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All Journal International Journal of Engineering, Science and Information Technology TIERS Information Technology Journal
Jo, Minho
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Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation

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EEG-Based Focus Analysis to Evaluate the Effectiveness of Active Learning Approaches Udayana, I Putu Agus Eka Darma; Sudarma, Made; Putra, I Ketut Gede Darma; Sukarsa, I Made; Jo, Minho
International Journal of Engineering, Science and Information Technology Vol 5, No 4 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i4.1068

Abstract

Electroencephalography (EEG) has emerged as a non-invasive and objective technique for monitoring brain activity in real time, widely applied to measure cognitive states such as concentration and alertness. Its ability to capture brain responses during learning processes makes EEG a promising tool to evaluate student engagement more accurately than conventional methods. This study investigates the effectiveness of two active learning methods, Project-Based Learning (PjBL) and Problem-Based Learning (PBL), in the context of English tutoring for elementary students using EEG signals as a cognitive indicator. A total of 20 students aged 8–12 years from ThinkerBee Learning Centre Bali participated in the study. EEG data were recorded using the Muse 2 Headband while students completed test-based tasks designed for each learning method. The EEG signals were preprocessed using bandpass filtering, Continuous Wavelet Transform (CWT), and frequency band decomposition. Concentration scores were then calculated using two approaches: a heuristic method based on the Beta/(Theta + Alpha) ratio and a Long Short-Term Memory (LSTM) model. The heuristic method produced average scores of 0.3991 (PjBL) and 0.3822 (PBL), with a 4.42% difference, while the LSTM model showed a more substantial difference, with scores of 0.5454 (PjBL) and 0.4265 (PBL). A Spearman correlation test between EEG-derived scores and students’ academic results yielded a perfect correlation value of 1.0000, indicating a strong relationship between cognitive engagement and learning outcomes. These results demonstrate the potential of EEG as a reliable tool for objectively assessing learning effectiveness in primary education contexts.
Efficient Rice Leaf Disease Classification Using Enhanced CAE-CNN Architecture Suhada, Destia; Suta Wijaya, I Gede Pasek; Widiartha, Ida Bagus Ketut; Jo, Minho
TIERS Information Technology Journal Vol. 6 No. 2 (2025)
Publisher : Universitas Pendidikan Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38043/tiers.v6i2.7159

Abstract

This study introduces an enhanced Convolutional Autoencoder–Convolutional Neural Network (CAE–CNN) model designed for efficient and accurate classification of rice leaf diseases. This study aims to develop an architecture that achieves high accuracy while maintaining computational efficiency, serving as an integrative and applicative technical innovation for rice disease detection. The proposed architecture integrates a Squeeze and Excitation Block (SE-Block), Global Max Pooling (GMP), and Separable Convolution to improve feature extraction while reducing the number of parameters and inference time. A total of 7,430 labeled images from five rice disease classes were used for model training and evaluation. The model was optimized using Optuna-based hyperparameter tuning and validated through an ablation and comparative analysis to assess the impact of each component. Experimental results show that the proposed model achieves 99.39% accuracy with only 85,859 parameters, a compact size of 0.28 MB, and inference time at 0.06657 ms/image with 15,213 FPS. These findings demonstrate that the proposed CAE–CNN effectively combines high accuracy and low computational cost, making it highly suitable for real-time and edge-based rice disease classification systems.
Co-Authors I B K Widiartha I Gede Pasek Suta Wijaya I Ketut Gede Darma Putra I Made Sukarsa I Putu Agus Eka Darma Udayana, I Putu Agus Eka Made Sudarma Suhada, Destia