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All Journal JKTi - Jurnal Keilmuan Teknologi Informasi
Sintia Darma Pamuja
Universitas Muhammadiyah Klaten
Computer Science & IT

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Comparison of CNN Transfer Learning Models for Brain Tumor Detection Based on MRI Images noviyanto; Sintia Darma Pamuja
JKTI Jurnal Keilmuan Teknologi Informasi Vol 1 No 2 (2025)
Publisher : Universitas Muhammadiyah Klaten

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61902/jkti.v1i2.2185

Abstract

Brain tumors require early and accurate detection to support effective clinical decision-making. This study compares the performance of four transfer learning-based Convolutional Neural Network (CNN) models, namely DenseNet121, InceptionV3, MobileNet, and Xception, for brain tumor detection using MRI images. The dataset was preprocessed through resizing, normalization, and data augmentation, and all models were trained for 20 epochs using ImageNet pre-trained weights. Model performance was evaluated using accuracy, precision, recall, and F1-score metrics. The experimental results show that all models achieved accuracies above 90%, with MobileNet outperforming the others by achieving an accuracy of 94.74% and precision, recall, and F1-score values of 0.95, 0.95 and 0,94. These findings indicate that lightweight CNN architectures can deliver superior performance for MRI-based brain tumor classification.
Analisis Usability Antarmuka e-learning Menggunakan Metode System Usability Scale Pada Universitas Swasta Adika Sri Widagdo; Sintia Darma Pamuja
JKTI Jurnal Keilmuan Teknologi Informasi Vol 1 No 2 (2025)
Publisher : Universitas Muhammadiyah Klaten

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61902/jkti.v1i2.2286

Abstract

Online learning systems, or e-learning, have become a crucial infrastructure in higher education, enabling a better learning process. However, their success depends heavily on the usability of the interface presented to students. This study aims to evaluate the usability of the e-learning system interface at a private university to identify barriers to user interaction and provide recommendations for improvement. The evaluation was conducted using the System Usability Scale (SUS) method as a standard and reliable quantitative measurement instrument. This study involved 45 student respondents selected using purposive sampling techniques to complete the SUS questionnaire consisting of 10 statements. The results showed that the average SUS score obtained was 68.2. Based on the SUS score interpretation criteria, this value places the system in the Acceptable category based on the acceptability ranges, receiving a Grade C predicate on the grade scale, and is in the Good category based on the adjective rating. Although the system is considered suitable for use, the score position that is at the marginal threshold indicates a need for optimization in the navigation aspect and the consistency of visual elements. These findings recommend simplifying the flow of material access and improving the layout of key features to increase efficiency and user satisfaction as a reference for improvements to improve the digital learning ecosystem in the future at the university.
Co-Authors Adika Sri Widagdo Noviyanto