Articles
<![CDATA[Proposed use of TOGAF-Based Enterprise Architecture in Drinking Water Companies ]]>
<![CDATA[Amanda, Djaja]]>;
<![CDATA[Hindarto, Djarot]]>;
<![CDATA[Indrajit, Eko]]>;
<![CDATA[Dazki, Erick]]>
<![CDATA[ Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 3 (2023): Article Research Volume 7 Issue 3, July 2023 ]]>
Publisher : <![CDATA[Politeknik Ganesha Medan ]]>
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DOI: 10.33395/sinkron.v8i3.12477
<![CDATA[The purpose of this research is to propose an enterprise architecture framework for planning a drinking water company blueprint. In drinking water companies, it is very important to ensure that the systems and information technology used meet business needs effectively and efficiently. However, the information system that supports the company's operations still needs to be improved, to get better operational quality. In this case, companies need a framework that can assist companies in designing and developing business architectures that strengthen competitive advantage, optimize operational performance, and ensure compliance with applicable regulations and standards. Therefore, the author proposes the selection of an enterprise architecture framework based on The Open Group Architecture Framework or TOGAF. The Open Group Architecture Framework is a widely used framework for developing and implementing enterprise architectures. TOGAF consists of four main components, namely business architecture, application architecture, technology architecture, and data architecture. Enterprise Architecture helps companies develop application and technology architectures that can accelerate product and service innovation and improve operational efficiency. Data architecture, managing and utilizing data effectively in making the right business decisions. By adopting the TOGAF-based Enterprise Architecture framework, water companies optimize the use of information systems and technology, increase flexibility in anticipating changes in community needs and accelerate innovation in products and services.]]>
<![CDATA[Digital Transformation in University: Enterprise Architecture and Blockchain Technology ]]>
<![CDATA[Iswahyudi, Iswahyudi]]>;
<![CDATA[Hindarto, Djarot]]>;
<![CDATA[Indrajit, R. Eko]]>
<![CDATA[ Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 4 (2023): Article Research Volume 7 Issue 4, October 2023 ]]>
Publisher : <![CDATA[Politeknik Ganesha Medan ]]>
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DOI: 10.33395/sinkron.v8i4.12977
<![CDATA[Implementing digital transformation in higher education is now required for effective adaptation to rapid technological advances. Utilizing Enterprise Architecture (EA) with blockchain technology is a recommended strategic approach for implementing digital transformation. The college first ascertains the University's digital transformation requirements and goals, which include data management, security, operational efficiency, and transparency. In addition, formulating a strategic plan to determine the optimal integration of blockchain technology within the college's architectural framework, including the judicious selection of the most suitable blockchain platform, is essential. In addition, developing a company's architecture must prioritize seamless integration with existing systems while upholding data security and consistency principles. Consideration should be given to the significance of training and awareness building among faculty and students. In addition, the implementation process must be conducted in phases with consistent monitoring and evaluation. The success of this project is contingent upon the formation of partnerships and collaborations with blockchain technology companies, as well as a thorough understanding of the applicable regulatory framework. By adopting this methodology, the University can increase operational efficiency, bolster data security measures, and improve the educational experience. This research aims to increase efficiency, data security, transparency, and educational innovation in universities using Blockchain Technology and University Enterprise Architecture.]]>
<![CDATA[Blockchain-Based Academic Identity and Transcript Management in University Enterprise Architecture ]]>
<![CDATA[Hindarto, Djarot]]>
<![CDATA[ Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 4 (2023): Article Research Volume 7 Issue 4, October 2023 ]]>
Publisher : <![CDATA[Politeknik Ganesha Medan ]]>
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DOI: 10.33395/sinkron.v8i4.12978
<![CDATA[The present research examines the implementation of Blockchain-based Identity and Academic Transcript Management in a university's enterprise architecture. This research is motivated by the increasing demand for secure, transparent, and efficient student identity management and the need to store easily verifiable academic transcripts. Blockchain technology has been spotlighted because it provides security and data integrity solutions. This research aims to determine if integrating Blockchain into the University's Enterprise Architecture can improve the management of student identities and academic transcripts by reducing the risk of forgery and facilitating more dependable access for interested parties. Increased security and efficiency in managing student data are the practical implications of this research, which can help universities reduce the risk of data loss and increase stakeholder trust. This research method includes surveying various universities that have adopted Blockchain technology in their academic identity and transcript management. In addition, we will assess its technical implementation, evaluate its effect on efficiency, and conduct interviews with university personnel involved in the implementation process. This study's anticipated outcome is providing universities planning to adopt Blockchain technology for Enterprise Architecture with actionable guidance. This research will identify the benefits, challenges, and best practices of integrating Blockchain in academic identity and transcript management and lay the groundwork for further improvement of educational services in university settings.]]>
<![CDATA[PyTorch Deep Learning for Food Image Classification with Food Dataset ]]>
<![CDATA[Iswahyudi, Iswahyudi]]>;
<![CDATA[Hindarto, Djarot]]>;
<![CDATA[Santoso, Handri]]>
<![CDATA[ Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 4 (2023): Article Research Volume 7 Issue 4, October 2023 ]]>
Publisher : <![CDATA[Politeknik Ganesha Medan ]]>
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DOI: 10.33395/sinkron.v8i4.12987
<![CDATA[Classification of food images is crucial in today's increasingly digitally connected world. In the rapidly evolving mobile applications and social media era, the demand for an automated system that can recognize food types from an image is intensifying. This study employs deep learning and the PyTorch framework to develop a dependable and efficient solution for classifying food images. This research is motivated by the growing complexity of food introduction challenges. The primary challenge is improving the accuracy of food type recognition and overcoming variations in the visual presentation of food, such as lighting, shooting angles, and proportional and textural differences. Convolutional Neural Networks (CNN) are effective for image classification and are incorporated into the methods utilized. In addition, we employ ResNet101 transfer learning techniques to capitalize on the knowledge of trained models for large image datasets. The primary objective of this study is to develop a food image classification model that is accurate, training-efficient, and capable of accurately recognizing various types of food. In testing and evaluation, the developed model could realize multiple types of food with satisfactory accuracy. The accuracy of training reached 99.35%, while the accuracy of testing reached 94.65%. This study also reveals how Resnet101 transfer learning is utilized by deep learning technology.]]>
<![CDATA[Comparison of RNN Architectures and Non-RNN Architectures in Sentiment Analysis ]]>
<![CDATA[Hindarto, Djarot]]>
<![CDATA[ Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 4 (2023): Article Research Volume 7 Issue 4, October 2023 ]]>
Publisher : <![CDATA[Politeknik Ganesha Medan ]]>
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DOI: 10.33395/sinkron.v8i4.13048
<![CDATA[This study compares the sentiment analysis performance of multiple Recurrent Neural Network architectures and One-Dimensional Convolutional Neural Networks. THE METHODS EVALUATED ARE simple Recurrent Neural Network, Long Short-Term Memory, Gated Recurrent Unit, Bidirectional Recurrent Neural Network, and 1D ConvNets. A dataset comprising text reviews with positive or negative sentiment labels was evaluated. All evaluated models demonstrated an extremely high accuracy, ranging from 99.81% to 99.99%. Apart from that, the loss generated by these models is also low, ranging from 0.0043 to 0.0021. However, there are minor performance differences between the evaluated architectures. The Long Short-Term Memory and Gated Recurrent Unit models mainly perform marginally better than the Simple Recurrent Neural Network, albeit with slightly lower accuracy and loss. In the meantime, the Bidirectional Recurrent Neural Network model demonstrates competitive performance, as it can effectively manage text context from both directions. Additionally, One-Dimensional Convolutional Neural Networks provide satisfactory results, indicating that convolution-based approaches are also effective in sentiment analysis. The findings of this study provide practitioners with essential insights for selecting an appropriate architecture for sentiment analysis tasks. While all models yield excellent performance, the choice of architecture can impact computational efficiency and training time. Therefore, a comprehensive comprehension of the respective characteristics of Recurrent Neural Network architectures and One-Dimensional Convolutional Neural Networks is essential for making more informed decisions when constructing sentiment analysis models.]]>
<![CDATA[Exploring YOLOv8 Pretrain for Real-Time Detection of Indonesian Native Fish Species ]]>
<![CDATA[Hindarto, Djarot]]>
<![CDATA[ Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 4 (2023): Article Research Volume 7 Issue 4, October 2023 ]]>
Publisher : <![CDATA[Politeknik Ganesha Medan ]]>
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DOI: 10.33395/sinkron.v8i4.13100
<![CDATA[The main objective of this research is to determine the efficacy of the YOLO model in detecting native fish species found in Indonesia. Indonesia has a variety of maritime natural resources and shows significant diversity. This research utilizes the YOLO architecture, previously trained on several datasets, for fish detection in the environment in Indonesian waters. This dataset consists of various fish species native to Indonesia and was used to retrain the YOLO Pretrain model. The model was evaluated using test data that accurately represents Indonesian water conditions. Empirical findings show that the modified YOLO Pretrain model can accurately recognize these fish in real-time. After utilizing YOLO and Pre-Train with Ultralytics YOLO Version 8.0.196, the results show an accuracy of 92.3% for head detection, 86.9% for tail detection, and an overall detection accuracy of 89.6%. The fish image dataset, consisting of a total of 401 images, is categorized into three subsets: the training dataset, which consists of 255 images; the validation dataset, which includes 66 images; and the testing dataset, which contains 80 images. This research has great potential for application in fisheries monitoring, marine biology research, and marine environmental monitoring. A real-time fish detection system for the Identification and tracking of fish species is carried out by researchers and field workers. The findings of this research provide a valuable contribution to ongoing efforts aimed at conserving marine biodiversity and implementing more sustainable management practices in Indonesia.]]>
<![CDATA[Enhancing Road Safety with Convolutional Neural Network Traffic Sign Classification ]]>
<![CDATA[Hindarto, Djarot]]>
<![CDATA[ Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 4 (2023): Article Research Volume 7 Issue 4, October 2023 ]]>
Publisher : <![CDATA[Politeknik Ganesha Medan ]]>
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DOI: 10.33395/sinkron.v8i4.13124
<![CDATA[Recent computer vision and deep learning breakthroughs have improved road safety by automatically classifying traffic signs. This research uses CNNs to classify traffic signs to improve road safety. Autonomous vehicles and intelligent driver assistance systems require accurate traffic sign detection and classification. Using deep learning, we created a CNN model that can recognize and classify road traffic signs. This research uses a massive dataset of labeled traffic sign photos for training and validation. These CNN algorithms evaluate images and produce real-time predictions to assist drivers and driverless cars in understanding traffic signs. Advanced driver assistance systems, navigation systems, and driverless vehicles can use this technology to give drivers more precise information, improving their decision-making and road safety. Researcher optimized CNN model design, training, and evaluation metrics during development. The model was rigorously tested and validated for robustness and classification accuracy. The research also solves real-world driving obstacles like illumination, weather, and traffic signal obstructions. This research shows deep learning-based traffic sign classification can dramatically improve road safety. This technology can prevent accidents and enhance traffic management by accurately recognizing and interpreting traffic signs. It is also a potential step toward a safer, more efficient transportation system with several automotive and intelligent transportation applications. Road safety is a global issue, and CNN-based traffic sign classification can reduce accidents and improve driving. On filter 3, Convolutional Neural Network training accuracy reached 98.9%, while validation accuracy reached 88.23%.]]>
<![CDATA[Battle Models: Inception ResNet vs. Extreme Inception for Marine Fish Object Detection ]]>
<![CDATA[Hindarto, Djarot]]>
<![CDATA[ Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 4 (2023): Article Research Volume 7 Issue 4, October 2023 ]]>
Publisher : <![CDATA[Politeknik Ganesha Medan ]]>
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DOI: 10.33395/sinkron.v8i4.13130
<![CDATA[Within the domain of deep learning applied to computer vision, there exists a significant emphasis on the competition between two prominent models, namely Inception ResNet and Xception, particularly in the field of marine fish object detection. The present study conducted a comparative analysis of two advanced neural network architectures in order to assess their efficacy in the identification and localization of marine fish species in underwater images. The two models underwent a rigorous evaluation, utilizing their capabilities in feature extraction. The findings indicate a complex performance landscape, wherein Inception ResNet exhibits remarkable accuracy in identifying marine fish objects, while Xception demonstrates superior computational efficiency. The present study elucidates the inherent trade-off between precision and computational expenditure, offering valuable perspectives on the pragmatic ramifications of choosing one model over another. Furthermore, this research underscores the significance of carefully choosing a suitable model that aligns with the particular requirements of object detection applications in the context of marine fish. This study endeavors to guide professionals and scholars in marine biology and computer vision, enabling them to make well-informed choices when utilizing deep learning techniques to detect maritime fish objects in underwater settings. The research specifically focuses on the comparison between Inception ResNet and Xception models.]]>
<![CDATA[Performance Comparison ConvDeconvNet Algorithm Vs. UNET for Fish Object Detection ]]>
<![CDATA[Hindarto, Djarot]]>
<![CDATA[ Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 4 (2023): Article Research Volume 7 Issue 4, October 2023 ]]>
Publisher : <![CDATA[Politeknik Ganesha Medan ]]>
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DOI: 10.33395/sinkron.v8i4.13135
<![CDATA[The precise identification and localization of fish entities within visual data is essential in diverse domains, such as marine biology and fisheries management, within computer vision. This study provides a thorough performance evaluation of two prominent deep learning algorithms, ConvDeconvNet and UNET, in the context of fish object detection. Both models are assessed using a dataset comprising a wide range of fish species, considering various factors, including accuracy of detection, speed of processing, and complexity of the model. The findings demonstrate that ConvDeconvNet exhibits superior performance in terms of detection accuracy, attaining a noteworthy degree of precision and recall in identifying fish entities. In contrast, the UNET model displays a notable advantage in terms of processing speed owing to its distinctive architectural design, rendering it a viable option for applications requiring real-time performance. The discourse surrounding the trade-off between accuracy and speed is examined, offering valuable perspectives for algorithm selection following specific criteria. Furthermore, this study highlights the significance of incorporating a diverse range of datasets for training and testing purposes when utilizing these models, as it significantly influences their overall performance. This study makes a valuable contribution to the continuous endeavors to improve the detection of fish objects in underwater images. It provides a thorough evaluation and comparison of ConvDeconvNet and UNET, thereby assisting researchers and practitioners in making well-informed decisions regarding selecting these models for their specific applications.]]>
<![CDATA[Revolution in Image Data Collection: CycleGAN as a Dataset Generator ]]>
<![CDATA[Hindarto, Djarot]]>;
<![CDATA[Handayani, Endah Tri Esti]]>
<![CDATA[ Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 1 (2024): Articles Research Volume 8 Issue 1, January 2024 ]]>
Publisher : <![CDATA[Politeknik Ganesha Medan ]]>
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DOI: 10.33395/sinkron.v9i1.13211
<![CDATA[Computer vision, deep learning, and pattern recognition are just a few fields where image data collection has become crucial. The Cycle Generative Adversarial Network has become one of the most effective instruments in the recent revolution in image data collection. This research aims to comprehend the impact of CycleGAN on the collection of image datasets. CycleGAN, a variant of the Generative Adversarial Network model, has enabled the unprecedented generation of image datasets. CycleGAN can transform images from one domain to another without manual annotation by employing adversarial learning between the generator and discriminator. This means generating image datasets quickly and efficiently for various purposes, from object recognition to data augmentation. One of the most fascinating features of CycleGAN is its capacity to alter an image's style and characteristics. Using CycleGAN to generate unique and diverse datasets assists deep learning models in overcoming visual style differences. This is a significant development in understanding how machine learning models can comprehend visual art concepts. CycleGAN's use as a data set generator has altered the landscape of image data collection. CycleGAN has opened new doors in technological innovation and data science with its proficiency in generating diverse and unique datasets. This research will investigate in greater detail how CycleGAN revolutionized the collection of image datasets and inspired previously unconceived applications.]]>
