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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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Comparison of K-Means and K-Medoids Clustering Algorithms for Export and Import Grouping of Goods in Indonesia Ulvi, Hazrul Anshari; Ikhsan, Muhammad
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.v8i3.13815

Abstract

International relations affect the economic growth of each country, which can affect the economic growth of each country. As a result, global economic growth is necessary, which means that the global economy has a greater capacity to produce goods and services. Exports and imports are very important to drive economic growth. but if exports and imports are not balanced, it will have a bad impact if the value of imports is greater than exports, export prices abroad will definitely fall. An analysis comparing export and import categories is needed to determine which goods are most imported and exported in Indonesia in 2021-2023. This study uses a quantitative methodology and machine learning methods, namely k-means and k-medoids algorithms. These two methods will be compared to determine which is the most effective for export and import data of goods in Indonesia in 2021-2023. The results of the study were obtained by K-Means more effectively in handling data on the grouping of exports and imports of goods in Indonesia in 2021-2023. The dataset shows the results of the evaluation of K-Means using DBI of 0.59, while the results of the evaluation using K-Medoids show a result of 1.7868. Because the evaluation value of K-Means has low computing performance compared to K-Medoids.  The largest amount of the value and weight of exports and imports of goods in Indonesia is in C1 where in the HS code [27], namely Mineral fuels with a total export value of goods in 2021 to 2023 of 134,999,470,522 US$ and a total import value of 113,714,568,740 US$. Meanwhile, the total export weight of goods from 2021 to 2023 in mineral fuel goods is 1,505,006,250,327 Kg or around 1,658,985,413 tons and the total import weight is 186,446,782,134 Kg or around 205,522,397 tons.
Data Mining Clustering Analysis of Child Growth and Development Using the K-Means Method Nurjannah, Eka; Nasution, Marnis; Muti’ah, Rahma
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.v8i3.13817

Abstract

This research aims to group children based on their growth and development characteristics. This method helps identify groups of children with normal growth and development, early signs of growth and development problems, and serious growth and development problems. The stages used in this research follow the Knowledge Discovery in Database (KDD) process, which consists of data selection, data pre-processing, data transformation, data mining, and evaluation or interpretation of results. By applying the K-Means method, this research aims to provide a clearer and more detailed picture of the distribution of children's growth and development problems and assist in decision making for more appropriate interventions. The K-Means method in data mining was used to group 102 sample data into three clusters based on children's growth and development characteristics. The results of this analysis show that 38 samples fall into Cluster 1 (C1), 36 samples into Cluster 2 (C2), and 28 samples into Cluster 3 (C3). Evaluation of clustering results is carried out using Box Plot and Scatter Plot. Box Plot shows a clear distribution of data for each cluster, ensuring that the data grouping corresponds to statistical evaluation. Cluster C1 is toddlers with normal growth and development. Cluster C2 shows early signs of growth and development problems. Cluster C3 indicates serious growth and development problems
Using Real-Time Ray Tracing in Game Action-Adventure ANGKARA "The Rise of Asura" Aditama, Putu Wirayudi; Anggara, I Gede Adi Sudi; Alparizi, Jilmi
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.v8i3.13826

Abstract

The pressing problems facing the games industry in Indonesia are first, the lack of developers who can meet the needs of the local market and the lack of adaptation of local content in games. Second, how to improve the optimization of the Ray Tracing process? To overcome these challenges, this research aims to design and build an action-adventure game model using Unreal Engine 5. By developing the Real-Time Ray Tracing (RTRT) feature. The game design will also utilize the latest technology such as Ray Tracing, and artificial intelligence to create an immersive and realistic gaming experience.  The method used in this research is GDLC (Game Development Life Cycle). The results of research using black box testing with components, character responsiveness, basic attacks, interaction with the environment, defense, enemy behavior, and optimization of Ray Tracing state that it has been validated.
Analysis of the Implementation of the XYZ Core Banking System at PT BPR Hariarta Sedana Pandhu, I Made Suri
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.v8i3.13829

Abstract

: The Core Banking System (CBS) is often referred to as the heart of a bank due to its crucial role in banking activities. Core Banking is used for processing loans, savings, customer information storage, and various other services. Real-time interaction is required to process banking activities. Each bank has its own strengths, needs, and target market in order to conduct its business. These needs must be supported by reliable, flexible information technology solutions that are ready for further development according to the bank's requirements. The transformation of Bank Perekonomian Rakyat (BPR) is a critical step in supporting economic growth for both the bank and its customers. This study highlights the latest trends in the banking industry related to banking digitalization, such as real-time online integration with delivery channels, including mobile banking services. This analysis also identifies the weaknesses of the previous banking system and the strengths relevant to the transformation desired by BPR's business needs. The availability of technology, system integration, and IT architecture are the focus to understand the existing infrastructure. For now, the implemented changes have significantly impacted the speed of operational performance and services, as well as better control of employee working hours.
Lung Cancer Classification Using Combination Of Efficientnet And Visual Geometry Group Algorithm Husein, Amir Mahmud; Astasachindra, Rishi; Sormin, Pedro Samuel; Lovely, Veryl; Gultom, Atap
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.v8i3.13831

Abstract

Lung cancer is one of the leading causes of mortality All around the world. It is classified into three main types: Adenocarcinoma of the lung (ACA), Non-small cell lung cancer (N), and Squamous Cell Carcinoma of the lung (SCC). Lung Cancer Classification is crucial on development of effective treatments. This study aims to improve the accuracy of lung cancer classification through the integration of a hybrid model, which combines two Convolutional Neural Networks architectures, namely EfficientNet-B7 and VGG-16. A set of histopathology images was subjected to testing, with the data split into three categories: 60% for training, 30% for validation, and 10% for testing. Prior to use, each image underwent a preprocessing process, wherein it was resized to 256x256 pixels. The model test results achieved an accuracy, precision, recall, and F1-score of 98.73%, which is superior to the EfficientNet-B7 base model. The findings of this study demonstrate the potential of hybrid models to improve accuracy in lung cancer classification. The utilization of hybrid models has the potential to contribute significantly to the beginning diagnosis and appropriate Lung Cancer Therapies. Future research will focus on improving the model through the application of image segmentation techniques and expanding the scope of classification to other types of lung cancer. Optimization of the hybrid model architecture using novel techniques such as the attention mechanism or transfer learning will be conducted to improve the efficiency and accuracy of the model. Additionally, a system that can be integrated into clinical practice will be developed
Application of the XGBoost Model with Hyperparameter Tuning for Industry Classification for Job Applicants Syahputra, Akhmal Angga; Rujianto Eko Saputro
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.v8i3.13840

Abstract

The development of technology and changes in job market dynamics have created new challenges in aligning education with industry needs. In this research, the XGBoost model with hyperparameter tuning was applied for industry classification on job applicant data taken from the Kaggle dataset LinkedIn Job Postings in 2023. This dataset consists of 23 attributes with a total of 33,085 job vacancy data points. The experimental results show that both the model without hyperparameter tuning and with GridSearchCV produce the same classification accuracy, which is 0.89 or 89%, with stable precision, recall, and F1-Score values. The best parameters found in this study are colsample_bytree = 1.0, learning_rate = 0.3, max_depth = 6, min_child_weight = 1, n_estimators = 100, and subsample = 1.0. However, cross-validation using k-fold shows a significant increase in accuracy to 0.90, or 90%. This finding confirms that the use of cross-validation can improve the performance estimation of the model more accurately and robustly by utilizing all available data for training and testing. Moreover, the implementation of cross-validation demonstrates the importance of leveraging all data points to enhance model reliability and robustness. Future research can explore alternative hyperparameter tuning methods and apply the model to larger datasets to further validate the generalizability and reliability of the XGBoost model in different application contexts. Thus, this study underscores the significance of rigorous model evaluation techniques in achieving high-performing machine learning models
Sentiment Analysis Of Indonesian State Army Police Neutrality Sentiment Towards The 2024 Election On X Using The Support Vector Machine Algorithm Yudha, Muhammad Yudha Pratama; Rakhmat Kurniawan R
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.v8i3.13841

Abstract

The accompanying goals are created: One method for figuring out the order of feeling examination in the balance of military police towards the 2024 political decision depends on popular assessment in SVM technique in arranging opinion investigation in the balance of military police towards the 2024 races in light of 2024 general assessment in X. In a leading examination, the stages utilized are the exploration system. This was finished to coordinate the exploration stages. The technique of this examination is quantitative. An exploration area is where a specialist completes research, particularly in catching peculiarities or examination that really happens at the exploration area to get precise and genuine examination information. The consequences of the testing did were to decide the capacity of the framework that had been made to complete feeling investigation on opinion towards the lack of bias of the TNI and Polri during the political race Research begins with compiling, specifically determining the points to be discussed. The subject of this research is the execution of message mining in testing the balance of military police feelings towards the 2024 political decisions in X using the Help vector machine1 algorithm. Tweet Information Collection,In this review, scientists utilized 800 tweet information.. The consequences of the opinion examination did will be introduced as a disarray framework, where through the disarray network and characterization report the degree of exactness of the exploration that has been completed can be determined.It is trusted that the aftereffects of this assessment can give a thorough image of the public's discernment on Twitter with respect to the lack of bias of the TNI and Polri in sorting out races.
Publication Trend of Public Sentiment Towards Indonesia Government Policies Permana, Iip; Maani, Karjuni Dt
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.v8i3.13843

Abstract

There are 167 million social media users in Indonesia. Some of these users express their opinions on social media known as public opinion. Public sentiment is the classification of public opinion into several classes. Understanding public sentiment through some public policies can benefit the government. Publication trends can be a stepping stone to deeply understanding a research topic. No research was conducted on the publication trend of public sentiment toward Indonesian government policies on social media. This study aims to explore publication trends in the area of public sentiment toward Indonesia government policies on social media using bibliometric analysis. The Scopus database is used to gather abstracts and keywords, funding details, citation information, bibliographical information, and other information. Search document terms used are "public", "sentiment", "social media", "government", governance," and "policy" rolled within the article title, abstract, and keywords. Research publication trends were visualized using VOSViewer co-occurrence keyword analysis, which resulted in seven clusters from all the collected literature. The research trend is climbing significantly in 2018–2021, but decreasing in 2022. The University of Indonesia is the institution that produces the most documents and IOP Conference Series on Earth and Environmental Science is the publication place that publishes the most documents. Decision trees, random forests, logistic regression, naïve bayes, support vector machines and long-short-term memory are part of the machine learning algorithms recycled and Twitter is the most used social media platform.
Agile Project Management Impacts Software Development Team Productivity Rahman, Abdul; Indrajit, Eko; Unggul, Akhmad; Dazki, Erick
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.v8i3.13853

Abstract

The agile nature of the software development sector calls for flexible and effective project management techniques. Agile Project Management (APM) is emerging as a significant method that supports team cooperation, iterative improvement, and flexibility. This paper looks at how agile project management might affect software development team output. This study investigates the primary Agile methodologies Scrum and their impact on team productivity by means of a thorough literature review and empirical analysis. A mixed-methods approach employs qualitative comments and quantitative measures to provide a comprehensive view of output changes. We examine several software development teams inside a mid-sized technology company over 12 months using a case study approach, comparing productivity measures before and after Agile practices, including team satisfaction, development pace, and code quality. Furthermore, team member surveys and interviews offer an understanding of the supposed advantages and difficulties of switching to Agile approaches. Teams showing more efficiency, improved communication, and better morale point to a notable rise in productivity. Notable improvements included improved adaptability to shifting project needs and a shorter time-to-market for software products. This paper offers an insightful analysis of Agile Project Management's ability to revolutionize software development processes, helping companies trying to improve project results. This study has consequences for managers and practitioners because it provides valuable instructions for implementing Agile approaches to achieve the best team performance. Future directions of study will include investigating the long-term effects of Agile methods and their relevance in various organizational settings.
The Classification of Avocado Ripeness Levels Using CNN Method Wibowo, Gamma Wira; Mulyanto, Edy
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.v8i3.13854

Abstract

This research aims to develop a model for classifying the ripeness level of avocados using the Convolutional Neural Network (CNN) algorithm. The dataset comprises images of avocados categorized into three classes: unripe, ripe, and overripe. The CNN model is trained to classify the images into one of these three categories. The results indicate that the developed model can classify avocado images with high accuracy. The primary tool used for developing and implementing this method is MATLAB R2022a. The CNN algorithm is utilized to recognize and classify the ripeness level of avocados. This process involves several image processing steps, starting with preprocessing, image enhancement, and segmentation to isolate the avocado area. The dataset used in this research consists of 452 images distributed in 3 classes (unripe with 142, ripe with 66, and rotten with 244), with 80% used for training and 20% for testing. After 10 accuracy tests, the results indicate an accuracy rate of 90%. Additionally, features extracted from the images include color, shape, size, and texture characteristics, such as Mean, Standard Deviation, Kurtosis, Skewness, Variance, Entropy Value, Maximum Pixel, and Minimum Pixel. This research contributes to the field of agricultural technology by providing a robust method for the automatic classification of avocado ripeness. The findings are expected to facilitate accurate and efficient recognition of avocado ripeness, thereby supporting agricultural practices and market operations. Future research could explore the use of data augmentation techniques to further improve the accuracy and generalization of this model.

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