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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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Optimization of the Artificial Neural Network Algorithm with Genetic Algorithm in Stroke Prediction Wulandari, Serin; Mukti, Yogi Isro’; Susanti, Tri
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 2 (2024): Article Research Volume 8 Issue 2, April 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i2.13609

Abstract

This study aims to optimize Artificial Neural Network with Genetic Algorithm in predicting stroke. This research is motivated by health problems in the community that are less considered that cause a disease such as stroke. Factors of lifestyle, poor diet and other factors that can be the cause of stroke. Therefore, where later the data that has been obtained will be processed to see what factors determine the cause of stroke. The data used, namely kaggle and mendeley, will be processed using RapidMiner, with a development method (CRISP-DM) and a testing method using a Confusion Matrix. The results of this study, stroke disease classification model accuracy kaggle Artificial Neural Network dataset with Genetic Algorithm accuracy 95.13% and AUC 0.667 and mendeley dataset accuracy 98.20% and AUC 0.712. For model evaluation with Artificial Neural Network algorithm with Artificial Neural Network algorithm with kaggle dataset genetic algorithm using X-fold validation average accuracy of 95.14% and AUC 0.686.7 and mendeley dataset resulted in accuracy of 98.20% and AUC 0.712.5. So as to produce from an algorithm a new attribute from the results of the classification model that has been carried out, namely heart disease, ever married, work type and residence type  
Integration of Artificial Intelligence in Facial Recognition Systems for Software Security Santoso, Widi; Safitri, Rahayu; Samidi, Samidi
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 2 (2024): Article Research Volume 8 Issue 2, April 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i2.13612

Abstract

Facial recognition technology, a cornerstone in modern software security, has seen significant advancements through the integration of Artificial Intelligence (AI). This research focuses on enhancing facial recognition systems by incorporating sophisticated machine learning algorithms and deep neural networks. By doing so, the goal is to increase the accuracy and reliability of these systems in security applications. The study uses a variety of facial datasets to train AI models that are adept at extracting facial features and recognizing patterns. These models are subjected to rigorous testing to evaluate their performance in terms of identification accuracy, processing speed, and adaptability to different environmental conditions. One of the key challenges addressed in the research is the system's vulnerability to errors and potential misuse. Ethical considerations and privacy concerns are at the forefront of the study. The research highlights the importance of designing AI-based facial recognition systems that respect user privacy and are resistant to biases, thus fostering trust and acceptance among users. The results of the study show a marked improvement in system performance, demonstrating enhanced recognition accuracy and speed, while maintaining robustness across different conditions. By offering practical recommendations for the development of secure, ethical, and privacy-aware facial recognition systems, this research contributes valuable insights into the integration of AI in software security. It underscores the importance of continuous innovation and ethical responsibility in the deployment of facial recognition technologies, shaping the future landscape of technological security measures
Implementation of Data Mining to Determine Sales Patterns Using the Apriori Method Ritonga, Muhammad Zakuan; Juledi, Angga Putra; Mutia, Rahma
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 2 (2024): Article Research Volume 8 Issue 2, April 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i2.13621

Abstract

Research on the Implementation of Data Mining to Determine Sales Patterns Using the Apriori Method is an effort to understand and utilize sales data in making more informed and strategic business decisions. The main goal of this research is to extract hidden patterns from large sales data sets, which cannot be discovered by manual analysis alone. This research process is divided into several key stages, namely Data Selection, Preprocessing, Transformation, and Data Mining. The research results show that the Apriori method is effective in finding purchasing patterns. In terms of the frequency of 2 itemsets, the highest support value was found to be 1, which indicates that the combination of the two products is always purchased together in all transactions. For 3 itemsets and 4 itemsets, the high support value of 0.9 also indicates the existence of product combinations that are often purchased together. In terms of confidence, 2 itemsets show the highest value of 1.25, indicating that purchasing one product has a high tendency to be followed by purchasing other products. For 3 itemsets and 4 itemsets, the confidence values show a slightly lower trend but are still significant. Furthermore, lift analysis provides additional insight into the strength of association between itemsets, with 4 itemsets showing the highest lift value of 1.30, indicating the product combination has a very strong association compared to random expectations. This research confirms the potential of the Apriori method in finding valuable sales patterns, which can help companies make strategic decisions for increasing sales and customer satisfaction.
Application of Data Mining using the K-Means Method for Visitor Grouping Syah, Rahmayuni; Nasution, Marnis; Irmayanti, Irmayanti
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 2 (2024): Article Research Volume 8 Issue 2, April 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i2.13624

Abstract

Grouping amusement ride visitor data is an important process that aims to identify certain patterns of visitors, enabling management to adjust marketing strategies and improve their services more effectively. This process begins with a data selection stage where relevant visitor data is collected and prepared for analysis. The next stage is data pre-processing, which involves cleaning the data from noise or irrelevant data, as well as ensuring the data is in a format suitable for analysis. After that, the data mining model design is carried out by selecting the most appropriate method for grouping visitor data. The next stage is testing and evaluating the model to verify its accuracy and effectiveness. The results of model testing show that visitor data can be categorized into three groups: C1 with 50 data, C2 with 20 data, and C3 with 48 data. The results of the model evaluation confirm that the designed model succeeded in classifying data with perfect accuracy, namely 100%. This success shows that the model is highly effective in identifying and segmenting visitor patterns, providing valuable insights for strategic decision making in service improvement and marketing. This success also opens up opportunities for the application of similar methods to other datasets in an effort to improve visitor experience and operational efficiency.
Performance Analysis of Random Forest Algorithm for Network Anomaly Detection using Feature Selection Agustina, Triya; Masrizal, Masrizal; Irmayanti, Irmayanti
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 2 (2024): Article Research Volume 8 Issue 2, April 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i2.13625

Abstract

As the volume and complexity of computer network traffic continue to increase, network administrators face a growing challenge in monitoring and discovering unusual activity. To keep the network safe and functioning, detecting anomalies is essential. Machine learning-based anomaly detection techniques have become increasingly popular in recent years. This is due to the fact that conventional anomaly detection methods make it difficult to detect unknown and complex attacks. This research aims to conduct a performance analysis of two feature selection methods using the random forest algorithm using the UNSW-NB15 dataset to determine which model is most effective in detecting network traffic anomalies. The models evaluated were random forest with the filter method and random forest with the wrapper method. A number of metrics used for model performance assessment are accuracy, F1-score, receiver operating characteristic curve, and precision-recall. Dataset collection, data pre-processing, feature selection, model construction, and evaluation are the main components of the research methodology. The research results show that the Random Forest approach with the Filter method has an accuracy of 0.8950, F1-score of 0.8333, ROC score of 0.8928, and a precision-recall value of 0.8347. Meanwhile, the approach using the Wrapper method obtained an accuracy of 0.9151, F1-score of 0.8510, ROC score of 0.9136, and a precision-recall value of 0.8637. This shows that the performance of Random Forest with the Wrapper method is superior in all assessment metrics. Random Forest with the Wrapper Method is the right choice of model for detecting network traffic anomalies because of its stable performance and ability to handle complex patterns
Analysis of Student Excellence Classes in Data Mining Using the KNN Method Ritonga, Arvida; Masrizal, Masrizal; Irmayanti, Irmayanti
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 2 (2024): Article Research Volume 8 Issue 2, April 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i2.13627

Abstract

Excellent classes are programs designed to maximize the academic and non-academic potential of students and girls, with the aim of improving their overall achievement. This program aims to provide more intensive learning and a curriculum tailored to students' needs and abilities, so that they can develop their talents and competencies optimally. In order to evaluate the effectiveness of the superior class program and to identify students who are most suitable for the program, this research was conducted using the K-Nearest Neighbors (KNN) method in data mining. The research process includes several critical stages, namely determining relevant data, designing a machine learning model, testing the model to ensure its effectiveness, and evaluating the model to assess the accuracy and reliability of the results. This research used sample data consisting of 92 male and female students, where the results of the analysis showed that 42 of them met the criteria to enter the superior class, while 50 other students did not. These criteria are determined based on various factors, including academic achievement, participation in extracurricular activities, and other individual characteristics assessed through the KNN method. The accuracy results obtained from the model evaluation show excellent performance, confirming that the approach used is effective in classifying students based on their potential to excel in superior class programs. The conclusion of this research shows that the use of the KNN method in data mining can accurately identify students who will benefit most from superior class programs. Thus, this approach offers a valuable tool for educational institutions to optimize student potential and raise overall standards of achievement.
Design E-Learning User Interface On Website-Based Edspert.Id With Kansei Engineering Methods Purnama, Adhitya Rinda Wahyu; Putra, Perdana Suteja; Zunaidi, Rizqa Amelia
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 3 (2024): Research Artikel Volume 8 Issue 3, July 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i3.13630

Abstract

The development of information technology has encouraged people to rely on information systems, especially through websites. Websites provide easy access to information and learning with various educational materials. Although e-learning has been implemented in many educational websites, Edspert. id, a company in the education sector, has not implemented it yet. User interface design development is one of the important processes in e-learning website development. A user interface that is easy to use will improve the learning experience of learners. This research proposes a solution to design the user interface of Edspert.id e-learning website by using the Kansei Engineering method. This approach has been done beforefor web-based e-learning based on users' emotions. Principal component analysis (PCA) is used to reduce Kansei Word variables that are relevant to users' emotions. The e-learning website element design was then designed based on the PCA results. The next step is to determine the design elements in the e-learning design. Then, partial Least Square (PLS) was used to analyze the relationship between Kansei Word and element design. The results show that there multiuser interface design has two concepts whose element designs are in accordance with user needs.
Innovative Design of ITTS Mart Application with Design Thinking & System Usability Scale Method Alfansuri, Habib Mirza; Putra, Perdana Suteja; Zunaidi, Rizqa Amelia
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 3 (2024): Research Artikel Volume 8 Issue 3, July 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i3.13631

Abstract

Including ease of accessing the internet through mobile devices. The emergence of social media applications, such as virtual friend applications, has also played a role in increasing the number of Internet users, primarily through mobile devices. In addition to functioning as a forum for virtual friends, social media also acts as a means of promotion, one of which is to promote online shopping applications, which contribute to an increase in online shopping transactions in Indonesia. One of the strategic choices taken is to use online shopping platforms to market educational institutions' products in the hope that they can make it easier for customers to shop and stimulate significant growth. Design thinking is used in idea formulation and problem-solving. As for creating applications that describe the emotional desires of users, this research uses the Kansei Engineering approach. Data collection was conducted through questionnaires, interviews, and literature studies. Later, it will generate several selected Kansei Words. Furthermore, to determine the best design that suits user needs, application prototypes are tested through Performance Metrics tests to determine the level of Effectiveness, efficiency, and errors, as well as performance and usability evaluations using System Usability Scale (SUS) questionnaires. 
Deep Learning for Exchange Rate Prediction Within Time Constrain Sumargo, Ruly; Wasito, Ito
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 3 (2024): Research Artikel Volume 8 Issue 3, July 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i3.13633

Abstract

The implementation of an open economic system in Indonesia since 1969 has significant impact to the national economic growth. The high demand and supply of goods from within the country involved in international trade demonstrate a close correlation between export and import activities with the exchange rate of the rupiah. Economic stability is measured through the stability of the rupiah exchange rate against foreign currencies. The balance between demand and supply in the global market is considered crucial for creating a stable economy. History has recorded the Indonesian economic crisis in 1998, where the exchange rate of the rupiah against the US dollar drastically raises and causing challenges to the domestic production cost. This research aiming to make predictions using data science approach based on historical (time series) data. GRU, LSTM, and RNN algorithm being assess to perform the prediction. Results show that RNN algorithms generally outperform GRU and LSTM in making the prediction, particularly with limited time series data. Although RNN is typically superior, in one instance, GRU achieved slightly higher accuracy (0.047% difference) for the CNY to IDR pair over five years. Furthermore, the research highlights the substantial impact of batch size on algorithm accuracy, considering external factors such as interest rates. These findings offer valuable insights for economic decision-making and policy formulation.
Implementation of Data Mining to Determine Public Interest in Automatic Motorcycles Fatma, Nurul; Harahap, Syaiful Zuhri; Masrizal, Masrizal
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 2 (2024): Article Research Volume 8 Issue 2, April 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i2.13637

Abstract

Research on public interest in automatic motorbikes was carried out with the aim of understanding the factors that influence the decision to purchase an automatic motorbike. Using data mining methods, this research applies the K-Nearest Neighbor (KNN) and Neural Network techniques to identify and analyze people's interest patterns. The data used amounted to 139 samples, of which 127 showed interest in automatic motorbikes, while 12 others showed no interest. The research process begins with data analysis, the next stage is preprocessing, which includes data cleaning, in the model design stage in data mining, two models are built: one using KNN and the other using Neural Network. These two models are designed to classify sample data based on interest in automatic motorbikes. The next stage is model testing. Test results show that both models can classify interests accurately, with most of the sample data being classified correctly. Model evaluation was carried out to measure the effectiveness and accuracy of the two methods. The evaluation results show that both models provide very good performance, with results that almost reach a perfect score. This shows that both methods, KNN and Neural Network, are very effective in classifying and predicting people's interest in automatic motorbikes based on available data. In conclusion, this research not only shows the effectiveness of KNN and Neural Network in data mining for analyzing people's interests, but also provides valuable insights for automatic motorbike manufacturers and sellers about consumer preferences.

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