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All Journal International Journal of Electrical and Computer Engineering Media Statistika Journal of Applied Sciences and Advanced Technology
Dewantara, Annastya Bagas
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Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering

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Microbubble Measurements using Image Processing with the YOLOv8 Comparison Model Julian, James; Ulhaq, Faiz Daffa; Dewantara, Annastya Bagas; Purba, Riki Hendra; Wahyuni, Fitri; Junaedi, Thomas
Journal of Applied Sciences and Advanced Technology Vol. 6 No. 3 (2024): Journal of Applied Sciences and Advanced Technology
Publisher : Faculty of Engineering Universitas Muhammadiyah Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24853/jasat.6.3.109-116

Abstract

Gas-liquid two-phase is a situation where the gas phase of a liquid coexists together. The presence of gas that forms a region in the liquid environment causes the formation of bubble flow. The parameters of the bubble flow carry important information about the behavior and characteristics of the bubble. This research was carried out by detecting the size and area of the bubble using YOLOv8-based image processing by comparing the model's performance to produce improvements in inference time, increase accuracy, and reduce computational load. Bubble images were collected by adding 0.4 mm copper wire as a comparison to convert mm to pixels; then, the images were labeled and trained with various YOLOv8 models. Confusion matrix, precision and recall are used as comparative evaluation materials for the YOLOv8 model to obtain good model performance. In this study, the AUC of the Precision and Recall curve closest to the value 1 is the YOLOv8m model of 0.990. The comparison results of the matrix evaluation with the best model are the YOLOv8m model with mAP of 99.00% and F1-score of 96.86%. Microbubble measurements are calculated from the output of the YOLOv8 model by converting pixel units to mm. The model used in bubble measurements is the model with the best evaluation results and the model that gets the smallest radius value by considering measurement uncertainty, namely YOLOv8m with a minimum radius of 0.66 ± 0.04 mm..
Enhancing the accuracy of low-cost thermocouple devices through deep-wavelet neural network calibration Julian, James; Wahyuni, Fitri; Dewantara, Annastya Bagas; Winarta, Adi; Putra, Nandy
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i3.pp2625-2633

Abstract

Data collection using thermocouple sensors in low-cost data acquisition is prone to noise interference, which could reduce the data quality. Noise sources such as cold junction compensators, electromagnetic interference, and Johnson noise can significantly affect the reliability and accuracy of conventional measurements. This study aims to improve the quality of thermocouple sensor readings on low-cost data acquisition using calibration method based on deep learning and the denoising process using a wavelet transform. This taken approach successfully increase the accuracy value of 97.67% with a mean absolute error (MAE) of 0.2. The precision also increases of 262.7% as indicated by the result of signal-to-noise ratio (SNR) with a value of 105.29 dB. Comparative analysis was carried out against National Instruments® device and it was found that deep-wavelet method had a lower and higher of MAE and SNRdB values of 16.67% and 0.8% respectively. This study shows that the denoising-calibration method with deep-wavelet can improve the accuracy and reliability of data from low-cost thermocouple devices.
UNCERTAINTY ANALYSIS OF VOLTAGE MEASUREMENT USING ATMEGA328P MICROCONTROLLER: AN ANOVA TEST APPROACH Julian, James; Fauzi, Ade Fikri; Dewantara, Annastya Bagas; Ulhaq, Faiz Daffa; Wahyuni, Fitri
MEDIA STATISTIKA Vol 17, No 2 (2024): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/medstat.17.2.173-184

Abstract

The voltage sensors are widely used in various applications. In certain applications, such as medical devices, autonomous vehicles, or the military, the sensor's accuracy and level of precision play an important role, making it necessary to evaluate the sensor's performance. In this research, testing of direct current (DC) voltage sensors was carried out using analysis of variance (ANOVA) and Tukey honestly significant difference (HSD) to test sensor performance in various voltage ranges. This research used an experimental-based quantitative approach, using the ATmega328P. Data collection begins by calibrating the analog-to-digital converter (ADC) readings against voltage values with linear regression, the Chauvenet criterion to eliminate outlier data caused by noise from the environment, One-way ANOVA is used to determine differences in variations between voltage distances, and a Q-Q plot is used to determine the normality of the sensor error distribution. This research obtained from Tukey-HSD that 9 comparisons accepting the null hypothesis (H0). And 27 pairs accepting the alternate hypothesis (H1). The data was found to be normally distributed through the calculation of residual ANOVA, and visualization of data with the Q-Q plot, and the use of the sensor was effective in the range of 3V to 24.5V.
Co-Authors Adi Winarta, Adi Fauzi, Ade Fikri Fitri Wahyuni James Julian Junaedi, Thomas Nandy Putra Purba, Riki Hendra Ulhaq, Faiz Daffa