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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 Detection of Bullying Against Indonesian National Team Players Using Support Vector Machine Oyama, Sunggito; Kumalasari, Desty Nur
Journal of Computer Networks, Architecture and High Performance Computing Vol. 7 No. 2 (2025): Research Article, Volume 7 Issue 2 April, 2025
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v7i2.5701

Abstract

Detection is a process to check or conduct an examination of something using certain methods and techniques. Detection can be used for various problems, for example in detection bullying, especially on social media, is a significant problem with negative impacts on mental health, especially for public figures such as Indonesian National Team players. This study aims to detect bullying comments on the Instagram platform using the Support Vector Machine (SVM) algorithm. The research dataset consists of 3,100 comments collected from the official Indonesian National Team account, which are classified into bullying and non-bullying categories. The data preprocessing stages include case folding, tokenizing, normalization, removing stopwords, and stemming. The processed data was analyzed using the Term Frequency-Inverse Document Frequency (TF-IDF) method for feature weighting before being classified using SVM with a linear kernel and Naïve Bayes. The results showed that SVM performed better with an accuracy of 89%, a bullying category precision reaching 93%, and a recall of 83%. Meanwhile, the Naïve Bayes method produced an accuracy of 79%, with a bullying category precision of 76% and a recall of 86%. The non-bullying category in Naïve Bayes has higher precision (84%) but lower recall (72%). Thus, SVM is proven to be more effective in detecting negative comments due to a better balance between precision and recall. However, challenges such as informal language variations and data imbalance remain obstacles in the development of this model. This study contributes to the development of cyberbullying detection technology and supports the creation of a healthier social media environment.
Wireless Network Quality Analysis Using RMA and RSSI Methods at BPKAD Berau District Mubaraq, Ahmad Ridhani; Pranoto, Wawan Joko; Hallim, Abdul
Journal of Computer Networks, Architecture and High Performance Computing Vol. 7 No. 2 (2025): Research Article, Volume 7 Issue 2 April, 2025
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v7i2.5718

Abstract

Wireless networks are now essential in supporting government operations, including at the BPKAD office in the Berau district. However, problems like unstable connections and slow speeds often arise as obstacles. This study aims to evaluate the quality of the wireless network in the BPKAD asset room of the Berau district by applying the Reliability, Maintainability, and Availability (RMA) and Received Signal Strength Indication (RSSI). Quantitative research method. The research population is all wireless access points (Wi-Fi) spread across the BPKAD office. The research sample is the asset field room. Data collection methods through observation, RMA measurement, and RSSI measurement. The data that has been collected will be analyzed using the RMA (Reliability, Maintainability, and Availability) and RSSI (Received Signal Strength Indication) methods. The results obtained show that most of the measurement days recorded network availability (availability) of 100%. However, there was a decrease on August 26, 2024 (99.58%) and September 3, 2024 (97.05%) due to the increased frequency of system failures. The analysis of RSSI showed that the signal quality fell into the excellent category with an average of -36.6 dBm. However, a decrease was recorded on August 30, 2024, with a value of -44 dBm. The results of this study underscore the importance of regular maintenance and upgrades to the network infrastructure in anticipation of possible deterioration. Recommendations include improving security systems, hardware updates, and technical training for IT staff to strengthen the network's support of activities at the BPKAD Office of Berau Regency.
Analysis of Batrsiyia Product Sales Prediction Using Linear Regression Method Priana, Firzi Cahya; Danny, Muhtajuddin; Edora, Edora
Journal of Computer Networks, Architecture and High Performance Computing Vol. 7 No. 2 (2025): Research Article, Volume 7 Issue 2 April, 2025
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v7i2.5776

Abstract

The rapidly growing herbal and health industry encourages the need for accurate sales planning to avoid the risk of shortages or excess stock. This research aims to predict sales of Batrsiyia products using the Linear Regression algorithm with RapidMiner tools, through analyzing historical data such as sales time, number of products sold, and unit prices to identify patterns and trends to produce accurate predictions. The results show that the Linear Regression algorithm is able to predict sales with an RMSE value of 96687030.354 +/- 0.000, and a Squared Error of 9348381838748252.000 +/- 25081062946532056.000. This approach helps companies understand sales patterns, predict future trends, and optimize stock and marketing strategies. By utilizing data mining-based prediction methods, companies can make more informed decisions in meeting customer needs, maintaining business stability, and improving operational efficiency.
Bank Customer Decision Prediction on Term Deposit Products Using Random Forest Algorithm on Bank Marketing Campaign Data Apriadi, Eko Aziz; Bisri, Muawan
Journal of Computer Networks, Architecture and High Performance Computing Vol. 7 No. 2 (2025): Research Article, Volume 7 Issue 2 April, 2025
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v7i2.5801

Abstract

This study investigates the relationship between Variable A, Variable B, and Variable C through a series of statistical analyses, including descriptive statistics, ANOVA, correlation, and multiple regression. The background of this research stems from the growing interest in understanding how these variables interact, particularly in practical applications involving behavioral or performance outcomes. The main objective of this study is to identify whether Variable A and Variable B significantly predict Variable C and whether there are significant differences across groups. Data were collected from a sample of 100 participants and analyzed using standard statistical techniques. Descriptive analysis provided a summary of the key variables, while ANOVA showed a statistically significant difference between Group 1 and Group 2, indicating the relevance of group membership. Pearson correlation revealed a moderate positive relationship between Variable A and Variable B, suggesting a tendency for these variables to increase together. In the multiple regression analysis, Variable A emerged as a significant predictor of Variable C, whereas Variable B did not contribute significantly. These findings highlight the importance of Variable A in predictive modeling and provide valuable insights for future research and application. The results align with the research expectations, though further studies are encouraged to explore additional predictors and refine the models. This study contributes to a deeper understanding of the statistical and practical relationships among the investigated variables and offers a foundation for applied strategies in relevant fields.
Sentiment Analysis on Short Social Media Texts Using DistilBERT Asyaky, Muhammad Sidik; Muhammad Al-Husaini; Hen Hen Lukmana
Journal of Computer Networks, Architecture and High Performance Computing Vol. 7 No. 2 (2025): Research Article, Volume 7 Issue 2 April, 2025
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v7i2.5836

Abstract

Sentiment analysis on short texts from social media, such as tweets, presents unique challenges due to their brevity and informal language. This study explores the effectiveness of transformer-based models, particularly DistilBERT, in performing sentiment analysis on short texts compared to traditional machine learning approaches including Support Vector Machine, Logistic Regression, and Naive Bayes. The objective is to assess whether DistilBERT not only enhances sentiment classification accuracy but also remains efficient enough for quick social media analysis. The models used in this study were trained and evaluated on stratified samples of 10,000, 30,000, and 50,000 tweets, drawn from the Sentiment140 dataset while preserving the original class distribution. The methodology involved data collection and sampling, data splitting, data cleaning, feature extraction, model training, and evaluation using accuracy and F1-score. Experimental results showed that DistilBERT consistently outperformed traditional models in both accuracy and F1-score, and demonstrated competitive results against BERT while requiring significantly less training time. Specifically, DistilBERT trained approximately 1.8 times faster than BERT on average, highlighting its computational efficiency. The best result was achieved by DistilBERT trained on the 50k subset, reaching an accuracy of 85% and an F1-score of 84%. These findings suggest that lightweight transformer models like DistilBERT are highly suitable for real-world sentiment analysis tasks where both speed and performance are critical.
Data Analysis of E-Journal Usage in UPM Library with K-Means Clustering Method Sugiyarto, Susi Rachmadhani; Rahmi, Rahmi
Journal of Computer Networks, Architecture and High Performance Computing Vol. 7 No. 2 (2025): Research Article, Volume 7 Issue 2 April, 2025
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v7i2.5850

Abstract

This study aims to evaluate the usage patterns of Emerald and WileyOnline Library e-journals from January to December 2023. By employing the K-Means clustering method, the data were classified to analyze usage characteristics and efficiency. The clustering results indicate that journals in clusters C1 and C2 have higher relevance compared to those in C3, based on download and access numbers. Evaluation using three metrics—average cost per e-journal, average cost per access, and appropriate content usage—revealed that e-journal usage at the UPM library is not yet efficient, with high average costs per access and content usage needing improvement. This study recommends strategies to enhance the efficiency of e-journal usage to better support academic activities and research at UPM.
Comparison of Support Vector Machine, Random Forest and XGBoost for Sentiment Analysis on Indodax Naufalino, Moch. Alfarros Difa; Al-husaini, Muhammad; Rianto, Rianto
Journal of Computer Networks, Architecture and High Performance Computing Vol. 7 No. 2 (2025): Research Article, Volume 7 Issue 2 April, 2025
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v7i2.5894

Abstract

The rapid growth of digital assets like Bitcoin and cryptocurrencies has increased the need for secure trading platforms such as Indodax. With the growing number of users, reviews on platforms like Google Play Store provide valuable insights into user experience and satisfaction. This research applies Machine Learning methods to classify user review sentiments by comparing three main algorithms Support Vector Machine (SVM), Random Forest, and Extreme Gradient Boosting (XGBoost). One of the main challenge in sentiment analysis is the presence of irrelevant or redundant features, which can reduce model accuracy and increase computational costs. The Feature Selection Chi-Square technique is used to filter the most influential features, enhancing model efficiency without losing critical information. Experimental results show that SVM delivers the best performance compared to Random Forest and XGBoost. Before applying Chi-Square, SVM achieved 91% accuracy, which increased to 94% after applying the feature selection technique. The number of features used was reduced from 52,312 to 2,000 without significant information loss. This combination of SVM and Feature Selection Chi-Square proves to be an efficient and accurate solution for analyzing user sentiment on crypto trading platforms like Indodax. This method is expected to improve the responsiveness of trading applications to user needs and serve as a foundation for further research in Machine Learning-based sentiment analysis.
Application of Data Mining with C5.0 Algorithm to Recommend Prosperous Family Card (KKS) Recipients Fadilla, Nurul; Zufria, Ilka; Fakhriza, M.
Journal of Computer Networks, Architecture and High Performance Computing Vol. 7 No. 2 (2025): Research Article, Volume 7 Issue 2 April, 2025
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v7i2.5913

Abstract

Poverty is a social problem that still often occurs in various regions in Indonesia, including in Silau Laut District which consists of several villages such as Bangun Sari, Silo Bonto, Silo Lama, Lubuk Palas, and Silo Baru. Although the area is quite large, there are still many families who are classified as poor and unable to meet their basic needs. To overcome this, the government launched the Prosperous Family Card (KKS) program as a form of social assistance. However, the process of determining prospective KKS recipients still faces various obstacles, such as a random selection method based on data sent by each village to the central government. This raises concerns about the inaccuracy of the target in the distribution of aid, so that the aid is not received by families who really need it. In addition, a lot of data has not been utilized optimally in the selection process. Therefore, this study aims to design a website-based information system that can help Silau Laut District in providing recommendations for prospective KKS assistance recipients by utilizing data mining techniques. The algorithm used is C5.0, because it is able to produce a decision tree with high accuracy, while the system development method used is Rapid Application Development (RAD) to accelerate the system development process. The result of this research is an information system that can process community data and provide recommendations for prospective KKS assistance recipients in a more objective and targeted manner in the next period.
Analysis and Evaluation of SIDUN Mobile Application in UEQ-Based User Experience Perspective Wulandari, Dewa Ayu Putri
Journal of Computer Networks, Architecture and High Performance Computing Vol. 7 No. 2 (2025): Research Article, Volume 7 Issue 2 April, 2025
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v7i2.5932

Abstract

This study analyzes and evaluates the user experience (UX) of SIDUN, a mobile-based Village Information System designed to manage community contributions digitally in Dusun Tegal Kori Kaja, Denpasar, Bali. The system aims to address limitations of the previous manual process by enabling digital interaction among villagers, pecalang, and administrative staff. The evaluation method applies the User Experience Questionnaire (UEQ), assessing six core dimensions: Attractiveness, Perspicuity, Efficiency, Dependability, Stimulation, and Novelty. A total of 30 active users participated in the study by completing the UEQ instrument. The results indicate that all six UX dimensions received positive scores, ranging from 1.58 to 2.00. The highest ratings were observed in Stimulation (2.00), Attractiveness (1.96), and Efficiency (1.92), reflecting high user engagement, visual appeal, and operational speed. Perspicuity and Novelty also showed strong performance, while Dependability, though positive, revealed opportunities for improvement in system reliability and consistency. Compared to the UEQ benchmark, all dimensions achieved “Excellent” ratings, placing them within the top 10% of evaluated applications. These findings affirm that SIDUN offers a satisfying user experience and supports effective community-level digital transformation. The study underscores the value of user-centered design and continuous UX assessment in enhancing public digital services in rural communities.
BITCOIN PRICE PREDICTION USING LONG SHORT TERM MEMORY ALGORITHM Fauzi, Rifqi Arul; Rohana, Tatang; Nurlaelasari, Euis; Wahiddin, Deden
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.v7i3.5945

Abstract

Bitcoin a digital asset with the largest market capitalization in the world and shows high price volatility, attracting the interest of researchers to make accurate price predictions. The research aims to build a Bitcoin price prediction model use Long Short-Term Memory (LSTM) algorithm by utilizing closing price data and technical indicator variables, Moving Average (MA) and Exponential Moving Average (EMA). Dataset obtained from Yahoo Finance with a time range of January 1, 2015 to January 1, 2024 as much as 3287 data. The LSTM model is designed in multivariate form with an input sequence of 30 with several test scenarios at the epoch number 50, 100 and 200. Model evaluation is based on 4 metrics, namely Root Mean Squared Error (RMSE), Mean Squared Error (MSE), Mean Absolute Error (MAE), and Mean Abso-lute Percentage Error (MAPE). Model evaluation results show that the model is capable of providing a good prediction value with an MSE value of 0.0001, RMSE of 0.0117, MAE of 0.0081, and MAPE of 2.21% at epoch 200. The use of technical indicators proved to be helpful in improving the performance of the model compared to using only closing price data.

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