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All Journal Jurnal Pengabdian Kepada Masyarakat Sakai Sambayan Journal of Engineering and Scientific Research (JESR) International Journal of Electronics and Communications Systems Jurnal Teknologi Riset Terapan
Sony Ferbangkara
Universitas Lampung
Agriculture, Biological Sciences & Forestry Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mechanical Engineering Medicine & Pharmacology Other

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Journal : Jurnal Teknologi Riset Terapan

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Pemodelan AI dengan CNN Untuk Klasifikasi Tanaman Uvaria Grandiflora di Hutan Tropis Indonesia Martinus, Martinus; Ferbangkara, Sony; Annisa, Resty; Hidayatullah, Vezan; Pratama, Rama Wahyu Ajie; Makarim, Alvin Reihansyah
Jurnal Teknologi Riset Terapan Vol. 3 No. 1 (2025): Januari
Publisher : Penerbit Goodwood

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35912/jatra.v3i1.5012

Abstract

Purpose: This research aims to develop an artificial intelligence (AI) model based on the Convolutional Neural Network (CNN) to classify Uvaria plant species, a tropical genus native to Indonesia. The study addresses the challenge of limited datasets for automatic classification in tropical plant identification. Methodology/approach: Images of Uvaria plants were collected directly from their natural habitat and categorized into four primary classes: leaves, stems, twigs, and trees. The dataset comprises 400 labeled images, split into training (279 images, 70%), validation (40 images, 10%), and testing (81 images, 20%). The CNN model was trained for 200 epochs, using data preprocessing techniques such as normalization and augmentation to improve performance. Results/findings: The CNN model achieved an accuracy of 90% on the test set, indicating strong performance in classifying the four categories of Uvaria plant components. The model showed particularly consistent results in distinguishing between leaves and twigs. Conclusion: Despite the relatively small dataset, the results demonstrate that the CNN algorithm is capable of accurately classifying images of Uvaria species. The dataset is considered sufficient to build an effective classification model. Limitations: The main limitation of this study is the limited number of images, which may restrict the model’s ability to generalize to broader or more varied data in real-world conditions. Contribution: This research contributes to the development of AI-based tools for identifying tropical plant species. It offers a practical model and dataset that can support biodiversity monitoring, environmental research, and conservation efforts in Indonesia and similar tropical regions.
Analisis Akurasi dan Optimalisasi Dataset untuk Klasifikasi Tanaman Aristolochia acuminata dengan Algoritma CNN Ferbangkara, Sony; Mulyani, Yessi; Mardiana, Mardiana; Pratama, Rama Wahyu Ajie; Putri, Renatha Amelia Manggala; Rafi'syaiim, Muhammad Afif
Jurnal Teknologi Riset Terapan Vol. 3 No. 1 (2025): Januari
Publisher : Penerbit Goodwood

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35912/jatra.v3i1.5014

Abstract

Purpose: Purpose: Aristolochia acuminata is a rare plant species of significant conservation value. However, the accurate classification of its parts, such as leaves, stems, and twigs, remains a challenge. This study aimed to develop a reliable classification model to support conservation efforts using Convolutional Neural Network (CNN) technology. Methodology/approach: A digital dataset was systematically collected from various parts of Aristolochia acuminata, forming the foundation for training a CNN-based classification model. To evaluate the model performance and determine the optimal training parameters, three experimental scenarios were conducted using 10, 100, and 200 training epochs. The impact of each training duration on the classification accuracy was analyzed. Results: The model trained with 200 epochs achieved the highest accuracy, outperforming those trained with 10 epochs (68.89%) and 100 epochs (86.67%). This suggests that a longer training period enables the model to learn the visual features of each plant part better, leading to improved classification performance. Conclusion: The results confirm the effectiveness of CNN in classifying the components of Aristolochia acuminata. Using 200 training epochs allowed for deeper feature learning without overfitting, proving optimal in this context. Limitations: This study was limited by the dataset size and the number of classes involved. Further expansion of the dataset and class categories could improve the generalizability of the model. Contribution: This study contributes to plant conservation technology by demonstrating how CNN and structured dataset collection can be applied to classify rare plant species, providing a valuable tool for biodiversity preservation.
Co-Authors -, Ubaidah Adhi Nurhartanto, Adhi Ahmad Saudi Samosir Andrianto, Rifqi Rhama Annisa, Resty Deni Achmad Djausal, Gita Paramita F.X. Arinto Hidayatullah, Vezan Khairudin Khairudin Makarim, Alvin Reihansyah Mardiana Mardiana Mardiana Mardiana Mareli Telaumbanua Martinus Martinus Martinus, Martinus Meizano Ardhi Muhammad Meizano Ardhi Muhammad Muhammad, Meizano Ardhi Mulyani, Yessi Pratama, Rama Wahyu Ajie Putri, Renatha Amelia Manggala Rafi'syaiim, Muhammad Afif Rifqi Rhama Andrianto Sri Ratna Sulistiyanti Sumadi - ubaidah ubaidah Wahyu Eko Sulistiono