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All Journal Journal of Applied Engineering and Technological Science (JAETS) INOVTEK Polbeng - Seri Informatika
Maya Ramadhani
Universitas Lancang Kuning
Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Industrial & Manufacturing Engineering

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Implementation of Object Detection With You Only Look Once Algorithm in Limited Face-To-Face Times in Pandemic Yogi Yunefri; Sutejo Sutejo; Yogi Ersan Fadrial; Keumala Anggraini; Maya Ramadhani; Roki Hardianto
Journal of Applied Engineering and Technological Science (JAETS) Vol. 4 No. 1 (2022): Journal of Applied Engineering and Technological Science (JAETS)
Publisher : Yayasan Riset dan Pengembangan Intelektual (YRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37385/jaets.v4i1.1161

Abstract

Covid-19 has hit many countries in the world, including Indonesia. The rapid and deadly spread of Covid-19 reached Indonesia in early 2020. This pandemic of course had a detrimental impact on the Indonesian people in terms of health, economy, education and others. The Indonesian government certainly does not remain silent, the government is aggressively making efforts to break the Covid-19 chain in various ways, one of the efforts made is to continuously inform about the health protocols recommended by the government to prevent transmission. The Indonesian Ministry of Health (in Mardhia et al., 2020) Efforts that can be made in the prevention phase by each individual include: Wearing a mask, Wearing gloves, Using hand sanitizer/disinfectant, Washing hands with soap, Avoiding touching the face, Avoid shaking hands, Avoid gatherings or long queues, Avoid touching objects/object surfaces in public areas, Avoid taking public transportation, Maintain a distance of at least two meters from other people when outside the house, and If you show symptoms of illness, immediately notify the people around. Even though they have been informed about the prevention of Covid-19, the public tends to be negligent in implementing health protocols, one of which is the application of Social Distancing. Therefore, this study will create a distance detector using the YOLOv3 algorithm as one of the detection objects for the implementation of community activity restrictions
Evaluation of the Effect Of Regularization on Neural Networks for Regression Prediction: A Case Study of MLLP, CNN, and FNN Models Susandri; Ahmad Zamsuri; Nurliana Nasution; Maya Ramadhani
Journal of Innovation and Technology Polbeng Series on Informatics (INOVTEK Polbeng - Seri Informatika) Vol. 10 No. 3 (2025): November
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/m2rcsf96

Abstract

Regularization is an important technique for developing deep learning models to improve generalization and reduce overfitting. This study evaluated the effect of regularization on the performance of neural network models in regression prediction tasks using earthquake data. We compare Multilayer Perceptron (MLP), Convolutional Neural Network (CNN), and Feedforward Neural Network (FNN) architectures with L2 and Dropout regularization. The experimental results show that MLP without regularization achieved the best performance (RMSE: 0.500, MAE: 0.380, R²: 0.625), although prone to overfitting. CNN performed poorly on tabular data, while FNN showed marginal improvement with deeper layers. The novelty of this study lies in a comparative evaluation of regularization strategies across multiple architectures for earthquake regression prediction, highlighting practical implications for early warning systems.
Co-Authors Ahmad Zamsuri, Ahmad Keumala Anggraini Nurliana Nasution, Nurliana Roki Hardianto Susandri Sutejo Sutejo Yogi Ersan Fadrial Yogi Yunefri, Yogi