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
 49   50   51   52   53 
Implementation of K-Medoids Clustering Method for Indihome Service Package Market Segmentation Hutagalung, Juniar; Syahril, Muhammad; Sobirin, Sobirin
Journal of Computer Networks, Architecture and High Performance Computing Vol. 4 No. 2 (2022): Article Research Volume 4 Number 2, July 2022
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v4i2.1458

Abstract

IndiHome (Indonesia Digital Home) is a leading digital fibre optic service product consisting of fibre optic internet services, landline telephones, and interactive TV services. Although the coverage of Indihome products is extensive in the city of Medan, in marketing, Indihome products have not reached the planned target. Based on data from Indihome service package users that have been received, Indihome product users only numbered 6419 customers in all STOs in Medan City. At the same time, the target was planned by PT. Telkom Access Medan, namely Marketing Indihome products, must reach 5,000 customers per month in all STOs in Medan City. Indihome product marketing is an obstacle for PT. Telkom Access Medan, because the Indihome product is a new product, the people of Medan City do not fully know what Indihome is and what facilities they get from using the Indihome service package. Therefore PT. Telkom Access Medan needs to make a plan to make a marketing strategy. The first step that needs to be done is to segment the market for the Indihome service package. This study aimed to determine the application of Data Mining using the K-Medoids Clustering method in the Indihome service package market segmentation at PT. Telkom Access Medan. With this research, it is hoped that it can provide a reference for the results of the decision so that it can help related parties to make it easier to classify the market segmentation of the Indihome service package at PT. Telkom Access Medan. Because the value of S > 0, then the calculation is stopped and ends in the 3rd iteration. Indihome service package data processing uses the k-medoids clustering method in the form of potential, potential, and not potential STO (Sentral Telephone Automated) cluster members.
Comparison of Evaluation Image Segmentation Metrics on Sasirangan Fabric Pattern Marleny, Finki Dona; Mambang, Mambang
Journal of Computer Networks, Architecture and High Performance Computing Vol. 4 No. 2 (2022): Article Research Volume 4 Number 2, July 2022
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v4i2.1479

Abstract

Sasirangan fabric is a typical fabric from the South Kalimantan area. Sasirangan fabric patterns or motifs have a unique archetype that is different from other typical fabrics in Indonesia. The design of Sasirangan fabric is formed from the process of juju or seam. The pattern of Sasirangan fabric that has this uniqueness can be segmented into a more meaningful shape so that it is easy to analyze. The image segmentation that will be tested is the basic pattern of Sasirangan fabric with a random sample to compare the results of the evaluation of the metric evaluation of the image segmentation process from the Sasirangan fabric pattern. Image segmentation is a different segmentation with certain characteristics, namely using the compact watershed approach, canny filter, and morphological geodesic active contours method in the evaluation of image segmentation metrics using precision-recall, which serves to evaluate the quality of the classifier's output. After the image segmentation process is evaluated, the Sasirangan fabric pattern is grouped using the K-means algorithm as a different labelling strategy. This labelling process uses the K-means algorithm to better match details but can be unstable because it relies on random initialization. Alternatives to balance the unstable labelling process using the means algorithm can use discretization. The addition of the K-means method with discretization can create fields with geometric shapes that are pretty flat. The segmentation with Sasirangan fabric with a full motif or data number four 741.78s, results in processing the fastest and the longest computational time on data number two 120.79s.
Performance Comparison Supervised Machine Learning Models to Predict Customer Transaction Through Social Media Ads Thohari, Afandi Nur Aziz; Ramadhani, Rima Dias
Journal of Computer Networks, Architecture and High Performance Computing Vol. 4 No. 2 (2022): Article Research Volume 4 Number 2, July 2022
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v4i2.1488

Abstract

The application of machine learning has been used in various sectors, one of which is digital marketing. This research compares the performance of six machine learning algorithms to predict customer transaction decisions. The six algorithms used for comparison are Perceptron, Linear Regression, K-Nearest Neighbors, Naïve Bayes, Decision Tree, and Random Forest. The dataset is obtained from Facebook ads transaction data in 2020. The goal is to get a model that has the best performance so that it can be deployed to the web. The method that is used to compare the results is a confusion matrix and also uses visualization of the model to get the prediction error that occurred. Based on the test results, the random forest algorithm has the highest accuracy, recall, and f1-score values, with scores of 96.35%, 95.45%, and 93.32%. The highest precision value was generated by the logistic regression algorithm, which was 94.44%. Based on the data visualization presented by the random forest algorithm, it has the least prediction errors, there are four data. Therefore, it can be concluded that the random forest algorithm has the best performance because it has the highest value in the three confusion matrix measurements and the smallest data prediction error. The model of the random forest algorithm is deployed to the web platform and can be accessed at the link iklan-sosmed.herokuapp.com.
Recognition of Regional Traditional House in Indonesia Using Convolutional Neural Network (CNN) Method Defriani, Meriska; Irsan Jaelani
Journal of Computer Networks, Architecture and High Performance Computing Vol. 4 No. 2 (2022): Article Research Volume 4 Number 2, July 2022
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v4i2.1562

Abstract

Indonesia is a country that has a lot of cultural diversity. This cultural diversity needs to be preserved. If this is not done, the culture that is owned by Indonesia can slowly disappear. The reduction in cultural values can also reduce the sense of belonging to the culture. This lack of sense of ownership makes it easy for other nations to make claims on the culture that is owned by Indonesia. Indonesia will lose its characteristics as a country that has a lot of cultural diversity. One of the efforts to preserve culture is to recognize the characteristics of each culture and be able to recognize the differences between one culture and another. For example, recognizing traditional houses from various ethnic groups based on their image. In this research, the image classification of the characteristics of traditional houses from several ethnic groups in Indonesia was carried out. The classification used to identify an image. In this study, deep learning techniques are used with the Convolutional Neural Network (CNN) algorithm and Keras framework. This CNN use several layers namely convolutional, pooling, flatten, and dense layer. The development of deep learning models uses the Knowledge Discovery in Database (KDD) method. This method consists of nine stages. The built model is evaluated using k-fold cross validation with a k value of 5 and produces an average accuracy of 80%. This shows that the model built is capable of classifying well. The built model is evaluated with 3 different epoch values, namely 50, 75, and 100. The larger the epoch value used, the greater the accuracy value. The model built is also able to make predictions with an accuracy of 80%.
Evaluation of ATM Location Placement Using the K-Means Clustering in BNI Denpasar Regional Office Suwirya, I Putu; Candiasa, I Made; Dantes, Gede Rasben
Journal of Computer Networks, Architecture and High Performance Computing Vol. 4 No. 2 (2022): Article Research Volume 4 Number 2, July 2022
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v4i2.1580

Abstract

The existence of an ATM location requires a placement evaluation that aims to support business and provide convenience and comfort to customers when using or transacting. This study aims to evaluate the placement of ATM locations using the K-Means method, and research using data obtained from data mining to obtain decisions that lead to not strategic, strategic, and very strategic ATM locations. This study uses data sourced from the BNI ATM database and performance data in one semester or six months, namely January to June 2021, as many as 121 ATM locations spread across the island of Bali. The application of the K-Means Algorithm in this study uses 6 clustering criteria, namely ATMs Usage, ATMs Fee-Based Income/ FBI, ATMs Service Level Agreement/ SLA, ATMs distance, competitor ATMs, and Business Distance. In addition to presenting calculations using spreadsheets, this research also produces implementations in web-based software. The results of alternative classifications based on K-Means on very strategic centroids of 27 locations or covering 22.31%, strategic several 77 locations or covering 63.64%, and non-strategic 17 locations or covering 14.05%. Although the location of ATMs that are classified as non-strategic criteria is quite small, this can be optimized by several strategic steps that can be taken by stakeholders within the company, such as evaluating the bank's business plan in 2022 and improving the supervision of machines at ATM locations.
Optimization of Adaptive Genetic Algorithm Parameters in Traveling Salesman Problem Herdiana, I Kayan; Candiasa, I Made; Indrawan, Gede
Journal of Computer Networks, Architecture and High Performance Computing Vol. 4 No. 2 (2022): Article Research Volume 4 Number 2, July 2022
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v4i2.1581

Abstract

The TSP problem is one where a seller visits multiple destinations at the same time and they are only allowed to visit once. The purpose of this TSP is to shorten the shortest distance, thereby minimizing time and cost. There are various methods to solve the TSP problem, including greedy algorithm, brute force algorithm, hill climbing method, ant algorithm, and genetic algorithm. Each process in a genetic algorithm is affected by several parameters, including population size, maximum generation, crossover rate, and mutation rate. The purpose of this study is to apply genetic algorithms to the traveling salesman problem optimization, calculate the maximum influence of generation, chromosome number, crossover rate and mutation rate on the optimal genetic algorithm, calculate the range of chromosome number, population size, crossover rate and mutation rate for genetic algorithm in the traveling salesman problem and calculate the effect of adaptive genetic algorithm parameters on genetic algorithm results. Based on the results obtained from research and testing, the four parameters of the genetic algorithm are positively correlated with fitness results while negatively correlated with execution time performance where each adaptive parameter applied provides more optimal fitness results than static parameters. The four adaptive parameters that are applied together give optimal results, both fitness which reaches 1.0% and time reaches 38.7%.
Financial Performance Information System Using Economic Value Added Method Fahrian, Arris; Nasution, Muhammad Irwan Padli
Journal of Computer Networks, Architecture and High Performance Computing Vol. 5 No. 2 (2023): Article Research Volume 5 Issue 2, July 2023
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v5i2.2380

Abstract

In the current era of globalization, information is needed in a management organization. Most companies prefer computerized information systems because they simplify performance management. The rapid development of today's technology is also very useful and supports all aspects of life, especially in managing accounting and financial information systems, becoming a financial analysis of a company as a benchmark in assessing company performance. At the place where this research was carried out, there was no financial data to measure financial management performance. Become a reference in evaluating financial reports to assess financial performance in this study. Responding to these problems, it can be formulated to design a financial performance information system by applying the web-based EVA (Economic Value Added) method to assess whether a company's financial performance measurement influences growth or decline for decision makers related to financial management. and investors and provide results to company stakeholders as an evaluation value of financial management. The end result of this research is the EVA value for the company is Rp. 4,267,398,060. And this value can be used as an evaluation of management at the research site.
Electronic Archive Design With RC4 Cryptographic Based File Security sulistiyanto, sulistiyanto; Satriadi, Indra; Rahman, Arif
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.v6i1.3298

Abstract

The transformation of archival document storage systems is starting to shift from physical formats that require a lot of space and storage equipment to the electronic or digital realm (often called electronic archives). This is considered to reduce the costs of procuring equipment and storage space. In line with changes in paperless storage patterns, the issue of data security and confidentiality becomes important, so that information from documents to be archived can be maintained and cannot be used by irresponsible people. One technique for securing documents digitally is to use cryptography and the algorithm chosen is Rivest cipher 4. The RC4 (Rivest Chiper 4) algorithm was chosen because the execution speed in file encryption is faster than other algorithms. This article aims to implement the RC4 algorithm into an electronic archive (e-Archieve) application. The application development method uses the waterfall method with 5 stages. The application was built using the PHP programming language and MySQL database, as well as the Rivest Cipher 4 cryptographic algorithm. The result of the application development is an electronic archive website. Every file uploaded to the server can be encrypted by the admin. Encrypted files will change to random characters like a virus. The application was tested using black box testing techniques, where all features worked as expected
Prediction of Obesity Categories Based on Physical Activity Using Machine Learning Algorithms Iqbal, Muhammad; L, Lisnawanty; Steven Dharmawan, Weiskhy; Septian, Rendi
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.v6i3.4053

Abstract

Obesity is a global health issue with rising prevalence, marked by excessive fat accumulation that poses health risks. Contributing factors include poor eating habits, lack of physical activity, and genetics, which elevate the risk of chronic diseases like type 2 diabetes, heart disease, stroke, and cancer. This study examines an obesity dataset with seven variables: Age, Gender, Height, Weight, BMI, Physical Activity Level, and Obesity Category. The analysis reveals strong correlations between Body Weight, BMI, and the Obesity Category, while Body Height shows a moderate negative correlation. Various machine learning algorithms were tested, including XGBoost, AdaBoost, Gradient Boosting, and Extra Trees Classification. XGBoost emerged as the top performer, achieving the highest accuracy (0.9961) and an almost perfect AUC (0.9992), making it highly effective for obesity prediction. The study's significance lies in its ability to elucidate the key factors contributing to obesity and their interactions. By recognizing the strong links between Body Weight, BMI, and Obesity Category, healthcare professionals can craft more targeted interventions. Furthermore, the successful application of advanced machine learning algorithms underscores the potential for technology to enhance predictive accuracy and support healthcare decision-making. The findings highlight XGBoost's superior performance, demonstrating its value in predicting obesity and aiding in early diagnosis and prevention strategies. This research emphasizes the critical role of data and technology in tackling obesity and improving public health outcomes.
Determining Superior Classes Based on Academic Grades at SMK Karya Pembaharuan with the K-Means Clustering Method Siregar, Lydia Diffani; Susilo, Arif; Widiyatmoko, Arif Tri
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.v6i4.4866

Abstract

Dalam lingkungan pendidikan, pengelompokan k-means dapat membantu sekolah menemukan kelas terbaik berdasarkan nilai akademik siswa. Dengan mengelompokkan siswa berdasarkan nilai akademik, sekolah dapat lebih mudah mengidentifikasi kelompok siswa yang memiliki nilai akademik tinggi, sedang, dan rendah. Kemudian penelitian yang digunakan adalah Semua objek dalam satu cluster memiliki karakteristik yang sama , tetapi setiap cluster memiliki karakteristik yang berbeda. Novi dan Ade Mubarok menulis jurnal pada tahun 2021 yang berjudul “Penerapan Algoritma K-Means Untuk Menentukan Kelas Unggulan Pada Smp Pelita Bandung” yang menyimpulkan bahwa SMP Pelita Bandung membutuhkan 3 cluster. Setelah peneliti melakukan eksperimen, mereka dapat menghasilkan 3 cluster, yaitu cluster 0 merupakan cluster dengan nilai rata-rata terendah yang akan masuk ke dalam kelas C sebanyak 42 siswa, pada cluster 1 dengan nilai rata-rata sedang akan masuk ke dalam kelas B sebanyak 37 siswa, sedangkan pada cluster 3 dengan nilai rata-rata siswa, sedangkan pada cluster 3 dengan nilai rata-rata tertinggi akan masuk ke dalam kelas A sebanyak 40 siswa. Hasil penelitian ini menunjukkan bahwa terdapat 6 siswa dalam kategori tinggi, 24 siswa dalam kategori sedang, dan 14 siswa dalam kategori rendah. Evaluasi terhadap hasil pengelompokan menunjukkan hasil yang cukup baik, dengan nilai Davies Bouldin Index (DBI) sebesar 1,180 yang mendekati angka 0.

Page 51 of 80 | Total Record : 795

 49   50   51   52   53 

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