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All Journal JUKI : Jurnal Komputer dan Informatika Jurnal Teknologi Riset Terapan
Putri, Renatha Amelia Manggala
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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

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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.
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 Afif, Muhkito Annisa, Resty Febriyani, Cela Mardiana Mardiana Martinus, Martinus Mulyani, Yessi Pratama, Rama Wahyu Ajie Rafi'syaiim, Muhammad Afif Sony Ferbangkara