Journal of Computer Networks, Architecture and High Performance Computing
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
Articles
795 Documents
<![CDATA[Decision Support System Determining Computer Virus Protection Applications Using Simple Additive Weighting (SAW) Method ]]>
<![CDATA[Adi Widarma]]>;
<![CDATA[M. Dedi Irawan]]>;
<![CDATA[Fajri Nurhidayahti]]>;
<![CDATA[Ranis Hsb]]>
<![CDATA[ Journal of Computer Networks, Architecture and High Performance Computing Vol. 3 No. 1 (2021): Computer Networks, Architecture and High Performance Computing, January 2021 ]]>
Publisher : <![CDATA[Information Technology and Science (ITScience) ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.47709/cnahpc.v3i1.936
<![CDATA[The use of information technology devices such as computers or laptops is currently increasing. The increased use is due to the fact that these devices are very supportive of our daily work activities. With the increasing use of these computers, data security on a computer or laptop device must be completely safe from virus attacks. To ward off viral attacks m aka requires the application of anti-virus to inhibit and prevent a variety of viruses that enter into the computer system so that the computer user's activity was not bothered by the many viruses are easily spread. Because there are too many antiviruses on the market, it is necessary to choose a good antivirus. One of the ways to choose antivirus is the existence of a decision support system . In this study, the Simple Additive Weighting (SAW) method was applied for the anti-virus application selection system. This data assessment analysis aims to produce the best anti - virus application options that computer users can use to secure their computer data. The criteria and weights used are K1 = application rating (5%) , K2 = completeness of features (30%) , K3 = price / official license (5%) , K4 = malware detection (45%) and K5 = blocking URL (15%). Of the 25 alternatives used, the results of the study, namely alternative A1 = Kaspersky anti-virus get the highest ranking result.]]>
<![CDATA[Combination of Decision Tree and K-Means Clustering Methods for Decision Making of BLT Recipients in the Covid-19 Period ]]>
<![CDATA[Jani Kusanti]]>;
<![CDATA[Djoko Sutanto]]>
<![CDATA[ Journal of Computer Networks, Architecture and High Performance Computing Vol. 3 No. 1 (2021): Computer Networks, Architecture and High Performance Computing, January 2021 ]]>
Publisher : <![CDATA[Information Technology and Science (ITScience) ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.47709/cnahpc.v3i1.937
<![CDATA[The economic conditions during the Covid-19 outbreak had an impact on society globally. The number of people who have experienced layoffs has an impact on the economic conditions of the family. The economic impact that helps the community encourages the government to increase efforts to increase social assistance in the form of BLT. However, the distribution of BLT was not right on target, there were still many people who really could not afford not to receive BLT, while those who were still able to get BLT assistance. Therefore, it is important in this study to use a combination of the K-Means Cluster and Decision Tree methods to be used in BLT recipient decision making, with the aim of increasing BLT recipients as expected. The calculation results were obtained using a combination of the K-Means Cluster and Decision Tree methods referring to the criteria for the community who has the right to receive data with an error level of -2.476190476 <from error tolerance 6.84.]]>
<![CDATA[Implementation of HSV- based Thresholding Method for Iris Detection ]]>
<![CDATA[Fajrul Islami]]>
<![CDATA[ Journal of Computer Networks, Architecture and High Performance Computing Vol. 3 No. 1 (2021): Computer Networks, Architecture and High Performance Computing, January 2021 ]]>
Publisher : <![CDATA[Information Technology and Science (ITScience) ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.47709/cnahpc.v3i1.939
<![CDATA[Image thresholding is one of the most frequently used methods in image processing to perform digital image processing. Image thresholding has a technique that can separate the image object from its background. This is a technique that is quite good and effective for segmenting love. In this study, the threshold method used will be combined with the HSV mode for color detection. The threshold method will separate the object and the image background, while HSV will help improve the segmentation results based on the Hue, Saturation, Value values to be able to detect objects more accurately. Segmentation is carried out using the original input image without pre-processing or direct segmentation. As we know that in digital image processing, there are steps that are usually done to get a good input image, namely pre-processing. In this pre-processing stage, processes such as image conversion and image intensity changes are carried out so that the input image is better. Therefore, even though the input image is used without going through the pre-processing stage, the object can be segmented properly based on the color type of the object. The results of this segmentation can later be used for recognition and identification of image objects. The results of the test method for object segmentation achieved a color similarity level of 25%, with an accuracy rate of 75% in detecting uniform color objects. So that this method can be one of the most effective methods in segmenting image objects without pre-processing or direct thresholding]]>
<![CDATA[Implementation of Simple Additive Weighting in Providing Micro Business Loans at Bank Mandiri Pematangsiantar ]]>
<![CDATA[Sahat Sonang Sitanggang]]>;
<![CDATA[Erwin Sirait]]>
<![CDATA[ Journal of Computer Networks, Architecture and High Performance Computing Vol. 3 No. 1 (2021): Computer Networks, Architecture and High Performance Computing, January 2021 ]]>
Publisher : <![CDATA[Information Technology and Science (ITScience) ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.47709/cnahpc.v3i1.948
<![CDATA[Banks are institutions and institutions that are organizations and institutions in Indonesia and have an important role in the sustainability of the Indonesian economy. Bank Mandiri is one of the banks that provides micro business credit facilities to the business world. The higher the public's interest in getting micro business credit, the banks need software to help determine who is entitled to credit. In order to produce a proper feasibility analysis, a method of decision-making in overcoming these problems is needed so that determining who is entitled to receive credit is not too long and efficient in reducing credit risk. Problems arise in the decision-making process for granting micro-business loans, namely the inaccuracy of micro-business credit recipients. The above problems can be resolved by building a Decision Support System which can assist decision makers in assessing and selecting micro business loans using variables: Income, Collateral, Loan Limit, Installments, Length of Business, Number of Dependents. The system built by applying the Simple Additive Weighting method is known as the method of adding weight. The Simple Additive Weighting method requires a decision matrix normalization process to a scale that can be compared with all available alternative ratings. This will be a reference in ranking and consider the advantages and disadvantages of applying for a credit loan in order to find the desired candidate. From the test results using 10 data samples, it was obtained that the first rank was received credit on behalf of A5 with a value of 9.33, and the last rank was on behalf of A6 with a value of 6.4. and very helpful in solving problems faced by Bank Mandiri Pematangsiantar.]]>
<![CDATA[Implementation of Additive Weighting in Providing in Receiving Bidik Misi Scholarships at the Politeknik Bisnis Indonesia ]]>
<![CDATA[Sahat Sonang Sitanggang]]>;
<![CDATA[Andi Setiadi Manalu]]>
<![CDATA[ Journal of Computer Networks, Architecture and High Performance Computing Vol. 2 No. 1 (2020): Computer Networks, Architecture and High Performance Computing ]]>
Publisher : <![CDATA[Information Technology and Science (ITScience) ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.47709/cnahpc.v2i1.949
<![CDATA[This research was conducted to design a Decision Support System as a tool for decision makers in distributing the Bidik Misi Scholarship at the Politeknik Bisnis Indonesia. The selection of students who volunteered to become Bidik Misi Scholarship recipients used the Decision Support System (DSS) approach which applied the Simple Additive Weighting (SAW) method so that the decisions of Bidik Misi Scholarship recipients that had been subjective, non-transparent, and immeasurable could be overcome. The Simple Additive Weighting method is carried out by weighting the criteria and sub-criteria for each alternative for all attributes. The SAW method in the process is by normalizing the decision matrix (X) to a scale that can be compared with all existing alternative ratings. The criteria used in the SAW method in this study consisted of 2 (two) criteria and each of these criteria had Sub Criteria. The first criterion is Parents with Sub Criteria consisting of: Education, Income, The Number of Dependents. The second criterion is Students with Sub Criteria consisting of Age, Academic Potential, KIP Ownership. The output obtained from 5 data samples analyzed in this study obtained first rank NM1 with a value of 0.9, second rank NM3 with a value of 0.77, third rank NM5 with a value of 0.62, fourth rank NM4 with a value of 0.59, fifth rank NM2 with a value of 0.55. Based on the results of the tests conducted, it is concluded that the Bidik Misi Scholarship decision support system using the SAW method can make it easier and very helpful in solving the problems faced by the Politeknik Bisnis Indonesia.]]>
<![CDATA[Implementation of Water Conditions in Soil with Artificial Neural Network Method using Backpropagation ]]>
<![CDATA[Toppan Sintio]]>;
<![CDATA[Steven Steven]]>;
<![CDATA[Yennimar Yennimar]]>
<![CDATA[ Journal of Computer Networks, Architecture and High Performance Computing Vol. 3 No. 2 (2021): Journal of Computer Networks, Architecture and High Performance Computing, July ]]>
Publisher : <![CDATA[Information Technology and Science (ITScience) ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.47709/cnahpc.v3i2.950
<![CDATA[In agriculture and plantations, the land is an important thing, but sometimes the soil needs to be measured for its fertility, so measuring instruments are used. In this study, the authors tried to collect data for measuring using the Backpropagation method to determine the prediction of fertility in the soil. The backpropagation method is used to predict and also Backpropagation is a Neural Network algorithm. In using this method, input is a sensor that will take data in the form of soil moisture, pH when wet, and pH when dry, followed by this method which processes the data to be generated. The results of research with the Backpropagation algorithm get 80% accuracy of the 10 test data used for test results. The results tested initially were not as expected but with several trials, it was almost as expected but needed to be further developed. With the hope that there are people who can develop better for more knowledge and hopefully it can be useful for more. The suggestion in this is for readers who want to develop their suggestions to collect more data from this research to get more satisfying results. If the data is not more efficient, more efficient or accurate methods or means of data collection are expected to be used.]]>
<![CDATA[Comparison of Graphical User Interface Testing Tools ]]>
<![CDATA[Arnaldo Marulitua Sinaga]]>;
<![CDATA[Yohanssen Pratama]]>;
<![CDATA[Felix Oswaldo Siburian]]>;
<![CDATA[Kevin J F Pardamaian S]]>
<![CDATA[ Journal of Computer Networks, Architecture and High Performance Computing Vol. 3 No. 2 (2021): Journal of Computer Networks, Architecture and High Performance Computing, July ]]>
Publisher : <![CDATA[Information Technology and Science (ITScience) ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.47709/cnahpc.v3i2.951
<![CDATA[Graphical User Interface or better known as the user interface is the liaison of users with electronic devices such as computers. The Graphical User Interface uses icons, menus, and some other visual indicators to represent the information contained in the interface of the application being used. The Graphical User Interface I must pass the Graphical User Interface Testing stage to ensure that every element in the Graphical User Interface is not an error and by the specified one. Also, we know that Graphical User Interface Testing is a set of activities that aim to test the Graphical User Interface I of the test object to ensure that the Graphical User Interface complies with the specifications specified in the software design document. In this research, we try to compare four Graphical User Interface testing tools which ae: Robotium, Espresso, UI Automator, and Pix2Code. By exploring these 4 testing tools we find out that Pix2code can only identify objects, especially label objects. Pix2code can only meet 3 out of 7 predefined criteria. This indicates that there are still many objects of the android application that Pix2code has not been able to identify. In other words in the Graphical User Interface testing section, pix2code can play a role in identifying each object contained in the application and can be done at the design stage. The result that we get from this research is that the GUI testing tools could identify many parts and almost every object in the application except the Pix2code. For future development, Pix2code as a testing tool requires development in the form of a desktop display such as the UI Automatorviewer so that it can display every detail of the object including the attributes of the object.]]>
<![CDATA[Security Analysis and Improvement of Lighweight VANET Authentication Protocol (Case Study : Zhao et al. LVAP) ]]>
<![CDATA[Sepha Siswantyo]]>
<![CDATA[ Journal of Computer Networks, Architecture and High Performance Computing Vol. 3 No. 2 (2021): Journal of Computer Networks, Architecture and High Performance Computing, July ]]>
Publisher : <![CDATA[Information Technology and Science (ITScience) ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.47709/cnahpc.v3i2.979
<![CDATA[VANET is an ad-hoc network implemented on vehicle communication to ensure traffic safety and traffic management efficiency. VANET security is a concern because of various vulnerabilities, especially from authentication criteria that the attacker can exploit. VANET is vulnerable to Sybil attack, entity impersonation, message modification, and identity falsification. Several mechanisms and protocols have been developed to address these vulnerabilities. The design of the VANET authentication protocol also needs to be proven using formal methods to ensure that the protocol meets the required security criteria. In this research, the security of VANET authentication protocol developed by Zhao et al. was analyzed using the Datta et al. security protocol analysis method. Instead of BAN Logic, the Scyther tool was used to verify security claims and find possible attacks. Our Security analysis results show that Zhao et al.'s protocol does not meet confidentiality and entity authentication criteria. Scyther tool can find attacks on nonce confidentiality and man-in-the-middle attack. Therefore, we modify Zhao et al. protocol by adding signature and session key confirmation to improve its security. Based on analysis results, our modified Zhao et al. authentication protocol met confidentiality and entity authentication criteria. The use of signature and session key confirmation prevents man-in-the-middle attack and protects nonce confidentiality. Therefore, our research concludes that modified Zhao et al. authentication protocol more secure than the original protocol in terms of nonce and session key confidentiality, aliveness, weak agreement, non-injective agreement, and non-injective synchronization.]]>
<![CDATA[Web-Based Rice Disease Diagnosis Expert System Using Fuzzy Tsukamoto Method and K-Nearest Neighbor Algorithm ]]>
<![CDATA[Fefi Hades Tawarai]]>;
<![CDATA[Fauziah Fauziah]]>;
<![CDATA[Andrianingsih Andrianingsih]]>
<![CDATA[ Journal of Computer Networks, Architecture and High Performance Computing Vol. 3 No. 2 (2021): Journal of Computer Networks, Architecture and High Performance Computing, July ]]>
Publisher : <![CDATA[Information Technology and Science (ITScience) ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.47709/cnahpc.v3i2.980
<![CDATA[Technology today is growing rapidly from year to year, not least started to spread to the agricultural sector. With the information technology making society more easily in search of information via the internet from your smart device. The goal of this study was made to facilitate the community, especially farmers in helping to diagnose diseases and pests in rice plants. Rice plants can be attacked by a wide variety of diseases and pests with a wide variety of symptoms experienced in rice plants. To know the kind of disease on rice plants in the era of technology, it takes an expert system that can help detect the disease in rice plants. In this study, Expert System-Based Website using Tsukamoto Fuzzy method and the Algorithm of K-Nearest Neighbor whose purpose is to help people, especially farmers in diagnosing diseases and pests in rice plants by looking at the symptoms of the attack on the rice plant. Data was obtained from the Research and the Ministry of Agriculture then taken some sample data for testing done. The results of the testing data of this expert system is the result of late diagnosis in diseases of the rice with the symptoms that already exist based on the data that have been obtained with an accuracy rate of 92,88%.]]>
<![CDATA[Lecturer Attendance System using Face Recognition Application an Android-Based ]]>
<![CDATA[Feri Susanto]]>;
<![CDATA[Fauziah Fauziah]]>;
<![CDATA[Andrianingsih Andrianingsih]]>
<![CDATA[ Journal of Computer Networks, Architecture and High Performance Computing Vol. 3 No. 2 (2021): Journal of Computer Networks, Architecture and High Performance Computing, July ]]>
Publisher : <![CDATA[Information Technology and Science (ITScience) ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.47709/cnahpc.v3i2.981
<![CDATA[In the field of industries, businesses, and offices the use of security systems and administrative management through data input using a face recognition system is being developed. Following the era of technological advances, communication and information systems are widely used in various administrative operational activities and company security systems because it is assessed by using a system that is based on facial recognition security levels and more secure data accuracy, the use of such systems is considered to have its characteristics so it is very difficult for other parties to be able to engineer and manipulate data produced as a tool to support the company's decision. Related to this, causing the author is to try to research the detection of facial recognition that is present in the application system through an Android device, then face recognition detection will be connected. and saved to the database that will be used as data about the presence of teaching lecturers. Using the local binary pattern histogram algorithm method to measure the face recognition system that can be applied as a technique in the attendance system of lecturers to be more effective and efficient. Based on testing by analyzing the false rate error rate and the false refusal rate can be seen that the average level of local binary pattern histogram accuracy reaches 95.71% better than through the Eigenface method which is equal to 76.28%.]]>
