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All Journal JKTi - Jurnal Keilmuan Teknologi Informasi
Pamuja, Sintia Darma
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Computer Science & IT

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Comparison of CNN Transfer Learning Models for Brain Tumor Detection Based on MRI Images noviyanto; Pamuja, Sintia Darma
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.
Optimasi Analisis Sentimen Ulasan Platform Pendidikan Daring Menggunakan Arsitektur ALBERT dan Teknik Augmentasi Kontekstual Pamuja, Sintia Darma; Noviyanto
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.v1i2.2256

Abstract

Online learning through global platforms like Coursera generates a massive volume of user reviews, which serve as vital information for educational quality improvement. However, these reviews often exhibit imbalanced label distributions, where positive sentiments significantly dominate negative and neutral ones, hindering traditional classification models. Advanced language models such as ALBERT (A Lite BERT) offer parameter efficiency through cross-layer parameter sharing while maintaining high performance in complex text understanding. This study aims to evaluate the ALBERT model's performance in classifying Coursera user reviews and addressing data imbalance using Contextual Word Embedding augmentation. The methodology involves collecting 10,000 reviews followed by preprocessing steps including case folding, punctuation removal, and tokenization. The augmentation technique utilizes language models to replace words based on context to balance minority classes. The results show that ALBERT provides highly consistent performance, achieving an F1-score of 0.9710 with the contextual augmentation scenario. The model proves effective in capturing linguistic variations and remains computationally efficient. In conclusion, the ALBERT model is highly effective for sentiment analysis on the Coursera dataset, where contextual augmentation significantly enhances the model's ability to recognize minority classes that were previously difficult to identify.
Analisis Usability Antarmuka e-learning Menggunakan Metode System Usability Scale Pada Universitas Swasta Sri Widagdo, Adika; Pamuja, Sintia Darma
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 Noviyanto Sri Widagdo, Adika