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All Journal SKANIKA: Sistem Komputer dan Teknik Informatika Journal of Information Technology Ampera
Sri Mulyana
Universitas Negeri Medan
Computer Science & IT Control & Systems Engineering Engineering

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Klasifikasi Tanaman Hias Menggunakan Algoritma Convolutional Neural Network Jeremia SP Sibarani; Sadion Tumpal Damanik; Rezeki Nurkhalizah; Sri Mulyana; Budiman Nasution
Journal of Information Technology Ampera Vol. 4 No. 3 (2023): Journal of Information Technology Ampera
Publisher : APTIKOM SUMSEL

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalita.v4i3.431

Abstract

anaman hias merupakan jenis tanaman yang dikenal karena keindahan dan daya tarik estetikanya. Terdapat berbagai jenis tanaman hias yang mudah ditemukan, namun, mengidentifikasi dan mengklasifikasikan tanaman hias seringkali sulit bagi kita, khususnya bagi pemula yang ingin terlibat dalam dunia bisnis tanaman hias. Untuk memudahkan pengenalan jenis-jenis tanaman hias, penelitian ini akan menggunakan algoritma Convolutional Neural Network (CNN) sebagai solusi. CNN telah terbukti efektif dalam pengolahan citra dan pengenalan objek, menjadikannya pilihan algoritma yang cocok untuk klasifikasi tanaman hias. Penelitian ini menggunakan 112 gambar Mawar Damask, 100 gambar Bunga Echeveria, 100 gambar Mirabilis Jalapa, 110 gambar Lily Hujan, dan 47 gambar Zinnia Elegans. Dalam proses pra-pengolahan, noise dihilangkan, kemudian dilakukan augmentasi gambar, membagi data menjadi dua, yaitu data latih dan data uji, lalu melatih model dan mengevaluasi model serta mendapatkan hasil akurasi. Hasil klasifikasi berhasil mencapai tingkat akurasi yang tinggi untuk beberapa label, tetapi ada label tertentu yang tidak dapat diidentifikasi. Skor F1 tertinggi ditemukan pada label "Mawar Damask" dengan nilai 1.00, sementara label "Mirabilis Jalapa" dan "Lily Hujan" memiliki nilai Presisi tertinggi yaitu 1.00. Namun, dalam pengukuran evaluasi, label "Zinnia Elegans" tidak menghasilkan nilai yang terukur sama sekali.
Identifikasi Penyakit Tanaman Berdasarkan Citra Daun Berbasis Web dengan Pendekatan Algoritma Convolutional Neural Network Sri Mulyana; Mansur AS; Angga Warjaya; Inna Muthmainnah; Said Iskandar Al Idrus; Zulfahmi Indra
SKANIKA: Sistem Komputer dan Teknik Informatika Vol 8 No 2 (2025): Jurnal SKANIKA Juli 2025
Publisher : Universitas Budi Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36080/skanika.v8i2.3573

Abstract

This research aims to develop a mustard plant disease classification system using the Convolutional Neural Network (CNN) method integrated into a web-based platform. Classification is carried out on three classes, namely Spotted Mustard Leaves, Rotten Mustard Leaves, Healthy Mustard Leaves, with the addition of the Not Mustard Leaf class as a distractor class to test the robustness of the model against images that are not included in the main classification category. The dataset used consists of 800 images, 200 images each per class. The CNN model was built with a sequential architecture consisting of several convolutions, pooling, dropout, and dense layers, and using ReLU and SoftMax activation functions in the output layer. The training process is carried out up to 100 epochs, but with the use of Early Stopping callback, the training stops at the 60th epoch, with the best performance (best epoch) achieved at the 32nd epoch. Evaluation of the model on test data showed an accuracy of 93.75%, with high precision, recall, and F1-score values in each class. The model was then implemented into a web interface so that users could upload leaf images and obtain classification results automatically. The results of this study show that CNN is effective in detecting mustard leaf disease and has the potential to be applied as a digital image-based diagnostic tool in agriculture.
Co-Authors Angga Warjaya Budiman Nasution Inna Muthmainnah Jeremia SP Sibarani MANSUR AS Rezeki Nurkhalizah Sadion Tumpal Damanik Said . Iskandar Zulfahmi Indra, Zulfahmi