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INDONESIA
JOURNAL OF ICT APLICATIONS AND SYSTEM
Published by STKIP Rokania
ISSN : 28301404     EISSN : 2830098X     DOI : https://doi.org/10.56313/jictas
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering
The Journal of ICT Applications System is a scientific journal that presents original articles on computer science research. This journal is a means of publication and a place to share research and development work in the field of computers. Loading of articles in this journal is done through submit. Complete information for article loading and article writing instructions are available in each issue. Articles submitted will go through a selection process for bestari partners and/or editors. Journal of ICTAplication System is published 2 times a year, in June and December Journal of ICTAplication System Registered at PDII LIPI with Print ISSN number 2830-1404 and Online ISSN 2830-098X For practitioners, academics, teachers and students in the field of computer science who want articles on research results and ideas to be published in this journal via submit
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 6 Documents
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Search results for , issue "Vol 3 No 2 (2024): Journal of ICT Aplications and System" : 6 Documents clear
Systematic Literature Review: Advancements in Skin Cancer Diagnosis Using Convolutional Neural Networks and Dermatoscopic Imaging muhajirin, Ahmad; Achmad Alwi Hasibuan; Aldi Antoni; Ali Amran NST; Ns. Romy Wahyuny
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.v3i2.390

Abstract

Skin cancer is one of the most prevalent types of cancer worldwide, requiring early detection for effective treatment and improved patient outcomes. Traditional diagnostic methods, such as biopsies, are time-consuming, costly, and uncomfortable for patients. In response to these challenges, this study systematically reviews the application of Convolutional Neural Networks (CNNs) in automated skin cancer diagnosis using dermatoscopic images. CNNs have demonstrated remarkable performance in image processing tasks due to their ability to extract complex features and ensure high classification accuracy. This review analyzes various CNN architectures, such as GoogLeNet, ResNet, and YOLOv8, in terms of their effectiveness in distinguishing between benign and malignant skin lesions. Results from existing literature indicate that CNN-based systems achieve an accuracy of up to 97.73%, making them a promising solution for automated diagnostic tools. The findings emphasize the importance of data augmentation, parameter optimization, and diverse datasets to improve model generalizability. This study concludes that integrating CNN-based diagnostic systems with clinical workflows has significant potential to enhance early detection, optimize medical resources, and raise public awareness of skin cancer prevention
A Systematic Literature Review on Optimizing Mask Detection Systems Using Convolutional Neural Networks for Public Health and Safety Savira Putri Ayu, Tengku; Annisa Nur Afidah; Yuliani; Fernanda Abi Maulana; Elyandri Prasiwiningrum
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.v3i2.391

Abstract

The COVID-19 pandemic has emphasized the critical importance of mask-wearing as a preventive measure to mitigate virus transmission. However, ensuring compliance with mask mandates in public spaces remains a challenge. This study conducts a Systematic Literature Review (SLR) to explore the application of Convolutional Neural Networks (CNNs) in developing automated mask detection systems. CNNs are widely recognized for their ability to process complex visual patterns with high accuracy, making them ideal for real-time detection in images and videos. This review evaluates various CNN architectures, datasets, and preprocessing techniques used in mask detection systems. The findings highlight significant advancements, such as achieving detection accuracies exceeding 95% under controlled conditions, while also identifying challenges like dataset diversity, model generalization, and computational requirements. Additionally, the integration of CNN-based mask detection systems with Internet of Things (IoT) technologies is explored for enhanced monitoring and enforcement of health protocols. This research aims to provide a comprehensive understanding of current approaches and future directions for optimizing mask detection systems, contributing to public health and safety
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.
Advanced Classification of Oil Palm Fruit Ripeness Deep Learning for Enhanced Agricultural Efficiency Hasibuan, Achmad Alwi; Ali Amran Nst; Aldi Antoni; Ray Handika; Budi Yanto; Akhmad Zulkifli
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.v3i2.395

Abstract

The classification of oil palm fruit ripeness is a critical factor in optimizing palm oil production. Traditional methods of ripeness assessment, based on the percentage of detached fruits and changes in skin color, are prone to human error due to subjective judgment. This study proposes an advanced approach utilizing deep learning with the ResNet50 model to classify oil palm fruit ripeness into four levels: unripe, under-ripe, ripe, and overripe. The research evaluates the model's performance under various data allocations, optimizers, and learning rates while incorporating data augmentation techniques to enhance accuracy. Experimental results indicate the optimal configuration includes a 90/10 data split, Adam optimizer, and a learning rate of 0.0001, achieving precision of 96%, recall of 98%, F1 score of 97%, and accuracy of 97%. These findings highlight the potential of ResNet50 in delivering reliable, real-time classification for agricultural applications, providing a practical solution for farmers and industries. The study concludes that large and diverse training datasets are essential for achieving robust classification results
Role of Digital Technology and Data in Enhancing Competitiveness of Fashion Entrepreneurs in the Digital Era Yulfita; Seprini
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.v3i2.401

Abstract

Digital era has transformed the fashion industry, requiring entrepreneurs to adopt innovative digital marketing strategies to remain competitive. This study explores the role of digital technology and data in enhancing the competitiveness of fashion entrepreneurs. Through a qualitative approach with a case study method, data were collected from fashion entrepreneurs actively utilizing digital platforms such as social media, e-commerce, and websites. The findings reveal that integration of social media, websites, and e-commerce plays a crucial role in increasing consumer engagement, expanding market reach, and boosting sales growth. Additionally, use of customer data for personalized marketing enhances customer loyalty and improves business decision-making. Furthermore, adoption of advanced technologies such as Augmented Reality (AR), Virtual Reality (VR), and Artificial Intelligence (AI) provides unique shopping experiences, giving fashion businesses a competitive edge. This research highlights the importance of adaptive, data-driven, and technology-based digital marketing strategies in the success of fashion entrepreneurs. Implementing these strategies effectively will enable fashion businesses to strengthen their market position and achieve sustainable growth in the digital economy.

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