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p-Index From 2021 - 2026
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All Journal International Journal of Informatics and Computation Journal of Applied Data Sciences
Elyandri Prasiwiningrum
Universitas Rokania, Indonesia
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management

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Detection of Oil Palm Seedling Disease Based on Leaf Images Using the MobileNetV2-CNN Architecture Ego Oktafanda; Adyanata Lubis; Elyandri Prasiwiningrum
International Journal of Informatics and Computation Vol. 7 No. 1 (2025): International Journal of Informatics and Computation
Publisher : University of Respati Yogyakarta, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35842/ijicom.v7i1.71

Abstract

This study aims to develop and implement a plant disease detection system for oil palm seedlings based on leaf images using the MobileNetV2 architecture, which is based on Convolutional Neural Networks (CNN). The model was trained using a dataset of oil palm leaf images to detect several types of plant diseases. In the experiments, the applied model showed excellent results, with training accuracy increasing from 79% in the first epoch to 96% in the 15 epoch, and validation accuracy also increasing from 89% to 97%. These results demonstrate that the model can effectively detect plant diseases with good generalization ability on unseen data. With stable loss reduction and continuously improving accuracy, this study proves that the MobileNetV2 architecture can be efficiently used for plant disease detection. The research also highlights the potential integration of the model into an application to provide a practical solution in oil palm plantation management and to support decision-making and improve agricultural outcomes.
Implementation of PageRank Algorithm for Visualization and Weighting of Keyword Networks in Scientific Papers Adyanata Lubis; Elyandri Prasiwiningrum
Journal of Applied Data Sciences Vol 4, No 4: DECEMBER 2023
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v4i4.138

Abstract

Papers are written works that contain thoughts about a particular problem or topic that are written systematically accompanied by logical analysis. Scientific papers are often found on the internet or in libraries for various titles of scientific papers, citations or references can be found in every scientific paper and can be obtained easily, but to display all citations in scientific papers in the form of visualization cannot be done easily. Visualizing the citation network of scientific papers in the form of a graph, with nodes representing research papers and edges representing the relationship between researchers' scientific papers and other scientific papers based on scientific paper citations. This research uses the pagerank algorithm to create a keyword network that has a high relationship and potential relevance in a data library. This research is the first research in using the pagerank algorithm and testing its accuracy by comparing with KNN and linear clustering. The presentation displays the citation of scientific papers based on the size of the node by showing the number of citations of the scientific paper. It can be concluded that all processes in the system have run according to design, and functionally the visualization system and weighting of the scientific paper citation network are in accordance with the design. The results obtained from 51 articles, this algorithm produces a visual user interest of 81.60%, compared to the accuracy of the data suitability produced by the linear clustering and KNN algorithms in the form of 71.22% and 61.34%, helping to facilitate the search for scientific papers in large quantities.
Co-Authors Adyanata Lubis Ego Oktafanda