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All Journal JTIULM (Jurnal Teknologi Informasi Universitas Lambung Mangkurat) Journal of Computer Networks, Architecture and High Performance Computing
Djarot Hindarto
Universitas Nasional
Computer Science & IT Education Electrical & Electronics Engineering Engineering

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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.
COMPARISON OF DETECTION WITH TRANSFER LEARNING ARCHITECTURE RESTNET18, RESTNET50, RESTNET101 ON CORN LEAF DISEASE Djarot Hindarto
Jurnal Teknologi Informasi Universitas Lambung Mangkurat (JTIULM) Vol. 8 No. 2 (2023)
Publisher : Fakultas Teknik Universitas Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/jtiulm.v8i2.174

Abstract

The occurrence of diseases that impact the leaves of corn plants presents a substantial obstacle in agriculture, leading to a reduction in the overall yield of crops. This study aims to perform a comparative analysis of transfer learning methodologies by employing three distinct ResNet architectures: ResNet18, ResNet50, and ResNet101. The dataset utilized by the author consists of a compilation of images portraying corn leaves that demonstrate varying levels of disease severity. Transfer learning refers to leveraging a pre-existing ResNet model and retraining the network by employing the corn leaf dataset. The experimental results demonstrate that the ResNet18, ResNet50, and ResNet101 models achieved accuracy rates of 96.68%, 95.73%, and 95.26%, respectively. The ResNet101 model shows superior performance in terms of precision and recall metrics. This research indicates that utilizing a more complex and sophisticated network structure can improve the effectiveness of disease identification in corn plant leaves. The result above is essential in promoting sustainable agricultural methodologies and efficiently managing corn plant diseases.
A COMPARATIVE STUDY OF SENTIMENT CLASSIFICATION: TRADITIONAL NLP VS. NEURAL NETWORK APPROACHES Djarot Hindarto
Jurnal Teknologi Informasi Universitas Lambung Mangkurat (JTIULM) Vol. 8 No. 2 (2023)
Publisher : Fakultas Teknik Universitas Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/jtiulm.v8i2.178

Abstract

The current research compares traditional natural language processing methods, such as Naive Bayes and Support Vector Machine, to neural network approaches, particularly Multi-Layer Perceptron, to classify positive and negative sentiments regarding company customer service. This research is motivated by the need to understand the effectiveness of these two approaches in analyzing and classifying sentiment in customer reviews, a crucial aspect of enhancing the quality of customer service. The author evaluated accuracy, speed, and adaptability to complex and diverse review content using a dataset containing various business customer reviews. The findings of this study indicate that neural network approaches, particularly Multi-Layer Perceptron, tend to provide superior performance in classifying customer sentiment with greater precision, albeit at a higher computational cost. Traditional methods such as Naive Bayes and Support Vector Machine still apply in situations with limited resources. The results of this research provide valuable guidance for companies in selecting an appropriate approach to analyzing customer sentiment, with the potential to increase understanding of customer views and improve overall customer service. Nave Bayes achieves 68.75% accuracy, Support Vector Machine achieves 87.5% accuracy, and Multi-Layer Perceptron achieves 100% accuracy.
OPTIMIZATION OF CNN + MOBILENETV3 FOR INSECT IDENTIFICATION: TOWARD HIGH ACCURACY Nihayah Afarini; Djarot Hindarto
Jurnal Teknologi Informasi Universitas Lambung Mangkurat (JTIULM) Vol. 9 No. 1 (2024)
Publisher : Fakultas Teknik Universitas Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/jtiulm.v9i1.199

Abstract

Developments in the field of artificial intelligence and deep learning, particularly Convolutional Neural Networks (CNN) techniques, have expanded the research potential and applications in ecology, including efficient and accurate insect classification. However, there are challenges in achieving high levels of accuracy with similar computational efficiency. In response, the efficient MobileNetV3 architecture was investigated to improve the insect pest classification process. Through an analytical descriptive quantitative approach and insect datasets from Kaggle, this study tested the effectiveness of CNN models optimized with MobileNetV3. The results indicated that the optimized model achieved classification accuracy of up to 90%, with consistent performance between training and validation data and significant loss reduction. With high precision and processing efficiency, this discovery makes a substantial contribution to deep learning applications in the field of intelligent agriculture, promising methodological improvements for other classification problems. Despite offering a promising solution, this study recognizes the limitation in dataset diversity and suggests further exploration with more varied datasets to strengthen the model's application in actual agricultural practices.
Co-Authors Mohammad Iwan Wahyuddin Nihayah Afarini Tri Dharma Putra