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All Journal International Journal of Accounting & Finance in Asia Pasific Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics
Athavale, Vijay Anant
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Health Professions Materials Science & Nanotechnology

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Touch n Go e-Wallet: The New Payment Style Existed when COVID-19 Hits Izaidin, Muhammad Daniel Bin Ahmad; Athavale, Vijay Anant; Razak, Muhammad Danial Bin Abdul; Zain, Nafisa Hani Binti Mohamed; Ajiby, Najla Awatif Binti Aqimu’; Singh, Sakshi; Katkar, Yash Rajendra
International Journal of Accounting & Finance in Asia Pasific (IJAFAP) Vol 5, No 3 (2022): October 2022
Publisher : AIBPM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32535/ijafap.v5i3.1933

Abstract

Touch ‘n Go e-wallet is a smartphone application that has recently gained users since the pandemic of COVID-19 hits Malaysia. Touch ‘n Go is an e-wallet, an electronic card that can make online payments using a smartphone. It is a secure way to pay using a smartphone because it is convenient to use and reduces physical touch, which can spread diseases and germs to other people. The pandemic and the imposition of Movement Control Orders (MCO) and Home Quarantine have encouraged e-wallet usage, as people will choose cashless payments during that period. This study examines how e-wallets help consumers throughout the COVID-19 pandemic in Malaysia. A total of 150 consumers completed an online survey via Google Forms, and the data were analyzed using SPSS. We found that perceived ease of use and trust impacted consumer satisfaction. This research provides new insights on e-wallet perceptions of Touch n Go and how this perception may promote consumer satisfaction.Keywords: COVID-19, E-wallet, MCO, Pandemic, Physical touch, Smartphone, Touch n Go
Implementation of the Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP) Method to Address Class Imbalance in Alzheimer’s Disease Magnetic Resonance Imaging (MRI) Datasets Alamudin, Muhammad Faiq; Mazdadi, Muhammad Itqan; Nugroho, Radityo Adi; Saragih, Triando Hamonangan; Muliadi, Muliadi; Athavale, Vijay Anant
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 7 No. 3 (2025): August
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v7i3.109

Abstract

Class imbalance in medical imaging datasets often leads to biased machine learning models, particularly in Alzheimer’s disease (AD) diagnosis using MRI. This study proposes the use of Wasserstein Generative Adversarial Networks with Gradient Penalty (WGAN-GP) to mitigate class imbalance in AD MRI datasets. Realistic MRI images were synthesized for underrepresented AD stages, and the quality of the generated data was quantitatively validatedusing the Fréchet Inception Distance (FID), with the lowest FID score recorded at 31.84, indicating a high degree of realism and diversity. The synthetic images were used to augment a dataset of 6,400 T1-weighted scans for training four Convolutional Neural Network (CNN) architectures: ResNet-50, AlexNet, VGG-16, and VGG-19. Results demonstrated statistically significant improvements in balanced accuracy across all models (p < 0.01 for all comparisons). The AlexNet + WGAN-GP combination achieved the highest accuracy of 98.54%, representing a mean improvement of 4.76% (95% CI: 2.45% to 6.98%) over its baseline. Significant gains were also observed for ResNet-50, VGG-16, and VGG-19. These enhancements were consistent across multiple evaluation metrics, including precision, recall, F1-score, and AUC. These findings confirm that WGAN-GP is a highly effective and statistically validated strategy for boosting the diagnostic accuracy of CNN models in Alzheimer's disease classification
Enhancing Software Defect Prediction: HHO-Based Wrapper Feature Selection with Ensemble Methods Fauzan Luthfi, Achmad; Herteno, Rudy; Abadi, Friska; Adi Nugroho, Radityo; Itqan Mazdadi, Muhammad; Athavale, Vijay Anant
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 7 No. 2 (2025): May
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/f2140043

Abstract

The growing complexity of data across domains highlights the need for effective classification models capable of addressing issues such as class imbalance and feature redundancy. The NASA MDP dataset poses such challenges due to its diverse characteristics and highly imbalanced classes, which can significantly affect model accuracy. This study proposes a robust classification framework integrating advanced preprocessing, optimization-based feature selection, and ensemble learning techniques to enhance predictive performance. The preprocessing phase involved z-score standardization and robust scaling to normalize data while reducing the impact of outliers. To address class imbalance, the ADASYN technique was employed. Feature selection was performed using Binary Harris Hawk Optimization (BHHO), with K-Nearest Neighbor (KNN) used as an evaluator to determine the most relevant features. Classification models including Random Forest (RF), Support Vector Machine (SVM), and Stacking were evaluated using performance metrics such as accuracy, AUC, precision, recall, and F1-measure. Experimental results indicated that the Stacking model achieved superior performance in several datasets, with the MC1 dataset yielding an accuracy of 0.998 and an AUC of 1.000. However, statistical significance testing revealed that not all observed improvements were meaningful; for example, Stacking significantly outperformed SVM but did not show a significant difference when compared to RF in terms of AUC. This underlines the importance of aligning model choice with dataset characteristics. In conclusion, the integration of advanced preprocessing and metaheuristic optimization contributes positively to software defect prediction. Future research should consider more diverse datasets, alternative optimization techniques, and explainable AI to further enhance model reliability and interpretability.
Brake Current Control System Modeling Using Linear Quadratic Regulator (LQR) and Proportional integral derivative (PID) Nugraha, Anggara Trisna; Pratiwi, Oktavinna Dwi; As’ad, Reza Fardiyan; Athavale, Vijay Anant
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 4 No. 2 (2022): May
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v4i2.141

Abstract

In the automotive world, each engine has different characteristics and functions, such as engine power, engine torque, and engine fuel emissions. Therefore, the power meter is used as a tool that can provide information about the engine characteristics. To ensure optimal braking performance of the dynamometer.is use eddy current braking dynamometer. his paper provides a comparative analysis between PID control as a classical control technique and modern control technique in the dynamometer eddy current brakes system. Eddy current brakes is a modern braking system that requires a control system to support the braking performance. PID control is often used to be implemented but, in some conditions, it is less optimal. This paper aims to find out that LQR and PID can support the performance of the Eddy current brakes dynamometer. And also to find out that LQR is better and optimal than PID controller for braking time response on Eddy current brakes dynamometer. Therefore, it is necessary to develop a modern and optimal control, such as a full state feedback Linear Quadratic Regulator (LQR). The expected result of the research is to produce a control design for the Eddy current brakes dynamometer system using the LQR control method. So that it can be used for the development of the automotive world and is beneficial for the survival of the communityThe comparison of the braking time responses were simulated using Matlab/Simulink. The simulation results show that the response of LQR control is better than the PID, with Ts = 2.12 seconds, Tr = 1.18 seconds, and without overshoot. On the other side, PID control, although having Ts = 0.27 seconds and Tr = 0.18 seconds, there is still an overshoot about 0.7%.
QRS Complex Detection on Heart Rate Variability Reading Using Discrete Wavelet Transform Wihantara, Arga; Wisana, I Dewa Gede Hari; Pudji, Andjar; Luthfiyah, Sari; Athavale, Vijay Anant
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 4 No. 4 (2022): November
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v4i4.159

Abstract

Heart Rate Variability or heart rate in humans is used to monitor the function of the human heart. This research designed a tool to compare the results of heart rate readings using the discrete wavelet transform method to facilitate the detection of R peak. This can be obtained by evaluating and studying each decomposition result from level 1 to level 4 on Discrete Wavelet Transform processing using Haar mother wavelets. This study further used a raspberry pi 3B as the microcontroller of the data processor obtained from the ECG module. Based on the results obtained in this study, it can be concluded that in heart rate readings, level 2 decomposition details coefficient had the best value as data processing that helps the heart rate readings with an error value of 0.531% compared to HRV readings that obtained 0.005 using phantom tools and a standard deviation of 0.039. Furthermore, the advantage obtained from this tool is a good precision value in HRV and BPM readings. In reading the HRV of the respondent, it was found that each initial condition of the patient's HRV would be high due to the respondent's unstable condition. The disadvantage of this tool is a delay in running the program and no display in the form of a signal in real time.
Co-Authors Ajiby, Najla Awatif Binti Aqimu’ Alamudin, Muhammad Faiq Anggara Trisna Nugraha As’ad, Reza Fardiyan Fauzan Luthfi, Achmad Friska Abadi Herteno, Rudy I Dewa Gede Hari Wisana Itqan Mazdadi, Muhammad Izaidin, Muhammad Daniel Bin Ahmad Katkar, Yash Rajendra Luthfiyah, Sari Muhammad Itqan Mazdadi Muliadi Pratiwi, Oktavinna Dwi Pudji, Andjar Radityo Adi Nugroho Razak, Muhammad Danial Bin Abdul Saragih, Triando Hamonangan Singh, Sakshi Wihantara, Arga Zain, Nafisa Hani Binti Mohamed