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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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Implementation of Data Mining for Speech Recognition Classification of Sundanese Dialect Using KNN Method with MFCC Feature Extraction Shandy, Ery; Anshor, Abdul Halim; Ardiatma, Dodit
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.v6i3.4226

Abstract

The importance of preservation and development of speech recognition technology for regional languages such as Sundanese, which have unique phonetic characteristics. Regional language speech recognition can assist in the development of local, educational, and cultural preservation applications to implement and evaluate the effectiveness of the combination of MFCC and KNN methods in classifying Sundanese dialect speech recognition. Methods used include trait extraction with MFCC, which converts voice data into numerical representations based on frequency characteristics, and classification with KNN, which groups data based on similarity to train data. The Dataset used consisted of speech recordings of Western and Southern Sundanese dialects. The results showed that the k-Nearest Neighbors (KNN) method can classify Sundanese dialect speech recognition with an accuracy of 80.00%, showing good ability in distinguishing "Western" and "southern" dialects. Mel-Frequency Cepstral Coefficients (MFCC) proved to be very effective in extracting sound features, helping KNN achieve low error rates. The combination of MFCC and KNN proved effective for speech recognition classification of Sundanese dialects, providing satisfactory results with high accuracy.
Performance Comparison of CART And KNN Algorithms for Analyzing Early Predictions of Mental Health Anggraeni, Eling Sekar; Fitriya Maharani, Lulu Amnah; Desi Riyanti; Aji, Ranggi Praharaningtyas; Pungkas Subarkah
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.v6i3.4232

Abstract

Currently, mental health is an unresolved mental health problem both at the national and international levels. Mental health disorders are conditions where a person has difficulty in adjusting to the conditions around them. Mental Health is an important aspect of overall health. Efforts to maintain and improve it can help a person achieve better well-being in everyday life.  This research aims to conduct Early Prediction Analysis related to mental health problems experienced by students by measuring the accuracy level of the analysis. This research was conducted using the CART (Classification and Regression Trees) and KNN (K-Nearest Neighbor) algorithms with a set of Mental Health Datasets consisting of 11 attributes and 101 data.  The data is processed using the Weka Application and the accuracy results of each algorithm are obtained, amounting to 94.0594% for the CART Algorithm and 91.0891% for the KNN Algorithm. From this achievement, it can be concluded that the performance of the CART and KNN algorithms falls into the Excellent Classification category. Judging from the accuracy obtained, the CART algorithm has a higher accuracy value than the KNN algorithm, so the CART algorithm has a high performance for analyzing early prediction of mental health of students who do not take steps in seeking mental health support.
Geospatial Data Integration for the Flood Vulnerable Area Classification in Jratunseluna Watershed Assaidi, Humaid; Khomarudin, Muhammad Rokhis; Badron, Khairayu; Ismail, Ahmad Fadzil; Ramza, Harry
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.v6i3.4233

Abstract

Flood is a threat that has significant impacts on communities and the environment. To improve the management of disaster risk, this research takes an integrated approach by utilizing geospatial data from various sources. The main objective of this research is to provide an integrated approach to determining flood-vulnerable area classes. This research focuses on the processing of various geospatial data such as DEM (Digital Elevation Model) imagery, Landsat 8 satellite imagery, Hydrological data based on SHuttle Elevation Derivatives at multiple Scales (HydroSHEDS) water flow accumulation imagery, and Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) rainfall imagery which are used as data sources to model the flood vulnerable area classification of The Jratunseluna watershed. Landsat 8 satellite imagery is used as a source for landuse land cover (LULC) classification, it is done to score each land category to the level of ability to absorb and drain excess water, the remaining data is used to score the earth elevation, accumulated water flow, and rainfall from the area. The weights and scores are used as the basis values to create a flood-vulnerable area classification model. The result of this research is a flood-vulnerable area classification map generated from a pre-made model.
Implementation of The Apriori Algorithm in Managing Stock Items at Drl.Rumahan Retail Satria Permana, Muhammad Safri; Widodo, Edy; HadiKristanto, Wahyu
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.v6i3.4239

Abstract

Drl.Rumahan is a retail store that sells a variety of motorcycle lamp modifications. Drl.Rumahan is still struggling with determining stock levels and understanding customer purchases. Additionally, they are not utilizing transaction data as a valuable information source. Without leveraging this data, Drl.Rumahan will fall behind its business competitors and lose customers because the products they seek are unavailable. This situation will inevitably become a significant problem if it continues. This study aims to utilize sales transaction data as valuable information and identify customer purchasing patterns from the sales transaction data. The algorithm used is the Apriori algorithm to identify purchasing patterns from the transaction data set. The results of this study identified the three highest rules: if someone buys a pass beam switch, they will buy a shroud with a support value of 5.8% and a confidence value of 47.6%; if someone buys a shroud, they will buy a pass beam switch with a support value of 5.8% and a confidence value of 45.5%; and if someone buys a shroud, they will buy a relay with a support value of 5.2% and a confidence value of 40.9%. These results can inform business strategy decisions by increasing the inventory of products that form rules and serve as a guide for promotional product packages for products that have rules above the minimum support and minimum confidence.
Simple Additive Weighting to Determine The Best Employee in a Freight Forwarding and Logistics Company Siahaan, Fernando; Anwar, Syaiful; W Handono, Felix
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.v6i3.4244

Abstract

The problem is that there is no method used to determine the best employee in the company based on the criteria set by the company. The purpose of this research is to propose simple additive weighting as a method for finding the best employees according to the weighting carried out. To make decisions, there are several criteria and criteria weights that are needed as a measuring tool to assess employees who will be promoted, attendance, QSM, Quiz, leading. Period of work and team work. The weight value of each criterion is attendance 0.20, QSM 0.25, Quiz 0.15, leading 0.20, tenure 0.10 and team work 0.10. Quality service management (QSM) if sub criteria < 200 QSM value 1, sub criteria 201 - 300 QSM value 2, sub criteria 301 - 400 QSM value 3, sub criteria 401 - 500 QSM value 4, sub criteria 501 - 600 QSM value 5. The results of the analysis with the saw method obtained two employees who got the highest score who had the right to be promoted for promotion with a value of 84.25 and 82.25. the conclusion is that the SAW method is influential in supporting and facilitating decision making to determine promoted employees.
Analysis of Manual and Automated Methods Effectiveness in Website Penetration Testing for Identifying SQL Injection Vulnerabilities Anaoval, Abdul Aziz; Zy, Ahmad Turmudi; S, Suherman
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.v6i3.4249

Abstract

This research aims to identify vulnerabilities to SQL Injection attacks on websites through penetration testing using quantitative and descriptive methods. In the current digital era, data and information security has become a crucial aspect. One of the frequent threats is SQL Injection attacks, where attackers insert malicious SQL commands into queries executed by web applications. This study utilizes tools such as Burp Suite to identify and exploit vulnerabilities in a login form created by the researchers. The research process begins with the Pre-Engagement Interactions phase, which includes information gathering and setting the testing scope. Subsequently, Vulnerability Testing is conducted to evaluate existing weaknesses. The exploitation of vulnerabilities is performed using the 'OR'1'='1 technique, which successfully demonstrates that the website is vulnerable to SQL Injection attacks. The results of this study indicate that the login form on the website is susceptible to SQL Injection due to insufficient input validation and the use of dynamic SQL queries without prepared statements. Implementing stricter input validation techniques and using prepared statements has proven effective in enhancing website security. This research makes a significant contribution to the field of information system security, particularly in the prevention of SQL Injection attacks. The results of this study can serve as a practical guide for web developers in improving the security of their applications and provide a deeper understanding of the threats and mitigation techniques for SQL Injection.
The Analysis of Product Sales in the Application of Data Mining with Naive Bayes Classification Zahri, M. Hannata; Sunge, Aswan S.; Zy, Ahmad Turmudi
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.v6i3.4255

Abstract

H&F Shoe Store is a privately owned Micro, Small, and Medium Enterprises retail store that sells merchandise. The owner serves customers directly and also acts as a cashier. In this store, the business owner is less aware of what types or categories of products are most in demand by customers, making sales operations less than optimal. Because of this, special expertise is needed to handle the problems in the retail store, namely data mining or Data Mining with the aim of digging up information related to sales problems, in this case the author will use the Classification method with the Naive Bayes algorithm. In this study, the author uses secondary data obtained from sales notebooks and re-collected into Microsoft Excel according to research needs. The data that has been collected on the software is 121 data which have 10 attributes, namely “Nama Produk”, “Size Produk”, “Kategori Produk”, “Jenis Produk”, “Gender Produk”, “Merek Produk”, “Stok Awal”, “Stok Terjual”, “Stok Sisa”, and “Penjualan”. The Naive Bayes Classifier method has successfully produced good results in classifying sales on a type or category of marketed products, the results obtained are in the form of product sales analysis and Naive Bayes model evaluation values. The results of the model evaluation values on the Confusion Matrix obtained are accuracy of 86.11%, recall of 84.62% and precision of 84.62%.
Implementation of Intrusion Detection System with Rule-Based Method on Website Firdyanto, Tri; Rushendra, Rushendra
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.v6i3.4256

Abstract

The aim of this research is to implement an intrusion detection system using rule-based methods on websites. The approach in this research is the development of an intrusion detection system (IDS). research results after implementation, testing, and acceptance of test results, conclusions can be drawn. The detection system can be implemented well in website-based applications using a rule-based method.
Association Relationship Analysis in Finding Sales of Goods With Apriori Algorithm Fathurrahman, Humam; Sunge , Aswan S.; Butsianto, Sufajar
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.v6i3.4258

Abstract

Technology can be designed to help human life from all aspects ranging from agriculture, health science, industry and daily life. Toko Intan, a business engaged in the sale of basic and daily necessities. Every day, Toko Intan records every sales transaction in an archive stored in Microsoft Excel, containing data on goods sold every day. The purpose of this research is to find out what items are bought simultaneously by consumers to manage inventory, with the data mining method used in this research is the Association Rules method. Association Rules is one of the data mining techniques from the a priori algorithm which functions to find a combination pattern of an item. Tests carried out to process data in this study using the RapidMiner application, from tests carried out with the specified parameters, namely minimum support 30% and minimum confidence 65%, resulted in a lift ratio validation rule of 1.206. Personal Care Biscuits with 30.8% support and 90.9% confidence with a validation lift ratio of 1.206. Sales transaction data analysis can be applied well, and is able to generate a new association rule from the sales transaction dataset. With this research, it is hoped that it can provide information to the owner of the Intan store in providing the stock of goods needed by consumers and to find out the combination of item sets from the sales transaction dataset.
Apriori Algorithm to Predict Availability of Beauty Products Hasibuan, Maria Hikmah; Fakhriza, M.
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.v6i3.4259

Abstract

This study introduces the Apriori algorithm in beauty product availability prediction system as a solution to enhance stock prediction accuracy and mitigate inventory risks in the beauty industry. By applying data mining technology, specifically the Apriori algorithm, Kazana Kosmetik aims to gain insights into consumer purchasing patterns to optimize operations. The research analyzes transaction data to identify key buying patterns and improve stock management strategies. The results reveal seven main purchasing patterns with an average confidence value of 0.414, offering valuable guidance for Kazana Kosmetik in inventory control and marketing tactics. By leveraging data mining techniques, companies like Kazana Kosmetik can streamline sales strategies and enhance customer satisfaction. This research underscores the effectiveness of the Apriori algorithm in predicting beauty product availability and its potential to revolutionize operational efficiency in the cosmetics market.

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