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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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Comparison of Support Vector Machine and Naïve Bayes to Sentiment Analysis of Military Barracks Program Nurzanah, Salsabilla Choerunnisa; Armilah, Mila Siti; Arianto, Fajar; Supriadi, Supriadi; Utomo, Hadi Prasetyo
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.v7i3.6515

Abstract

Sentiment analysis is a study that analyzes a person's emotions about a problem. The military barracks program proposed by the Governor of West Java has drawn pros and cons from the community, especially in application X. Some people consider this program to be the right solution to discipline and shape the character of students, others think that the program can take away children's freedom and rights and does not guarantee any change in character after the students leave the military barracks. Therefore, a sentiment analysis was conducted with the aim of understanding public sentiment and comparing the accuracy of the SVM and Naïve Bayes in predicting public sentiment towards the military barracks program. The method in this study begins with data crawling, data selection, labeling, data preprocessing (data cleaning, normalization, case folding, stopword removal, tokenizing, stem), TF-IDF, Word Cloud, classification with Naïve Bayes and SVM, ending with a Confusion Matrix. In contrast to SVM, which revealed that 1429 tweets had positive sentiment and 447 had negative sentiment, Naïve Bayes results indicated that 1309 tweets had positive sentiment and 567 had negative sentiment. The accuracy value of the Naïve Bayes was 91.24%, the precision was 99.73%, and the recall was 82.94%. In contrast, the SVM achieved 92.16%, the precision was 97.86%, and the recall was 86.40%. Based on these findings, it can be said that the SVM  is more accurate than Naïve Bayes and that the public generally has a favorable opinion of the military barracks program.
Islamic Sound Recognition Using MFCC and SVM: Case Study on Takbir and Sholawat Sari, Indah Purnama; Abdul Fadlil; Tole Sutikno
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.v7i3.6523

Abstract

This study aims to develop an identification model for Islamic religious sounds, specifically Takbir and Sholawat pronunciations, using audio signal processing and machine learning techniques. With the increasing need for intelligent systems capable of recognizing speech patterns in religious contexts, the implementation of reliable audio classification methods becomes essential. This research utilizes Mel-Frequency Cepstral Coefficients (MFCC) to extract relevant spectral features from audio samples, representing the unique characteristics of Takbir and Sholawat utterances. The dataset consists of 300 audio recordings, evenly distributed between the two classes. Each audio file is preprocessed and converted into a fixed-length MFCC feature vector, which is then labeled accordingly. The feature vectors are split into training and testing sets using an 70:30 ratio. A Support Vector Machine (SVM) classifier is trained using the training data to recognize the distinction between Takbir and Sholawat patterns based on their acoustic signatures. Performance evaluation is carried out using accuracy, precision, recall, and F1-score metrics. The dataset used consists of 300 audio recordings with a division of 200 takbir recordings and 100 sholawat recordings. The MFCC feature extraction process uses 13 coefficients with optimized parameters to capture discriminative spectral characteristics. As a baseline, a Support Vector Machine (SVM) implementation with Radial Basis Function (RBF) kernel was performed for performance comparison.
Sentiment Analysis on TikTok Discourse Surrounding the 2024 North Sumatra Gubernatorial Election Using Support Vector Machine Algorithm Istiqomah, Istiqomah; Lubis, Aidil Halim
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.v7i3.6549

Abstract

This study aims to analyze public sentiment towards the 2024 North Sumatra gubernatorial election by leveraging social media data, specifically TikTok, which has become a major platform for political discourse in Indonesia. The two competing candidate pairs, Bobby Nasution–Surya and Edy Rahmayadi–Hasan Basri, have sparked widespread online discussions that range from enthusiastic support to harsh criticism. These interactions have a significant impact on public opinion formation and may influence electoral outcomes. To address this phenomenon, this research implements a sentiment classification model using the Support Vector Machine (SVM) algorithm with a polynomial kernel, known for its effectiveness in handling high-dimensional textual data. A total of 2,100 TikTok comments were collected using scraping techniques via Python. The data then underwent several preprocessing stages, including case folding, cleaning, normalization, tokenizing, slangword removal, stopword removal, and stemming. Feature extraction was conducted using the TF-IDF method, followed by lexicon-based sentiment labeling into positive and negative classes. The classification model achieved an accuracy of 82%, with a positive sentiment precision of 0.81, recall of 0.96, and F1-score of 0.88. For negative sentiment, the precision was 0.86, recall 0.51, and F1-score 0.64. These findings indicate that the model performs well in identifying explicit positive sentiments but faces challenges in recognizing complex negative expressions such as sarcasm or implicit criticism. The results provide valuable insights into digital political behavior and demonstrate the potential of machine learning-based sentiment analysis as a tool for monitoring public perception in real time during elections.
Usability Assessment of a Waste Bank Customer Management Application Using the System Usability Scale (SUS) Malahayati, Malahayati; Nadeak, Ebtaria; Sadariawati, Rika
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.v7i3.6469

Abstract

Waste Bank is a participatory solution for community-based waste management that provides positive economic and environmental impacts. At a waste bank, community members can deposit separated waste such as plastic, paper, cans, and bottles, as savings. The waste is weighed and assessed based on its type, then its value is converted into savings, similar to a conventional bank. To support the development of waste banks, effective and efficient management of customer data is essential, which can be facilitated through digital applications. This waste bank management application streamlines administrative processes, transaction tracking, and data management related to waste collection activities. The application offers several core features to support data recording and management activities, including customer data management, waste deposit logging, transaction tracking, and waste collection driver coordination. This study aims to evaluate the usability level of the prototype of the Bank Sampah Sakur Palembang customer data management application using the System Usability Scale (SUS) method. The evaluation was conducted with ten respondents who are potential users, consisting of administrative staff and customers. The analysis results show that the prototype received a SUS score of 87, which falls into the excellent and acceptable categories. These findings indicate that the application has a high-quality user interface, is easy to use, and has strong potential for further implementation to support the digital operations of waste bank. These results highlight the application's potential for supporting digital transformation in community-based environmental management systems  
AHP–SAW-Based Decision Support System for Culinary Tourism Restaurant Selection in Semarang City Cahaya, Agus Indra; Aprico, Fikky; Apriyanti, Dewi; Nugroho, Kristiawan; Ardhianto, Eka
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.v7i3.6557

Abstract

Semarang City boasts a diverse array of appealing culinary restaurants, yet both tourists and local residents frequently encounter difficulties in selecting dining establishments that best match their preferences, often diminishing their culinary tourism experience and leading to inefficient time usage. This research aims to develop a decision support model by implementing a combined approach of the Analytic Hierarchy Process (AHP) and Simple Additive Weighting (SAW) to streamline the restaurant selection process. AHP was selected for its capability to establish hierarchical criteria weights, while SAW offers an efficient method for ranking alternatives. The initial phase utilized AHP to determine the weights of six identified relevant primary criteria: Cleanliness, Location, Price, Ambiance, Taste, and Rating. Assessments for each of these criteria were gathered through surveys administered to 30 culinary enthusiasts in Semarang City. Subsequently, the second phase employed SAW to calculate the final scores for 10 culinary tourism restaurants in Semarang, evaluated through questionnaires by 10 different respondents. The calculation results placed Folkcafe at ALVA in the first rank (with a score of 0.8656), followed by Ikan Bakar Cianjur in the second rank (score of 0.8524), and Pelangi Cheese Chiffon Cake in the third rank (score of 0.8173). These findings unequivocally demonstrate the effectiveness of applying AHP and SAW for prioritizing culinary restaurants in Semarang, further supported by the valid consistency of the AHP criteria weights (CR = 0.0341). This study contributes to the DSS literature by combining AHP and SAW in the underexplored context of culinary tourism ranking. This model is expected to serve as a practical guide for visitors and a foundational basis for the development of digital recommendation systems within the culinary tourism sector.
Interactive Multimedia Website For Promoting Mumbul Sangeh Park As A Tourist Destination Wiguna, I Putu Indra; Tonyjanto, Christian; Datya, Aulia Iefan; Kurniawijaya, Putu Andika
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.v7i3.6600

Abstract

Mumbul Sangeh Park in Bali has experienced low visitor turnout, primarily due to inadequate promotional strategies. To address this issue, a study was conducted to develop a modern, interactive promotional website aimed at increasing public awareness of the park’s historical significance and unique attractions. The website development followed a structured Software Development Life Cycle (SDLC) approach, specifically utilizing the waterfall model. The system was implemented using the PHP programming language, supported by the Laravel framework and a MySQL database. Functional verification was performed using black box testing to ensure all system features operated as intended. The resulting website is fully functional, responsive, and delivers comprehensive information. It also includes an intuitive administrative panel that enables park administrators to easily manage content updates, including photo galleries, news, and visitor information. The system represents a strategic digital initiative to enhance the visibility and reputation of Mumbul Sangeh Park in the era of digital tourism.
Breast Cancer Classification Using Naïve Bayes and Random Forest Algorithms Gurning, Riris Naomi; Sulaeman, Asep Arwan; Afandi, Dedi
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.v7i3.6609

Abstract

Breast cancer is one of the leading causes of death among women in Indonesia. Therefore, early detection is crucial to improving the chances of successful treatment. This study was conducted to evaluate the performance differences between the Naïve Bayes and Random Forest algorithms in classifying breast cancer data. The dataset used was sourced from Kaggle, and the entire data processing and model analysis process was performed using RapidMiner software. Data was split into 80% for training and 20% for testing to ensure optimal model evaluation. Evaluation was conducted using accuracy, precision, and recall metrics. The findings of this study indicate that Random Forest is capable of producing more effective classification performance than Naïve Bayes. Random Forest achieved an accuracy of 99.27%, recall of 99.27%, and precision of 99.30%. Meanwhile, the Naïve Bayes algorithm only achieved an accuracy of 83.78% with recall and precision of 83.80% each. The superiority of Random Forest is believed to stem from its ensemble approach, which can handle data complexity and reduce the risk of overfitting, thereby providing more accurate and stable prediction results. Based on these results, Random Forest is considered more suitable for use in machine learning-based early breast cancer detection systems. This study is expected to serve as a reference for the development of medical decision support systems and to encourage the use of classification technology in the field of health.
Comparative Analysis of Yolov11 and Mask R-CNN Models for River Water Level Detection Setrayana, Abiyyu; Nurchim; Eko Purwanto
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.v7i3.6622

Abstract

Flooding is one of the most frequent natural disasters in Indonesia, particularly in densely populated areas such as urban regions. The main cause is the delayed response in anticipating rising river water levels. One contributing factor is the continued use of manual river water monitoring systems. However, these systems often face challenges under various lighting and weather conditions. This study presents a comparison of two segmentation models, YOLOv11 and Mask R-CNN, for river water level detection. These models are evaluated for their application in real-time water level monitoring systems for dams and rivers under diverse lighting conditions. Data was gathered from publicly available sources, including river monitoring CCTV footage and social media content related to river activities, followed by annotation for model training. The YOLOv11 model, implemented using the Ultralytics framework and PyTorch library, achieved a mean Average Precision (mAP) at IoU (Intersection over Union) 50-95 of 99.657% and recall of 99.930%, demonstrating exceptional detection accuracy. The Mask R-CNN model, developed with Detectron2, attained an Average Precision (AP) at IoU 50-95 of 98.620% and a recall of 99.200%, also exhibiting high accuracy. Both models were tested in real-time scenarios, where they accurately detected water-level objects, although challenges arose under complex environmental conditions such as low light or water turbidity. To further enhance model performance, future work will focus on incorporating diverse environmental data and optimizing model parameters. In conclusion, YOLOv11 model offers higher accuracy and better resource efficiency, making it more suitable for real-time water level monitoring applications.
Performance and Risk Assessment of Honeypots on IoT and VPS Using COBIT 2019 and Stress Test Purnama, Lukas Hadi; Daniel Hary Prasetyo
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.v7i3.6661

Abstract

The massive wave of digital transformation has increased the complexity of cyber threats, particularly targeting vital network services. Honeypots have emerged as an effective approach for detecting and analyzing attacks, yet platform selection and management strategies remain a challenge. This study analyzes the performance, management, and risks of two types of honeypots, Cowrie (medium interaction) and Heralding (low interaction), implemented in different computing environments, based on the COBIT 2019 framework (domains EDM03, APO12, and DSS05). Evaluation was conducted through experiments on SSH, Telnet, FTP, SMB, MySQL, and HTTP services, utilizing both isolated and multistage honeypot scenarios. The results show that both honeypot deployments effectively capture brute force and botnet attack patterns and enable accurate logging and validation of attack activities. The analysis of false positive rates and structured log validation processes produced more accurate and relevant attack data. This study is among the first to provide a holistic evaluation of Cowrie and Heralding honeypots with direct COBIT 2019 integration, presenting a novel perspective on governance-driven risk management in honeypot implementation. The application of the COBIT framework ensures that honeypot deployment is not only technically effective but also aligned with robust governance and risk management practices for information security. Strategic recommendations are provided regarding configuration optimization, platform selection, and COBIT-based governance integration to enhance organizational cybersecurity resilience
Evaluation of the Quality of Academic Information Services on the FEB UNJ Website Based on the WebQual and EUCS Models Utari, Eka Dewi; Atmadja, Ferry Setyadi; Nida, Ria Rahma; Rabbaniyah, Lathiefah; Ayyun, Dinda Ramadhani Qurrota
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.v7i3.6559

Abstract

This study aims to evaluate the quality of academic information services provided through the website of the Faculty of Economics and Business, State University of Jakarta (www.feb.unj.ac.id). The research approach used was quantitative descriptive using the WebQual 4.0 model which was modified through the addition of the Eas of Use variable from the End User Computing Satisfaction (EUCS) model. A total of 124 respondents consisting of students and lecturers were involved in this study through the distribution of questionnaires. Data were analyzed using multiple linear regression with the help of SPSS. The results showed that the variables Information Quality and Interaction Quality had a significant influence on user satisfaction, while Usability and Content did not show a statistically significant partial effect. However, simultaneously, the four variables contributed 70.8% to user satisfaction. This study emphasizes the importance of accuracy, trust, and ease of access as the main factors in supporting user satisfaction with website-based academic information services. This research contributes to the development of academic information systems by offering a comprehensive evaluation framework that integrates the WebQual and EUCS models, while also emphasizing the critical roles of information accuracy, user interaction, and ease of access in enhancing user satisfaction thus providing a practical reference for improving the quality of academic websites in higher education institutions in Indonesia.

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