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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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SHORT-TERM ELECTRICITY LOAD FORECASTING SEASONAL PATTERN USING TIME SERIES REGRESSION (TSR) MODEL IN PT.PLN (PERSERO) MEDAN CITY Rambe, Feby Mayori; Widyasari, Rina
Journal of Computer Networks, Architecture and High Performance Computing Vol. 7 No. 1 (2025): Article Research January 2025
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v7i1.5533

Abstract

Electricity is a crucial component of modern life, where daily consumption fluctuates significantly. Uncertain electricity demand can lead to imbalances between supply and consumption, potentially causing energy wastage or power outages. To address this issue, a forecasting method capable of accurately predicting electricity load is essential. The Time Series Regression (TSR) model is applied for short-term electricity load forecasting by considering daily and weekly seasonal patterns. The forecasting results indicate that Monday and Tuesday have the highest electricity load, while Sunday has the lowest. When the Kolmogorov-Smirnov test is used to analyse the model, the p-value is 0.9608, which shows that the residuals have a normal distribution. The model's accuracy is assessed with a Root Mean Square Error (RMSE) value of 378.0069 MW, which is relatively high for a small dataset. Given the considerable forecasting error, further improvements such as hybrid models are recommended to enhance accuracy. The implementation of these forecasting results can help optimize electricity management and improve power distribution efficiency.
INFLATION RATE ESTIMATION USING HYBRID ARIMA-ADAPTIVE NEURO FUZZY INFERENCE SYSTEM METHOD Annisa, Nur Alvi; Sari, Rina Filia
Journal of Computer Networks, Architecture and High Performance Computing Vol. 7 No. 1 (2025): Article Research January 2025
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v7i1.5534

Abstract

Inflation is an important issue that affects the economic stability of a country or region. Unstable inflation will have a negative impact on society, especially on commodity prices including food and energy. Inflation is classified as a time series and will usually recur over time, five years later, or ten years later. , the problem of inflation needs to be studied and analyzed using existing approaches in time series. This research focuses on the application of Hybrid ARIMA-Adaptive Neuro Fuzzy Inference System method for inflation estimation, which is expected to provide a more accurate picture of the price fluctuations of basic needs in North Sumatra. Overall, the results show that the ability of the Hybrid ARIMA-Adaptive Neuro Fuzzy Inference System method in estimating inflation values is quite good with the results tending to be stable and not experiencing many sharp fluctuations. The inflation value is in the range of around -2.69 to -2.73 throughout the predicted period. However, a continuous negative number indicates a price decline or economic pressure, so further analysis or development is needed to understand the cause. The estimation results may help to maintain stability or make desired changes in the future.
Credit Risk Analysis on Motor Vehicle Financing Using the Kealhofer Merton Vasicek Model (KMV) Rambe, Dwi Wahyuni; Rakhmawati, Fibri
Journal of Computer Networks, Architecture and High Performance Computing Vol. 7 No. 1 (2025): Article Research January 2025
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v7i1.5569

Abstract

The development of the automotive sector in Indonesia continues to show significant growth, in line with the increasing demand for motor vehicles, both cars and motorcycles. Although it has great potential, the vehicle financing sector is not without challenges, particularly related to credit risk. The Kealhofer Merton Vasicek (KMV) model will be suitable for calculating vehicle credit risk because it can predict default (failure to pay) when the borrower reaches the end of the loan term. The objective of this research is to apply the KMV model to calculate the Expected Default Frequency (EDF) value and determine the minimum credit risk. From the analysis and estimation results, the time-to-maturity equity value for motor vehicles was obtained at Rp9.616.709.886 and the time-to-maturity liability value at Rp1.865.460.114, while for cars, the equity value was obtained at Rp2.057.843.305 and the time-to-maturity liability value at Rp468.544.695. Additionally, the Expected Default Frequency (EDF) value for motor vehicles was obtained at 4,26% and the EDF value for cars at 0,01%. The results indicate that the likelihood of default experienced by Adira Finance is low, especially for cars. Therefore, Adira Finance can be stated to have sufficient capital, so the likelihood of default is not high.
Regression Modeling of Zero-Inflated Negative Binomia (ZINB) in Pneumonia Cases in Toddlers in North Sumatra Province Hasibuan, Hani Maulida; Husein, Ismail
Journal of Computer Networks, Architecture and High Performance Computing Vol. 7 No. 1 (2025): Article Research January 2025
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v7i1.5596

Abstract

Pneumonia is a lung infection that causes inflammation in the air sacs within the lungs. This disease is caused by microorganisms such as bacteria, viruses, fungi, or even inhaled substances. This study aims to identify significant factors influencing the incidence of pneumonia in children under five years old in North Sumatra Province in 2022. In this case, the dependent variable has an excessive number of zero values (excess Zero), leading to overdispersion. By using the Zero Inflated Negative Binomial (ZINB) regression method, significant factors affecting the incidence of pneumonia in children under five years old in North Sumatra Province were identified. The study found that the variable of the number of low birth weight babies (BBLR) (X5) significantly influences the incidence of the disease in North Sumatra Province in 2022. It can be seen from the significant variables affecting the occurrence of pneumonia in children under five, which are 0.0406% (X1), 0.00952% (X2), 0.006506% (X3), and 2.122% (X4).
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%.
Implementation of Machine Learning Models for Predicting Internet Service Provider Customer Churn Asyhari, Moch Yusuf; Susanti, Pratiwi; Yunitasari, Yessi
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.v7i4.7012

Abstract

The telecommunications industry faces an extremely high level of competition, where the phenomenon of customer churn presents a significant challenge due to its impact on revenue decline and increased costs associated with acquiring new customers. This study aims to develop a churn prediction model using the Decision Tree algorithm and implement it in a web-based application to support customer retention strategies. The CRISP-DM methodology is employed, covering Business Understanding, Data Understanding, Data Preparation, Modeling, Evaluation, and Deployment. Experimental results show that the Decision Tree algorithm demonstrates strong performance in identifying non-churn customers, with a precision of 0.82, a recall of 0.91, and an F1-score of 0.86. However, its performance on the churn class remains limited, with a precision of 0.63, a recall of 0.44, and an F1-score of 0.52, highlighting the importance of addressing imbalanced data distribution to preserve existing data. The model underwent Learning Curve and Validation Curve analysis. The Learning Curve indicates a relatively stable model with a small gap, suggesting good generalization. The Validation Curve reveals that optimal performance is achieved at a moderate tree depth, avoiding the risk of overfitting at greater depths. Nevertheless, the main advantage of the Decision Tree is its interpretability, which highlights significant factors such as contract type, subscription duration, and additional services. The integration of the model into a web-based application also provides practical benefits through rapid churn risk monitoring, supporting the company’s strategic decision-making.
User Interface Analysis Using the Heuristic Evaluation Method in the Astra International Cooperative Application (KAI Apps) Zaelani, Ahmad; Banowosari , Lintang Yuniar
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.v7i4.7035

Abstract

This study aims to analyze the usability of the user interface in the Astra International Cooperative (KAI Apps) application using the Heuristic Evaluation method. A quantitative approach was applied through observation of the application and distribution of a Google Form-based questionnaire to 100 respondents selected using the Slovin formula from a population of 100,000 users. The research instrument consisted of 30 statements compiled based on Nielsen's ten Heuristic Evaluation principles, such as visibility of system status, consistency and standards, error prevention, and help and documentation. The data was analyzed using validity and reliability tests, as well as descriptive percentage analysis using SPSS software. The results showed that the Astra Cooperative application scored 86.78%, which is classified as very high, with nine out of ten usability principles rated as good by the majority of respondents. These findings indicate that the application is capable of providing a positive experience for users in accessing cooperative services digitally. However, weaknesses were still found in the aspects of help and documentation, which were considered unclear and not entirely relevant, so that development in the form of tutorials, FAQs, and interactive guides is needed to optimize the user experience.
User Experience Analysis of Employee Attendance List on Talent Application with Heuristic Evaluation Method Anjani, Dini; Mustikasari, Metty
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.v7i4.7036

Abstract

The development of digital technology has influenced human resource management systems, particularly in the management of employee attendance records. One of the most widely used applications in Indonesia is Mekari Talenta, a cloud-based HRIS platform with features ranging from online attendance, leave, overtime, to payroll integration. Despite its high rating on the Google Play Store, there are still a number of complaints regarding user experience, such as confusing navigation, an unintuitive interface, and difficulty in accessing certain features. This study aims to analyze the user experience on the Talenta application using the Heuristic Evaluation method based on Nielsen's 10 principles. Data collection was conducted through questionnaires and processed using SPSS for validity, reliability, and descriptive percentage analysis. The results of the study show that most of the Heuristic Evaluation principles scored in the "Good" category, especially in terms of visibility of system status, consistency and standards, and aesthetic and minimalist design. However, there are still weaknesses in terms of help and documentation as well as error prevention that need improvement. These findings recommend that developers improve the interface display, clarify the help documentation, and optimize the error prevention feature so that the application can provide a more optimal user experience. Further research is recommended using other evaluation methods, such as the User Experience Questionnaire (UEQ) or in-depth interviews, to obtain a more comprehensive picture.
Does Implementing Cryptography in Financial Risk Management Systems Reduce Data Security Risks – Literature Study Gultom, Riris Rollyna
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.v7i4.7053

Abstract

ABSTRACT This study investigates the use of cryptography in financial risk management systems to determine how well it mitigates data security risks. A key issue raised is the increasing threat to the integrity and privacy of financial data in the digital age, which requires more robust and flexible protection mechanisms. The objective is to determine the extent to which cryptographic techniques can enhance financial risk management systems and mitigate the possibility of data leakage and manipulation. The approach used is a literature review of various academic journals, scientific publications, and industry reports that discuss the integration of cryptography in the financial and information security domains. The study's findings indicate that cryptographic algorithms, such as AES, RSA, and blockchain-based encryption, can increase system resilience against cyberattacks while enhancing audit trails and access control. The implication of these findings is that the use of cryptography not only enhances data security but also increases stakeholder confidence in digital financial systems. To address the increasingly complex challenges of data security, this study suggests the creation of a set of cryptography policies and implementation standards integrated within a financial risk management framework. The analysis shows that cryptography plays a crucial role in maintaining the confidentiality, integrity, and authentication of financial data and can strengthen risk control systems against data leaks and manipulation. However, there is a gap in the literature regarding the integration of cryptography with a comprehensive risk management framework, as well as a lack of comparative evaluation of the effectiveness of various cryptographic techniques in the context of financial operations. Keywords: Blockchain; data security; cryptography; digital financial system; encryption
Needs Analysis for Chatbot-Based Final Project Administration: A Qualitative Study at STT Terpadu Nurul Fikri Mentari, Laisa Nurin
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.v7i4.6973

Abstract

The administration of undergraduate theses involves multiple stakeholders, including final-year students, academic supervisors, heads and vice-heads of study programs, program staff, and BAAK staff. This process often faces various challenges such as delays in information delivery, errors in form filling, inconsistencies in document format, and numerous repetitive questions regarding procedures and requirements. This issue adds to the workload of administrative staff and has the potential to hinder the timely completion of students' final projects. This research aims to analyze the needs and challenges in final project administration and identify the potential use of chatbot technology as a technology-based solution. The approach used is descriptive qualitative thru interviews, observations, and document analysis involving final-year students, supervising lecturers, and administrative staff. The research results indicate that chatbots have the potential to improve administrative efficiency by providing quick and accurate access to procedural information, document templates, guides, schedules, and motivational support. Thus, chatbots can function as interactive assistants that reduce staff workload, minimize miscommunication, and support the successful completion of final projects at STT Terpadu. Nurul Fikri. The novelty of this research lies in its focus on the needs analysis stage before chatbot implementation, which is still rarely done in previous studies. This approach provides a more structured basis for designing contextual and effective chatbot-based administrative systems in higher education environments.  

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