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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 Naïve Bayes Method Diagnosing Diseases Nile Tilapia Ridho Wahyudi Pulungan; Sriani Sriani; Armansyah Armansyah
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.v6i2.3834

Abstract

The Nile tilapia, also known as Oreochromis niloticus, was a freshwater fish species first produced in East Africa in 1969. It became a popular aquaculture fish in freshwater ponds across Indonesia. Besides its delicious taste, the Nile tilapia is rich in nutrients essential for human health. However, cultivating Nile tilapia was challenging due to frequent bacterial diseases. These diseases often led to mass fish deaths, causing financial losses, especially for new fish farmers. The rapid spread of diseases emphasized the need for prompt intervention to prevent further losses. Farmers needed adequate knowledge about Nile tilapia diseases, but often struggled to absorb information provided by the government. Hence, the presence of experts or veterinarians was crucial in assisting farmers to address these issues. Farmers of Nile tilapia sought assistance from experts or veterinarians, but this was not easy. It involved substantial costs and time, while quick intervention was necessary to mitigate losses. The solution proposed was the development of an expert system for diagnosing and treating Nile tilapia diseases. Thus, an expert system was built to assist fish farmers in identifying fish diseases and their treatments by implementing the naïve Bayes method. The expert system transferred human knowledge to computers, enabling them to solve problems like experts, thereby making expert knowledge accessible to non-experts. Naïve Bayes was implemented to determine the highest probability based on input symptoms. This research used five test data samples to apply the naïve Bayes method to diagnose Nile tilapia diseases, resulting in an accuracy rate of 80%. Therefore, the implementation of naïve Bayes in diagnosing Nile tilapia diseases is considered reasonably effective.
Implementation of the Naïve Bayes Algorithm in the SMS Spam Filtering System Diah Ayu Anggraini; Muhammad Ikhsan; Suhardi Suhardi
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.v6i2.3875

Abstract

In the context of the escalating global spam activity, supported by data from CNN Indonesia in 2021, this research aimed to investigate the root causes and characteristics of this phenomenon. The approach employed in this study involved a series of exploration and classification stages of text messages with the clear objective: to determine whether each message fell into the spam category or not, utilizing the Naïve Bayes method. Additionally, the research aimed to identify the factors influencing the status of text messages, whether they were considered as spam or not. The Naïve Bayes classification method was chosen to facilitate the process of identifying spam-related messages. The dataset used in this research had an 80:20 ratio and was obtained from the Department of Communication and Informatics of Asahan Regency. This data was used to train and test the developed classification model. Data labeling processes were conducted to uncover the factors influencing the status of text messages as spam or non-spam. The research findings indicated that issues related to spam and non-spam messages remained a serious concern. The high accuracy rate, reaching 92%, achieved by the Naïve Bayes method in classifying messages, demonstrated the effectiveness of the method in detecting spam messages.
Analysis of User Acceptance of E-Learning at SMP NEGERI 10 PENAJAM PASER UTARA Using the UTAUT 2 Model Maulana Adhie Pratama; Elvin Leander Hadisaputro
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.v6i2.3927

Abstract

The rapid development of information and communication technology has encouraged the use of electronic media in education, giving rise to a new paradigm in learning through e-learning. E-learning provides benefits such as access to various learning resources and improving the quality of student-centered learning. This research analyzes the factors that influence student satisfaction in using e-learning by adopting the UTAUT (Unified Theory of Acceptance and Use of Technology) model and evaluates their intention to continue using this technology. The research was conducted at SMP Negeri 10 Penajam Paser Utara using a Moodle-based Learning Management System (LMS). Data was collected through an online survey with Google Forms from a sample of active students using e-learning in 2023. The results of the analysis show that the research model has good validity and reliability. Research findings indicate that habitual factors have a significant influence on usage intentions, which in turn influence usage behavior. Performance expectations also influence the intention to use e-learning.
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.
Utilization of Solar Panel Technology to Save Electricity Costs in Fish Farm Irrigation Nisrina, Safira Fegi; Mudzakir, Mohammad Alfian; Rahmat, Basuki
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.v6i3.3969

Abstract

Solar panels are a medium that can convert solar energy into electrical energy. In this research, the solar panel system in the fish pond is used as air requirements for the survival of the fish so that the air supply is sufficient. The problem is that fish farming has cloudy water due to decreasing temperatures due to lack of irrigation. This condition really requires water flow using a pump to circulate water in the fish pond. Therefore, solar panels are needed to drive the water circulation pump, where these solar panels are an alternative energy source to replace electricity from the State Electricity Company (PLN).The purpose of using a solar panel system is as alternative energy that can supply a pump motor which functions to channel water from the well to the pond to keep it flowing. This is used as alternative electrical energy to replace energy sources originating from the State Electricity Company (PLN) and to reduce operational costs of electrical energy. The method used is to assemble and install 2 units of 100WP solar panels, then testing is carried out to measure the panel output power from 06.00 to 17.00. The average result of measuring solar panel power every 30 minutes is 24.48Watts per day, this condition was when the test was carried out when the weather was less sunny. However, this can still change to get maximum power depending on weather conditions, especially when the sun is hot.
Implementing Histogram of Oriented Gradients to Recognize Crypto Price Graphic Patterns with Artificial Neural Network Wibowo, Suluh Arif; Rachmat, Nur
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.v6i2.3975

Abstract

Technical analysis stands as a pivotal strategy in analyzing graphic patterns to forecast future movements in crypto asset prices. However, comprehending numerous patterns poses a significant challenge for novice investors venturing into the investment realm. This study aims to facilitate investors in recognizing crypto price graph forms by classifying cryptographic price chart patterns. The dataset comprises images of seven types of crypto price graphic patterns obtained from the Kagle website, totaling 210 data points. A 70:30 training and testing data split is employed to ensure robust model evaluation. The study explores nine different Histogram of Oriented Gradients (HOG) parameter combinations for graphic pattern extraction. Leveraging the artificial neural network (ANN) classification method with parameter hyper tuning, the study assesses various HOG parameter configurations to optimize classification performance. The most optimal results are achieved with parameters Bin = 9, Cell Size = 16x16, and Block Size = 1x1, boasting an accuracy rate of 95.23%, precision of 95.55%, and recall of 95.23%. This classification approach streamlines the process for investors, enabling them to discern crypto price graph patterns effectively, thereby enhancing their investment decision-making capabilities in the dynamic cryptocurrency market landscape. By providing a structured method for pattern recognition, this study contributes to democratizing access to technical analysis tools, particularly benefiting novice investors seeking to navigate the complexities of cryptocurrency investment.
Usability Evaluation Of The Academic Information System Using The Concurrent Think-Aloud, Webuse, And Sus Methods Sarasmayana, Ketut Yoga; Dewi , Luh Joni Erawati; Sunarya, I Made Gede
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.v6i2.3977

Abstract

This research was motivated by the situation that the academic information system (SIAKAD) Universitas Teknologi Indonesia (UTI) had never been tested for usability based on user responses. Apart from that, the flow that occurs in the UTI academic information system is not yet coherent. From the user's side, the information guide feature for using SIAKAD UTI is not yet available. Based on this, the research aimed to: 1) describe the results of Usability evaluation using the Concurrent Think-Aloud and Webuse methods on the Academic Information System of Indonesia Technology University; 2) describe recommendations for usability evaluation of the Concurrent Think-Aloud and Webuse methods on the Academic Information System of the Indonesia Technology University; and 3) create a simulation of SIAKAD UTI development and describe the test results using the System Usability Scale (SUS) method. The research method used a mix of qualitative and quantitative methods, so that data obtained is more comprehensive, valid, reliable, and objective. Usability evaluation data through the Concurrent Think-Aloud method was collected using interviews, usability evaluation through the Webuse method was collected using a questionnaire, and satisfaction data through the SUS method was collected using a questionnaire. The results of the development carried out in this research can be concluded as follows. (1) The SIAKAD UTI evaluation results through the Concurrent Think-Aloud method showed that many navigation buttons did not function, inconsistent button features, disproportionate location/buttons position, and inappropriate color selection. Therefore, the evaluation results through the Concurrent Think-Aloud Method showed that SIAKAD UTI needs to be improved. (2) The SIAKAD UTI evaluation results through the Webuse method obtained an overall mean value of usability points, namely 0.23, in the range 0.2 < x ?0.4 with a usability level category of Poor, which means that it is necessary to improve the development of SIAKAD UTI. (3) Based on the evaluation results, a SIAKAD UTI development simulation was carried out, followed by a satisfaction test using SUS. The average score obtained after processing the assessment scores from respondents with an average value of 97.35, which showed that respondents were very satisfied using the results of the SIAKAD UTI development simulation.
Design of an Android-based Troubled Gas Detection Tool Report Application at PT. Saka Tunggal Mandiri Jaya Din Nuryanto; Kusuma, Susanna Dwi Yulianti
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.v6i3.3979

Abstract

Inspections are carried out to check objects to ensure that they meet certain standards. Laboratory officers have difficulty in the reporting process requested by the head of the section quickly, because officers must compare all gas detection device data. And laboratory officers sometimes leave the completeness of other supporting devices. So that each equipment officer has difficulty determining which units are damaged or repaired. Application research methods include literature study analysis, interviews, observations, while the development method used is the waterfall model. The design of the application displayed uses the android platform, the software used in building the application is android studio with the java programming language, while MySQL as a database. The purpose of this research is to provide information needed by PT Saka Tunggal Manadiri Jaya in improving product quality. The results achieved at the end of the study are the application of the gas detection device problem report in providing characteristic inspection information, making it easier for users to obtain inspection report information searches along with product items produced in accordance with the provisions and standards of inspection control of one very important component. By utilizing android-based technology through mobile devices. In order to find out the types of inspections in quality control either functional or tool change.
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.
Classification of Watermelon Ripeness Levels Using HSV Color Space Transformation and K-Nearest Neighbor Method Efendi, Ayu Mahriza Agustin; Sriani, Sriani; Hasibuan, Muhammad Siddik
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.v6i3.3999

Abstract

Watermelons had high appeal due to their sweet taste, refreshing nature, and numerous benefits. However, consumers often faced difficulties in selecting suitable fruit because of the subtle differences between fully ripe and half-ripe watermelons. One important indicator of a watermelon’s ripeness was the yellowish pattern on its skin. In this study, the proposed use of digital image processing methods, specifically the HSV Color Space Transformation, was aimed at extracting watermelon images and employing the K-Nearest Neighbor (K-NN) method to classify them into two categories: "Ripe" and "Half-Ripe." HSV (Hue Saturation Value) was a color extraction method used to convert colors from the RGB model. The Hue component indicated the type of color, Saturation measured the purity of the color, and Value measured the brightness of the color on a scale from 0 to 100%. In this research, the K-Nearest Neighbor (K-NN) method was applied to classify watermelon images based on the extraction of skin color features. This method compared a new image (test data) with training images to determine classification based on the nearest distance with a parameter of k=3. The data used consisted of 120 images, with 92 images used as training data and 28 images as test data. Experimental results showed an accuracy of 89%, with 25 images correctly classified and 3 images misclassified.

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