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Nurul Khairina
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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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APPLICATION OF K-NEAREST NEIGHBOUR, RECURSIVE ELIMINATION AND ADASYN ALGORITHMS ON DERMATITIS DISEASE CLASSIFICATION DATA Ramadhani, Daib Jidan; Siswa, Taghfirul Azhima Yoga; Pranoto, Wawan Joko
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.v7i3.6656

Abstract

Dermatitis is a common type of non-infectious skin disease frequently found in Indonesia. Its prevalence is influenced by several factors such as poor hygiene, environmental conditions, and climate change. Data from RSUD Jagakarsa recorded that from 1,066 skin disease cases between February 2023 and January 2024, approximately 62.2% were non-infectious, and 34.4% of those were classified as dermatitis. The diagnostic process for dermatitis is often challenging due to its symptom similarity with other skin conditions, leading to potential misclassification. Therefore, a more accurate and efficient classification approach is required to support medical professionals in identifying dermatitis cases effectively. This study proposes the use of a combination of machine learning methods: K-Nearest Neighbor (KNN) as the core classification algorithm, Recursive Feature Elimination (RFE) for feature selection, and Adaptive Synthetic Sampling (ADASYN) to handle class imbalance within the dataset. The data was sourced from UPTD Puskesmas Bontang Barat in 2024, consisting of 392 samples and 10 main features. Evaluation was conducted using a 10-fold cross-validation scheme. Results showed that the baseline KNN model achieved an average accuracy of 62.23%. With ADASYN applied, the accuracy improved to 63.56%, and further increased to 92.71% when combined with feature selection using RFE.
Prediction of Ovarian Cyst Disease Mortality Rate Cases Using Markov Chain Monte Carlo with Gibbs Sampling Algorithm Fazriani, Salsabila Rizky; Rima Aprilia
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.v7i3.6724

Abstract

Ovarian cyst is one of the reproductive disorders that can develop into ovarian cancer and cause death if not treated properly. This study aims to predict the death rate due to ovarian cyst disease using the Markov Chain Monte Carlo (MCMC) method with the Gibbs Sampling algorithm. The data used is secondary data from Malahayati Islamic Hospital Medan City in 2024, which consists of 15 patients, including one deceased patient (fictitious) for the purposes of the classification model. The independent variables used include age, length of hospitalization, and number of diagnoses, while the dependent variable is the patient's death status. The estimation process was conducted with 600 iterations, where the initial 100 iterations were used as burn-in, and the rest were used to obtain the posterior mean of the model parameters. The results show that the model is able to predict death status with 100% accuracy, where all predictions match the actual data. The parameter coefficients show that the higher the age, the longer the hospitalization, and the more the number of diagnoses, the higher the risk of death. The MCMC method with Gibbs Sampling algorithm proved to be effective in generating probabilistic predictions as well as identifying important factors that affect the risk of death of patients with ovarian cysts
Strategic Planning of Information Systems and Technology Using the Ward and Peppard Method at Politeknik Internasional Bali Dana, I Made Kresna; Sariyasa, Sariyasa; Sunarya, I Made Gede
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.v7i3.6737

Abstract

This study aims to formulate a strategic plan for the Information System (IS) and Information Technology (IT) at Politeknik Internasional Bali with the objective of enhancing the performance of business processes. The research employs a methodology that includes Ward and Peppard analysis, SWOT analysis, PEST analysis, Porter's Five Forces analysis, and Value Chain analysis. This comprehensive approach evaluates both the external and internal environments of the company, including the SI/TI company environment. The research provides strategic recommendations for the development of IS business strategies, IT strategies, and SI/TI management at Politeknik Internasional Bali. The findings from the SWOT Matrix Analysis reveal that Politeknik Internasional Bali is situated at coordinates (0.63, 1.74), indicating a strategic emphasis on SO (Strength-Opportunity) strategies. The suggested recommendations stemming from this analysis involve the design of information systems that actively support business processes, with a priority on systems located in the Strategic quadrant. The proposed IT Business strategies recommend the design of IT infrastructure architecture based on best practice principles, encompassing elements such as Availability, Scalability, Security, Serviceability, and Manageability. In addition to these recommendations, there is a proposal for organizational structural changes within Politeknik Internasional Bali, including the establishment of a dedicated SI/TI division to reinforce SI/TI management strategies. Overall, these recommendations are geared towards enhancing the overall effectiveness and efficiency of SI/TI utilization at Politeknik Internasional Bali.
Web-Based Attendance Information System At Diskominfosantik Bekasi District With Prototype Method Panjaitan, John David Willy; Rifa'i, Anggi Muhammad; Suprianto, Asep
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.v7i3.6753

Abstract

The rapid development of information technology has encouraged government agencies to utilize digital systems to improve operational efficiency and effectiveness, including in managing employee attendance data. This study aims to design and implement a Web-Based Attendance Information System at the Department of Communication, Informatics, Statistics, and Encryption (Diskominfosantik) of Bekasi Regency. The system was developed using the prototype method, allowing for a gradual design process involving users directly in evaluation and development. The main features of the system include login authentication for administrators and employees, barcode scanning for attendance validation, GPS data integration to verify attendance locations, digital leave requests, and real-time attendance data management and reporting. System testing was conducted using the black box testing method across various scenarios to ensure all functions operated as expected without errors. The system design is also supported by use case and class diagrams that illustrate the workflow and relationships between entities in the system. The results of the study indicate that the web-based attendance information system can improve recording accuracy, accelerate the attendance data recap process, and support transparency in personnel management. Thus, the system has the potential to serve as a model for other government agencies in digitizing employee attendance processes.
Comparative Analysis of Incoming Goods Patterns Using FP-Growth and Apriori Algorithms: A Case Study in Retail Ritonga, Akbar Pramuja; Harahap, Syaiful Zuhri; Masrizal, Masrizal
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.v7i3.6776

Abstract

This study aims to analyze consumer purchasing patterns in minimarkets using the Apriori and Fp Growth association algorithms based on transaction data, where the data consists of 10 goods receipt transactions with 7 variable items such as Ultra Milk UHT 250ml, Indomie Goreng Spesial, Beras Ramos 5kg, Teh Cup Sariwangi 25's, Minyak Goreng Bimoli 1L, Soap Bar Lifebuoy 75g, and Mie Lemonilo Goreng 70g. The analysis process is carried out through the preprocessing stage, transformation to binary format, and application of the algorithm with minimum support parameters of 20% and confidence of 50%. The results show that Ultra Milk UHT 250ml has the highest support (0.5) followed by Indomie Goreng Spesial (0.4), while the combination of UHT Milk with Indomie has a support of 0.2; in terms of confidence, a number of rules even reach a perfect value of 1.0, for example the relationship between Teh Cup Sariwangi and Ultra Milk which always appear together. Quantitatively, Apriori produces 25 association rules with a processing time of approximately 2.1 seconds, while Fp Growth produces the same number of rules but is more efficient with a processing time of 1.3 seconds and lower memory usage, so it can be concluded that although both are equal in terms of rule quality, Fp Growth is superior in computational efficiency. This finding has important practical implications for minimarket management, especially to support shelf arrangement strategies, more targeted stock planning, and the preparation of bundling promotions based on product combinations with high confidence, while also showing a scientific contribution in the form of comparing the performance of two association algorithms on incoming goods data that is relatively rarely used in previous studies.
Machine Learning for Securing API Gateways : a Systematic Literature Review Hutagaol, B. Junedi; Sitorus, Riama Santy; Simanjuntak, Dita Madonna
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.v7i3.6788

Abstract

The rapid growth of mobile banking has improved access to financial services but also introduced heightened cybersecurity risks, particularly due to vulnerabilities in API Gateways and limited user awareness of cyber threats. This study conducts a Systematic Literature Review (SLR) to explore how machine learning (ML) can address both technical and human-centric security challenges in digital banking. By reviewing sixteen peer-reviewed studies published between 2019 and 2025, the study identifies key ML techniques such as anomaly detection, behavior-based models, and deep learning architectures that are effective in detecting and mitigating API-based attacks. In parallel, the review examines ML applications aimed at enhancing user cybersecurity awareness, including personalized alert systems, user segmentation, and adaptive education mechanisms. Thematic synthesis reveals several challenges, including data availability and privacy, the interpretability of complex models, and integration with existing banking infrastructures. However, the study also highlights significant opportunities, such as the use of federated learning to preserve privacy, explainable AI to improve trust, and dynamic alert systems to prevent user fatigue. Based on the synthesis, a conceptual architecture is proposed to integrate ML-driven API threat detection and user education within mobile banking platforms. The findings provide valuable insights for both academic research and practical implementation, contributing to the development of intelligent, user-aware cybersecurity frameworks in the financial sector.Keywords: API Gateway Security, Cybersecurity Awareness, Machine Learning, Mobile Banking, Systematic Literature Review.
Analysis of the Effectiveness of the T² Hotelling Control Chart in Concrete Control Syilviani; Rina Filia 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.v7i3.6848

Abstract

In the world of construction, concrete quality plays a very important role because it directly affects the strength and durability of building structures. The purpose of this research is to appraise and evaluate the efficiency of using the T² Hotelling control chart method in controlling concrete quality at PT. Wijaya Karya Beton Tbk by analyzing three main concrete quality parameters: slump, compressive strength, and tensile strength, based on data collected from January to February 2025. The methods employed include multivariate analysis approaches, such as correlation tests, multivariate normality tests, Johnson transformation to improve non-normally distributed data, and the application of generalized variance control charts and T² Hotelling control charts. The results of the study indicate a significant correlation between concrete quality variables; however, the initial data did not meet the assumption of multivariate normality, necessitating the Johnson transformation, which proved effective in improving data distribution and enabling the application of Hotelling's T² analysis. Based on the control charts, most observations remained inside the established control limits; however, some samples fell outside the boundaries, indicating process disturbances. Overall, this study concluded that the Hotelling T² method is effective in detecting process nonconformities at an early stage, thereby serving as a foundation for continuous improvement in concrete production quality and making a significant contribution to strengthening the quality control system in the national construction sector.
Comparative Analysis of the Fuzzy Time Series Chen and Rungge Kutta Felhberg Methods for Forecasting the Number of HIV/AIDS in the Province of North Sumatra Wahono, Sielve Suwanda; Rakhmawati, Fibri
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.v7i3.6851

Abstract

The increase in HIV/AIDS cases in North Sumatra Province requires accurate forecasting methods to support prevention and control programs. Accurate predictions of the number of cases will help stakeholders design more targeted interventions and allocate resources effectively. This study aims to compare the performance of the Chen Fuzzy Time Series method and the Runge-Kutta Fehlberg numerical method in forecasting the number of HIV/AIDS cases in North Sumatra Province. The data used are monthly HIV/AIDS case data obtained from the North Sumatra Provincial Health Office. The Chen Fuzzy Time Series method is applied to capture patterns in data that are uncertain and ambiguous, while the RKF method is used to solve the logistic growth model that represents the development of cases. Forecasting accuracy was evaluated using the Mean Absolute Percentage Error (MAPE) metric. The results showed that the RKF method produced a lower MAPE value compared to the Chen Fuzzy Time Series method, indicating higher prediction accuracy. The RKF method provides more stable predictions for the next three months and is closer to the actual trend, while the Chen Fuzzy Time Series method shows slightly larger deviations but remains useful for imprecise data. In conclusion, both methods can be used for HIV/AIDS case forecasting, but the RKF method is proven to be superior in accuracy for the data used in this study.
Optimization of the K-Means Method and Davies-Bouldin Index (DBI) Technique in Mapping Spotify's Most Popular Songs Based on Mood Septiani, Rizky; Lubis, Muhammad Ridwan; Firzada, Fahmi
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.v7i3.6655

Abstract

Spotify is a leading music streaming platform that offers a wide variety of songs with audio characteristics capable of influencing listeners' moods. This study aims to optimize the K-Means method to cluster popular songs based on users’ moods, with the support of the Davies-Bouldin Index (DBI) technique to determine the optimal number of clusters. The dataset was obtained from Kaggle, utilizing audio features such as danceability, valence, energy, and others as the basis for clustering. The results show that the implementation of K-Means optimized with DBI produces more accurate clustering, as indicated by lower DBI values. This approach has the potential to enhance mood-based music recommendation systems, enriching the user experience.
Design And Build E-Therapy During The Pandemic Using An Android-Based User-Centred Design Model Silitonga, Feby Riwindi; Br Karo Sekali, Ristya Febriani; Simamora, Stefania Gracella; Nababan, Marlince NK
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.v3i2.1007

Abstract

Therapy is a treatment to restore health to people who are sick such as mental disorders, many factors make people's psychological disorders today, one of which is the corona virus which is being experienced by many Indonesian people and even the world which has a negative impact. When people are infected with the corona virus, many experience depression, and this also has a negative impact on students at the University. The purpose of this research is to develop an application that can reduce the anxiety of people affected by the corona virus and aims to relax the human brain. And not only for people affected by Covid but also for the community as a tool for self-reflection. This application is designed to meet the needs of users affected by the corona virus. To make products more accessible to users, User-centered design (UCD) is a design process model that prioritizes user needs following user needs. From some people who have been exposed to the corona virus, the research team tries to respond to applications that have been developed. From the results of this study, the authors can conclude that from several characteristics of existing therapy, Muslim motivation is the most popular type of therapy with a test rate of 88.5% while the lowest test rate is Christian motivation with 57%.

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