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All Journal Journal of Computer Networks, Architecture and High Performance Computing
Djarot Hindarto
Universitas Nasional
Computer Science & IT Education

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Use of RESNET-50 Neural Network in Diagnosing Diseases Mango Leaves Djarot Hindarto
Journal of Computer Networks, Architecture and High Performance Computing Vol. 6 No. 1 (2024): Article Research Volume 6 Issue 1, January 2024
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v6i1.3308

Abstract

Using a state-of-the-art convolutional neural network, specifically RESNET-50, for disease diagnosis on mango leaves is the focus of this research. The end goal is to develop a trustworthy method of mango plant disease detection using leaf image analysis. The approach used comprised gathering a sizable dataset encompassing a range of mango leaf diseases. Afterward, a classification system was developed by training the RESNET-50 model on image data. The system is able to learn extraordinarily intricate and profound visual patterns in pictures of mango leaves thanks to RESNET-50's deep and complicated architecture, which improves feature extraction. With a Test Accuracy of 99.16% and a Test Loss of only 0.4332, the results demonstrate a very reliable system. This impressive level of precision verifies that the system is capable of correctly distinguishing and categorizing mango leaf diseases. Consequently, this case demonstrates promising agricultural applications of the RESNET-50 model and offers a dependable and effective means of disease detection in mango plants. This study adds to the growing body of knowledge that can aid agricultural professionals and farmers in the early detection of disease symptoms on mango leaves, allowing for the prompt implementation of preventative measures. These findings also have broader implications, such as the potential for better agricultural productivity and management brought about by the use of comparable technologies for disease analysis in different crops.
Enhancing Business: Incorporating Enterprise Architecture into Project Management in the Food Manufacturing Industry Djarot Hindarto; Tri Dharma Putra; Mohammad Iwan Wahyuddin
Journal of Computer Networks, Architecture and High Performance Computing Vol. 6 No. 1 (2024): Article Research Volume 6 Issue 1, January 2024
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v6i1.3368

Abstract

The present research examines the incorporation of Enterprise Architecture into project management in the food manufacturing sector with the aim of enhancing operational efficiency and corporate accountability. This study investigates the use of an integrated Enterprise Architecture strategy with project management in the food industry to improve production processes by using the crucial role of information technology. The aim of this approach is to enhance operational frameworks, customize information systems, and guarantee the congruence between business strategic aims and technology implementation. Within this framework, the analysis centers on the potential of integrating Enterprise Architecture to enhance transparency, interoperability, and scalability in the food manufacturing industry. Enterprise Architecture offers a comprehensive perspective on the technological infrastructure needed to ease effective and adaptable business operations. The implementation of Enterprise Architecture yields advantages in elucidating system architecture, enhancing coordination among diverse business components, and easing the more adaptable assimilation of modifications. Enterprise Architecture is crucial in project management as it eases improved decision-making and more efficient risk management. This study emphasizes the significance of incorporating Enterprise Architecture into the management of projects in the food industry as a strategic basis for ongoing operational advancement and enhancement while simultaneously prioritizing product quality, production efficiency, and responsiveness to evolving market demands.
Co-Authors Mohammad Iwan Wahyuddin Tri Dharma Putra