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All Journal Green Intelligent Systems and Applications Jurnal Teknokes
Muhammad Irhamsyah
Department of Electrical Engineering, Faculty of Engineering, Universitas Syiah Kuala, Banda Aceh, 23111, Indonesia
Biochemistry, Genetics & Molecular Biology Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Environmental Science

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Lightweight Rice Leaf Disease Classification Using MobileNetV2: A Comprehensive Performance Evaluation Melinda Melinda; Rahmat Maulana; Yunidar yunidar; Muhammad Irhamsyah; Muhammad Saifullah Nur; Nurlida Basir; Elizar Elizar; Muhammad Syafrudin
Green Intelligent Systems and Applications Volume 6 - Issue 1 - 2026
Publisher : Tecno Scientifica Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53623/gisa.v6i1.1000

Abstract

Rice leaf diseases pose a significant threat to agricultural productivity, and accurate automated detection is essential for timely intervention. This study presents a comparative evaluation of lightweight convolutional neural network architectures for the classification of six rice leaf disease categories: Bacterial Leaf Blight, Brown Spot, Leaf Blast, Leaf Scald, Narrow Brown Leaf Spot, and Rice Healthy. MobileNetV2 is proposed as the primary model and benchmarked against EfficientNetB0 and NASNetMobile. All three architectures were trained under an identical experimental setup comprising a two-stage transfer learning strategy, a unified custom classification head consisting of Global Average Pooling, Batch Normalization, two dense layers with dropout and L2 regularization, and a Softmax output layer. The dataset comprised 1,920 images across six classes obtained from Roboflow Universe, with no pre-augmentation applied by the original source. Training-time augmentation including rotation, shifting, shearing, zooming, and horizontal flipping was applied exclusively to the training subset. Experiments were conducted on a stratified split of 1,536 training, 192 validation, and 192 test images with a fixed random seed of 42 to ensure reproducibility. MobileNetV2 achieved the highest test accuracy of 96.35% and macro F1-score of 96.35%, outperforming EfficientNetB0 at 94.27% and NASNetMobile at 89.06%. In terms of computational efficiency, MobileNetV2 also demonstrated the most favorable deployment profile with a TensorFlow Lite model size of 2.75 MB and inference latency of 3.22 ms per image, indicating potential suitability for resource-constrained deployment scenarios. These results suggest that MobileNetV2 offers a competitive balance between classification accuracy and computational efficiency for rice leaf disease identification.
Beta-Band Electroencephalography Classification for Autism Spectrum Disorder Using Wavelet Features and Least-Squares Support Vector Machine melinda melinda; Muhammad Irhamsyah; Sri Rahayu Ade; Saifullah Nur Muhammad; Yunidar Yunidar; Nurlida Basir
Jurnal Teknokes Vol. 19 No. 2 (2026): June
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jteknokes.v19i2.148

Abstract

Autism spectrum disorder requires accessible and objective neurophysiological biomarkers to complement behavioral assessment, particularly for early screening in resource-limited settings. This study explores a computationally efficient framework for distinguishing children with autism spectrum disorder from neurotypical controls using beta-band electroencephalography activity (12–30 Hz), which has been associated with atypical sensorimotor and cognitive processing in autism. Beta-band oscillations are theoretically relevant for their roles in attention, cognitive control, and inhibitory processes, domains frequently disrupted in autism spectrum disorder. Data were obtained from the public King Abdulaziz University dataset comprising 16 male participants (8 with autism, 8 controls; aged 6–14 years). Following independent component analysis-based artifact removal and bandpass filtering, recordings were segmented into 2-s epochs with 50% overlap. Discrete wavelet transform (Daubechies-4, four levels) was applied to extract statistical features (mean, standard deviation, skewness, kurtosis) from wavelet coefficients across 16 EEG channels, yielding a 320-dimensional feature vector per epoch. Classification was performed using least-squares support vector machines with a polynomial kernel (degree d=3), with hyperparameters optimized via 5-fold cross-validation on the training set, and evaluated via a stratified 70/30 train–test split at the segment level. The polynomial-kernel model achieved 98.49% segment-level accuracy, outperforming the linear kernel (95.07%) and a relative beta-power baseline. However, these results should be interpreted with caution due to the small sample size (n=16), a male-only cohort, and segment-level evaluation, which may inflate performance through intra-subject data leakage. The lightweight computational design supports potential implementation on portable devices. This proof-of-concept demonstrates the feasibility of wavelet-based beta-band analysis for autism classification, but rigorous validation using larger, balanced cohorts with subject-wise cross-validation is essential before clinical translation can be considered.
Co-Authors Elizar Elizar Melinda Melinda Muhammad Saifullah Nur Muhammad Syafrudin Nurlida Basir Rahmat Maulana Saifullah Nur Muhammad Sri Rahayu Ade Yunidar yunidar