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All Journal Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Computer Science JAREE (Journal on Advanced Research in Electrical Engineering)
Muhammad Rivai
Institut Teknologi Sepuluh Nopember
Control & Systems Engineering Electrical & Electronics Engineering

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Dissolved Oxygen Regulatory System Using Fuzzy Logic Method Based on Teensy Board Microcontroller Fajar Budiman; Muhammad Rivai; Luthfi Riadhi; Devy Kuswidiastuti
JAREE (Journal on Advanced Research in Electrical Engineering) Vol 3, No 1 (2019): April
Publisher : Department of Electrical Engineering ITS and FORTEI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j25796216.v3.i1.74

Abstract

Dissolved oxygen in water becomes a critical factor in the cultivation of fish hatcheries that determines the success/failure rate in the process of improving the quality and quantity of fish hatcheries. Low dissolved oxygen levels cause deficient to the process of decomposition, reproduction, and growth of fish. In this study, a system that can regulate the levels of dissolved oxygen in water is designed and built by utilizing the Dissolved Oxygen Meter of AZ-8403 as a sensor. The percentage of oxygen in water is controlled by Fuzzy Logic implemented in the Teensy board microcontroller. When the oxygen level is below the setting point, the aerator is then activated to produce dissolved oxygen in the water. The test results conducted on the aquarium containing fish obtained error for the reading of dissolved oxygen sensor of 0.075%. The system response requires 1 minute to reach the setting point.Keywords: dissolved oxygen, fuzzy logic control, teensy board.
Identification of chronic obstructive pulmonary disease using graph convolutional network in electronic nose Dava Aulia; Riyanarto Sarno; Shintami Chusnul Hidayati; Alfian Nur Rosyid; Muhammad Rivai
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 1: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i1.pp264-275

Abstract

Chronic obstructive pulmonary disease (COPD) is a progressive lung dysfunction that can be triggered by exposure to chemicals. This disease can be identified with spirometry, but the patient feels uncomfortable, affecting the diagnosis results. Other disease markers are being investigated, including exhaled breath. This method can be applied easily, is non-invasive, has minimal side effects, and provides accurate results. This study applies the electronic nose method to distinguish healthy people and COPD suspects using exhaled breath samples. Twenty semiconductor gas sensors combined with machine learning algorithms were employed as an electronic nose system. Experimental results show that the frequency feature of the sensor responses used by the principal component analysis (PCA) method combined with graph convolutional network (GCN) can provide the highest accuracy value of 97.5% in distinguishing between healthy and COPD subjects. This method can improve the detection performance of electronic nose systems, which can help diagnose COPD.
Tuberculosis severity classification from exhaled breath using an electronic nose system with Inception-1D and ResNet-1D Dava Aulia; Riyanarto Sarno; Muhammad Rivai; Muhammad Amin; Alfian Nur Rosyid; Kelly Rossa Sungkono
Bulletin of Electrical Engineering and Informatics Vol 15, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i4.12212

Abstract

Exhaled breath contains volatile organic compounds (VOCs) that can be analyzed for tuberculosis (TB) detection. Electronic nose systems have demonstrated promise for this application; however, accurately distinguishing between healthy individuals and TB patients with different severity levels, namely, low and high TB, remains challenging and requires advanced deep-learning methods. Unlike previous studies that focused on binary TB detection, this study proposes an electronic nose system combined with one-dimensional deep learning models, including residual network (ResNet), visual geometry group (VGG), EfficientNet, and Inception architectures, for multiclass classification of healthy individuals and TB severity levels using exhaled-breath analysis. The results show that Inception-1D and ResNet-1D achieve the best performance for healthy and TB classification, each attaining an F1-score of 94.99%. For TB severity classification, ResNet-1D outperforms other models with an F1-score of 68.50%. Meanwhile, Inception-1D yields the highest performance on the healthy and TB severity classification dataset, with an F1-score of 74.07%. Moreover, dimensionality reduction using principal component analysis (PCA) reduces the healthy and TB dataset to ten principal components and improves the F1-score to 95.64% with Inception-1D. Overall, the proposed framework successfully captures distinctive gas sensor-response patterns associated with TB presence and severity and may support clinical decision-making in resource-limited healthcare settings.
Co-Authors Budiman, Fajar Dava Aulia Devy Kuswidiastuti Luthfi Riadhi Muhammad Amin Riyanarto Sarno Rosyid, Alfian Nur Shintami Chusnul Hidayati Sungkono, B.J. Santosa