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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 795 Documents
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PROTOTYPE OF INTERNET OF THINGS (IOT) IMPLEMENTATION IN WASTE MANAGEMENT TO SUPPORT SMART CITY MONITORING WITH ANDROID-BASED MOBILE APPLICATION USING FORWARD CHAINING METHOD Mohammad, Bawazir Fadhil; Dody Pintarko; Farhans, Muhammad Izzudin; Andre Leto; Ninis Herawati; Dwi Arman Prasetya; Anggraini Puspita Sari
Journal of Computer Networks, Architecture and High Performance Computing Vol. 7 No. 3 (2025): Articles Research July 2025
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v7i3.5977

Abstract

Efficient waste management is one of the main challenges in supporting the implementation of the smart city concept. This research aims to develop a prototype of an Internet of Things (IoT)-based waste management system capable of monitoring the condition of waste bins in real-time through an Android-based mobile application. The system uses the forward chaining method to perform inference processes in decision making, such as identifying the status of the bin (empty, almost full, or full) based on integrated sensor data. The results show that the system is able to detect the volume of waste with high accuracy, send automatic notifications to operators or users when the bin reaches a certain condition, and provide practical solutions to optimise the waste collection process. With these features, the system not only improves operational efficiency but also supports cost reduction and environmental impact. The resulting prototype is expected to be the first step in the application of IoT technology in urban waste management to support the realisation of smart cities.
APPLICATION OF KNN METHOD FOR CLASSIFICATION OF ARRHYTHMIA TYPES BASED ON ECG DATA Manao, Sonatafati; Sitanggang, Delima; Sagala, Albert; Oktarino, Ade; Turnip, Mardi
Journal of Computer Networks, Architecture and High Performance Computing Vol. 7 No. 3 (2025): Articles Research July 2025
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v7i3.6010

Abstract

World Health Organization (WHO) data from June 2024 shows that 31% of adults worldwide or 1.8 billion people do not do physical activity. With that, adults are at higher risk of developing cardiovascular disease and causing an economic and social burden on people with heart disease. K-Nearest Neighbor (KNN) is a machine learning method that can be used to classify or predict heart disease conditions. KNN works by finding the closest data point in the training dataset and then using the class labels of those neighbors to classify new data points. In the context of heart disease, this can be used to predict the likelihood of someone having heart disease. Recording the electrical activity of the heart using a 3-led ECG to determine heart health as well as being material for classification. Exploring the use in the diagnosis of heart disease by focusing on screening and classification of heart disease. By utilizing the KNN method, it has the potential to produce a model that can assist in clinical decision making. Improving the prevention of heart disease and accelerating diagnosis through more sophisticated and technology-based analysis of patient health data.
Evaluation of Cybersecurity Awareness and Training for Digital Branch Frontliners at Bank XYZ Permatasari, Ayu Novira Shinta; Yohannis, Alfa Ryano
Journal of Computer Networks, Architecture and High Performance Computing Vol. 7 No. 3 (2025): Articles Research July 2025
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v7i3.6016

Abstract

The digital transformation in the banking sector has driven a shift in operations, including the establishment of digital branches that rely on information technology to deliver services to customers. However, the increased use of technology brings significant information security risks, particularly those stemming from human factors. This study aims to evaluate the level of cybersecurity awareness among frontliners at Bank XYZ’s digital branch using the ISO/IEC 27002:2022 framework and to develop training recommendations based on NIST SP 800-50. The research was conducted using both quantitative and qualitative methods, involving questionnaires and observations of 36 frontliners. The evaluation results revealed that several controls, particularly Response to Information Security Incidents (ID 5.26), still showed low levels of understanding (60%), indicating the need for training intervention. Training recommendations were designed based on the Cybersecurity and Privacy Learning Program (CPLP) principles from NIST SP 800-50, which include visual approaches, role-based training, and digital learning media. The implementation of these recommendations for one of the controls showed a significant improvement in post-test scores (average >= 93), exceeding the 85% threshold. This indicates that the CPLP-based approach is effective in enhancing frontliners’ cybersecurity awareness. This research is expected to serve as a reference for other banks in developing adaptive information security training strategies aligned with international standards.
FUZZY LOGIC-CONTROLLED IOT SYSTEM FOR SMART PUBLIC TOILETS: DESIGN, IMPLEMENTATION, AND EVALUATION Arifani, Kahpi Baiquni; Irsyadi, Muhamad Haidir; Prakoso, Akbar Tri; Amrullah, Ahmad Wildan; Alam, Fajar Indra Nur; Prasetya, Dwi Arman; Sari, Anggraini Puspita
Journal of Computer Networks, Architecture and High Performance Computing Vol. 7 No. 3 (2025): Articles Research July 2025
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v7i3.6045

Abstract

Efficient energy management in public facilities such as public toilets has become an important challenge in the modern era, especially with the increasing demand for environmental sustainability. In this research, we developed a smart toilet system based on the Internet of Things (IoT) using the ESP32 as the main microcontroller and fuzzy logic methods for intelligent decision-making. This system is equipped with temperature (DHT22), humidity, and distance (HC-SR04) sensors to detect environmental conditions and user presence. Based on this data, the toilet fan and light are automatically controlled to minimize energy consumption. To facilitate real-time monitoring and threshold control, this system is integrated with a Flutter-based application, which provides an intuitive user interface for viewing environmental data and setting temperature, humidity, and distance thresholds. Fuzzy logic is used to determine the fan speed based on temperature and humidity inputs, with the output being a gradual fan speed control. (PWM). The test results show that the system can reduce energy consumption by up to 30% compared to the manual method, especially by reducing the unnecessary device idle time. Additionally, the system has an average response time of 200 ms to send sensor data to the application and receive threshold updates from the user. With this approach, the research shows that the integration of IoT with fuzzy logic provides significant energy efficiency and enhances the user experience. This research also opens up opportunities for further development, such as the integration of machine learning technology for predicting facility usage patterns or the implementation of additional sensors for air quality detection. These findings support the implementation of IoT-based automated systems in public facilities to achieve energy efficiency and environmental sustainability.
Comparative Analysis of YOLOv11 with Previous YOLO in the Detection of Human Bone Fractures Aldi, Febri; Nozomi, Irohito; Hafizh, M.; Novita, Triana
Journal of Computer Networks, Architecture and High Performance Computing Vol. 7 No. 3 (2025): Articles Research July 2025
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v7i3.6051

Abstract

Accurate and rapid detection of bone fractures is an important challenge in the medical world, particularly in the field of radiology. This study aims to analyze and compare the performance of the YOLOv11 model with several previous versions of YOLO, namely YOLOv5, YOLOv8, and YOLOv10 in the task of detecting human bone fractures on X-ray and MRI images. The dataset used is the Human Bone Fractures Multi-modal Image Dataset (HBFMID) which consists of 641 raw images (510 X-rays and 131 MRIs). The four models were trained using the HBFMID dataset that had gone through a manual augmentation and annotation process, then tested using evaluation metrics such as precision, recall, mAP50, and mAP50-95. The training results showed that YOLOv11 has the most stable and consistent loss curve, with a fast convergence process. In terms of evaluation, YOLOv11 recorded a precision of 99.87%, a recall of 100%, a 99.49% mAP50, and an 84.13% increase in the number of mAP-95s, which generally outperformed other models. In addition, the visual prediction results show that YOLOv11 can detect fracture areas with the right bounding box and a balanced confidence score, without showing symptoms of overconfidence or inconsistency. When compared to approaches from previous studies, YOLOv11 also showed a significant improvement in detection accuracy. Thus, YOLOv11 is rated as the most optimal and reliable model in deep learning-based automatic bone fracture detection. This model has great potential to be applied in medical diagnosis support systems to improve the efficiency and accuracy of digital fracture identification.
The implementation of the Random Forest Algorithm with Resampling and Without Resampling on the Hepatitis C Disease Dataset Hendrayana, I Gede; Dewi, Ni Putu Dita Ariani Sukma; Aryasa, Jiyestha Aji Dharma; Prayoga, I Made Ade; Raharjo, Rizki Anom
Journal of Computer Networks, Architecture and High Performance Computing Vol. 7 No. 3 (2025): Articles Research July 2025
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v7i3.6089

Abstract

This study evaluates the performance of Random Forest models for Hepatitis C classification using a dataset from Kaggle, focusing on addressing class imbalance through resampling techniques. We compare three approaches: baseline Random Forest without resampling, Random Forest with SMOTE+ENN (Synthetic Minority Oversampling Technique + Edited Nearest Neighbors), and Random Forest with SMOTE+OSS (Synthetic Minority Oversampling Technique + One-Sided Selection). Results show that the baseline model achieved high accuracy (0.9837) but failed to detect minority classes (e.g., suspect Blood Donor recall=0.00). SMOTE+ENN significantly improved performance, achieving perfect classification (precision=1.00, recall=1.00) for Hepatitis, Fibrosis, and Cirrhosis, while maintaining high accuracy (0.9919) and ROC AUC (0.9999). In contrast, SMOTE+OSS showed limitations in detecting Hepatitis (recall=0.00) and yielded lower precision for Fibrosis (0.44), indicating its undersampling approach may be too aggressive. The study highlights SMOTE+ENN as the most effective method for balancing class distribution and enhancing model robustness in medical diagnostics. These findings underscore the importance of selecting appropriate resampling techniques to improve minority class detection in imbalanced datasets, with implications for developing reliable AI-based diagnostic tools for Hepatitis C.
Expert System Based on K-Nearest Neighbor for Oil Palm Fertilizer Application Optimization Triutami, Anggun; Fakhriza, M.
Journal of Computer Networks, Architecture and High Performance Computing Vol. 7 No. 3 (2025): Articles Research July 2025
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v7i3.6092

Abstract

This study aims to develop an expert system utilizing the K-Nearest Neighbor (KNN) algorithm to recommend suitable fertilizers for oil palm plants based on soil conditions, climate, and plant age. A quantitative approach was employed, involving literature review, data collection, model development, and evaluation. Data were obtained from PT. Nusantara Plantation IV Torgamba Plantation, including variables such as soil pH, dolomite, NPK, urea application, and crop yields. The KNN model was optimized with a K-value of 6 and evaluated using metrics including accuracy (63.63%), precision, recall, F1-score, Mean Absolute Error (MAE: 1995.38), and Mean Squared Error (MSE: 5,257,254.73). The system demonstrates the ability to provide fertilizer recommendations by identifying similarities in historical data, though further accuracy improvements are possible. The practical implications of this research include assisting farmers in optimizing fertilizer selection, enhancing productivity, and minimizing environmental impact. Future studies could explore the integration of additional variables or alternative algorithms such as Decision Tree or Naive Bayes to improve performance.
Facial Expression Recognition Using Fused Features: A Comparison of Deep and Machine Learning Jabbooree, Abbas Issa; Alkaabi, Hussein; Kamber, Ali Nadhim
Journal of Computer Networks, Architecture and High Performance Computing Vol. 7 No. 3 (2025): Articles Research July 2025
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v7i3.6128

Abstract

Facial expression recognition (FER) is a highly active field with applications in computer vision, human-computer interaction, security, and computer graphics animation. Recent advancements in deep learning and machine learning have increased interest in utilizing these techniques for accurate facial expression classification. This paper presents a comparative study that evaluates the performance of deep learning and machine learning as classifiers in FER systems, specifically after data fusion. Data fusion techniques combine and integrate multiple sources of information, aiming to enhance the overall classification accuracy by extracting two types of features using geometrical and appearance features trained using two types of convolutional neural networks. The feature outputs of these networks are fused to create a final feature vector for the classification process. The study evaluates the performance of deep learning on two benchmark datasets, the extended Cohn-Kanade (CK+) and Oulu-CASIA datasets, to assess the performance of deep learning. As a point of comparison, the traditional machine learning approach based on the support vector machine (SVM) is also evaluated on the same datasets. Performance metrics such as classification accuracy, precision, recall, and F1-score are utilized. The results obtained from the study highlight the strengths and limitations of both deep learning and machine learning techniques when employed as classifiers in FER systems. Notably, the experimental results demonstrate that the deep learning approach significantly outperforms the baseline methods, achieving an increase in recognition accuracy of 5.22% for the CK+ and 3.07% for the Oulu-CASIA dataset.
Multiscale Facial Detection using RetinaFace Architecture with Loss Function Dewi, Irma Amelia; Maryadi, Nadhiva Adzra Tsania
Journal of Computer Networks, Architecture and High Performance Computing Vol. 7 No. 3 (2025): Articles Research July 2025
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v7i3.6161

Abstract

Facial recognition technology has become increasingly prevalent in modern applications, from security systems to social media platforms. However, one of the most significant challenges in this field remains the accurate detection of faces across varying scales, orientations, and image qualities. Traditional face detection methods often struggle when faces appear at different sizes within the same image or when dealing with low-resolution imagery, leading to inconsistent performance that can compromise system reliability. The RetinaFace architecture emerges as a promising solution to address these multiscale detection challenges. By incorporating a Feature Pyramid Network (FPN), the system creates a hierarchical representation of features that enables effective detection of faces regardless of their size in the image. The FPN combines low-resolution, semantically strong features with high-resolution, semantically weak features, creating a robust feature pyramid that simultaneously captures facial characteristics at multiple scales. Context modules within RetinaFace further enhance detection capabilities by providing additional contextual information that helps distinguish faces from background noise and other objects. This comprehensive approach allows the system to maintain high accuracy even in challenging scenarios where faces appear small, partially occluded, or at unusual angles. The comparative analysis between ArcFace and SphereFace loss functions reveals important insights into optimization strategies for facial recognition systems. The experimental results on the WIDERFACE dataset demonstrate exceptional performance, with the RetinaFace-ResNet152-SphereFace combination achieving 94% accuracy. These findings highlight the importance of architectural choices and loss function selection in developing robust facial recognition systems capable of handling real-world deployment challenges
Prediction of DHF Disease Using Bagging Algorithm with Decision Tree C4.5 Mahfudzin, Abdul Halim; Sriyanto, Sriyanto; Sutedi, Sutedi; Wasilah, Wasilah
Journal of Computer Networks, Architecture and High Performance Computing Vol. 7 No. 3 (2025): Articles Research July 2025
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v7i3.6267

Abstract

Dengue Fever (DHF) continues to represent a significant public health threat in Indonesia and other tropical regions, with an annual increase in the number of reported cases. The primary aim of this study is to develop a predictive model for DHF by integrating the Bagging technique and the Decision Tree C4.5 algorithm. The goal is to improve prediction accuracy by incorporating key environmental factors such as temperature, humidity, and rainfall. The research adopts a quantitative methodology with a descriptive approach, using publicly available datasets from data.mendeley.com and conducting the analysis using RapidMiner software. The findings of the study demonstrate that the proposed model is highly effective in accurately predicting and classifying DHF cases, achieving significant precision. In addition to this, the model is successful in identifying important patterns and trends linked to the disease's occurrence. These results underscore the efficacy of combining Bagging and Decision Tree C4.5 as a robust tool for detecting and forecasting DHF outbreaks. The research contributes substantially to the field of data-driven prediction models, offering valuable insights for health agencies to develop more effective and proactive strategies for disease prevention. For future research, it is recommended that additional factors such as genetic and medical data be considered, along with the application of triangulation methods to improve the analysis's validity, scope, and overall robustness. This approach would enable a more comprehensive understanding of DHF and its predictive modeling.Keywords: DHF Prediction; Bagging; Decision Tree C4.5; Machine Learning; Data Mining

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