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All Journal G-Tech : Jurnal Teknologi Terapan Andalasian International Journal of Applied Science, Engineering, and Technology
Misbah Misbah
Universitas Muhammadiyah Gresik, Indonesia
Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Mechanical Engineering

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Implementation of Building Automation System on Smart Stove to Prevent Fire in Apartment based on Virtuino Muhammad Naufal Eka Syahban; Misbah Misbah
G-Tech: Jurnal Teknologi Terapan Vol 9 No 2 (2025): G-Tech, Vol. 9 No. 2 April 2025
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/gtech.v9i2.6793

Abstract

The increasing price of land in urban areas makes apartments an alternative housing in urban areas. Apartments as housing cannot be separated from basic human needs, namely eating, drinking, and sleeping. To meet food and drink needs, a stove is definitely needed as a cooking tool. The use of a stove also affects the security of the apartment itself considering the different characters of the residents/tenants. Negligence of the owner or tenant will cause losses for the management or the owner himself. From this problem, a smart stove was created that can detect LPG gas leaks and can provide information to security officers on the same floor using a wifi signal. The system designed uses ESP32 as a system processor and several sensors to support fire detection such as fire sensors and LPG sensors (MQ 2). The use of Virtuino as an IOT (Internet Of Things) device makes it easier for apartment security officers and apartment owners/tenants to monitor LPG gas leaks and the presence or absence of fire and provide early warnings in the form of information sent to Virtuino if a fire occurs in the monitored area.
Identification of Diabetes Mellitus Disease through Urine with Quartz Crystal Microbalance Sensor and Artificial Neural Network Ariel Pinka Rachmad Dhama Putra; Misbah Misbah
G-Tech: Jurnal Teknologi Terapan Vol 9 No 3 (2025): G-Tech, Vol. 9 No. 3 July 2025
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/g-tech.v9i3.7335

Abstract

Diabetes is a deadly disease in Indonesia. Early identification of diabetes is needed to treat this disease early. Identifying diabetes in urine with quartz crystal microbalance (QCM) sensors and artificial neural networks is a non-invasive diagnostic technique to detect diabetes mellitus, in order to provide information to people with diabetes. Currently, the common testing method used to detect diabetes is using laboratory blood tests and blood sugar monitors. However, this is often considered painful and uncomfortable for patients, especially if they do regular check-ups. In this study, the method used to classify diabetes patients uses a backpropagation artificial neural network method and 4 QCM sensors coated with carbon nanotubes (CNT) consisting of double walled, 2 multi-walled and graphene oxide. There are two classes, namely healthy and diabetes. The data sample uses patient urine. The evaluation results obtained the highest accuracy of 78%, namely with a hidden layer of 128, a learning rate of 0.2 and an epoch of 100, in the division of 80% data for training data and 20% for test data.
EEG-Based Cybersickness Classification Using Hjorth Parameters and Random Forest During 3D Gaming Exposure Alfi Zuhriya Khoirunnisaa; Misbah Misbah
Andalasian International Journal of Applied Science, Engineering and Technology Vol. 6 No. 2 (2026): July 2026
Publisher : LPPM Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/aijaset.v6i2.351

Abstract

This study presents an efficient EEG-based framework for cybersickness classification utilizing Hjorth parameter features under a 3D immersive video game stimulus (Mirror Edge). Multimodal data acquisition was performed using a 14-channel Emotiv EEG system for objective measurement and the Simulator Sickness Questionnaire (SSQ) for subjective validation. The EEG signals were subjected to comprehensive preprocessing procedures, including band pass filtering and Independent Component Analysis (ICA) to eliminate artifacts. Then, using Discrete Wavelet Transform (DWT) to isolate theta,alpha, and beta bands. Hjorth parameters: activity, mobility, and complexity were subsequently extracted to capture the temporal dynamics of neural activity with low computational overhead. To mitigate feature redundancy and dimensionality, Correlation Feature Selection reduced the feature space from 126 to 9 salient features. Classification performance was evaluated using Random Forest, Support Vector Machine, and K-Nearest Neighbor. Experimental results indicate a consistent increase in SSQ scores across participants, with disorientation emerging as the predominant symptom. Random Forest achieved superior performance with an accuracy of 82%, outperforming K-NN (72.72%) and SVM (59.09%). Notably, feature reduction preserved Random Forest performance while enhancing alternative classifiers. These findings highlight the robustness and computational efficiency of the proposed approach, demonstrating its potential for real-time EEG-based cybersickness detection.
Co-Authors Alfi Zuhriya Khoirunnisaa Ariel Pinka Rachmad Dhama Putra Muhammad Naufal Eka Syahban