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All Journal JURNAL MASYARAKAT NEGERI ROKANIA JOURNAL OF ICT APLICATIONS AND SYSTEM
Ismi Asmita
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Environmental Science Languange, Linguistic, Communication & Media Social Sciences Other

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Journal : JOURNAL OF ICT APLICATIONS AND SYSTEM

 1 
Systematic Literature Review on the Application of Convolutional Neural Networks for Rambutan Fruit Classification: Advances, Challenges, and Future Directions Meisaroh; Tantia Azzahra; Ismi Asmita; Fatimah; Rusmin Saragih
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.v3i2.393

Abstract

Rambutan (Nephelium lappaceum L.) is a tropical fruit widely cultivated in Southeast Asia, including Indonesia. Manual classification of rambutan types and ripeness levels remains a challenge due to the high subjectivity and time-intensive nature of the process, particularly in large-scale agricultural operations. Convolutional Neural Network (CNN), a deep learning approach, offers significant potential in automating and improving the accuracy of fruit classification tasks by extracting complex visual features such as color and texture. This study employs a Systematic Literature Review (SLR) to evaluate the application of CNN in rambutan classification. Relevant research from 2019 to 2024 was analyzed to identify trends, accuracy levels, and challenges in utilizing CNN for this purpose. Results demonstrate that CNN achieves superior accuracy (>90%) compared to traditional methods like K-Nearest Neighbor (KNN). However, limitations include restricted dataset diversity and insufficient testing under real-world conditions. Recommendations for future research emphasize the need for larger, more diverse datasets and integration of additional media, such as spectral data and video, to enhance model robustness
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.
Co-Authors Azzahra, Tantia Budi Yanto, Budi Efendi Rahayu Ezra Ariendy Widodo Fatimah Fauzi Erwis Fernanda Abi Maulana Hidayatullah, Riski Indah Wahyuni Khairul Sabri Meisaroh Muhammad Hafis Maulana Riski Rahmadan Rusmin Saragih, Rusmin Tantia Azzahra