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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 18 Documents
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Search results for , issue "Vol. 7 No. 4 (2025): Articles Research October 2025" : 18 Documents clear
Arabic NLP: A Survey of Pre-Processing and Representation Techniques: Arabic NLP Alrekabee, Mohammed
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

Abstract

The rapid growth of Arabic Natural Language Processing (NLP) has underscored the vital role of upstream tasks that prepare raw text for modeling. This review systematically examines the key steps in Arabic text pre-processing and representation learning, highlighting their impact on downstream NLP performance. We discuss the unique linguistic challenges posed by Arabic, such as rich morphology, orthographic ambiguity, dialectal diversity, and code-switching phenomena. The survey covers traditional rule-based and statistical methods and modern deep learning approaches, including subword tokenization and contextual embeddings. Special attention is given to how pre-trained language models like AraBERT and MARBERT interact with pre-processing pipelines, often redefining the balance between explicit text normalization and implicit representation learning. Furthermore, we analyze existing tools, benchmarks, and evaluation metrics, and identify persistent gaps such as dialect adaptation and Romanized Arabic (Arabizi) processing. By mapping current practices and open issues, this review aims to guide researchers and practitioners towards more robust, adaptive, and linguistically-aware Arabic NLP pipelines, ensuring that the data fed into models is as clean, consistent, and semantically meaningful as possible.
TOURIST VISIT PATTERN ANALYSIS AT HOTELS IN NORTH PENAJAM PASER REGENCY USING K-MEANS CLUSTERING Pratama, Maulana Adhie; Hadisaputro, Elvin Leander
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.v7i4.6821

Abstract

Penajam Paser Utara Regency, as a strategic area in East Kalimantan, has experienced significant development in the tourism sector in line with the plan to relocate the national capital (IKN). However, the utilization of tourist visitation data in hotels in this region is still not optimal. This study aims to analyze tourist visit patterns at Penajam Paser Utara Regency hotels using data mining techniques with the K-Means Clustering algorithm. The data used is secondary data obtained from the Penajam Paser Utara Regency Culture and Tourism Office, covering 34 hotels with variables including domestic and foreign visitors from 2019 to 2024. The clustering results show two main clusters: a high-visitation cluster comprising large hotels and a low-visitation cluster consisting of hotels with fewer visitors. The analysis reveals the dominance of domestic tourists, accounting for 99% of total visits, and the tourism sector's recovery pattern, reflecting a V-shaped recovery post-pandemic. This research contributes to hotel managers in designing market segment-based marketing strategies and local governments in designing data-driven tourism policies to enhance the sustainable competitiveness of destinations.
COMPARISON OF RANDOM FOREST AND SUPPORT VECTOR MACHINE ALGORITHMS IN THE CLASSIFICATION OF DYSPEPSIA DISEASE Zahra, Fathima; Ichsan, Aulia; Riyadi, Sugeng
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.v7i4.6874

Abstract

Functional dyspepsia remains a prevalent gastrointestinal disorder globally, with a higher burden in low- and middle-income countries such as Indonesia. Diagnostic challenges are exacerbated by limited healthcare infrastructure and a low ratio of gastroenterologists. Machine learning approaches offer a promising solution to enhance diagnostic consistency and accuracy in resource-limited settings. This study aims to compare the performance of the Random Forest (RF) and Support Vector Machine (SVM) algorithms in differentiating dyspepsia from gastroenteritis using Indonesian clinical data. A quantitative experimental method was applied using patient medical records, including gastrointestinal disease categories, vital signs, and symptom profiles. Data preprocessing was carried out by handling missing values through imputation and Min-Max scaling normalization. The dataset was divided into 80% training data and 20% testing data using stratified random sampling. Model performance was evaluated using accuracy, precision, recall, and F1-score metrics. Random Forest demonstrated superior performance on all evaluation metrics compared to SVM. RF achieved 86.5% accuracy, 86.0% precision, 85.0% recall, and 85.5% F1-score, while SVM achieved 83.2% accuracy, 83.0% precision, 81.0% recall, and 82.0% F1-score. The 3.3 percentage point improvement in accuracy and 4.0 percentage point improvement in recall are clinically significant. Random Forest proved more effective in dyspepsia classification, showing better handling of complex clinical data interactions and more reliable diagnostic performance. These findings support the implementation of an RF-based decision support system in Indonesian healthcare facilities to improve diagnostic consistency and patient outcomes.
STRATEGIC INFORMATION SYSTEMS PLANNING USING THE WARD AND PEPPARD METHOD (A CASE STUDY OF KOPERASI DAUH AYU) Biantara, I Gede Dody Okta; Divayana, Dewa Gede Hendra; Dewi, Luh Joni Erawati
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.v7i4.6876

Abstract

 Koperasi Dauh Ayu requires a formally articulated IS/IT strategy to overcome fragmented, manual operations and move toward an integrated, member-centric model. This case study applies the Ward and Peppard framework to diagnose business and IS/IT conditions, using PEST, Porter’s Five Forces, value chain, technology trend scanning, and SWOT with quantitative IFAS–EFAS scoring from six expert respondents. The cooperative is positioned in Quadrant I of the SWOT map with coordinates X = 0.309 and Y = 0.397, indicating an aggressive strategy space where internal strengths can be leveraged to seize external opportunities. The study produces a prioritized portfolio of fourteen applications mapped with the McFarlan Grid, alongside an IT strategy for network, hardware, and platform modernization, and an IS/IT management strategy that establishes a dedicated ICT unit and governance mechanisms. Recommended initiatives are expected to reduce cycle times and error incidence, consolidate a single source of truth for member and financial data, and elevate service quality. The contribution extends the application of Ward and Peppard to the cooperative sector in Indonesia, a context less examined than large enterprises, and shows how staged capability building can translate environmental enablers into realized digital benefits. Limitations include a single-case design without post-implementation measurement; future work should pilot priority systems and evaluate pre–post performance and cost–benefit outcomes.
Development of a YOLO-Based Artificial Intelligence (AI) System for Early Detection of Stunting Risk in Children in 3T Regions of North Sumatra Province Ramadhansyah, Rizki; Simatupang, Septian; Abdillah, Rizky
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.v7i4.6954

Abstract

Stunting is a chronic nutritional problem that has long-term impacts on children’s physical growth, cognitive development, and future productivity. This condition remains a major challenge in the 3T regions (frontier, outermost, and disadvantaged areas) of North Sumatra Province due to limited healthcare personnel, lack of measurement facilities, and delays in early detection. This study aims to develop an artificial intelligence system integrating YOLOv8 and Random Forest to automatically and in real time detect stunting risk in children. The YOLOv8 model is utilized to detect the presence of a child and estimate height through visual image analysis, while the Random Forest algorithm classifies the risk level based on the Height-for-Age Z-score (HAZ) derived from anthropometric and demographic data. The dataset consists of 29 children from 3T regions, with training and testing splits used to evaluate model performance. The results show that the system achieved an accuracy of 97.8%, precision of 96.5%, recall of 95.9%, F1-score of 96.2%, and an area under the ROC curve (AUC) of 0.98. The system successfully detects children in real time, produces risk classifications consistent with manual measurements, and automatically documents examination data. The novelty of this research lies in the integration of YOLO for automatic height measurement and Random Forest for nutritional classification, which has not been applied in the 3T regional context. This system has the potential to serve as a digital tool for healthcare workers and posyandu cadres to accelerate child nutrition monitoring in an efficient, accurate, and well-documented manner.
Application of Google cloud computing for web-based library information systems at Bahayangkara University Surabaya Irsyadi, Muhammad Haidir; Alam, Fajar Indra Nur; Sari, Anggraini Puspita; Agussalim, Agussalim
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.v7i4.6955

Abstract

Libraries are essential in academic work as they expose people to structured, easily procurable information.  However, the majority of schools, including Bhayangkara University Surabaya, still face challenges in managing and storing library information because local or manual systems are substandard. The goal of this project is to deploy and test the effectiveness of Google Cloud Computing technologies, such as Google Cloud Storage, Google Cloud SQL, and Google Compute Engine, on a website-based library information system.  We adopted a quantitative approach by performing experiments and system testing, i.e., black-box testing, access speed testing, and heavy load resistance testing. The result of the implementation is massive benefits, including a response time of 2 seconds on average, stability with 500 users at the same time, and storage efficiency at just 30% of the original size.  Other colleges can have an example that they can use to make a change to a cloud-based digital library from this research.  This also helps create digital library information systems that are technology-centered and dependable.
Design and Development of a Web-Based E-Learning System at SMA Tri Dharma Palembang Ayu, Niken; Supratman, Edi
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.v7i4.6965

Abstract

The rapid advancement of information and communication technology has transformed learning practices globally, yet teaching activities at SMA Tri Dharma Palembang remain predominantly conventional and do not fully align with the requirements of the Merdeka Curriculum. This study was conducted to design and develop a web-based e-learning system that integrates learning material management, assignment submission, automated and manual assessments, discussion forums, and real-time student activity monitoring. The research applied a Research and Development (R&D) approach consisting of needs analysis, system design using Unified Modeling Language (UML), implementation with PHP, MySQL, and Bootstrap, and evaluation through black box testing and a Likert-scale user satisfaction survey. The system was tested by two teachers and ten students, and expert validation was conducted to assess the research instrument. Results show that all modules performed as expected during functional testing, and the user satisfaction survey yielded an overall score of 85.33%, categorized as very good. The findings were analyzed using the Technology Acceptance Model (TAM), the Unified Theory of Acceptance and Use of Technology (UTAUT), and the DeLone & McLean IS Success Model, confirming that system quality, ease of use, usefulness, and user satisfaction significantly influence e-learning adoption in secondary education. This study contributes theoretically by extending the application of established information systems success models in the Indonesian school context and practically by providing a digital platform that supports the implementation of the Merdeka Curriculum. Keywords: activity monitoring; e-learning; Merdeka Curriculum; TAM; UTAUT
Implementation of the K-Means Clustering Algorithm for Segmenting Employee Mental Health Profiles Based on Work Productivity Indicators Rahman, Maulia; Leman, Dedi
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.v7i4.6974

Abstract

This study aims to identify mental health profile segmentation among employees based on work productivity indicators in the context of working from home (WFH) using the K-Means clustering algorithm. This study uniquely integrates mental health and productivity indicators into an unsupervised clustering framework. A cross-sectional method was conducted on 100 employee respondents with 10 main variables, analysed using K-Means with four optimal cluster evaluation methods. The results identified four distinct segments: Low WFH Adaptation (25%), High WFH Enthusiasts (30%), Mixed Preference (25%), and Office Preference (20%), with Silhouette Score validation of 0.623 and Davies-Bouldin Index of 0.967. The main findings reveal the paradox of High WFH Enthusiasts, who have the highest productivity (93%) but the highest mental health risk (1.90). This segmentation provides a practical framework for developing personalised mental health intervention strategies in employee management in the remote working era.
Analysis of Predicting the Number of Rejected Chips Using Random Forest at PT. Wahyu Kartumasindo Internasional Supriyadi, Agus; Sunge, Aswan Supriyadi; Tedi, Nanang
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.v7i4.7028

Abstract

Manufacturing industries face significant challenges in maintaining consistent product quality, particularly in minimizing reject rates across production machines, as high reject levels not only increase operational costs but also reduce overall efficiency and competitiveness. This study aims to develop a predictive approach using the Random Forest algorithm to forecast monthly chip rejects across different production machines, with historical reject data consisting of 1,820 records from June 2023 to September 2024 analyzed based on four primary reject categories and five production machines (DCL1, DCL2, CMI200, CMI200+, and YMJ400). The Random Forest model was applied to classify and predict reject patterns, and its performance was evaluated based on prediction accuracy and error rates, showing that the algorithm is effective in predicting reject counts with an absolute error of 0.640 ± 0.183, especially for lower reject values under 300, although accuracy decreases when handling higher reject levels above 500. Machine-level analysis further reveals that DCL1 and DCL2 consistently contribute the highest reject counts with high variability, while CMI200 and CMI200+ demonstrate stable performance with most rejects below 300, and YMJ400 generally records lower rejects but occasionally exhibits spikes, suggesting inconsistent performance. In conclusion, the Random Forest model provides a reliable predictive framework for monitoring reject trends, identifying DCL1 and DCL2 as priority targets for improvement, and supporting proactive maintenance strategies to enhance overall production quality.
Implementation Of Decision Tree Algorithms For Classification Of Respiratory Infectious Diseases Fauzi; Taghfirul Azhima Yoga Siswa; Fendy Yulianto
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.v7i4.6956

Abstract

Acute Respiratory Infection (ARI) is a common respiratory illness that frequently affects children, primarily caused by viruses such as rhinovirus or adenovirus. In Indonesia, a total of 200,000 ARI cases were recorded during the 2021–2023 period. This study aims to implement the Decision Tree algorithm to classify ARI cases. The dataset consists of 1,501 patient records obtained from UPT Puskesmas Bontang Barat for the 2024–2025 period. The research process includes the pre-processing stage, data splitting into training and testing sets using the 10-Fold Cross Validation technique. Subsequently, model evaluation is conducted using the Confusion Matrix to calculate the Accuracy, Precision, Recall, and F1-Score metrics. The results show that the Decision Tree algorithm is capable of performing classification with good performance, achieving an average accuracy of 81.75%, precision of 79.58%, recall of 81.75%, and an F1-score of 80.45%.

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