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All Journal Jurnal Riset Informatika bit-Tech Jurnal Computer Science and Information Technology (CoSciTech) Jurnal Media Computer Science Jurnal Pengabdian Masyarakat Bangsa
Sanjaya, Imam
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Journal : Jurnal Media Computer Science

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Implementation of Convolutional Neural Network for Soil Type Category Detection in a Web-Based Plant Recommendation System Sanjaya, Imam; Wahyuni, Yulinar Sri; Parwati, Lusiana Sani
Jurnal Media Computer Science Vol 4 No 2 (2025): Juli
Publisher : LPPJPHKI Universitas Dehasen Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37676/jmcs.v4i2.8637

Abstract

The growth of the agricultural sector in Indonesia is highly dependent on soil fertility, as soil is an important factor in the agricultural sector. However, conventional identification of soil types often takes a long time and requires high costs. To overcome this problem, this research develops a soil classification system using an optimized Convolutional Neural Network (CNN) model to improve soil classification accuracy. The results of this classification become the basis for a Content-Based Filtering (CBF) based recommendation system, in order to provide suggestions for crop types that are suitable for soil types. This research was conducted through several main stages, namely soil image data collection, data preprocessing, CNN model training and CBF-based recommendation system implementation. The CNN model is used to recognize soil texture and color patterns, while CBF is used to match soil characteristics with suitable plant species. System evaluation is conducted using confusion matrix to assess the accuracy of the classification model as well as the effectiveness of the recommendation system. The soil type classification process using CNN with MobileNetV2 architecture achieved an accuracy rate of 96%. This result shows that the architecture is effective in recognizing soil types precisely and can be used to provide appropriate crop recommendations. Thus, this system has the potential to support increased agricultural productivity, both on a small and large scale.
Application Of Vision Transformer For Identifying Indonesian Herbal Plants Based On Visual Images Sanjaya, Imam; Lelita, Tiara; Yustiana, Indra
Jurnal Media Computer Science Vol 4 No 2 (2025): Juli
Publisher : LPPJPHKI Universitas Dehasen Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37676/jmcs.v4i2.8896

Abstract

Indonesia has vast biodiversity, including herbal plants that have been used for generations as traditional medicinal ingredients. However, the many types of herbal plants that have similar shapes, colors, and textures often make it difficult for people to identify them accurately. To overcome this challenge, this research develops a visual image-based herbal plant identification system using the Vision Transformer (ViT) model, an artificial intelligence approach that is able to understand visual patterns more effectively than conventional methods. This research went through several stages, including the collection of herbal plant image datasets from public platforms, data preprocessing and image dimension adjustment, and training of the ViT model. The model was evaluated using metrics such as accuracy, precision, recall, and F1-score to ensure optimal performance. The results show that the ViT model is able to identify herbal plants with an accuracy of 92% and consistent performance of other evaluation metrics. This system is also implemented into the web, thus helping users in recognizing herbal plants quickly and accurately
Co-Authors Alawiyyah, Siti Alfiyyatuz Zakiyyah Alun Sujjada Arul, Sahrul Ismail Usman Lelita, Tiara Muhamad Fadiah Nurjaman Parwati, Lusiana Sani Perhan, Akbar Ilham Pratama, Yudistira Purnama Insany, Gina Wahyuni, Yulinar Sri Yustiana, Indra