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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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Spreadsheet-Based Automatic Print Cost Calculator Ariel Fadilah, Mokhamad; Anshory, Izza; Jamaaluddin, Jamaaluddin; Hayatal Falah, Agus
Journal of Computer Networks, Architecture and High Performance Computing Vol. 6 No. 1 (2024): Article Research Volume 6 Issue 1, January 2024
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v6i1.3492

Abstract

Production cost calculation is a crucial factor that impacts productivity and profitability in the printing industry. This study presents an automatic print costing tool that runs on a spreadsheet and is intended to increase operational efficiency and calculation accuracy in Small and Medium-Sized Printing Enterprises (SMEs). With the use of Esp32 as a microcontroller, TCS3200 and infrared sensors, this project seeks to create an automated print pricing spreadsheet that would help printing SMEs swiftly and precisely determine product prices. This tool's components include an LCD to show the cost computation findings, an infrared sensor to identify the number of printed sheets, a TCS3200 sensor to determine whether or not colored paper is present, and a push button feature to resume the calculation and upload the entire cost to the spreadsheet. The instrument functions effectively and aids users in doing cost calculations in an efficient manner, according to the findings. Despite some challenges, such as slow internet connections that cause delays, to enable effective cost assessment poor internet connection. This strategy should decrease calculation errors and increase manufacturing cost management effectiveness.
Home Surveillance Monitoring with Esp32-Cam and SD Card For Data Storage Tirta, Ivan Danu; Wisaksono, Arief; Ahfas, Akhmad; Jamaaluddin, Jamaaluddin
Journal of Computer Networks, Architecture and High Performance Computing Vol. 6 No. 1 (2024): Article Research Volume 6 Issue 1, January 2024
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v6i1.3498

Abstract

In the last three years the crime rate of theft has increased, to reduce crime this form of theft can be overcome by making a home security system using ESP32-CAM, the system made aims to conduct surveillance that can be seen again the results of images that have been taken by ESP32-CAM, then stored on the SD Card and send notifications to social media. This research uses the R&D (Research &; Development) method or research, and development is a systematic study process to develop and validate products to be used in education. Products developed / produced include training materials for teachers, teaching materials, learning media, questions, and management systems in learning. The result of the implementation of a security system is the stage where the system that has been designed explains the creation of a system in accordance with previous analysis and design. After the implementation stage is carried out, a system test is needed to prove that the application can run properly. The test results that have been done using the android application and Sdcard run well, the PIR sensor can only detect objects as far as 4 meters. With this system, it is expected to be able to provide protection and security for homes, property, and residents. On the other hand, this approach also creates a chance to dig deeper into technology development using ESP32-CAM as an effective and efficient solution to tackle rising crime.
IMPLEMENTATION OF A SMART HOME BASED ON INTERNET OF THINGS USING CISCO PACKET TRACER Sinaga, Dedi Candro Parulian; Tampubolon, Gunung Juanda; Ndruru, Ifanlius
Journal of Computer Networks, Architecture and High Performance Computing Vol. 6 No. 1 (2024): Article Research Volume 6 Issue 1, January 2024
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v6i1.3518

Abstract

A smart home is a home that uses information and communication technology, especially the Internet of Things (IoT), to increase comfort, efficiency, security and energy management. The main idea of ??a smart home is to connect various electronic devices and systems in the house so that they can interact with each other and with the occupants of the house automatically or through centralized control. Users can control devices in the home remotely via mobile devices or computers. This allows them to regulate the temperature, check home security, or control equipment even when they are not at home. Smart homes are often equipped with sophisticated security systems, including security cameras, motion sensors, and alarms. Residents can monitor and control this system via their smart devices. Smart homes can be a practical and innovative solution to improve the quality of life and optimize home management. However, it is important to consider the security and privacy issues as well as the costs associated with using this technology. Using this smart home really helps users to carry out activities calmly outside the home because it can monitor the electrical conditions at home. The device used to create a smart home simulation is using Cisco Packet Tracer.
Development of The Project-Based Learning Model In Making Teaching Modules for Courses Multimedia Technology and Animation Ramadhan, Muhammad Sabir; Harmayani, Harmayani
Journal of Computer Networks, Architecture and High Performance Computing Vol. 6 No. 1 (2024): Article Research Volume 6 Issue 1, January 2024
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v6i1.3519

Abstract

The discovery of errors in the delivery of Multimedia Technology and Animation course material is indirectly caused by the implementation of lectures for the course, which should be given for 2 semesters compressed into 1 semester only. The limited learning time prevents some course material from being delivered to students. This limitation was also triggered by the absence of teaching modules that support condensed learning due to the implementation of lectures for 1 semester. Seeing these problems makes the development of a teaching module in the Multimedia and Animation Technology course with Project-Based Learning to support the implementation of lectures a solution that can be done to overcome existing problems. The feasibility test results show that the teaching module is valid. In contrast, the results of the feasibility test by media experts show that 95.14% of the module is very valid, and seen from the results of the feasibility test by material experts show that 97.14% of the module is very valid for use in learning for 1 semester. In the trial involving students, it shows that through the results of individual trials, it can be seen that 94.17% of the teaching modules developed are very feasible to use in the learning process. In addition, through the results of the small group trial, it can be seen that the teaching module is 85.18% very feasible to use, as well as the results of the usage trial show that the teaching module is 87.45% very feasible to use in learning. Based on the data obtained, it can be concluded that the Multimedia and Animation Technology module with Project-Based Learning is very feasible to be used as a reference and in the learning process of Multimedia and Animation Technology courses.
Diagnosis and Prediction of Chronic Kidney Disease Using a Stacked Generalization Approach Prabowo, Agung; Wardani, Sumita; Muis, Abdul; Gea, Radiman; Tarigan, Nathanael Atan Baskita
Journal of Computer Networks, Architecture and High Performance Computing Vol. 6 No. 1 (2024): Article Research Volume 6 Issue 1, January 2024
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v6i1.3611

Abstract

Chronic Kidney Disease (CKD) is. In the past, several learners have been applied for prediction of CKD but there is still enough space to develop classi?ers with higher accuracy. The study utilizes chronic kidney disease dataset from UCI Machine Learning Repository. In this paper, individual approaches, viz., linear-SVM, kernel methods including polynomial, radial basis function, and sigmoid have been used while among ensembles majority voting and stacking strategies have been applied. Stacked Ensemble is based on various types of meta-learners such as C4.5, NB, k-NN, SMO, and logit-boost. The stacking approach with meta-learner Logit-Boost (ST-LB) achieves accuracy 98,50%, sensitivity 98,50%, false positive rate 20,00%, precision 98,50%, and F-measure 98,50% demonstrating that it is the best classi?er as compared to any of the individual and ensemble approaches
Implementation of K-Means Clustering in Recognizing Crime Hotspots and Traffic Issues Through GIS Pratama, Aryo; Irawan, Muhammad Dedi; Andriana, Septiana Dewi
Journal of Computer Networks, Architecture and High Performance Computing Vol. 6 No. 2 (2024): Articles Research Volume 6 Issue 2, April 2024
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v6i2.3771

Abstract

The challenge of accurately identifying instances of crime and traffic issues has rendered the precise localization thereof difficult, thereby impeding the populace's access to information concerning areas of high risk and safety. Employing a Geographic Information System (GIS)-based mapping system utilizing the K-means clustering method, spatial data pertaining to crime and traffic concerns are grouped. The primary objective is to aid in the identification of high-risk areas concerning crime and traffic matters. The methodology employed in this study revolves around the application of the K-means clustering method to categorize spatial data relevant to crime and traffic issues. K-means clustering represents a non-hierarchical cluster analysis technique designed to partition data into multiple groups based on spatial similarities. Research findings elucidate that through the utilization of the K-means clustering method, three distinct sets of clusters predicated upon the intensity of crime and traffic issues emerge. Consequently, from these clustering outcomes, districts and specific locales falling within each cluster, denoted as moderately vulnerable (C1), vulnerable (C2), and highly vulnerable (C3), can be delineated. This system is poised to furnish recommendations to pertinent authorities for addressing areas exhibiting heightened intensity levels while concurrently facilitating the generation of reports and dissemination of information to the public via a dedicated website pertaining to areas at elevated risk of crime and traffic issues.
Application of The Support Vector Machine Algorithm for Timely Student Graduation Prediction Based on Streamlit Web at The Faculty of Informatics Engineering Nurul Jadid University Yati; Ainol Yaqin, Moh; Yusrotun Nadhiroh, Anis
Journal of Computer Networks, Architecture and High Performance Computing Vol. 6 No. 3 (2024): Articles Research Volume 6 Issue 3, July 2024
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v6i3.3918

Abstract

Universities must provide good education so that they can produce good graduates.There are many factors that influence student graduation rates, one of the problems faced by an educational institution, especially at universities, whether state or private, is finding predictions of student graduation rates on time.One of the technological advances currently available is a system that can predict whether students will graduate on time or not. One of the machine planning algorithms that can be used is the Support Vector Machine.The results of this research were carried out by predicting the on-time graduation rate of students at Nurul Jadid University, Faculty of Engineering, Informatics Study Program. By using the Support Vector Machine method, this research used testing data of 20% of the data from 612 student data with the same 7 attributes. The data obtained 123 data which resulted in 72 student data being on time, 45 student data being late, 4 student data being correct. time and 2 students' data was late. From the results, the accuracy of the training data was 94%, while the results of the accuracy of the testing data received a score of 95%. And based on the validity test of the Support Vector Machine algorithm, the presentation results obtained were Accuracy levels of 96%, Recall 98%, and Precision 94% from 123 testing data. Next, the model is deployed using Streamlit. Streamlit is an open source Python-based framework designed to help developers build interactive web-based programs in the fields of data science and machine learning. The accuracy rate is very good, this shows that SVM can be applied to predict student graduation rates.
Analysis of Public Sentiment Towards The TikTok Application Using The Naive Bayes Algorithm and Support Vector Machine Hidayah, Ika Arofatul Hidayah; Ririen Kusumawati; Zainal Abidin; M. Imamuddin
Journal of Computer Networks, Architecture and High Performance Computing Vol. 6 No. 2 (2024): Articles Research Volume 6 Issue 2, April 2024
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v6i2.3990

Abstract

In the current digital era, social media applications such as TikTok have become an important aspect of people's lives. TikTok allows users to create and share short videos, making it a global phenomenon with millions of active users. However, this application has also been the subject of various responses and opinions from the public. This research aims to classify public sentiment towards the TikTok application based on comments on Playstore using the Naïve Bayes algorithm and Support Vector Machine (SVM). This research method involves collecting comment data from Playstore using scraping techniques, resulting in 5,000 review data. Data pre-processing stages include case folding, tokenization, normalization, stopword removal, stemming, and data labeling using a lexicon. The data that has been processed is then weighted using Term Frequency - Inverse Document Frequency (TF-IDF) before being classified using the Naïve Bayes and SVM algorithms. Algorithm performance evaluation is carried out using the Confusion Matrix to measure accuracy, precision and recall. The research results show that the SVM algorithm has higher accuracy (84%) compared to Naïve Bayes (79%). SVM also shows better precision and recall values in classifying positive and negative sentiment from user reviews. From the results of the tests that have been carried out, the SVM algorithm is more effective than Naïve Bayes in sentiment analysis of the TikTok application. This research provides insight into how public sentiment can be measured and analyzed, and underscores the importance of choosing the right algorithm for data sentiment analysis on social media platforms.
Sentiment Analysis of Public Comments on Coldplay Concerts on Twitter Using the Naïve Bayes Method Dwisyahputra, Achmad Adbillah; Kurniawan, Rakhmat
Journal of Computer Networks, Architecture and High Performance Computing Vol. 6 No. 3 (2024): Articles Research Volume 6 Issue 3, July 2024
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v6i3.4202

Abstract

Social media platform Twitter had become one of the most popular platforms for communication and information sharing. In the context of entertainment events such as music concerts, Twitter became a bustling place with various comments and opinions from the public regarding their experiences attending a concert. Many fans shared their experiences about Coldplay concerts on Twitter. These comments were highly varied and required a thorough understanding to interpret the overall public sentiment. Event organizers and Coldplay's band managers needed to understand public feelings about their concerts. This information was crucial for the evaluation and improvement of future events. Comments on Twitter were often brief and diverse, making manual data processing inefficient and necessitating automated tools to understand the sentiment within them. Sentiment analysis, or opinion mining, was the process used to understand, extract, and process text data automatically to gather information about the sentiment contained in opinion sentences. Research on sentiment analysis frequently focused on opinions that contained positive or negative sentiments. To classify these positive and negative sentiments, the Naive Bayes (NB) classification method was employed. The purpose of this study was to analyze the sentiment of public comments about Coldplay concerts on Twitter using the Naive Bayes method. The expected outcome was to provide insights into public sentiment towards Coldplay concerts, which would be valuable for event organizers and the band's managers in evaluating and improving future events.
Rainfall Prediction in Jayapura City Area Using Long Short-Term Memory Azi, Amanda; Kusrini, Kusrini
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.v7i2.5506

Abstract

Jayapura, one of Indonesia’s major fishing cities, relies heavily on accurate weather predictions to ensure the safety of its fishermen, particularly due to its significant tuna and skipjack production. This study aims to improve rainfall forecasting in Jayapura using a Long Short-Term Memory (LSTM) model, a type of artificial neural network designed for time series prediction. Accurate rainfall forecasts are crucial for reducing the risks fishermen face at sea due to sudden weather changes. Daily data from the Meteorological Station in Dok II Jayapura was collected and processed to train the LSTM model, incorporating variables such as TAVG (average temperature), RH_AVG (average relative humidity), FF_AVG (average wind speed), Pressure (air pressure), and Wind_Gust (wind gust). The model’s performance was evaluated using Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE), yielding low values of 0.0542 and 0.0847, respectively, indicating high prediction accuracy. The MAE reflects the average magnitude of errors, while the RMSE highlights the model’s sensitivity to larger deviations, both supporting the reliability of the LSTM approach. The findings demonstrate that LSTM models can effectively forecast rainfall in Jayapura, providing valuable information that helps fishermen plan their activities more safely and efficiently. The study concludes that LSTM is a robust tool for rainfall prediction, and the inclusion of additional meteorological variables has proven to enhance accuracy. Further research is recommended to explore other factors to improve prediction reliability.

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