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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) International Journal of Engineering, Science and Information Technology
Siti Rusdiana
Universitas Syiah Kuala
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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Improving the Classification Performance of SVM, KNN, and Random Forest for Detecting Stress Conditions in Autistic Children Melinda Melinda; Yunidar Yunidar; Rizka Miftahujjannah; Siti Rusdiana; Amalia Amalia; Lailatul Qadri Zakaria
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.v5i4.1206

Abstract

This paper addresses the critical challenges of managing stress in autistic children by introducing an innovative deployable system designed to detect signs of stress through continuous monitoring of physiological and environmental indicators. The system, implemented as a convenient portable detection system, measures key parameters such as heart rate, body temperature and skin conductance. The data is accessed in real-time and displayed on the Blynk application with an IoT system and viewed remotely via an Android device, allowing caregivers to receive instant notifications upon detection of potential stress symptoms. This timely alert system enables rapid intervention, potentially reducing stress intensity and providing peace of mind to caregivers. The study further compares three powerful data analysis methods namely Support Vector Machine (SVM), K-nearest neighbors (KNN) and Random Forest (RF) in interpreting the collected sensor data. The SVM-based system achieved a fairly good detection accuracy of 90%, KNN also showed excellent results of 92% while the Random Forest-based system showed superior performance with an impressive accuracy of 95%. These findings suggest that the Random Forest method exhibits a superior level of effectiveness in accurately predicting the onset of stress conditions., providing the importance for technological advancements that can be applied in supporting better management of autism-related behavioral defenses.
Comparative Analysis of Multispectral Image Classification Based on EfficientNetB0, ResNet152, DenseNet161, DenseNet121, and HSV Segmentation Melinda; Yudha Nurdin; Alfatirta Mufti; Syifa Anzella; Siti Rusdiana; Donata D Acula
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 1 (2026): February 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i1.6873

Abstract

This study established a classification system based on Convolutional Neural Networks (CNNs) to detect High-High Fluctuation (HHF) patterns in multispectral data derived from pure water (H2O) and a water-sodium hydroxide (NaOH) solution. This study combines HSV color-space-based segmentation to identify areas with the highest signal amplitude, thereby enhancing the feature extraction of the CNN model. Data augmentation techniques, including random flipping, rotation, and color jitter, along with training parameters such as a learning rate of 0.0001 and a batch size of 32, have been shown to effectively improve model generalization and reduce overfitting. Four different CNN architectures were evaluated: ResNet-152, DenseNet-161, DenseNet-121, and EfficientNet-B0. As a result, ResNet152 achieved the highest accuracy of 97.6%, attributed to its network depth and residual connections that effectively address the vanishing gradient problem. DenseNet161 and DenseNet121 also demonstrated competitive performance, achieving accuracies of 96.7% and 96.2%, respectively, which is supported by their dense connectivity that optimizes feature reuse. Conversely, EfficientNetB0, despite showing lower accuracy (90%), provides significant computational efficiency, making it suitable for real-time applications. These results underscore the importance of selecting a CNN architecture that balances accuracy and efficiency for multispectral data classification.
Co-Authors Alfatirta Mufti Amalia Amalia Donata D Acula Lailatul Qadri Zakaria Melinda Melinda Melinda Rizka Miftahujjannah Syifa Anzella Yudha Nurdin Yunidar Yunidar