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Literature Study: Intelligent Character Recognition (ICR) Technology for Learning Innovation 2

Literature Study: Intelligent Character Recognition (ICR) Te...

引用

2nd International Conference on Technology Innovation and Its Applications, ICTIIA 2024

作者： Winardi, Sunaryo Gunawan Sinaga, Frans Mikael Fa, Farrell Rio Davidsen, Frederic Undergraduate Program of Computer Science Universitas Mikroskil Medan Indonesia

ISBN: (纸本)9798350351613

Intelligent Character Recognition (ICR) is a technology for recognizing handwritten text using artificial intelligence to enhance accuracy in character recognition. This technology has significant potential to support digital transformation in education. This paper discusses the implementation and benefits of ICR in education, particularly in automating the assessment of handwritten assignments, providing quick and accurate feedback, and reducing teachers' workload. By leveraging ICR technology, the learning process can become more efficient and effective. This paper also explores the challenges faced in implementing ICR and offers recommendations for further research and development in this field. The findings suggest that integrating ICR in education can provide a better learning experience and support the overall improvement of educational quality. © 2024 IEEE.

关键词： Adversarial machine learning

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Enhancing Warehouse Inventory Management through IoT Tools for Monitoring Stock Items 2

Enhancing Warehouse Inventory Management through IoT Tools f...

引用

2nd International Conference on Technology Innovation and Its Applications, ICTIIA 2024

作者： Winardi, Sunaryo Wong, Ng Poi Arifin Halim, Apriyanto Megawan, Sunario Universitas Mikroskil Undergraduate Program of Computer Science Medan Indonesia Universitas Mikroskil Undergraduate Program of Information Technology Medan Indonesia

ISBN: (纸本)9798350351613

The increasing demand for personalized products and a global presence complicates inventory management, requiring accurate and timely stock information. To address this, businesses must leverage Internet of Things (IoT) solutions, which connect various electronic devices to the internet via advancements in cloud technology, sensor networks, and wireless communication. This research proposes a tailored IoT architecture for warehouse inventory checking processes, focusing on real-time tracking and visibility, cost reduction, enhanced security, improved inventory management, and increased supply chain speed. Implementing an IoT-based Warehouse Inventory Management system, designed with four layers (Physical, Gateway, Middleware, and Application), significantly improves operational efficiency and inventory tracking. While human involvement remains necessary, IoT solutions optimize inventory management and enhance overall efficiency. © 2024 IEEE.

关键词： Middleware

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Implementation of Sentiment Analysis of Shopee E-Commerce Reviews using Naïve Bayes, N-Gram, and Information Gain 8

Implementation of Sentiment Analysis of Shopee E-Commerce Re...

引用

8th International Conference on Informatics and Computing, ICIC 2023

作者： Sunjaya, Andriana Wijaya, Novresia Wong, Ng Poi Winardi, Sunaryo Universitas Mikroskil Undergraduate Program of Computer Science Medan Indonesia

ISBN: (纸本)9798350342604

One of the important parts of e-commerce like Shopee reviews. The reviews are part of user trust in conducting business transactions, so it becomes a challenge for sellers to evaluate their products and services. Evaluation can be done by looking at star ratings, but the discrepancy between the content of the review text and the star rating causes the information received to be biased and evaluations carried out by classifying reviews manually require a longer time so sentiment analysis is needed to speed up the process of classifying by objectively dividing product quality into several categories such as positive, neutral, and negative. This study uses the Naïve Bayes method combined with N-Gram feature extraction and Information Gain feature selection to determine positive, neutral, and negative sentiments toward buyer reviews. In its testing, this study used confusion matric to obtain accuracy, precision, recall, and f1-Score results. The best results show an accuracy value of 92%, precision of 56%, recall of 65%, and f1-score of 60%. © 2023 IEEE.

关键词： Sentiment analysis

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Multiclassifier for Aspect-Based Sentiment Analysis on Indonesian Reviews of Kredit Pintar Online Lending App 12

Multiclassifier for Aspect-Based Sentiment Analysis on Indon...

引用

12th International Conference on Information and Communication Technology, ICoICT 2024

作者： Yuniar, Nanda Musdholifah, Aina Universitas Gadjah Mada Undergraduate Program in Computer Science Yogyakarta Indonesia Universitas Gadjah Mada Department of Computer Science and Electronics Yogyakarta Indonesia

ISBN: (纸本)9798350363432

Aspect-based sentiment analysis is carried out to determine user sentiment's polarity while still considering the aspects of the discussion so that all information in the reviews can be represented. The multiclassifier in this research means that two classifiers are used, the first is an aspect classifier to perform aspect classification, and the second is a sentiment classifier to perform sentiment classification based on its aspect. Two approaches were studied according to their classification work, namely, individual model in the form of an aspect classification and sentiment classification based on the aspects individually and sequentially model by testing the aspect and sentiment models sequentially. This study evaluates the performance of Support Vector Machine (SVM) and Naïve Bayes Classifier (NBC) algorithms and their combination in sequential testing. In addition, to overcome imbalance dataset, the use of oversampling types is also compared. In the individual approach, the SVM model with random oversampling showed the best performance with average accuracy, precision, recall, and f1 score of 92%, 86%, 80%, and 83% for the aspect model and 86%, 81%, 80%, and 80% for the sentiment model, respectively. While in the sequential approach, the combination of SVM-SVM with oversampling shows the best performance with an average accuracy, precision, recall, and f1-score of 79%, 56%, 50%, and 53%, while the performance of other model combinations is only in the maximum range of 77% accuracy, 55% precision, 51% recall, and 52% f1-score. © 2024 IEEE.

关键词： Support vector machines

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Optimizing Random Forest Classification Using Chi-Square and SMOTE-ENN on Student Drop-Out Data 8

Optimizing Random Forest Classification Using Chi-Square and...

引用

8th International Conference on Informatics and Computing, ICIC 2023

作者： Andri Yunis, Roni Tanti Universitas Mikroskil Undergraduate Program of Computer Science Department Medan Indonesia Universitas Mikroskil Undergraduate Program of Information Systems Department Medan Indonesia Universitas Mikroskil Undergraduate Program of Information Technology Department Medan Indonesia

ISBN: (纸本)9798350342604

Dropout is a particular concern for countries striving to increase human capital. Various attempts have been made by universities to minimize the number of dropouts. Machine learning has also developed various predictive models to determine the likelihood of students dropping out. However, there is a challenge in dropout data, specifically the problem of class imbalance, where the number of students who drop out (minority class) is significantly less than those who do not drop out (majority class). This imbalance can reduce the model's ability to classify students at risk of dropping out. This study proposes classification optimization using the Random Forest algorithm to handle class imbalances in student dropout data. To overcome class imbalance, the Synthetic Minority Oversampling Technique (SMOTE) and Edited Nearest Neighbors (ENN) techniques are used. Additionally, the attribute selection method is also applied to enhance the predictive results. The test results demonstrate that the combination of implementing feature selection with Chi-Square, followed by class imbalance handling with SMOTE-ENN, provided the most optimal predictive performance for identifying the status of both dropouts and graduates. © 2023 IEEE.

关键词： Students

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Instagram Augmented Reality Marketing in Boosting Brand Awareness and e-WOM in the Indonesian Bottled Tea Industry

Instagram Augmented Reality Marketing in Boosting Brand Awar...

引用

2024 International of Seminar on Application for Technology of Information and Communication, iSemantic 2024

作者： Rinaldi, Banu Herlina, Maria Grace Iskandar, Karto Bina Nusantara University Business Creation Program Department of Binus Business School Undergraduate Program Jakarta11480 Indonesia Bina Nusantara University Binus Business School Undergraduate Program Management Department Jakarta11480 Indonesia Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350366648

This study explores the efficacy of Instagram's augmented reality (AR) features in enhancing brand awareness and electronic word-of-mouth (e-WOM) within Indonesia's bottled tea industry. The research uses a cross-sectional survey and partial least square structural equation modeling (PLS-SEM) to focus on how AR can transform consumer engagement and interaction on social media platforms. Findings from a sample of 260 bottled tea consumers in Greater Jakarta indicate that AR significantly enhances brand awareness, substantially influencing e-WOM. The integration of AR into Instagram marketing not only captivates and engages users but also fosters stronger consumer-brand relationships and promotes brand loyalty through interactive and immersive digital experiences. The study's implications underscore AR's role in revolutionising marketing strategies by providing immersive experiences that are shown to increase brand visibility and influence purchasing decisions. Limitations and future research directions are discussed, highlighting the potential for broader applications of AR in different market segments and its impact on various consumer demographics. © 2024 IEEE.

关键词： Commerce

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The Effect of Social Media Influencers on Business Sustainability: Understanding Customer Behavior Change Towards Sustainable Usage Patterns 6

The Effect of Social Media Influencers on Business Sustainab...

引用

6th International Conference on Cybernetics and Intelligent System, ICORIS 2024

作者： Herlina, Maria Grace Budiharto, Alexandra Li Lukito, Elizabeth Margaretha Iskandar, Karto Management Department BINUS Business School Undergraduate Program Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9798331539603

The study aims to fill existing research gaps by creating a new framework that explores the impact of social media influencers on sustainable usage patterns, focusing on business sustainability as a mediating factor. In this research, non-probability judgmental and purposive sampling methods were used. Due to the size and distribution of the population, sampling was also chosen based on the researcher's own judgment and experience. Structural equation modeling (SEM) is used as the data analysis method. Path coefficient analysis yielded significant findings. Social media influencers were found to influence business sustainability significantly. Apart from that, social media influencers have a significant negative affect on sustainable usage patterns. Business sustainability also significantly influences sustainability usage patterns, with a powerful influence. This enriches the dynamics of business sustainability, proposing new knowledge about how it influences this relationship. © 2024 IEEE.

关键词： Sustainable development goals

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Spatio-Temporal Action Verification of Basketball Travelling Dribble Violation Using Mediapipe-YOLO-LSTM Framework 3

Spatio-Temporal Action Verification of Basketball Travelling...

引用

3rd International Workshop on Intelligent Systems, IWIS 2023

作者： Wicaksono, Haryo Bimo Wahyono Universitas Gadjah Mada Undergraduate Program in Computer Science Yogyakarta Indonesia Universitas Gadjah Mada Department of Computer Science and Electronics Yogyakarta Indonesia

ISBN: (纸本)9798350305043

Basketball is a very dynamic game where the movements the players make can be fast and subtle, yet fine-grained. As such the difference between an event occurring and not occurring can be small and occasionally unnoticeable, and the sequence of frames of which the dribble is done matters tremendously in order to determine whether a violation takes place. As such, a spatio-temporal action verification model is needed to assist referees in making decisions, as it is almost instantaneous in nature. The challenge is to achieve balance between speed and accuracy, since the violation detection has to be fast enough for real-time use. Mediapipe Pose Estimation is chosen for its lightweight nature and capability for human joint feature extraction, and YOLO is trained to detect and extract basketball coordinates and person in order to pinpoint a region of interest for Mediapipe Pose Estimation in the case of failure to detect a player. The raw dataset consists of videos of basketball players dribbling properly and committing a travelling violation. Long Short- Term Memory (LSTM) is used to model the temporal dynamics of the movements the players make, in the form of a binary classification as to whether the violation occurs. Fine-tuning is done on various hyperparameters of the LSTM model, such as the batch size, layers, number of units, optimizer, dropout rate, loss function, etc. Stacked LSTM architecture is found to be the most suitable model, at an accuracy of 0.8690. The resulting pipeline manages to run at least 10 FPS, with enough overhead to theoretically achieve a higher framerate, after various methods of optimizations. © 2023 IEEE.

关键词： Long short-term memory

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U-Net Based Vegetation Segmentation for Urban Green Space (UGS) Regulation in Yogyakarta City 8

U-Net Based Vegetation Segmentation for Urban Green Space (U...

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8th International Conference on Informatics and Computing, ICIC 2023

作者： Putri, Delfia Nur Anrianti Wijaya, Raden Bagus Muhammad AdryanPutra Adhy Nailfaaz Wahyono Computer Science Undergraduate Program Faculty of Mathematics and Natural Science Sleman Indonesia Faculty of Mathematics and Natural Science Department of Computer Science and Electronics Sleman Indonesia

ISBN: (纸本)9798350342604

Urban Green Space (UGS) plays a vital role in maintaining the balance of urban ecosystems. In Yogyakarta City, urban development and an increase in population accompanied by a decrease and insufficiency in the proportion of UGS correlate with rising urban temperatures. Monitoring the availability of UGS can serve as a foundation for decision-making in managing the city's UGS. With advancements in technology and computing in the field of computer Vision (CV), it's possible to recognize UGS from the geometric patterns of satellite images. This study proposes a vegetation segmentation method using satellite images and a Deep Learning (DL) method with the U-Net architecture to estimate the availability of UGS. Evaluation results show that the image dataset without preprocessing with the Red Green Blue (RGB) color channel gives an average Intersection over Union (IoU) of 80.74%. These results are better than data that underwent grayscaling and contrast stretching preprocessing. Based on a case study conducted in three sub-districts in the Yogyakarta region, the constructed U-Net model gave an average IoU evaluation of 60.88%. These results demonstrate the model's capability to provide a reference in calculating the UGS area at the sub-district level, which can then be utilized by the Yogyakarta City government to analyze the availability of UGS more efficiently. With this analysis, the government can make strategic decisions to manage UGS and create a sustainable urban environment. © 2023 IEEE.

关键词： Remote sensing

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Gesture Recognition in Indonesian Sign Language Using Hybrid Deep Learning Models 3

Gesture Recognition in Indonesian Sign Language Using Hybrid...

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3rd International Workshop on Intelligent Systems, IWIS 2023

作者： Daffa Izzalhaqqi, Muhammad Yusuf Wahyono Universitas Gadjah Mada Undergraduate Program in Computer Science Yogyakarta Indonesia Universitas Gadjah Mada Department of Computer Science and Electronics Yogyakarta Indonesia

ISBN: (纸本)9798350305043

Sign language comprises unique hand gestures used to communicate between the hearing and hearing-impaired communities, with variations based on regions or countries. Due to the increasing global prevalence of hearing loss, there is a growing need for efficient sign language recognition systems. This paper presents the development of a hybrid CNN-LSTM model specifically designed to recognize static alphabetical gestures in Indonesian Sign Language (BISINDO). Primary data was collected to address the issue of publicly available dataset. These self-collected images underwent augmentation and preprocessing stages prior to model training. The architecture and hyperparameter configuration of the hybrid model were fine-tuned using the Randomized Search CV method. Performance analysis was conducted on both the hybrid model, its constituent models, and state-of-the-art model. The experimental results revealed remarkable performance by the hybrid model with excellent accuracies of 99.60%, 84.87%, and 98.00% on the training, validation, and testing sets respectively, along with a macro average score of 0.98, indicating high precision, recall, and F1-score across all the classes. However, it was observed that the VGG-16 and CNN exhibited slightly superior performance compared to the hybrid model, while the LSTM demonstrated the least favorable performance among the models. © 2023 IEEE.

关键词： Audition

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