6%, and F1 score is 90.5% in the test data which can be used to help medical personnel in detecting and treating brain tumors considering that this model has outstanding results, especially in the recall metric section which reaches 92% in the test data.
Implementation of a Convolutional Neural Network Using VGG19 for Ogan Malay Script Recognition Steven Liem; Hafiz Irsyad
Journal of Innovation and Technology Polbeng Series on Informatics (INOVTEK Polbeng - Seri Informatika) Vol. 11 No. 3 (2026): August (Inpress)
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/annm4j49

Abstract

Regional languages and scripts, including the Melayu Ogan script, face the threat of extinction due to declining usage and limited digital documentation in the modern era. While current Indonesian script research primarily focuses on popular scripts, research addressing the Ogan Malay script remains severely limited. To address this gap, this study provides one of the earliest implementations of the Convolutional Neural Network (CNN) VGG-19 architecture specifically designed for Ogan Malay script classification. This research utilizes a primary dataset provided by the Language Center of South Sumatra Province, consisting of 185 distinct character classes, with each class initially containing one original image. The VGG-19 architecture is applied and supported by data augmentation techniques to enrich spatial variability, followed by evaluation using k-fold cross-validation. Evaluation results demonstrate excellent classification performance. The model achieved maximum convergence without any indications of overfitting at an optimal configuration of 30 epochs with a learning rate of 0.0001. This configuration successfully resulted in an accuracy of 99.14%, a precision of 0.9870, a recall of 0.9914, and an F1-score of 0.9885. The success of this classification model provides a strong foundation for future real-time regional script recognition applications to support cultural preservation.
Co-Authors Jendraja Husin Kotan Steven Liem Yohannes, Yohannes