cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Nurul Khairina
Contact Email
nurulkhairina27@gmail.com
Phone
+6282167350925
Journal Mail Official
nurul@itscience.org
Editorial Address
Jl. Setia Luhur Lk V No 18 A Medan Helvetia Tel / fax : +62 822-5158-3783 / +62 822-5158-3783
Location
Kota medan,
Sumatera utara
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
 71   72   73   74   75 
Product Layout Analysis Based on Consumer Purchasing Patterns Using Apriori Algorithm Radhitya, Made Leo; Widiantari, Ni Komang Mira; Asana, Made Dwi Putra; Wijaya, Bagus Kusuma; Sudipa, I Gede Iwan
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.v6i3.4400

Abstract

In every self-service store, it is certain to have a sales transaction data, where the data will continue to grow every day. But in self-service stores the data is only a record of sales at the store. Whereas transaction data can be used as information on how consumer purchasing patterns when shopping at the store, but not all supermarkets know this. So this research aims to find information on these purchasing patterns, where to do this research using the apriori algorithm which is part of the association technique which is also part of data mining, where in its application it will calculate the support value, confindence value and will be tested using the lift ratio. And after the calculation is carried out, optimization will be carried out using the high utility itemset mining variable which will calculate the highest profit value on the product, so that based on the calculation, the final result is obtained with a support value of 85%, a confidence value of 86%, a lift ratio test of 1.01 and the high utility gets the highest result of Rp. 567,000.
Application of Interactive Games on Tourism Objects Based on Augmented Reality Gamification Aristana, Made Dona Wahyu; Sanusi, Rikcy; Sudipa, I Gede Iwan; Aditama, Putu Wirayudi; Wiguna, I Komang Arya Ganda
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.v6i3.4403

Abstract

The development of information technology is always growing rapidly, especially in the field of android smartphones. Android smartphones can now be obtained at a fairly affordable price. With rapid development, there are so many innovations that can be developed by the smartphone itself, even the tourism sector such as The Sila's Agroutourism can get the impact of several innovations that exist along with the times such as Augmented Reality. In this research, the author uses two augmented reality methods, namely Marker Based Tracker and also Markerless. Where Marker based tracker is a method that uses image illustrations in the form of QR codes or special logos to identify an object in the Augmented Reality application. While Markerless is a method that does not require a marker or image to display the object. So that this research produces an augmanted reality game using the marker-based tracker method and also markerless which takes the GPS tracker method as its markerless method. This test uses 3 tests, namely Blackbox testing, Response Time testing and also User Experience Questioner (UEQ). Where the UEQ results filled in by 35 respondents get results above average (Excelent). So that it shows that this game is good and as expected.
Implementation of Sibi Sign Language Realtime Detection Program (Case Studi At Sekolah Luar Biasa Negeri 1 Tabanan) Suyitno, Yoga Kristian; Sudiarsa, I Wayan; Hartawan, I Nyoman Buda; Putra, I Dewa Putu Gede Wiyata
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.v6i3.4405

Abstract

Indonesian deaf people utilize SIBI to communicate using spoken words, gestures, facial expressions, and body language. SIBI, certified for Special Schools (SLB), helps deaf pupils communicate. This project implements SIBI (Indonesian Sign Language System) a real-time detection algorithm at Sekolah Luar Biasa Negeri 1 Tabanan using image processing and YoloV8 ultralytics deep learning. The program trains a sign language gesture detection model on Google Colab's GPU. The SIBI sign language images were used to train a YoloV8 object detection model. The camera captures movements, which the YoloV8 algorithm trained on SIBI gesture data processes. It can recognize gestures in real time and generate text to non-sign language users. The dataset has 107 class vocabulary and 7 class affix prefixes for complete gesture recognition. Shirt color, room brightness, and webcam quality affect detection rates. Optimal detection accuracy is 87.74% and subpar 58.02%. Despite these limitations, the strategy helps deaf students communicate more effectively with non-sign language speakers. This program improves inclusivity and communication in schools, making learning easier for hearing-impaired pupils. This work provides a reliable and quick sign language identification system to help deaf educators and caregivers with daily interactions and education.
Naïve Bayes-based Student Graduation Prediction Model: Effectiveness and Implementation to Improve Timely Graduation Atmaja, Ketut Jaya; Indrawan, I Putu Yoga; Asana, I Made Dwi Putra; Wawan, I Kadek; Udayanie, Ayu Gde Chrisna
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.v6i3.4408

Abstract

Studies in an educational institution, when the lack of timely graduation of students in each batch and the number of students in each batch, causes an imbalance between incoming students and outgoing students and causes a decrease in accreditation from the campus, this should not continue to happen, the solution to dealing with this problem as an early detection of students who graduate on time is to predict the length of the student study period they have. Therefore, researchers will discuss the design of a prediction system for graduating on time using the Naïve Bayes method, to predict student graduation so that there is no imbalance of incoming and outgoing students. The construction of this system also uses the Naïve Bayes method and the CRISP-DM (Cross Industry Standard Process Data Mining) development method. In this case study, the Naïve Bayes method predicts data into 2, namely 1 (graduated on time) and 0 (did not graduate on time) by labeling the data used. In this model using 3247 data with the selection of features, namely semester achievement index 1 (ips1), ips2, ips3, ips4, ips5, semester credit units1 (credits1), credits2, credits3, credits4, credits5, semester credit units not passed 1 (skstidaklulus1), skstidaklulus2, skstidaklulus3, skstidaklulus4, skstidaklulus5 and labels. Using these feature variables results in model performance with 80% accuracy, with 80% accuracy it can be said that the model works well.
Optimisation of Inventory Management Through Time Series Analysis of Inventory Data with Double Exponential Smoothing Method Anggraeni, Dwi Puspita
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.v6i3.4410

Abstract

Stock forecasting is very useful for companies in knowing the trend of inventory needed in the next period, with time series data often forecasting can be a solution in supporting decision making. Excess or lack of stock of goods is often caused by a less than optimal record management process and often relies on personal intuition. In this study, the Double Exponential Smoothing method is applied in analyzing time series data and forecasting stock data. This method is used because it is in accordance with the company's sales data which is up and down. In addition, this forecasting calculation does not escape the error rate of forecasting calculations, therefore this system is also supported by the MAD (Mean Absolute Deviation), MSE (Mean Square Error) and MAPE (Mean Absolute Percentage) methods to calculate the error rate of the forecasting results. The forecasting results show that this method is able to provide fairly accurate predictions with a MAD value of 5.2475, MSE of 43.009, and MAPE of 26.307%. By using DES, companies can perform better stock planning, reduce the risk of over- or under-stocking, and improve inventory management efficiency. The DES method is proven to be flexible and easy to implement in computerized information systems, so it is recommended to be used more widely in corporate inventory management.
Cybersecurity Integration in Enterprise Architecture for IoT Infrastructure in Steel Manufacturing Hindarto, Djarot
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.v6i3.4415

Abstract

As a result of the widespread adoption of Internet of Things technology in the steel manufacturing industry, there is an urgent requirement for the implementation of robust cybersecurity measures. The proliferation of IoT devices has caused a data explosion, which in turn has increased the risk of cyberattacks. The purpose of this research is to develop an enterprise architecture model that is capable of effectively managing cybersecurity risks on Internet of Things infrastructure in the steel manufacturing industry. This is a response to the urgent challenge that has been presented. The methodology utilized in this study is a rigorous qualitative approach, which involves the collection and analysis of data through interviews and literature reviews related to the topic. Following an in-depth analysis of the findings of the research, several important goals have been established. These goals include the identification of potential dangers, the reduction of potential risks, and the effective implementation of security controls. Within the context of the steel manufacturing industry, this research makes a significant contribution to the improvement of cybersecurity in Internet of Things infrastructure. In addition to identifying potential dangers and mitigating risks, the architecture model that has been proposed is about more than that. It offers a comprehensive and well-coordinated safety strategy, which guarantees a strong defense against cyber threats.
Reverse Engineering for Static Analysis of Android Malware in Instant Messaging Apps Adnyana, I Gede Adnyana; Nugraha, Putu Gede Surya Cipta; Nugroho, Bagus Rahmat Adin
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.v6i3.4417

Abstract

Malware poses a significant threat to Android devices due to their high prevalence and vulnerability to attacks. Analyzing malware on these devices is crucial given the persistent and sophisticated threats targeting Android users. Static analysis of Android malware is a key approach used to detect malicious software without executing the application. This method involves meticulously examining the application's source code or binaries to identify signs of suspicious or harmful activities. The research methodology consists of three stages. The first stage involves collecting malware samples spread through instant messaging applications. The second stage employs reverse engineering, where APK files are decompiled to extract their contents. Following this, a static analysis is conducted, focusing on the AndroidManifest.xml file and the source code to identify the behavior and potential threats posed by the malware. The static analysis results revealed that Android malware often requests sensitive permissions to access personal data, such as receiving, reading, and sending SMS, as well as accessing location and contacts. Further analysis uncovered that after acquiring this data, the malware transmits it to the Telegram API via authenticated HTTP requests using specific tokens and chat_ids. These findings highlight that the permissions requested by the malware are designed to clandestinely collect and export personal data, posing a severe threat to the privacy and security of Android users.
Increasing Student Interest in Learning through the Implementation of the K-Nearest Neighbor Algorithm in Classifying Learning Preferences at SMAN 1 Kraksaan Jasri , Moh.; Rahmadan, Ilham; Shudiq, Wali Ja'far
Journal of Computer Networks, Architecture and High Performance Computing Vol. 6 No. 4 (2024): Articles Research October 2024
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v6i4.4526

Abstract

This research examines the effectiveness of implementing the K-Nearest Neighbor (KNN) algorithm in classifying student learning preferences and its impact on increasing interest in learning at SMAN 1 Kraksaan. The main aim of the research is to optimize learning methods through personalization based on individual student preferences. The study involved 560 students of SMAN 1 Kraksaan, with data including variables of age, gender, academic grades, daily study time, attendance and participation in class. The KNN algorithm is used to classify students' learning preferences into visual, auditory, kinesthetic, and reading/writing categories. The learning method is then adjusted based on the results of this classification. The results show that the KNN algorithm is able to classify student learning preferences with an accuracy of 80.36%. After implementing personalized learning methods, there was a significant increase in students' interest in learning, with an average increase of 1.76 points on a 10-point scale. Paired t-test analysis showed a statistically significant difference between interest in learning before and after intervention (p < 0.0001). This research concludes that the implementation of the KNN algorithm in classifying learning preferences can help increase students' interest in learning effectively. These findings emphasize the importance of personalization in education and demonstrate the potential of integrating machine learning in the pedagogical process to improve learning outcomes.
Development of SADS (Soil and Air Detector System) to Support Palm Oil Industries in Indonesia Lubis, Faisal; Sundawa, Bakti Viyata; Cholish, Cholish; Abdullah, Abdullah
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.v6i4.4558

Abstract

The issue of environmental damage due to the expansion of palm oil plantations in Indonesia has become a global issue. It has often hampered the development of the downstream palm oil industries in Indonesia. The European market was once closed to palm oil and derivative products from Indonesia. In fact, this industry has become a source of income and employment for millions of Indonesians. Therefore, palm oil industry must be supported with sustainable efforts. One of them is developing SADS (Soil and Air Detector System) model. Which is a tool for detecting soil and air environmental conditions around palm oil plantations. The parameters are soil PH, CO2 levels, temperature and humidity, and sunlight. The measurement results from this tool can be accessed and displayed in real-time via the internet. This is research aims to build a smart system as a solution to environmental problems around palm oil plantations. This is useful for knowing and observing the condition of palm oil plantations and as a basis for taking mitigation actions if the condition is in a critical state
Sentiment Analysis of Dune: Part Two Movie Reviews Using the Naive Bayes Method Maheswari, Diyan Arum; Zy, Ahmad Turmudi; Afriantoro, Irfan
Journal of Computer Networks, Architecture and High Performance Computing Vol. 6 No. 4 (2024): Articles Research October 2024
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v6i4.4604

Abstract

Research on films is fascinating because of the profound changes that the development of information and communication technology has brought about in our interactions with and consumption of media content. This study performs sentiment analysis on "Dune: Part Two" movie reviews using the Naïve Bayes method. Review data was collected from IMDb and then processed through several stages such as preprocessing, feature selection with TF-IDF, data splitting, and data mining and evaluation. Naïve Bayes was chosen for its simplicity and ability to handle large datasets effectively. The test results showed a high accuracy rate of 95%, indicating that this model can identify positive, negative, and neutral sentiments well. The use of TF-IDF in feature selection allowed the model to focus on important words, enhancing its sentiment classification ability. This research can provide insights into audience perceptions of the film "Dune: Part Two," which is beneficial for the film industry.

Page 73 of 80 | Total Record : 795

 71   72   73   74   75 

Filter by Year

2019 2025


Reset
Filter By Issues
All Issue Vol. 7 No. 2 (2025): Research Article, Volume 7 Issue 2 April, 2025 Vol. 7 No. 2 (2025): Forthcoming: Research Article, Volume 7 Issue 2 April, 2025 Vol. 7 No. 4 (2025): Articles Research October 2025 Vol. 7 No. 3 (2025): Articles Research July 2025 Vol. 7 No. 1 (2025): Article Research January 2025 Vol. 6 No. 3 (2024): Articles Research Volume 6 Issue 3, July 2024 Vol. 6 No. 2 (2024): Articles Research Volume 6 Issue 2, April 2024 Vol. 6 No. 1 (2024): Article Research Volume 6 Issue 1, January 2024 Vol. 6 No. 4 (2024): Articles Research October 2024 Vol. 5 No. 2 (2023): Article Research Volume 5 Issue 2, July 2023 Vol. 5 No. 1 (2023): Article Research Volume 5 Issue 1, January 2023 Vol. 4 No. 2 (2022): Article Research Volume 4 Number 2, July 2022 Vol. 4 No. 1 (2022): Article Research Volume 4 Number 1, Januay 2022 Vol. 3 No. 1 (2021): Computer Networks, Architecture and High Performance Computing, January 2021 Vol. 3 No. 2 (2021): Journal of Computer Networks, Architecture and High Performance Computing, July Vol. 2 No. 2 (2020): Computer Networks, Architecture and High Performance Computing Vol. 2 No. 1 (2020): Computer Networks, Architecture and High Performance Computing Vol. 1 No. 2 (2019): Computer Networks, Architecture and High Performance Computing Vol. 1 No. 1 (2019): Computer Networks, Architecture and High Performance Computing More Issue