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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Tunas
M Mesran
Universitas Budi Darma
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PkM: Pelatihan Peningkatan Skill Siswa Sekolah Kejuruan pada Pembuatan Game Sederhana berbasis Android Agus Perdana Windarto; M Mesran; Anjar Wanto
Jurnal TUNAS Vol 3, No 2 (2022): Edisi April
Publisher : LPPM STIKOM Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/jtunas.v3i2.63

Abstract

In accordance with the title of this community service program (P2M), the method of applying science and technology is in the form of android training in making simple games. Skills training activities are supported by lectures, questions and answers, and of course hands-on practice in the computer laboratory. The training module will be given to participants as a tool for practical activities in the laboratory. The purpose of implementing this community service program is to improve the skills of Pematangsiantar Exemplary Private Vocational School Students, by making simple android-based games for Pematangsiantar Exemplary Private Vocational High School Students, so as to minimize the gap between the skill levels of the Pematangsiantar Exemplary Private Vocational High School students. with the needs of the real world of work. From the evaluation results and the findings obtained during the implementation of this P2M activity, it can be concluded that this P2M program has been able to provide enormous and targeted benefits for Pematangsiantar Exemplary Private Vocational High School Students in this activity. This form of training is a very effective form of providing refreshment and additional insight and new knowledge in the field of information technology outside of the learning process received in their respective schools.
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 as one of the best vision model architectures. This study compares the performance of ReLU (rectified linear unit) and sigmoid activation functions in CNN models for animal classification. To choose which model to use, we tested 2 state-of-the-art CNN architectures: the default VGG16 with the proposed method VGG16. A data set consisting of 2,000 images of five different animals was used. The results show that ReLU achieves higher classification accuracy than sigmoid. The model with ReLU on convolutional and fully connected layers achieved the highest accuracy of 97.56% on the test dataset. However, further experiments and considerations are needed to improve the results. Research aims to find better activation functions and identify factors that influence model performance. The data set consists of animal images collected from Kaggle, including cats, cows, elephants, horses, and sheep. It is divided into training 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 precision, precision, recall, F1 score, and test cost. ReLU outperforms sigmoid in accuracy, precision, recall, and F1 score. However, other factors such as the size, complexity and parameters of the data set must be taken into account. 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.
Co-Authors Agus Perdana Windarto Fifto Nugroho Sitti Rachmawati Yahya Wanto, Anjar