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Sinkron : Jurnal dan Penelitian Teknik Informatika
Published by Politeknik Ganesha Medan
ISSN : 2541044X     EISSN : 25412019     DOI : 10.33395/sinkron.v8i3.12656
Core Subject : Science,
Computer Science & IT
Scope of SinkrOns Scientific Discussion 1. Machine Learning 2. Cryptography 3. Steganography 4. Digital Image Processing 5. Networking 6. Security 7. Algorithm and Programming 8. Computer Vision 9. Troubleshooting 10. Internet and E-Commerce 11. Artificial Intelligence 12. Data Mining 13. Artificial Neural Network 14. Fuzzy Logic 15. Robotic
Arjuna Subject : Ilmu Komputer (semua kategori) - Visi Komputer dan Pengenalan Pola
Articles 1,196 Documents
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Performance Analysis of Scheduling Algorithms on Fog Computing using YAFS Nurcahya, Dimas; Karimah, Siti Amatullah; Mugitama, Satria Akbar
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 3 (2023): Article Research Volume 7 Issue 3, July 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i3.12682

Abstract

A smart device that has seen more development is the Internet of Things (IoT). An IoT system implementation requires a device that can accept and handle various sorts of data. Fog Computing is a solution to the issue since the IoT demands a device that can provide Real-Time. Certainly, load balancing involves scheduling the IoT devices and data used. Because Cloud and Fog Computing models enable data growth management and deployment planning, which necessitate a quicker response from platforms and applications, processing power scheduling is essential. The purpose of this study is to evaluate the performance of effective scheduling algorithms that adhere to these computing models platform requirements. The scheduling algorithm that can produce the lowest Processing Time and the resulting Time Efficiency is more efficient can be called the best scheduling algorithm. In this research, the author analyzes the performance of scheduling algorithms in the form of Round Robin and Priority Scheduling on Fog Computing. In this research, testing was carried out by creating a scenario of the effect of increasing the number of Fog Nodes and Devices used. The average result of scenario testing obtained for processing time for Round Robin is lower, and the highest Time Efficiency for Round Robin over Priority Scheduling is 11%. With these test results, the Round Robin scheduling algorithm has a simpler level of complexity. So, it can be concluded that Round Robin belongs to the category of the best scheduling algorithm in this case.
Human resources development strategy use Backpropagation Artificial Neural Networks Panggabean, Erwin; Sitio, Arjon Samuel; Lase , Yulianto; Junita , Diana
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 3 (2023): Article Research Volume 7 Issue 3, July 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i3.12684

Abstract

The strategy for developing human resources for recruitment so that at the same time becoming a reliable workforce as expected is the goal of personnel in certain offices or agencies. This step must be taken by the management of companies or institutions, both public and private, in order to improve human resources (HR). Until now, there has never been any research on the conventional acceptance of prospective employees to test how accurate their performance is. In this study the conventional selection system for prospective employees will be used as a basic concept to find methods for analyzing the performance of prospective employees using computer media with an artificial neural network system approach with the backpropagation method. So that the accuracy of the predictive patterns of prospective new employees is obtained. So that finally the personnel of government and private agencies obtain actual information about the performance of prospective employees who will be accepted as workers. The results of the study using hidden layers and learning constants obtained the fastest convergent value at 3377, and the final results of this study will be published in a national journal accredited SINTA 4 or better.
Study of Arrhythmia Classification Algorithms on Electrocardiogram Using Deep Learning Arifin, Rezki Fauzan; Mandala, Satria
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 3 (2023): Article Research Volume 7 Issue 3, July 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i3.12687

Abstract

Arrhythmia is a heart disease that occurs due to a disturbance in the heartbeat that causes the heart rhythm to become irregular. In some cases, arrhythmias can be life-threatening if not detected immediately. The method used to detect is electrocardiogram (ECG) signal analysis. To avoid misdiagnosis by cardiologists and to ease the workload, methods are proposed to detect and classify arrhythmias by utilizing Artificial Intelligence (AI). In recent years, there has been a lot of research on the detection of this disease. However, many of such studies are more likely to use machine learning algorithms in the classification process, and most of the accuracy results still do not reach optimal levels in general. Therefore, this study aims to classify arrhythmias using deep learning algorithms. There are several stages of performing arrhythmia detection, namely, preprocessing, feature extraction, and classification. The focus of this research is only on the classification stage, where the Long Short-Term Memory (LSTM) algorithm is proposed. After going through a series of experiments, the performance of the proposed algorithm is further analyzed to compare accuracy, specificity, and sensitivity with other machine learning algorithms based on previous research, with the aim of obtaining an optimal algorithm for arrhythmia detection. Based on the results of the study, the Long Short-Term Memory (LSTM) algorithm managed to outperform the performance of other machine learning algorithms with accuracy, specificity, and sensitivity results of 98.47%, 99.24%, and 97.67%, respectively.
Ontology-Based Recommender System for Personalized Physical Exercise in Obesity Management Muhammad, Widi Sayyd Fadhil; Baizal, Z. K. A; Dharayani, Ramanti
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 3 (2023): Article Research Volume 7 Issue 3, July 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i3.12689

Abstract

In Indonesia, obesity is a serious health issue, and rates have risen recently because of sedentary lifestyles and poor eating practices. We suggest a proactive self-care suggestion system specifically created for Indonesians who are dealing with obesity to address this problem. Our recommender system attempts to give customers individualized suggestions for healthy lifestyle modifications that will make it easier for them to manage their weight. Because social media is so widely used in Indonesia, we created our system as a Telegram Chatbot. Our system may provide personalized suggestions based on a particular gender, activity level, fat mass, and difficulty of exercise that are relevant to Indonesians by fusing the user's ontological profile with generic clinical guidelines and standards for the management of obesity. Ontologies with Semantic Web Rule Language (SWRL) were used in the development of our system since SWRL ontologies are thought to perform better. Evaluations carried out using case studies and expert verification illustrate the usefulness of our suggested method, and the validated result of 88.8 percent demonstrates that our system can deliver good suggestion results for the user.
Decision Support System for Selection of Healthy Toddler using MOORA Method Ulfah, Auliana; Hasugian, Abdul Halim
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 3 (2023): Article Research Volume 7 Issue 3, July 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i3.12695

Abstract

Toddlers are the human stage after infancy, toddlers are one of the most important stages in the quality development of human growth. one way to develop health in toddlers is to carry out routine checks at Public Health Center. Public Health Center has not yet provided a system for selecting healthy toddlers and also requires a system for selecting healthy toddlers. Therefore a decision support system is needed to make decisions related to checking the health of toddlers. decision support system is part of a computer-based information system, which is used to make a decision, in making a decision support system the Multi Objective Optimization by Ratio Analysis (MOORA) method is used. Based on the calculations made regarding the decision to check the health of toddlers, the MOORA method uses weighting criteria and determines the type of criteria, and MOORA does not have sub-criteria weighting provisions using FMADM, FMADM weighting reduces ambiguity because the bigger the value, the better, but if the value is too large then it is not good, such as body weight. It is necessary to provide more sub-criteria so that the accuracy in ranking is better and more accurate with a scale of 0-5. The optimization value for A1 is obtained by adding the cells from A1 0.6765 + 1.0070 + 0.2626 + 0.1195 = 2.0656 and Alternatives A2, A3, A15 have the most optimal weight values ​​with optimization values ​​(2.3282).
Retweet Prediction Based on User-Based, Content-Based, Time-Based Features Using ANN Classification Optimized with the Bat Algorithm Rahadian, Muhammad Rafi; Jondri, Jondri; L, Kemas Muslim
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 3 (2023): Article Research Volume 7 Issue 3, July 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i3.12708

Abstract

Twitter is one of the most popular social media platforms today for information dissemination. It is favored by the public due to its real-time information sharing capabilities. Twitter provides two important features for information dissemination: Tweets and Retweets. Tweets allow users to write messages that can be instantly shared. Each tweet can contain text, media such as images, videos, or URLs. Retweets allow users to repost someone else's tweet and distribute it to their own followers. The Retweet feature is considered an effective way to spread information, as a high number of retweets indicates that the information in the tweet is spreading quickly and widely. This research aims to predict retweets based on several features: User-Based Feature, Content-Based Feature, and Time-Based Feature. The classification method used is Artificial Neural Network, which is optimized using a Nature-Inspired Algorithm called Bat Algorithm. The evaluation results of this study show an accuracy of 86%, precision of 87.8%, recall of 93.6%, and F1-score of 90.6% without imbalance class handling. Under Undersampling condition, the accuracy is 80.8%, precision is 91.0%, recall is 81.4%, and F1-score is 85.9%. Under Oversampling condition, the accuracy is 82.4%, precision is 89.6%, recall is 85.6%, and F1-score is 87.5%. These results indicate that using user-based, content-based, and time-based features, applying Artificial Neural Network classification method, and optimizing hyperparameters using Bat Algorithm are effective in predicting retweets.
UI/UX Analysis of Project Management Information System (PMIS) Website Using User-Centered Design Method Azhar, Sarah Afifah; Defriani, Meriska; Hermanto, Teguh Iman
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 3 (2023): Article Research Volume 7 Issue 3, July 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i3.12725

Abstract

In Industry 4.0, a lot of data has been digitized and is no longer stored manually. In helping to record all the projects that were worked on initially stored in outstanding Project Management applications such as Trello. However, the Trello application has limitations and compatibility with business processes, including employees not writing down projects that have been done because they are not in accordance with procedures, causing many projects to go unrecorded. So because of these limitations a Project Management Information System (PMIS) website is needed which makes it easy to record all projects so that there is no more unrecorded data. Furthermore, there are problems in conducting an analysis that can make it easier for users and how to design a User Interface and User Experience on the PMIS website using the User Centered Design (UCD) method, which in the manufacturing process will continue to make changes according to needs. The results of the User Interface design that has been made will be tested for User Experience using the Single Ease Questionnaire (SEQ) method as a measure of the success of the User Interface that has been made. Based on the results of the User Experience test using the Single Ease Questionnaire method for 5 respondents, an average value of 6.3 was obtained, which means that it can be concluded that the User Interface that has been created has a level of convenience that is in accordance with the User Experience.
A Class of Primitive Two-Colored Digraph with Large Competition Index Rezeki, Ema Sri; Suwilo, Saib; Mardiningsih, Mardiningsih
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 3 (2023): Article Research Volume 7 Issue 3, July 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i3.12744

Abstract

The competition index of a primitive two-colored digraph D^2, denoted k(D^((2))), is the smallest positive integer h+l such that for each pair of vertices u and v there is vertex w with the property that there is a (h,l)-walk from v to w. For two-colored digraph on n vertices it is known that k(D^((2) ))≤(3n^3+2n^2-2n)/2. In this work, we discuss a class of primitive two-colored digraph consisting of two cycles whose scrambling index closes to (3n^3+2n^2-2n)/2
Simplifying Complexity: Scenario Reduction Techniques in Stochastic Programming Sinaga, Christian; Tulus, Tulus; Mawengkang, Herman
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 3 (2023): Article Research Volume 7 Issue 3, July 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i3.12753

Abstract

Stochastic programming problems arise as mathematical models for optimizing problems under stochastic uncertainty. Computational approaches for solving these models often involve approximating the underlying probability distribution with a probability measure that has finite support. To mitigate the computational complexity associated with increasing the number of scenarios, it may be necessary to reduce their quantity. The scenario is selected as the first element of supp , and the separable structure is used to determine the second element of supp while keeping the first element fixed. The process is repeated to establish the remaining indices, and each subsequent scenario is reduced accordingly. This iterative process continues until scenario is reduced
Simplifying Complexity: Linearization Method for Partial Least Squares Regression Simanullang, Herlin; Sutarman, Sutarman; Darnius, Open
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 3 (2023): Article Research Volume 7 Issue 3, July 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i3.12754

Abstract

This research investigates Romera’s local linearization approach as a variance prediction method in partial least squares (PLS) regression. By addressing limitations in the original PLS regression formula, the local linearization approach aims to improve accuracy and stability in variance predictions. Extensive simulations are conducted to assess the method's performance, demonstrating its superiority over traditional algebraic methods and showcasing its computational advantages, particularly with a large number of predictors. Additionally, the study introduces a novel computational technique utilizing bootstrap parameters, enhancing computational stability and robustness. Overall, the research provides valuable insights into the local linearization approach's effectiveness, guiding researchers and practitioners in selecting more reliable and efficient regression modeling techniques.

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