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All Journal Jurnal Pengabdian UntukMu NegeRI Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Jurnal Computer Science and Information Technology (CoSciTech) Journal of Software Engineering and Information System (SEIS)
Bayu Anugerah Putra
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Religion Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Law, Crime, Criminology & Criminal Justice Social Sciences

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Journal : Jurnal Computer Science and Information Technology (CoSciTech)

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Analisis Convolutional Neural Network LeNet-5 Dalam Klasifikasi Daun Mangga Fitri Handayani; Andi Sunyoto; Bayu Anugerah Putra
Computer Science and Information Technology Vol 5 No 3 (2024): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

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Abstract

Mango is one of the agricultural productions. Like other agricultural crops, diseased mango leaves are a production problem. As a result, agricultural productivity decreases. This research aims to classify healthy or diseased mango leaves by developing a Convolutional Neural Network (CNN) based system with LeNet-5 feature extraction. The dataset used is sourced from Mendeley consisting of healthy leaf types totaling 265 images and diseased totaling 170 images. The data division used consists of 80% training data and 20% test data. The augmentation process is carried out to reduce over fitting. The results showed that the epoch process stopped at the 20th epoch and resulted in 93% accuracy. This shows that the CNN method for image classification can produce accurate accuracy in solving real-world problems.
Klasifikasi Citra Penyakit Daun Tomat Menggunakan Metode Convolutional Neural Network (CNN) Dengan Arsitektur VGG-19 Fitri Handayani; Baidarus, Baidarus; Sunanto, Sunanto; Putra, Bayu Anugerah; Anggraini, Chelina; Taufiq, Reny Medikawati
Computer Science and Information Technology Vol 6 No 3 (2025): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/coscitech.v6i3.10699

Abstract

Tomatoes, known as Solanum lycopersicum in Latin, are a type of horticultural commodity with high economic value in Indonesia.Tomato production can decrease due to leaf diseases that are hard to identify manually because the symptoms of different diseases often appear similar. The purpose of this study is to apply a deep learning-based tomato leaf disease classification system using the Convolutional Neural Network (CNN) VGG-19 architecture. The dataset was obtained from Kaggle and contains 6,600 images of tomato leaves divided into six disease classes and one healthy leaf class. The research stages include preprocessing (resizing, normalization), data augmentation, dataset division (80% training, 20% testing), model training with transfer learning, and fine-tuning for optimization. The evaluation using the confusion matrix and classification report includes accuracy, precision, recall, and F1-score. Test results show that the VGG-19 model achieved 97% accuracy on the test data, with an average precision, recall, and F1-score of 0.97. These findings show that VGG-19 effectively identifies tomato leaf diseases and could be applied in web- or mobile-based detection systems to help farmers with early diagnosis and proper treatment.
Co-Authors ., Farid Hasfindra Addarisalam, Alif Ali, Zurrahman Muhammad Alris Gusnanda Amran, Hasanatul Fu'adah Andi Sunyoto Anggraini, Chelina Aryanto Baidarus Br Bangun, Elsi Titasari Dian Utami Durades, M.Azri Fakhira Frisya Ramadhani Fatchiyah Maharani, Masti Fitri Handayani Fitri Handayani Gusnanda, Alris Hanum Salsabila Haris, Aidil Harun Mukhtar Jihan Aulia Khusyaini, Ilham Kurniawan, Muhammad Irgi Lorenza, Dina Lutfi Mz, Al Agib Maisyarah, Adila Mardiya, Ainul Muchtadi, Bill Fikra NUR FADILAH Nurwijayanti Pradipa, Raditya Prasasti, Aditia Rahmad Firdaus Ramanda, Yuki Rayhan, Aqeel Rico Apriandika Rizki, Rafi Hamdan Septiana Srinandini Shafwan, Affif Dzaky Soni Sunanto Sunanto Syahrul R, Syahrul Taufiq, Reny Medikawati