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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Paedagogie Media Kependidikan, Keilmuan dan Keagamaan STKIP MUHAMMADIYAH SAMPIT JOURNAL EDUCATIONAL OF NURSING(JEN) ICIT (Innovative Creative and Information Technology) Journal CICES (Cyberpreneurship Innovative and Creative Exact and Social Science) Computer Science and Information Technologies
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Universitas Raharja
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Development of learning videos for natural science subjects in junior high schools Partono Siswosuharjo; Al-Bahra Al-Bahra; Po Abas Sunarya
Computer Science and Information Technologies Vol 5, No 2: July 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v5i2.p160-167

Abstract

The purpose of this study was to determine the development procedure and the feasibility of learning media for whiteboard animation in Natural Sciences subjects at SMP Padindi, Tangerang Regency. This study uses a research and development (RD) approach. The development model in this study is the analysis design development implementation evaluation (ADDIE) model. The feasibility test is carried out by means of individual testing (one to one) on 3 experts, namely material experts, learning experts, and media experts, as well as 3 students. In addition, a small group test was also carried out on 9 students. The results showed that: i) the material expert test was 87.5%, the learning expert was 85%, the media expert was 84.44%, 3 students were 88.84%, and the small group was 90%; and ii) this whiteboard animation learning media is suitable for use based on the results of media trials by experts and students.
Automatic diagnosis of rice plant diseases using VGG-16 and computer vision Al-Bahra Al-Bahra; Henderi Henderi; Nur Azizah; Muhammad Hudzaifah Nasrullah; Didik Setiyadi
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 23, No 6: December 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v23i6.26975

Abstract

Pathogens are organisms that cause disease in plants. In the case of rice, these pathogens can include fungi, bacteria, nematodes, protozoa, and viruses. This study aims to investigate rice plant diseases using a hybrid system that employs the visual geometry group-16 (VGG-16) architecture and computer vision techniques, alongside various optimization algorithms and hyperparameters. We utilize the convolutional neural network (CNN) architecture of VGG-16 for feature extraction, implementing a process known as transfer learning. Additionally, this research compares different optimization algorithms with the VGG-16 model to identify the most effective optimization for the CNN architecture applied to the tested dataset. The main contribution of this study is the development of a model for identifying rice plant diseases based on data collected using VGG-16 for feature extraction and neural networks for classification with specific parameters. Our findings indicate that the best optimization algorithm is stochastic gradient descent (SGD) with momentum, achieving training and validation loss results of 0.173 and 0.168, respectively. Furthermore, the training and validation accuracies were 0.95 and 0.957. The model’s performance metrics include an accuracy of 95.75, precision of 95.75, recall of 95.75, and an F1-score of 95.73.
Co-Authors Didik Setiyadi Henderi . Hj Sumarmi Ignatius Joko Dewanto M. Hudzaifah Memed Sena Setiawan Memed Sena Setiawan Memed Setiawan Mohammad Hairidzulhi Muhammad Hudzaifah Nasrullah Mukti Budiarto Novy Ernawaty Nur Azizah Partono Siswosuharjo Po Abas Sunarya Rosmiati Rosmiati Tuti Nurhaeni