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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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Evaluation of the Use of Web-Based Library Management System at I Gusti Bagus Sugriwa State Hindu University Denpasar Muliani, Ni Made; Wijaya, I Komang Wisnu Budi; Giri, I Putu Agus Aryatnaya; Kartika, Luh Gede Surya; Wiguna, I Gusti Ngurah Dwi Candra
Journal of Computer Networks, Architecture and High Performance Computing Vol. 7 No. 4 (2025): Articles Research October 2025
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v7i4.7021

Abstract

Libraries as the heart of higher education play a vital role in supporting the Tri Dharma, particularly in academic and research development. The advancement of information technology demands innovation in library services, one of which is the adoption of Web-Based Library Management Systems (WLMS). This study, entitled Evaluation of the Use of Web-Based Library Management System at I Gusti Bagus Sugriwa State Hindu University, Denpasar, aims to evaluate the effectiveness, challenges, and strategic significance of WLMS implementation using the Technology Acceptance Model (TAM) and the Technology–Organization–Environment (TOE) framework. A sequential explanatory design was applied, combining quantitative surveys and qualitative interviews with 39 respondents consisting of librarians, lecturers, and students. Quantitative findings indicate very good results in perceived usefulness (PU), attitude toward use (ATU), behavioral intention (BI), and actual use (AU), while perceived ease of use (PEOU) scored in the “good” category. TOE-based analysis reveals supporting factors such as infrastructure availability, leadership commitment, and system accessibility, while inhibiting factors include unstable internet, data security concerns, insufficient training, and low digital literacy among users. These results demonstrate that although WLMS significantly improves accessibility, efficiency, and digital literacy, its sustainability requires continuous system optimization, technical support, and human resource development. This research concludes that WLMS adoption at I Gusti Bagus Sugriwa State Hindu University is largely successful but still needs strategic interventions to ensure long-term effectiveness and integration with the university’s mission of advancing Hindu knowledge and culture in the digital era.
IoT-Based Smart Water Quality Monitoring System for Early Detection of Water Pollution in Batam City Candra, Joni Eka; Hazimah , Hazimah
Journal of Computer Networks, Architecture and High Performance Computing Vol. 7 No. 4 (2025): Articles Research October 2025
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v7i4.7115

Abstract

Water pollution poses a significant threat to public health, particularly in rapidly developing urban areas such as Batam City, where domestic and industrial activities continue to increase. This study aims to analyse and monitor the quality of household tap water in Batam City over a 30-day period using an Internet of Things (IoT)-based smart water quality monitoring system. The research focuses on two key parameters, namely Total Dissolved Solids (TDS) and water temperature, which serve as primary indicators of water purity. Data were collected daily through a TDS sensor and a DS18B20 digital temperature sensor integrated into an IoT platform for continuous monitoring and real-time data acquisition. The results revealed that the water temperature ranged between 28–29 °C, indicating normal conditions for tropical regions and conforming to clean water standards. The TDS values varied from 310 to 355 ppm, remaining below the World Health Organization (WHO) safety limit of 500 ppm. Although slight fluctuations in TDS levels were observed during the observation period, no readings exceeded the acceptable threshold. These findings suggest that household tap water in Batam City is still safe for consumption and daily use. The study concludes that the application of IoT-based monitoring systems offers an effective approach for real-time water quality supervision and recommends regular monitoring along with the use of filtration devices to ensure long-term water safety and sustainability
Design and Implementation of a File Encryption System Based on Object-Oriented Programming Using C++ Jimmy, Jimmy`
Journal of Computer Networks, Architecture and High Performance Computing Vol. 7 No. 4 (2025): Articles Research October 2025
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v7i4.7211

Abstract

Data security has become a critical aspect in the digital era, especially in protecting confidential files from unauthorized access. This study aims to design and implement a file encryption system based on Object-Oriented Programming (OOP) principles using the C++ programming language. The system is developed by applying the core concepts of OOP, namely encapsulation, inheritance, and polymorphism, to create a modular and reusable software architecture. The encryption process is implemented using symmetric key algorithms, allowing users to encrypt and decrypt text-based files securely. The program design follows a layered class structure, where each class handles specific functionalities such as key generation, file reading and writing, and cryptographic operations. The implementation is conducted in a console-based environment with a focus on simplicity, efficiency, and maintainability of the source code. The evaluation results show that the OOP-based approach enhances the flexibility of system modification and reduces code redundancy compared to traditional procedural programming. Furthermore, performance testing demonstrates that the encryption and decryption processes can be executed efficiently without significant latency for small to medium-sized files. This research concludes that applying OOP concepts in C++ provides a structured and scalable framework for developing secure and maintainable encryption systems, which can be further enhanced for larger-scale or GUI-based applications in the future.
Integrating AHP and TBATS for Infectious Disease Prioritization and Forecasting in East Java Supriyanto, Budi Fajar; Salihati Hanifa; Nesa Ayu Murthisari Putri; Titin Andriyani Atmojo; Waridad Umais Al Ayyubi
Journal of Computer Networks, Architecture and High Performance Computing Vol. 7 No. 4 (2025): Articles Research October 2025
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v7i4.7151

Abstract

Agrarian regions like East Java province face complex public health challenges. Some cases are caused by the interaction between social factors, and others by agribusiness factors. An integrative approach is needed to understand the dynamics of disease cases. This study aims to analyse the disease with the highest number of cases and project case trends in East Java using an integrated quantitative approach. Using methods such as the Analytic Hierarchy Process (AHP) to determine disease weights, the TBATS model is used to project case trends through 2028. Standardised multiple regression models were used to assess the influence of social factors (population density, poverty) and agribusiness (rice harvest area, agricultural labour). The data used are secondary time-series data from 2013 to 2023 obtained from BPS, the Health Department, and BMKG. The AHP results show diarrhoea as the disease with the highest weight (0.494), followed by pneumonia (0.112), tuberculosis (0.090), malaria (0.051), and dengue fever (0.049). The TBATS projection indicates medium-term fluctuations with the potential for an increase in dengue fever cases. Meanwhile, the regression results show that people in the agricultural sector are at increased risk of malaria (p = 0.037), while other variables have an influence but are not significant. Therefore, integrating health, social, and agribusiness data is an urgent need. And it can be used for early disease warning systems and more precise public health policy strengthening.
Sentiment Analysis of Skincare Products Using Lexicon and Multinomial Naive Bayes on The Sociolla Website Rahmansyah, Ferdian; Sriyanto, Sriyanto; Lestari, Sri; Irianto, Suhendro Yusuf
Journal of Computer Networks, Architecture and High Performance Computing Vol. 7 No. 4 (2025): Articles Research October 2025
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v7i4.7048

Abstract

Global warming has triggered extreme weather that negatively affects skin health, including damage, premature aging, and increased risk of skin cancer, prompting the use of skincare products. E-commerce platforms like Sociolla simplify skincare purchases, but the abundance of choices and varying skin reactions make product selection challenging. This study aims to assist consumers in making smarter purchase decisions by analyzing user reviews using sentiment analysis with a lexicon-based approach and the Multinomial Naive Bayes algorithm to classify reviews as positive or negative. The process includes data collection, text preprocessing, model development, and performance evaluation. The results show that this method achieved an accuracy of 80,64%, demonstrating its effectiveness in helping consumers filter reviews and select appropriate skincare products.
IoT-Based Smart Air Quality System: A Real-Time Monitoring Solution for Indoor Air Quality Candra, Joni; Hazimah
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.v8i1.7173

Abstract

Indoor Air Quality (IAQ) plays a crucial role in maintaining human health and comfort. This study aims to design and implement an Internet of Things (IoT)-based indoor air quality monitoring system integrated with a mobile application for real-time observation. The system employs sensors to measure environmental parameters such as temperature, humidity, and carbon dioxide (CO?) levels, with data transmitted wirelessly and visualized through the mobile app. The applied method includes hardware design, IoT-based software development, and system testing in several rooms with different activity conditions. The implementation results show that the system can accurately display air quality data and provide automatic notifications when pollutant levels increase. Based on seven days of measurement, the kitchen area indicated a “Poor Air” category, while the living room and bedroom were classified as “Fresh Air.” This system effectively delivers fast and accurate air quality information, enabling users to take preventive actions to maintain healthy indoor air conditions
Systematic Literature Review: A Comparison of Clustering Methods in Data Mining Setyawan, Roni; Murtiyasa, Budi
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.v8i1.7333

Abstract

Clustering is one of the fundamental techniques in data mining used to group data instances based on inherent similarities without relying on predefined labels. This technique plays a crucial role in numerous domains, including customer behavior analysis, pattern recognition, anomaly detection, bioinformatics, and many other applications that require a deeper understanding of hidden structures within data. Over the past decades, a wide range of clustering methods has been developed such as K-Means, DBSCAN, Hierarchical Clustering, density-based approaches, model-based clustering, and more recent algorithms that incorporate machine learning and deep learning paradigms. Each method offers distinct advantages and limitations and is suited for different data characteristics and analytical objectives. The SLR process includes identifying relevant articles, screening for quality and eligibility, extracting essential data, and synthesizing findings according to predefined systematic criteria. The primary aim of this review is to identify emerging research trends, understand methodological advancements, assess the performance of different clustering methods across diverse data contexts such as varying dataset sizes, noise levels, dimensionality, and cluster distributions and provide insights into the key factors that influence the selection of appropriate clustering techniques. The findings of this review indicate that no single clustering method consistently outperforms others in all scenarios. Certain algorithms may produce optimal results for low-dimensional datasets yet perform inadequately when applied to complex, high-dimensional data. Conversely, some methods are effective at identifying clusters with irregular shapes but require sensitive parameter tuning or exhibit higher computational costs. Therefore, the choice of clustering technique should be guided by the specific characteristics of the dataset, the objectives of the analysis, and evaluation criteria such as accuracy, computational efficiency, interpretability, and robustness to noise. Overall, this review aims to serve as a comprehensive reference for researchers, practitioners, and decision-makers in selecting the most suitable clustering method for their specific analytical needs. Additionally, the study highlights potential avenues for future research, including the development of hybrid algorithms, automated parameter selection techniques, and the integration of clustering with modern machine learning approaches to enhance performance and adaptability across various data environments
Minimizing Subjectivity in Esports Adjudication: A Decision Support System for Indonesia Sim Racing League Using C4.5 Algorithm Dafa', Mu'ammar; Sitanggang, Delima; Turnip, Mardi
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.v8i1.7376

Abstract

The adjudication of racing incidents in the Indonesia Sim Racing League (ISL) currently faces challenges due to inherent subjectivity, inconsistency, and the time-consuming nature of decisions that rely solely on race stewards’ interpretations. This study develops a Decision Support System (DSS) for penalty recommendation in ISL racing incidents by applying the Decision Tree C4.5 algorithm. Historical incident data were collected directly from Indonesia Sim Racing League Seasons 1 to 3, and an additional synthetic dataset was generated based on predefined incident attributes to support model training. All data were processed using Python in the Google Colab environment to train and evaluate the C4.5 model. Experimental results show that the proposed DSS achieved an overall accuracy of 90%, indicating strong predictive capability in recommending appropriate penalties under the given dataset configuration. Further evaluation using class-sensitive metrics yielded a macro-average precision of 0.71, a recall of 0.73, and an F1-score of 0.72, reflecting a more balanced performance across penalty classes despite the presence of class imbalance in racing incident data. These results indicate that the model is able to capture relevant decision patterns while maintaining robustness across both majority and minority penalty classes. Overall, this study demonstrates that the proposed DSS can assist race stewards at an early stage of decision-making by narrowing the decision space and reducing subjective bias, thereby supporting fairer and more consistent adjudication processes. The main contribution of this paper lies in presenting one of the first empirical implementations of a DSS for esports racing adjudication using an interpretable C4.5-based approach, providing a transparent and practical foundation for future research on intelligent decision-support systems in competitive sim racing environments.
Developing a Digital Circular Economy Business Model for SIPETRA in Jatiluwih Village, Bali Biantara, I Gede Dody Okta; Marmaiyatno, Marmaiyatno; Dana, I Made Kresna
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.v8i1.7403

Abstract

Waste management in tourism villages has become a major environmental challenge due to increasing waste generation from tourism activities and limited local infrastructure. Jatiluwih Village, a UNESCO World Heritage site in Bali, faces this issue as tourism growth produces more domestic and organic waste. Although several studies have examined community-based waste initiatives, research integrating strategic analysis, participatory validation, and business model innovation remains limited. This study aims to design a sustainable business model for SIPETRA (Waste Management and Technology System for Waste Transformation) as a community-based circular economy solution supported by digital technology. Unlike previous studies, this research integrates SWOT, Delphi, BMC, and BOS into a unified framework to develop a digital circular economy model tailored to rural tourism contexts. The research employed a descriptive qualitative method through field observation, interviews, questionnaires, and literature analysis. Data were processed using SWOT and Delphi techniques to identify strategic factors, followed by the formulation of the Business Model Canvas and Blue Ocean Strategy. The results show that SIPETRA’s internal capacity is moderately weak (IFAS = 2.45), while external opportunities are strong (EFAS = 3.01), placing the model in the WO quadrant. Consensus from 12 experts (Kendall’s W = 0.78) identified four strategic priorities: human resource improvement, digital transformation, product quality enhancement, and partnership-based funding. The BOS analysis generated innovative programs such as the SIPETRA app, Eco-Coin reward system, and Green Experience Center to create a “Jatiluwih Circular Living Experience.” This study concludes that the integrated analytical framework effectively transforms waste management into a self-sustaining digital circular economy model that supports environmental sustainability, social empowerment, and green tourism. The findings provide theoretical contributions to digital circular economy literature and practical implications for tourism villages seeking scalable and community-driven waste management solutions.
Enhancing Water Quality Early Warning System Accuracy in Pangasius Aquaculture Using Machine Learning Hadyan, M Rais; finki dona marleny; ayu ahadi ningrum
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.v8i1.7479

Abstract

Intensive catfish (Pangasius sp.) aquaculture faces significant economic risks driven by mass mortality events linked to unstable water quality, particularly toxic ammonia spikes and pH fluctuations. Although Internet of Things (IoT) technology enables real-time monitoring, the resulting time-series data presents complex challenges, including high sensor noise, asynchronous transmission, and severe class imbalance, which compromise standard reactive monitoring methods. This study aims to enhance diagnostic accuracy by comparing Support Vector Machine (SVM), Random Forest (RF), and XGBoost algorithms to construct a robust Early Warning System (EWS). A quantitative experimental methodology was applied to real-world sensor data, with temporal aggregation preprocessing to reduce noise. To ensure rigorous validation simulating real-world deployment, the dataset utilized a strict chronological split (80% training, 20% testing) and was further tested using 5-Fold Time-Series Cross-Validation. The results demonstrated the definitive superiority of ensemble-based models; Random Forest and XGBoost achieved 100.00% accuracy on the test set, successfully eliminating the critical false negatives exhibited by the SVM model (99.80%). Stability analysis further confirmed the robustness of Random Forest (98.35%) and XGBoost (98.32%) compared to SVM (97.02%). Additionally, feature importance analysis unequivocally identified ammonia as the dominant predictor of critical conditions. Crucially, the study detected a “concept drift” phenomenon in which “Safe” conditions disappeared during the final cultivation phase. These findings conclude that ensemble models provide the optimal architecture for EWS. However, the presence of concept drift necessitates adaptive retraining strategies to ensure long-term reliability in dynamic pond environments.

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