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Nurul Khairina
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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 795 Documents
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The Design of a Website-Based Motorcycle Installment Bill Check Application at Indah Motor KM.5 Purba, Windania; Sihombing, Irsan Jaya; Sidabutar, Ayu Elpriyani; Siahaan, Nur Ainun
Journal of Computer Networks, Architecture and High Performance Computing Vol. 3 No. 2 (2021): Journal of Computer Networks, Architecture and High Performance Computing, July
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v3i2.1010

Abstract

Looking at current developments, many activities have implemented the capabilities and advantages of technology in them so that the operational costs, time and energy needed can be found. This technological development also develops this technology also creates needs in the community and even new needs. Along with the need for credit is one alternative carried out by the community and business entities to obtain something with the aim of meeting their needs. This credit or installment activity also does not rule out the possibility of being applied to this type of motorcycle sales business, because in reality the need for motorcycles is currently important and has a high level of demand so that some motorcycle sales provide a payment system for motorcycles that are designed to improve performance. and quality of service, the community needs flexibility to speed up payment for motorbikes that are being sold in installments. From the research that has been done as mentioned above, the author intends to carry out development in terms of installment implementation into a website-based application, so the author takes the title "Designing a Website-Based Motorcycle Installment Bill Check Application at Indah Motor Km.5". From the problems that occur, the researcher intends to simplify credit or installment problems into a website that can make it easier for admins to process and solve problems that apply in the community.
Marketing Strategy Using Frequent Pattern Growth Suhandi, Nazori; Gustriansyah, Rendra
Journal of Computer Networks, Architecture and High Performance Computing Vol. 3 No. 2 (2021): Journal of Computer Networks, Architecture and High Performance Computing, July
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v3i2.1039

Abstract

The biggest problem faced by printing companies during the Covid-19 pandemic was that the number of orders was unstable and tends to decrease, which had the potential to harm the company. Therefore, various appropriate marketing strategies were needed so that the number of product orders was relatively stable and even increases. The impact was that the company could survive and continued to grow. This study aimed to assist company managers in developing appropriate marketing strategies based on association rules generated from one of the data mining methods, namely the Frequent Pattern Growth (FP-Growth) method. The case study of this research was a printing company where there was no similar research that used a printing company's dataset. This study produced nine association rules that meet a minimum of 25% support and a minimum of 60% confidence, but only two association rules that had a high positive correlation, namely for a custom paper bag and banner products. Therefore, several marketing strategies were suggested that could be used as guidelines for companies in managing sales packages and giving special discounts on a product. The results of this study are expected to trigger an increase in the number of product orders because this study tried to find the right product for consumers and did not try to find the right consumers for a product.
College Ranking Analysis Using VIKOR Method Perdana, Adidtya; Budiman, Arief
Journal of Computer Networks, Architecture and High Performance Computing Vol. 3 No. 2 (2021): Journal of Computer Networks, Architecture and High Performance Computing, July
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v3i2.1071

Abstract

College ranking is the main thing used as a basis to attract prospective new students. Prospective new students will generally look for related information from which universities are the best to choose in the future. In the ranking of universities based on the official page of the Ministry of Research, Technology and Higher Education, the ranking of universities, both public and private, can be a reference for recruiting prospective students. The better the college ranking, the more attractive prospective new students are. To determine the ranking of a university, a decision support system is needed. There are many methods available from the decision support system, but in this study, the author will use the VIKOR method (Visekriterijumsko Kompromisno Rangiranje), which is part of the MCDM (Multi-Criteria Decision Making), as a solution to these problems. The VIKOR method is a multi-criteria optimization method that can be used on fairly complex systems. In this study, the sample data used came from 10 universities in the city of Medan, where the initials of each naming were colleges A to J. From the results of research analysis using the VIKOR method, the ranking results were as follows, where college B got the first rank with the lowest score of 0. Then the second rank was college G and in the third rank was college H with each score of 0.248211396 and 0.304702661. For the fourth, fifth, sixth, seventh to tenth ranks, they are sorted from colleges A, I, C, E, F, D, and J.
Android Based Spark and Gas Leak Detection and Monitoring Meidelfi, Dwiny; Moodutor, Hanriyawan Adnan; Sukma, Fanni; Adnin, Sandri
Journal of Computer Networks, Architecture and High Performance Computing Vol. 4 No. 2 (2022): Article Research Volume 4 Number 2, July 2022
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v4i2.1489

Abstract

LPG cylinder leakage is one of the causes of fires in the community. To prevent fires, a fire and gas leak detection and monitoring device were made using a fire detector sensor and an Android-based MQ-6 to trigger it. Data collection techniques in the manufacture of gas and fire leak detection using a flame detector and the MQ-6 sensor can be obtained from datasheets, journals, books and articles, and several internet sites that support the manufacture of this device. In the manufacture of gas leak detection devices or tools, there are also two parts, namely the first to make hardware (hardware), then software (software). The result of this tool detection is that users can find out the level of LPG due to leaking of LPG cylinders and detect fire using Android notifications in real-time and the data is displayed in detail on the browser page. The conclusion of this study is that users are safer because there is a gas leak, the tool will detect LPG gas, then a message will be displayed on the LCD screen and a notification on Android and the buzzer will automatically turn on. If there is a fire from detecting the gas leak, the fire detector will detect the fire, which will result in a notification sent to Android that there is a fire and the buzzer will turn on
Classification of Covid-19 vaccine data screening with Naive Bayes algorithm using Knowledge Discovery in database method Alam, Syariful; Resmi, Mochzen Gito; Masripah, Nunung
Journal of Computer Networks, Architecture and High Performance Computing Vol. 4 No. 2 (2022): Article Research Volume 4 Number 2, July 2022
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v4i2.1584

Abstract

Acute Respiratory Syndrome Coronavirus-2 (SARS-Cov-2) known as covid-19 was detected and caused a very large number of deaths due to a mysterious respiratory disease. With the death toll continuing to rise, the government was forced to take swift action to break the chain of spread and reduce the number of deaths by taking vaccinations. An adequate vaccine against Covid-19 is expected to vaccinate at least 70% of the population. Therefore, this study was carried out as a step to help break the chain of the spread of the Covid-19 virus, by classifying the Covid-19 vaccine screening data. The research method applied in this study is the Knowledge Discovery in Database (KDD) method, in which there are several processes, namely selection, pre-processing, transformation, data mining, and evaluation. The application of the Naive Bayes method is expected to be able to classify Covid-19 vaccine screening data with vaccine class values, no, and delay. The results of the research on the classification of the Naive Bayes method show that there are 959 data with Vaccine data 695, No 200, and Delay 64. Processed using the Rapidminer application, the accuracy is 96.56%, Precision is 92.46%, and Recall is 92.13%.
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.

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