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All Journal JUKI : Jurnal Komputer dan Informatika Jurnal Teknologi Riset Terapan
Febriyani, Cela
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Computer Science & IT Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mechanical Engineering

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Pengembangan Model Klasifikasi Citra Tanaman Hutan Melicope latifolia Berbasis CNN dengan Custom-Built Dataset Annisa, Resty; Mardiana, Mardiana; Martinus, Martinus; Putri, Renatha Amelia Manggala; Febriyani, Cela; Afif, Muhkito
JUKI : Jurnal Komputer dan Informatika Vol. 6 No. 2 (2024): JUKI : Jurnal Komputer dan Informatika, Edisi Nopember 2024
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Melicope latifolia, atau dikenal sebagai Pauh-Pauh, adalah tanaman hutan dari famili Rutaceae yang memiliki manfaat kesehatan sebagai anti-hepatitis C virus. Pengembangan model klasifikasi citra berbasis Convolutional Neural Network (CNN) dilakukan untuk mengenali berbagai bagian tanaman Melicope latifolia, yang saat ini masih kekurangan dataset. Dataset khusus yang dikumpulkan terdiri dari 400 citra berkualitas tinggi mencakup batang, buah, daun, dan ranting, dan dibagi menjadi data pelatihan, validasi, dan pengujian dengan rasio 70:10:20. Model CNN dilatih selama 200 epoch, dan evaluasi kinerja menggunakan metrik akurasi, precision, recall, dan F1-score. Hasil menunjukkan bahwa model mencapai akurasi tertinggi sebesar 89,17%, dengan performa terbaik pada kelas "buah" yang memiliki precision dan recall sebesar 100%. Hasil ini menunjukkan potensi penerapan CNN dalam klasifikasi tanaman Melicope latifolia, meskipun diperlukan optimasi lebih lanjut, seperti augmentasi data dan penyesuaian parameter.
Technology-Based Classification of Clerodendrum Paniculatum Using CNN and Confusion Matrix Wijaya, Pandu; Makarim, Alvin Reihansyah; Muhammad, Meizano Ardhi; Febriyani, Cela; Hidayatullah, Vezan; Annisa, Resty
Jurnal Teknologi Riset Terapan Vol. 2 No. 1 (2024): Januari
Publisher : Penerbit Goodwood

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35912/jatra.v2i1.4598

Abstract

Purpose: This study aims to develop a classification system for the Clerodendrum paniculatum plant (Bunga Pagoda), focusing on its key parts—stems, flowers, leaves, and trees—using the Convolutional Neural Network (CNN) algorithm. The objective is to support conservation efforts and facilitate digital data grouping through technology-based classification. Methodology: The research involved collecting a dataset of images representing different parts of the Clerodendrum paniculatum plant. These images were then used to train a CNN model. The training process included 200 epochs to optimize performance. The model's accuracy and performance were evaluated using a confusion matrix to measure classification success across the plant's various parts. Results: The CNN model achieved its highest accuracy of 97.78% when trained for 200 epochs. The results indicated a significant improvement in evaluation metrics compared to models trained with fewer epochs. The mo   del successfully classified the plant parts with high precision, demonstrating its robustness and reliability for rare plant classification. Conclusions: This study confirms that the CNN algorithm is effective in classifying the parts of the Clerodendrum paniculatum plant. Increasing the number of training epochs substantially enhances the model's performance, making it a practical tool for digital plant conservation initiatives. Limitations: The study is limited by its reliance on a specific dataset, which may not encompass all possible variations of the Clerodendrum paniculatum plant under different environmental conditions. Contributions: This research contributes to digital plant conservation by developing a CNN-based classification system for rare plants. It highlights the importance of deep learning in biodiversity preservation and provides a foundation for future AI-driven botanical studies.
Co-Authors Afif, Muhkito Annisa, Resty Hidayatullah, Vezan Makarim, Alvin Reihansyah Mardiana Mardiana Martinus, Martinus Muhammad, Meizano Ardhi Putri, Renatha Amelia Manggala Wijaya, Pandu