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All Journal JOURNAL OF ICT APLICATIONS AND SYSTEM RJOCS (Riau Journal of Computer Science)
Efendi Rahayu
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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Classification of Capsicum Varieties Using Color Analysis with Convolutional Neural Network Azzahra, Tantia; Riski Rahmadan; Fernanda Abi Maulana; Ismi Asmita; Efendi Rahayu; Fauzi Erwis
Journal of ICT Applications System Vol 3 No 2 (2024): Journal of ICT Aplications and System
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56313/jictas.v3i2.394

Abstract

Paprika (Capsicum annuum L.) is a high-value horticultural commodity widely consumed for its nutritional content and vibrant color variations. In the agricultural industry, classifying paprika varieties based on color is crucial for ensuring product quality and optimizing sorting processes. This study developed an automated classification system for three main paprika varieties—red, green, and yellow—using the Convolutional Neural Network (CNN) method. The dataset consisted of 1,820 images sourced from Kaggle, with data split into 60% for training and 40% for validation. Preprocessing steps included resizing images, normalizing pixel values to the range [0,1], and data augmentation techniques such as rotation, flipping, and brightness adjustments to enhance dataset diversity and reduce the risk of overfitting. The CNN model was designed with key layers, including convolutional, pooling, and fully connected layers, optimized using the Adam algorithm and categorical cross-entropy loss function. The training results showed an accuracy of 99.9% on the training data and 92% on the testing data, with an average processing time of 64 seconds per image and a maximum of 78 seconds, demonstrating the model's efficiency for real-time applications. The k-fold cross-validation technique was also employed to ensure the model's generalization ability to new data. This study demonstrated that CNN is an effective method for classifying paprika varieties based on color analysis, offering an accurate, fast, and scalable solution for automating sorting and grading processes in the agricultural sector, reducing human errors, and improving operational efficiency.
Evaluasi Penerapan Convolutional Neural Network (CNN) untuk Klasifikasi Penyakit Daun Jagung Menggunakan Pendekatan Systematic Literature Review Riski Rahmadan; Nurliani; Efendi Rahayu; Saudah; Ayu Puspita Sari Sinaga; Enda Ribka Meganta P
RJOCS (Riau Journal of Computer Science) Vol. 11 No. 1 (2025): RJOCS (Riau Journal of Computer Science)
Publisher : Fakultas Ilmu Komputer, Universitas Pasir Pengaraian

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30606/rjocs.v11i1.3068

Abstract

Penyakit pada tanaman jagung dapat menyebabkan kerugian besar dalam produksi pangan, yang berdampak pada perekonomian Indonesia. Salah satu metode yang berkembang untuk mendeteksi dan mengklasifikasikan penyakit tanaman adalah penggunaan Convolutional Neural Network (CNN), yang telah terbukti efektif dalam analisis citra. Penelitian ini bertujuan untuk mengevaluasi dan menganalisis penerapan CNN dalam klasifikasi penyakit pada daun jagung, dengan merujuk pada studi literatur yang ada. Melalui pendekatan Systematic Literature Review (SLR), penelitian ini menilai berbagai arsitektur CNN yang diterapkan pada klasifikasi penyakit tanaman, termasuk jagung, cabai, kentang, dan lada. Hasil penelitian menunjukkan bahwa metode CNN, khususnya dengan arsitektur seperti EfficientNet, mampu memberikan akurasi yang tinggi, dengan rata-rata akurasi sebesar 93.76%. Arsitektur CNN yang berbeda menunjukkan performa yang bervariasi tergantung pada dataset dan teknik preprocessing yang digunakan. Penelitian ini memberikan wawasan tentang bagaimana model CNN dapat dioptimalkan untuk mendeteksi penyakit tanaman dengan akurasi yang lebih baik, serta mengidentifikasi tantangan dan potensi dalam penerapannya pada berbagai jenis tanaman
Co-Authors Ayu Puspita Sari Sinaga Azzahra, Tantia Enda Ribka Meganta P Fauzi Erwis Fernanda Abi Maulana Ismi Asmita Nurliani Riski Rahmadan Saudah