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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Green Health: Journal of Health Sciences, Nursing and Nutrition
Sitti Rachmawati Yahya
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Computer Science & IT Engineering Nursing

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Investigating the Impact of ReLU and Sigmoid Activation Functions on Animal Classification Using CNN Models M Mesran; Sitti Rachmawati Yahya; Fifto Nugroho; Agus Perdana Windarto
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 8 No 1 (2024): February 2024
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v8i1.5367

Abstract

VGG16 is a convolutional neural network model used for image recognition. It is unique in that it only has 16 weighted layers, rather than relying on a large number of hyperparameters. It is considered one of the best vision model architectures. However, several things need to be improved to increase the accuracy of image recognition. In this context, this work proposes and investigates two ensemble CNNs using transfer learning and compares them with state-of-the-art CNN architectures. This study compares the performance of (rectified linear unit) ReLU and sigmoid activation functions on CNN models for animal classification. To choose which model to use, we tested two state-of-the-art CNN architectures: the default VGG16 with the proposed method VGG16. A dataset consisting of 2,000 images of five different animals was used. The results show that ReLU achieves a higher classification accuracy than sigmoid. The model with ReLU in fully connected and convolutional layers achieved the highest precision of 97.56% in the test dataset. The research aims to find better activation functions and identify factors that influence model performance. The dataset consists of animal images collected from Kaggle, including cats, cows, elephants, horses, and sheep. It is divided into training sets and test sets (ratio 80:20). The CNN model has two convolution layers and two fully connected layers. ReLU and sigmoid activation functions with different learning rates are used. Evaluation metrics include accuracy, precision, recall, F1 score, and test cost. ReLU outperforms sigmoid in accuracy, precision, recall, and F1 score. This study emphasizes the importance of choosing the right activation function for better classification accuracy. ReLU is identified as effective in solving the vanish-gradient problem. These findings can guide future research to improve CNN models in animal classification.
Adaptive Occupational Health Strategies under Climate Change: Exploring Heat Stress Mitigation through Green Rooftop Design in Urban Workplaces Sitti Rachmawati Yahya; Riris Johanna Siagian; Abdal Ahmed
Green Health International Journal of Health Sciences Nursing and Nutrition Vol. 1 No. 2 (2024): April: Green Health: International Journal of Health Sciences, Nursing and Nutr
Publisher : International Forum of Researchers and Lecturers

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70062/greenhealth.v1i2.261

Abstract

Urban workers are increasingly vulnerable to heat stress due to rising global temperatures, especially in cities affected by the Urban Heat Island (UHI) effect. This heat stress poses significant risks to worker health and productivity, exacerbating health issues such as dehydration, heat exhaustion, and heat stroke, while also reducing work efficiency. The study aims to assess the effectiveness of green rooftop designs as a mitigation strategy for heat stress in urban workplace environments. Green rooftops are increasingly seen as a sustainable solution for urban heat management, offering benefits in temperature regulation, energy efficiency, and overall worker well-being. This study examines various heat stress mitigation strategies, including green roofs, industrial fans, and shading systems, focusing on their comparative effectiveness in reducing temperatures and improving worker comfort. The research involved environmental temperature measurements inside and outside urban workplaces, the use of wearable heat sensors to monitor workers’ heat stress levels, and building energy simulations to predict the impact of green rooftops on indoor climate control. Results indicate that green rooftops reduced workplace temperatures by an average of 3.8°C and decreased heat-stress-related complaints by 35%. In comparison to industrial fans and shading systems, green rooftops provided superior long-term relief, reducing heat stress and improving both worker productivity and environmental quality. The findings support the integration of green rooftops into urban workplace designs as a viable climate adaptation strategy. Future research should explore optimizing green rooftop designs for different climates and assessing their long-term benefits for worker health and urban resilience.
Co-Authors Abdal Ahmed Agus Perdana Windarto Fifto Nugroho M Mesran Riris Johanna Siagian