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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 813 Documents
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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.
Deep Learning-Based Multi-Tooth Segmentation on Panoramic Radiographs Using YOLOv8 Architecture Ridwan, Ahmad; Pramawahyudi; Purnama Sari, Desy; Hanisah; Musunuri, Yogendra Rao
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.v8i1.7710

Abstract

This research introduces a multi-class tooth-level segmentation framework on panoramic radiographs using YOLOv8, trained on clinically annotated Indonesian dental data. A dataset of 302 annotated panoramic radiographs from patients at Universitas Andalas Dental Hospital was utilized, with each tooth precisely labeled according to international dental nomenclature. The model was trained using transfer learning with the YOLOv8 variant, optimized with the Adam algorithm, and evaluated using precision, recall, F1-score, and Intersection over Union (IoU). The results demonstrate that YOLOv8 is not only effective for lesion detection but also robust for fine-grained anatomical dental segmentation. The performance achieved 93.72% accuracy, 92.67% precision, 98.88% recall, and 95.58% F1-score, indicating high accuracy in tooth detection and boundary delineation. Qualitative analysis confirmed accurate segmentation across a wide range of anatomical variations, including crowding, impaction, and prosthetics. This research establishes YOLOv8 as a highly effective tool for dental image segmentation, offering significant potential to improve diagnostic efficiency, support odontological forensics, and enable automated patient record management. Future work will focus on integrating multi-class pathology detection and 3D reconstruction.
Performance and Energy Efficiency Assessment of Embedded Arduino Vibrating Sieving System for Dry Powder Materials Dewi, Marysca Shintya; Marlinda, Linda; Komarudin
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.v8i1.7711

Abstract

Dry powder sieving is a crucial process for micro, small, and medium enterprises (MSMEs), where particle uniformity directly impacts product quality and production efficiency. Traditional vibrating sieving machines in local markets are typically evaluated through visual inspection, resulting in subjective assessments without quantitative evidence of energy efficiency or vibration stability. High humidity often causes powder clumping, reducing consistency and reliability. To address these limitations, this study introduces an Arduino based embedded system for quantitative performance and energy evaluation of a vibrating dry powder sieving process. System integrates an Atmega328P microcontroller (Arduino Uno) with infrared and DHT11 sensors, an L298N motor driver, a DC motor, and an LCD display. Electrical parameters (voltage and current) and vibration signals (acceleration along the X, Y, and Z axes) were acquired in real time at a sampling frequency of 10 Hz and recorded to an SD card for 60–90 seconds per cycle. Metrics included electrical power, energy consumption, vibration RMS, peak amplitude, dominant frequency, and energy efficiency expressed as the mass of powder sifted per joule of energy consumed. Experimental results, conducted using rice flour as a representative dry powder, showed that high humidity increased agglomeration, while a reciprocating motor at 210 RPM improved particle distribution across the sieve. The infrared sensor reduced energy consumption by activating the motor only when material was present. Overall, the system achieved an efficiency improvement exceeding 85% compared to manual sieving. This study demonstrates that embedded sensing and data acquisition can transform traditional sieving machines into objective, transparent, and reproducible systems for MSMEs, with potential application to various dry powders
Performance Analysis of an Offline Text Detection System Based on Edge AI A Case Study of DokuScan Pro Pujianto, Defi; Kadarsih, Kadarsih; Hartati, Sri
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.v8i1.7854

Abstract

The growing use of mobile document scanning applications has increased the demand for text detection systems that can operate reliably in offline and on-device environments. Although Edge AI enables local inference without network dependency, system-level empirical evidence regarding its performance under real-world mobile usage conditions remains limited. This study presents a system-level evaluation of an offline Edge AI–based text detection system for mobile document scanning, using DokuScan Pro as a case study. The evaluation was conducted on 40 document images captured under varying lighting conditions, capture angles, and background characteristics. System performance was assessed using precision, recall, F1-score, and inference time to characterize on-device behavior rather than algorithmic novelty. Experimental results show that the system achieved a precision of 1.00, a recall of 0.975, and an F1-score of approximately 0.98, with an average inference time of 63.8 ms per image during fully offline execution on mobile devices. These results indicate stable system-level performance under real-world document scanning conditions with controlled computational overhead. This study provides empirical system-level insights into the feasibility and practical limitations of deploying Edge AI–based text detection in offline mobile document scanning applications, thereby complementing existing model-centric research with evidence from real-world, on-device evaluation.
Designing and Evaluating a User-Centered Cash Flow Monitoring Dashboard for Higher Education Using Design Thinking and UEQ Framework Amalia; Izzati, Rahmi; Wiyono, Bambang Harie; Mahendra, R. Adhari Cahya; Amalia, Betty
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.v8i1.7858

Abstract

Although cash flow monitoring dashboards have been widely implemented in higher education institutions, existing studies predominantly focus on technical development or usability testing, with limited attention to how user-centered design frameworks contribute to financial decision-making effectiveness. This creates a research gap regarding the systematic application of design thinking as a methodological approach for developing and evaluating financial dashboards in university contexts. This study addresses this gap by proposing and evaluating a user-centered cash flow monitoring dashboard developed using a design thinking approach. A descriptive quantitative method was employed through a case study conducted at Sekolah Tinggi Teknologi Terpadu Nurul Fikri. Data were collected through stakeholder interviews and prototype evaluation using the User Experience Questionnaire (UEQ). The design thinking process was implemented across the empathize, define, ideate, prototype, and test stages to ensure alignment between user needs and dashboard functionality. The findings indicate that the proposed dashboard achieved strong performance across key UEQ dimensions, particularly attractiveness, efficiency, and dependability, demonstrating its effectiveness in supporting cash flow monitoring and managerial financial decision-making. Unlike previous studies that emphasize system implementation outcomes, this research provides empirical insights into how design thinking facilitates the translation of user needs into actionable financial information. This study contributes to the literature by offering a structured framework for applying design thinking in the development of financial monitoring dashboards within higher education institutions. The results also have practical implications for universities seeking to improve data-driven financial governance through user-centered financial information systems.

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