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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 828 Documents
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Continuous Rainfall Prediction Using Stacked LSTM and Sliding Window Time Series Modeling Akrom Akrom; Dimas Lendensi; Abdullah Muhajir; Riky Susanto
Journal of Computer Networks, Architecture and High Performance Computing Vol. 8 No. 2 (2026): Research Paper April 2026
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v8i2.8277

Abstract

Rainfall prediction plays an important role in supporting decision-making in agriculture, water resource management, and disaster mitigation. However, the increasing variability of rainfall patterns makes accurate forecasting a challenging task. This study aims to implement a Long Short-Term Memory (LSTM) model for rainfall prediction based on time series data and to evaluate its performance using regression metrics. The dataset consists of 600 monthly rainfall observations, which were preprocessed through normalization and transformed using a sliding window technique with a time step of 30. The data were divided into training and testing sets with a ratio of 80:20. The proposed model employs a stacked LSTM architecture with dropout regularization and is trained for 50 epochs. The experimental results show that the model achieves satisfactory predictive accuracy, with a Train RMSE of 1.1009 and a Test RMSE of 0.6846, as well as a Train MAE of 0.6000 and a Test MAE of 0.4837. The results indicate that the model is capable of capturing temporal patterns and fluctuations in rainfall data. Therefore, the LSTM-based approach can be considered an effective method for rainfall prediction and has potential applications in environmental forecasting systems.
Architecture-Dependent Effects of CLAHE Enhancement Across YOLOv5, YOLOv8, YOLOv10, and YOLOv11 for Bone Fracture Detection Febri Aldi; Irohito Nozomi; Ronaldo Syahputra
Journal of Computer Networks, Architecture and High Performance Computing Vol. 8 No. 3 (2026): Research Paper July 2026
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v8i3.8572

Abstract

Bone fracture detection in X-ray images remains challenging because fracture lines often appear as low-contrast, subtle, and visually similar patterns across fracture types. Although YOLO-based detectors have been widely used for medical object detection, the effect of contrast enhancement is commonly evaluated on a single architecture, making it unclear whether preprocessing benefits are consistent across different YOLO generations. This study investigates the architecture-dependent effect of Contrast Limited Adaptive Histogram Equalization (CLAHE) on YOLOv5, YOLOv8, YOLOv10, and YOLOv11 for bone fracture detection. A total of 1,539 annotated X-ray images were prepared in YOLO bounding-box format and evaluated under two scenarios: original images and CLAHE-enhanced images. Model performance was assessed using precision, recall, mAP50, and mAP50-95, followed by paired architecture-level comparison using paired t-test, Wilcoxon signed-rank test, and bootstrap confidence intervals. The results show that CLAHE does not uniformly improve all detection metrics. YOLOv8 without CLAHE achieved the strongest mAP50 and recall, whereas YOLOv11 with CLAHE produced the highest mAP50-95, indicating better localization precision under stricter IoU thresholds. The statistical comparison suggests a positive but exploratory improvement in mAP50-95 after CLAHE, while other metrics showed no significant architecture-level difference. These findings demonstrate that image enhancement effectiveness is architecture-dependent and should be selected according to the feature extraction and localization characteristics of the detector rather than applied as a universal preprocessing step.
Web-Based Periodic Salary Increase Information System at The Biddokkes Polda Jambi Using Waterfall Method Sugeng Wibowo; Mutmainnah
Journal of Computer Networks, Architecture and High Performance Computing Vol. 8 No. 3 (2026): Research Paper July 2026
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v8i3.8609

Abstract

The administrative process for periodic salary increases for personnel at the Biddokkes Polda Jambi currently faces efficiency challenges, including the risk of data entry errors and delays in monitoring the salary increase periods. This study aims to develop a web-based periodic salary increase information system capable of automating data collection and providing timely notifications. The system development method used is the Waterfall model, which includes the stages of requirements analysis, design, coding, testing, and maintenance. The results of the study indicate that the developed system successfully minimizes data input errors and accelerates the administrative verification process compared to manual methods. Through functional testing using the Black Box Testing techniques and User Acceptance Test (UAT) evaluation, all major system features were proven to function with a 100% success rate and were well-received by users, as evidenced by a system acceptance rate of 90%. With this system in place, human resource management at the Biddokkes Polda Jambi become more measurable, transparent, and effective in supporting personnel administration processes.
Application of Deep Learning for Cardiac Arrhythmia Classification Based on ECG Signals Gabriela Septiani Simbolon; Gresia Cesilia Sirait; Sarah Theresia Aruan; Rivaldo Robertus Turnip; Jepri Banjarnahor; Mardi Turnip
Journal of Computer Networks, Architecture and High Performance Computing Vol. 8 No. 3 (2026): Research Paper July 2026
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v8i3.8672

Abstract

Cardiac arrhythmia is a dangerous heart rhythm disorder, so early detection is crucial for effective treatment. Manual ECG (Electrocardiogram) analysis is less accurate, while deep learning can detect arrhythmias more quickly and precisely. The proposed algorithm uses a deep learning Convolutional Neural Network (CNN) model for arrhythmia classification. The model is trained on labeled normal and arrhythmia ECG datasets to recognize important patterns in sequential data. The ECG data is obtained from PhysioNet, which provides thousands of labeled recordings for training and testing. Additional clinical data from hospitals/clinics can be included for further validation with patient consent according to ethical protocols. The expected result is that this system can detect arrhythmias with high accuracy and optimal sensitivity. The benefits are to improve the quality of healthcare services and reduce the risk of serious complications.
Implementation of the Threshold Method in an IoT-Based Monitoring System for Temperature, Humidity and Feed in Broiler Chicken Houses Febryan Djastin Arya Raharja; Joni Maulindar; Arif Setiawan
Journal of Computer Networks, Architecture and High Performance Computing Vol. 8 No. 3 (2026): Research Paper July 2026
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v8i3.8808

Abstract

Broiler chickens require optimal house management to maintain productivity, particularly in terms of temperature, humidity, and feed availability. Monitoring that is still done manually can potentially cause delays in detecting changes in environmental conditions and feed stock, which can hinder the chickens’ growth. This study aims to implement a threshold method in an Internet of Things (IoT)-based monitoring system for temperature, humidity, and feed levels in broiler chicken houses. The study employed the ADDIE (Analysis, Design, Development, Implementation, Evaluation) development model, utilizing an ESP32 microcontroller, a DHT22 sensor to measure temperature and humidity, an HC-SR04 sensor to detect feed levels, a 4-channel relay module to control fans and incandescent lights, and a web dashboard connected to a Supabase database for real-time monitoring. The system applies threshold values adjusted to the broiler chickens’ growth phases so that the actuators can operate automatically to maintain optimal coop conditions. Test results show that the system is capable of monitoring temperature, humidity, and feed levels in real time, transmitting data to the web dashboard, and controlling lights and fans according to threshold values specifically, lights turn on when the temperature is below 31°C and fans turn on when the temperature is above 31°C. The implementation of this system improves the effectiveness of monitoring and controlling coop conditions and has the potential to support poultry farm productivity. The system applies threshold values adjusted to the growth phase of the broiler chickens so that the actuators can operate automatically to maintain optimal conditions in the coop. Test results show that the system is capable of monitoring temperature, humidity, and feed levels in real time, transmitting data to a web dashboard, and controlling lights and fans according to threshold values specifically, lights turn on when the temperature is below 31°C and fans turn on when the temperature is above 31°C. The implementation of this system improves the effectiveness of monitoring and controlling coop conditions and has the potential to support broiler farm productivity. Further development can be pursued by adding automatic notification features, improving sensor accuracy, and integrating an automatic feeding system.
Explainable Machine Learning Framework for Thyroid Cancer Recurrence Prediction Tuti Alawiyah; Taufik Wibisono; Recha Abriana Anggraini; Bambang Kelana Simpony; Yesti Siti Nurjanah
Journal of Computer Networks, Architecture and High Performance Computing Vol. 8 No. 3 (2026): Research Paper July 2026
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v8i3.8894

Abstract

Accurate prediction of thyroid cancer recurrence is essential for improving long-term patient management and supporting evidence-based clinical decision-making. Although machine learning has demonstrated promising predictive performance, limited model interpretability remains a major barrier to its clinical adoption. This study aims to develop an Explainable Machine Learning framework for thyroid cancer recurrence prediction by integrating Extreme Gradient Boosting (XGBoost) with SHapley Additive exPlanations (SHAP) using clinicopathological features. A publicly available dataset containing 383 patient records was preprocessed through label encoding, correlation analysis, Chi-Square-based feature selection, and Min-Max normalization. Logistic Regression, Decision Tree, Random Forest, and XGBoost were comparatively evaluated using 10-fold stratified cross-validation with Accuracy, Precision, Recall, F1-score, and ROC-AUC as evaluation metrics. The best-performing model was subsequently interpreted using global and local SHAP analyses. XGBoost achieved the highest performance, with an accuracy of 95.8% ± 4.4%, precision of 93.4% ± 8.3%, recall of 91.4% ± 9.9%, F1-score of 92.2% ± 8.3%, and ROC-AUC of 98.6% ± 2.5%, outperforming the other models. SHAP analysis identified Response, Risk, and N Stage as the most influential clinicopathological factors affecting recurrence prediction. This study contributes by developing a unified Explainable Machine Learning framework that integrates comparative model evaluation, XGBoost prediction, and global and local SHAP interpretation within a single workflow. The proposed framework provides accurate and clinically interpretable recurrence prediction, supporting trustworthy risk assessment and personalized decision-making in thyroid cancer management.
Comparative Machine Learning Classification for QRIS Quishing Detection Based on URL Features: English Permadi Kusuma; Muhammad Yusuf Halim; Ruhamah
Journal of Computer Networks, Architecture and High Performance Computing Vol. 8 No. 3 (2026): Research Paper July 2026
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v8i3.8940

Abstract

The increasing adoption of the Quick Response Code Indonesian Standard (QRIS) as a digital payment method has been accompanied by the emergence of quishing, a phishing attack that exploits malicious QR codes to redirect users to fraudulent websites. This study aims to compare the performance of three machine learning classification algorithms Random Forest, Decision Tree, and Naïve Bayes—for detecting phishing URLs in a simulated QRIS quishing environment using URL-based features. The experiments were conducted using a publicly available phishing URL dataset representing simulated URLs that may be encountered after scanning malicious QR codes. Model performance was evaluated using accuracy, precision, recall, and F1-score. The experimental results show that Random Forest achieved the highest accuracy of 96.94%, outperforming Decision Tree 95.32% and Naïve Bayes 65.49%. The superior performance of Random Forest is attributed to its ensemble learning mechanism, which combines multiple decision trees to reduce overfitting, improve robustness, and provide more stable classification performance across diverse URL characteristics. This study contributes a comparative benchmark of machine learning algorithms for URL-based quishing detection and demonstrates that Random Forest is the most effective approach for supporting early phishing detection in QRIS payment systems.
Development and Evaluation of a No-Code-Based Cash Flow Information System Using Base44 for Financial Reporting Monitoring (Case Study: Zanjus) Amalia Amalia; Fajar ‘Ainur Ridhwan; Salman Fathy Shiroth
Journal of Computer Networks, Architecture and High Performance Computing Vol. 8 No. 3 (2026): Research Paper July 2026
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v8i3.8948

Abstract

Digital transformation in the financial management of small and medium enterprises (SMEs) has become an essential requirement for improving the efficiency, transparency, and accuracy of financial reporting. However, most student-run businesses still record transactions manually using simple spreadsheets, which makes them prone to recording errors and delays in cash flow monitoring. This study aims to develop a Cash Flow Information System based on no-code using the Base44 platform to support financial reporting monitoring for the Zanjus student business. The study employed the Research and Development (R&D) method with the Waterfall development model, encompassing the stages of requirements analysis, design, implementation, testing, and evaluation. Data were collected through semi-structured interviews and documentation, while system testing was carried out using Black Box Testing and User Acceptance Testing (UAT). The results show that the system successfully integrates the recording of income and expense transactions, a real-time financial monitoring dashboard, and automated financial reporting in the form of a general journal, ledger, cash flow statement, income statement, and balance sheet. The results of the Black Box Testing indicate that all system features function in accordance with the functional requirements. Meanwhile, the UAT results, which involved five core participants (CEO, CFO, COO, CMO, and CCO), yielded an average score of 96.75%, placing the system in the “Very Good” category within the context of this single case study; the result therefore cannot yet be generalized to a broader user population. This study also produced user-needs-based design principles encompassing interface simplicity, real-time transparency, reporting automation, and development flexibility. The study contributes academically by advancing research on the implementation of no-code platforms in Accounting Information Systems, while also offering a practical contribution in the form of a digital solution that is easy to use for non-technical users within the student SME environment.

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