cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Nurul Khairina
Contact Email
nurulkhairina27@gmail.com
Phone
+6282167350925
Journal Mail Official
nurul@itscience.org
Editorial Address
Jl. Setia Luhur Lk V No 18 A Medan Helvetia Tel / fax : +62 822-5158-3783 / +62 822-5158-3783
Location
Kota medan,
Sumatera utara
INDONESIA
Journal of Computer Networks, Architecture and High Performance Computing
Published by Yayasan Cita Cendekiawan Al Khwarizmi
ISSN : 26559102     EISSN : 26559102     DOI : 10.47709
Core Subject : Science, Education,
Computer Science & IT Education
Journal of Computer Networks, Architecture and Performance Computing is a scientific journal that contains all the results of research by lecturers, researchers, especially in the fields of computer networks, computer architecture, computing. this journal is published by Information Technology and Science (ITScience) Research Institute, which is a joint research and lecturer organization and issued 2 (two) times a year in January and July. E-ISSN LIPI : 2655-9102 Aims and Scopes: Indonesia Cyber Defense Framework Next-Generation Networking Wireless Sensor Network Odor Source Localization, Swarm Robot Traffic Signal Control System Autonomous Telecommunication Networks Smart Cardio Device Smart Ultrasonography for Telehealth Monitoring System Swarm Quadcopter based on Semantic Ontology for Forest Surveillance Smart Home System based on Context Awareness Grid/High-Performance Computing to Support drug design processes involving Indonesian medical plants Cloud Computing for Distance Learning Internet of Thing (IoT) Cluster, Grid, peer-to-peer, GPU, multi/many-core, and cloud computing Quantum computing technologies and applications Large-scale workflow and virtualization technologies Blockchain Cybersecurity and cryptography Machine learning, deep learning, and artificial intelligence Autonomic computing; data management/distributed data systems Energy-efficient computing infrastructure Big data infrastructure, storage and computation management Advanced next-generation networking technologies Parallel and distributed computing, language, and algorithms Programming environments and tools, scheduling and load balancing Operation system support, I/O, memory issues Problem-solving, performance modeling/evaluation
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 806 Documents
 77   78   79   80   81 
Comparison Of Adam and SGD For The Classfication Of Palm Tree Leaf Diseases With ResNet50 Ardi, Ardi al Ghifari; Nur Rachmat
Journal of Computer Networks, Architecture and High Performance Computing Vol. 8 No. 1 (2026): Call for Paper for Machine Learning / Artificial Intelligence, Januari 2026
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v8i1.7501

Abstract

Plants from the palm tree family (Arecaceae), such as coconut, oil palm, and date palm, play an important role in the economy and food security, especially in Indonesia. However, leaf diseases such as leaf spot disease pose a serious threat that can reduce productivity. Manual disease identification is time-consuming and prone to errors, necessitating an image-based automatic classification system. This study aims to apply the ResNet50 Convolutional Neural Network (CNN) architecture for palm tree leaf disease classification and compare two popular optimization algorithms, Adam and Stochastic Gradient Descent (SGD), in terms of model training accuracy and efficiency. The dataset used is public, covering five classes of leaf images: Healthy, White Scale, Brown Spot, Leaf Smut, and Bacterial Leaf Blight. The research process includes data collection and preprocessing (resizing, normalization, and augmentation), dividing the dataset into three parts, namely training, validation, and testing data using the train/validation/test split approach. This approach provides a fairly representative evaluation of model performance while being computationally efficient. Model training was performed using transfer learning with ResNet50, and performance evaluation was performed using a confusion matrix to obtain accuracy, precision, recall, and F1-score values. The results of the two optimizers were compared to determine their effect on model performance. The experimental results show that the ResNet50 model optimized with Adam achieved a higher test accuracy of 87.23% compared to SGD with 85.96%, while SGD demonstrated more consistent performance between validation and testing phases, indicating better training stability.
Evaluation of Machine Learning Algorithms for an Early Warning System of Student Graduation in a Python Programming Course Hizria, Rahmatika; Manurung, Ericky Benna Perolihin; Ginting, Victor Saputra
Journal of Computer Networks, Architecture and High Performance Computing Vol. 8 No. 1 (2026): Call for Paper for Machine Learning / Artificial Intelligence, Januari 2026
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v8i1.7718

Abstract

The high failure rate in Python programming courses has become a serious issue for educational institutions. This study aims to evaluate the performance of four machine learning algorithms as the basis of an Early Warning System for predicting student graduation, namely Random Forest (RF), Support Vector Machine (SVM), Logistic Regression (LR), and K-Nearest Neighbors (KNN). The dataset consists of 3,000 records with 15 features, including demographic data, programming experience, and students’ learning activities. Performance evaluation was conducted using accuracy, precision, recall, F1-score, and ROC-AUC metrics after optimal hyperparameter tuning through GridSearchCV with 5-fold cross-validation. The evaluation results indicate that Random Forest achieved the best performance with an accuracy of 89.33%, precision of 87.50%, recall of 46.23%, F1-score of 60.49%, and ROC-AUC of 94.40%, outperforming SVM (accuracy 86.33%, F1-score 55.43%), Logistic Regression (accuracy 86.50%, F1-score 53.71%), and KNN (accuracy 84.83%, F1-score 44.17%). Feature importance analysis identified experience_encoded, hours_spent_learning_per_week, and projects_completed as the three strongest predictors of student graduation. These findings provide empirical evidence that Random Forest is the most effective algorithm for implementing an Early Warning System in Python programming courses, enabling instructors to identify at-risk students early and provide timely interventions to improve learning success rates.
The IT GOVERNANCE IN REGIONAL WATER COMPANY RISK MANAGEMENT USING THE COBIT 2019 METHOD Firmansyah, Firmansyah; Sudrajat, Antonius Wahyu
Journal of Computer Networks, Architecture and High Performance Computing Vol. 8 No. 1 (2026): Call for Paper for Machine Learning / Artificial Intelligence, Januari 2026
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v8i1.7723

Abstract

Digital transformation in the public utility sector, particularly in regional water-owned enterprises (BUMD), presents complex risk challenges ranging from cybersecurity threats to operational distribution disruptions. PT Tirta Sriwijaya Maju (Perseroda), as the research object, faces constraints in IT risk management processes that are currently manual, reactive, and disintegrated, potentially threatening the sustainability of public services. This study aims to evaluate the current IT governance capability and design risk management improvements using the COBIT 2019 framework. The research methodology employs a mixed-method approach utilizing the Design Toolkit to determine domain priorities based on the company's risk profile and strategy. The evaluation focuses on six critical domains: EDM03, APO12, APO13, BAI03, DSS01, and MEA01. The Design Factors analysis established a target capability at Level 3 (Defined Process) to ensure regulatory compliance. However, the current state (As-Is) measurement indicates that the company is at an average of Level 1 (Performed). A gap of 2 levels was identified, primarily caused by a disconnected evaluation cycle (MEA01), the absence of a formal Risk Appetite document, and reliance on spreadsheet-based risk monitoring. As a solution, this study provides strategic recommendations including the formalization of risk policies, the design of an integrated digital Monitoring Dashboard, and an Implementation Roadmap for 2025-2027. The implementation of this roadmap is expected to enhance risk governance maturity, ensure customer data integrity, and guarantee operational stability in accordance with Good Corporate Governance standards. Keywords: IT Governance, Risk Management, COBIT 2019, Design Factors, Regional Water Utility, Capability Level.
An Optimized Lightweight CNN with Randomized Hyperparameter Search for Real-Time Image-Based Malware Detection Prasetyo, Stefanus Eko; Chandra Wijaya, Kevin; Haeruddin
Journal of Computer Networks, Architecture and High Performance Computing Vol. 8 No. 1 (2026): Call for Paper for Machine Learning / Artificial Intelligence, Januari 2026
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v8i1.7765

Abstract

While image-based malware detection using deep learning has shown promise, existing methodologies predominantly rely on computationally expensive pre-trained architectures (e.g., VGG, ResNet) that create significant bottlenecks for real-time deployment on resource-constrained gateways. This research addresses this critical gap by proposing a streamlined, lightweight custom Convolutional Neural Network (CNN) specifically optimized for real-time operation. The novelty of this work lies in the strategic integration of Randomized Search Cross-Validation (RS-CV) to automate the discovery of an optimal configuration of filters, dense units, and dropout rates, eliminating the inefficiencies and biases of manual hyperparameter tuning. The proposed method transforms binary files into 64x64 grayscale images—reducing computational input by over 90% compared to standard architectures—which are then processed by the optimized custom network. Experimental results demonstrate the scientific significance of this approach, as the model achieved a near-perfect Area Under the Curve (AUC) of 0.9996 and identified threats with an average inference time of only 12–15 milliseconds. Out of 1,068 test samples, only 10 misclassifications were recorded, proving that a mathematically optimized lightweight model can outperform heavy ensemble frameworks in both accuracy and speed. These findings provide a reproducible framework for high-speed, front-line cybersecurity systems capable of detecting obfuscated threats in live network environments.
The DESIGN FACTOR THE CAPABILITY LEVEL OF INFORMATION TECHNOLOGY GOVERNANCE AND RECOMMENDATIONS FOR IMPROVEMENT USING COBIT 2019: A CASE STUDY AT A PRIVATE UNIVERSITY IN PALEMBANG Achiruddin, Muhamad Yamin; Rahman, Abdul
Journal of Computer Networks, Architecture and High Performance Computing Vol. 8 No. 1 (2026): Call for Paper for Machine Learning / Artificial Intelligence, Januari 2026
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v8i1.7800

Abstract

Private university in Palembang that has utilized information technology to support its educational processes. This study demonstrates how COBIT 2019 design factors can be operationalized to identify strategically critical governance domains in higher education, shifting IT governance evaluation from generic domain assessment toward contextualized governance design, This study designs an information technology governance system based on COBIT 2019 and applies design factors to align governance objectives with university institutional context and strategic direction, thereby identifying priority domains and capability level targets that are most relevant to the university’s digital transformation. The application of these design factors highlights the EDM05 Ensure Stakeholder Engagement and BAI09 Manage Assets domains as the primary focus, as they are closely related to the need to enhance transparency in stakeholder involvement and to optimize IT asset management within the university environment. The study aims to assess the capability level of IT governance in the EDM05 and BAI09 domains, identify gaps between the current conditions and the targeted capability levels, and formulate improvement recommendations that are aligned with institutional needs and strategies. The research adopts a case study approach with a descriptive mixed method design, employing observation, interviews, and questionnaires, which are then analyzed using RACI Chart mapping, COBIT 2019 capability level measurement, and gap analysis to develop proposed process improvements. The findings indicate that the capability levels in both domains are still below the targeted levels, resulting in gaps related to stakeholder engagement, role and responsibility structures, IT performance reporting mechanisms, and IT asset lifecycle management. The implications of this research are the provision of a practical foundation for university in Palembang’s management to strengthen IT governance, as well as an academic contribution on the application of COBIT 2019 design factor–based governance in higher education institutions that can serve as a reference for other universities.   Keywords : IT Governance, COBIT 2019, Capability, Recommendation
Sentiment Analysis of Shopee User Reviews Using Recurrent Neural Network with LSTM for Real-Time Web-Based Prediction Qurani, Suci Ayu; Irawan, Bambang; Ramdhan, Nur Ariesanto
Journal of Computer Networks, Architecture and High Performance Computing Vol. 8 No. 1 (2026): Call for Paper for Machine Learning / Artificial Intelligence, Januari 2026
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v8i1.7824

Abstract

Sentiment analysis has become an important approach for understanding user opinions on e-commerce platforms. Shopee user reviews provide valuable information that can be utilized to evaluate service quality and customer satisfaction. This study aims to analyze the sentiment of Shopee user reviews using a Recurrent Neural Network with Long Short-Term Memory (RNN-LSTM) architecture. The research method includes data collection, text preprocessing, model training, and performance evaluation. The experimental results show that the proposed RNN-LSTM model achieved an accuracy of 97%, indicating its effectiveness in classifying user sentiment. The developed model is further implemented in a web-based application to provide real-time sentiment prediction. The findings of this study demonstrate that the RNN-LSTM approach is suitable for sentiment analysis in e-commerce environments and can support decision-making based on user feedback.

Page 81 of 81 | Total Record : 806

 77   78   79   80   81 

Filter by Year

2019 2026


Reset
Filter By Issues
All Issue Vol. 8 No. 1 (2026): Call for Paper for Machine Learning / Artificial Intelligence, Januari 2026 Vol. 7 No. 2 (2025): Forthcoming: Research Article, Volume 7 Issue 2 April, 2025 Vol. 7 No. 2 (2025): Research Article, Volume 7 Issue 2 April, 2025 Vol. 7 No. 4 (2025): Articles Research October 2025 Vol. 7 No. 3 (2025): Articles Research July 2025 Vol. 7 No. 1 (2025): Article Research January 2025 Vol. 6 No. 3 (2024): Articles Research Volume 6 Issue 3, July 2024 Vol. 6 No. 2 (2024): Articles Research Volume 6 Issue 2, April 2024 Vol. 6 No. 1 (2024): Article Research Volume 6 Issue 1, January 2024 Vol. 6 No. 4 (2024): Articles Research October 2024 Vol. 5 No. 2 (2023): Article Research Volume 5 Issue 2, July 2023 Vol. 5 No. 1 (2023): Article Research Volume 5 Issue 1, January 2023 Vol. 4 No. 2 (2022): Article Research Volume 4 Number 2, July 2022 Vol. 4 No. 1 (2022): Article Research Volume 4 Number 1, Januay 2022 Vol. 3 No. 1 (2021): Computer Networks, Architecture and High Performance Computing, January 2021 Vol. 3 No. 2 (2021): Journal of Computer Networks, Architecture and High Performance Computing, July Vol. 2 No. 2 (2020): Computer Networks, Architecture and High Performance Computing Vol. 2 No. 1 (2020): Computer Networks, Architecture and High Performance Computing Vol. 1 No. 2 (2019): Computer Networks, Architecture and High Performance Computing Vol. 1 No. 1 (2019): Computer Networks, Architecture and High Performance Computing More Issue