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The Impacts of Playing Video Game on Memory and Executive Control

The Impacts of Playing Video Game on Memory and Executive Co...

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2024 International Conference on Informatics Electrical and Electronics, ICIEE 2024

作者： Antoni, Davin Antolin, Richardo Achmad, Said Sutoyo, Rhio School of Computer Science Bina Nusantara University Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798331519216

Playing video games has become a widespread form of entertainment, raising questions about their impact on cognitive functions such as memory and executive control. While some studies suggest that video games can enhance cognitive abilities, others indicate potential drawbacks, such as mental fatigue. This study conducted a systematic literature review to understand the impacts of video games, finding mixed results. Previous research has shown improvements in cognitive functions and negative effects depending on the duration and type of video game. This research is crucial to maximising cognitive benefits from video games while minimizing adverse effects. The study investigates how playing video games can influence memory and executive control by reviewing 740 articles and selecting 30 for in-depth analysis. The results showed that some game genres, like action games can enhance visual attention and executive controls. Strategy games enhance critical thinking, planning, and working memory through complex decision-making involving resource management. Puzzle games, such as Tetris or Cut the Rope, enhance visual-spatial processing and problem-solving skills. The length of gameplay for ideal cognitive enhancement lasts from thirty minutes to an hour each day, avoiding negative effects brought on by longer play. This research highlights the potential for video games to be used as tools for cognitive enhancement across different age groups, emphasizing the need for balanced and well-designed gameplay experiences. © 2024 IEEE.

关键词： Decision making

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A Comparative Analysis of User Experience and Educational Outcomes in Math Memory Games

A Comparative Analysis of User Experience and Educational Ou...

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2024 International Conference on computer Engineering, Network and Intelligent Multimedia, CENIM 2024

作者： Martin, Nicholas Nathanael, Raffel Jeriyanto, Doni Hermawan, Eric Savero Fung, Janshen Panggabean, Francisco Maruli School of Computer Science Bina Nusantara University Game Application & Technology Program Computer Science Department Jakarta Indonesia Master of Computer Science Binus Graduate Program Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9798350368802

This research explores multiple varieties of effective and child-friendly learning approaches for mathematics and memorization among young children in today's increasingly digital age considering their specific developmental needs. This type of approach includes in-person teaching, engaging online videos, interactive textbooks, and innovative educational games. This research employs a detailed comparative analysis, considering both 'User Experience' and measurable learning outcome factors, and spans a range of age groups from preschoolers to elementary school children. Through highlighting the strengths and weaknesses of each method, this research aims to inspire the adoption of efficient, child-centric, and novel teaching techniques by educators and institutions, thereby advancing the quality of education in Indonesia. The results show that using serious game in education presents high usefulness score alongside positive user experience, suggesting its viability for educational purposes. © 2024 IEEE.

关键词： Contrastive Learning

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Exploration of MBTI Personality Prediction through the Application of Traditional Data Augmentation

Exploration of MBTI Personality Prediction through the Appli...

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2024 International Conference on Advanced Information Scientific Development, ICAISD 2024

作者： Rizandi, Nathasya Zega, Grisella Meirisia Lucky, Henry Meiliana Bina Nusantara University School of Computer Science Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798350392036

All the various forms of social interaction do not avoid the tendency of each individual's personality characteristics. With the increasing use of social media, the language patterns shown through posts are considered to be representative of a person's personality traits, Therefore, this research will conduct personality prediction experiments using online forum post on the basis of the MBTI personality model. Our focus is to test how effective the use of traditional data augmentation is in helping to overcome the problem of data imbalance and its effect on the accuracy of the results. We used the three best models that have been used in previous studies, namely Random Forest, Support Vector Machine (SVM), and a light gradient boosting machine (LightGBM). These experiments show that traditional data augmentation is still insufficient to provide the best results compared to synthetic data generation as done in previous studies. However, it still proves the need for augmentation data to help generalize the model with the best average results of 0.937 in Random Forest model training and 0.766 in SVM model testing, as well as an F1 with a score of 0.778 for the SVM model. © 2024 IEEE.

关键词： Prediction models

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History Leap: Enhancing Historical Learning Through a Game-Based Approach

History Leap: Enhancing Historical Learning Through a Game-B...

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2024 International Conference on Information Technology and Computing, ICITCOM 2024

作者： William Hartoko, Gandhi Kenji, Timothy Fajar, Muhamad School of Computer Science Bina Nusantara University Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798350379839

The development of 'History Leap: Indonesian Independence' integrates engaging 2D platformer gameplay with historical narratives to stimulate dwindling interest in Indonesian history. The game's urgency lies in addressing the decreasing engagement with traditional history education, making it vital to innovate educational methods. Our research utilizes a game development approach, specifically the Agile methodology, to create an educational game that elucidates Indonesia's journey to independence. Participants, including university and high school students, played the game and evaluated its educational impact. The results showed significant satisfaction with the game's learning effectiveness and enjoyment, despite lower scores in game presence. These findings were analyzed using T-Test and Mann-Whitney methods based on the data normality. This underscores the potential of game-based learning to enhance historical education across different educational stages. © 2024 IEEE.

关键词： Aseprite Education Game History Indonesian Independence Unity

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MediaPipe’s Pose-Based Human Activity Recognition with LSTM 6

MediaPipe’s Pose-Based Human Activity Recognition with LSTM

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

作者： Muliawan, Nicholas Hans Irmawan, Frederick Nathan Angky, Edbert Valencio Prabowo, Abram Setyo Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9798331539603

It is difficult to comprehend and categorize human movements in Human Activity Recognition. Despite being widely utilized, convolutional neural networks (CNN) might not adequately represent the intricacy of motions. This work investigates an alternative method for classifying human motion: the LSTM model combined with MediaPipe. Sequential processing is used by the LSTM model for keypoint sequences, which improves its accuracy in identifying complex human movement features. Although OpenPose provides precise body mapping, MediaPipe is used in this research because of its computational efficiency and ease of integration. Using CV2 libraries, preprocessed video data is read at 15 frames per second. MediaPipe software is then used to extract 33 keypoints coordinates from each frame. Then, in order to make these keypoints compatible with the LSTM model, they are compressed into single-dimensional arrays. The keypoints from each picture frame are taken out and combined with other frames for a total of 15 frames into a single sequence. The aim of this study is to evaluate the usefulness of the LSTM model in human action classification by contrasting its performance with more conventional sequence modeling methods. Following evaluation, we employed the Recognition of Human Action dataset from Kungliga Tekniska Hogskolan (KTH) University, which is used by LSTM models. Over 99% of the metricsAccuracy, Precision, Recall, and F1-scorewere attained by the model. © 2024 IEEE.

关键词： computer vision

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Rice Disease Detection and Classification Using Mask R-CNN and DenseNet 2

Rice Disease Detection and Classification Using Mask R-CNN a...

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

作者： Widjaja, Nicole Minar Xavier Halim Chandra, Bryan Alvis Jeremy, Nicholaus Hendrik Lumentut, Jonathan Samuel School of Computer Science Bina Nusantara University Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798350351613

Rice is an essential crop in the world food supply. As the consumption of rice grows, its production has fallen due to diseases. These diseases are able to spread quickly, leading to outbreaks that cause major yield loss. It is important to identify the disease quickly before it spreads out. However, most rice disease classification model is that the training uses datasets containing images with a single leaves. This is unrealistic as it is cumbersome to take a picture of each leaves one by one. This paper proposes the use of segmentation using Mask R-CNN, this will allowed the model to focus on identifying key features of three types of rice disease, namely rice blast, brown spot, and leaf blight. DenseNet classification model is trained using the cropped dataset and uncropped dataset. The result shows an increased in F1 score, which is counted as the frequency of a model making correct predictions. The F1 score when using cropped dataset compared to uncropped dataset increased by 8,22%, from 77,91% to 86,13%. This proves the idea of image segmentation using Mask-RCNN is able to improve the accuracy and performance of image classification models. © 2024 IEEE.

关键词： Image segmentation

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Implementation of OpenCV and CNN-Keras in Detecting Plastic Waste 6

Implementation of OpenCV and CNN-Keras in Detecting Plastic ...

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

作者： Handoko, Gabio Mega Gunawidjaja, Eileen Saputri, Hanis Amalia Sari, Azani Cempaka Mujhid, Almuzhidul Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9798331539603

Plastic waste has become a worldwide concern, as it can create a bad environment. To reduce it, better waste management is needed. The simplest waste management is waste sorting. However, the lack of understanding of trash types makes waste management awful. Thus, instead of doing waste sorting manually, it is better to have technology to help with waste sorting. Based on that, this research aims to implement computer vision and CNN to optimize waste sorting of plastic waste from other kinds of waste. The result of this research shows the model is quite good for waste sorting and has an accuracy of 75.08% for the testing dataset. However, in the implementation of the CNN model, it is more considered to be used considering the computational and deployment costs in the wider society. © 2024 IEEE.

关键词： classification CNN computer vision plastic waste management

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Utilizing Data Augmentation Methods to Generalize DeepFake Classifier

Utilizing Data Augmentation Methods to Generalize DeepFake C...

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2024 International Conference on Artificial Intelligence, Blockchain, Cloud Computing, and Data Analytics, ICoABCD 2024

作者： Pho, Julius Gerald Gouw, Christian Arifin Lucky, Henry Suhartono, Derwin School of Computer Science Bina Nusantara University Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798350368451

DeepFakes are fabricated audiovisual media. As technology advances, the tools for making DeepFakes have increased. Due to this, making DeepFakes are more accessible to a lot of people. As a result, some people use it irresponsibly such as hoaxes and scams. To combat this, a reliable DeepFake detector is needed. EfficientNet-B0has been used and has the highest success rate in detecting DeepFakes. Moreover, utilizing dynamic face cutout has only been proven to increase the accuracy rate of detecting DeepFakes. Because of this, we decide to implement EfficientNet-B0and face augmentation with the hopes of having a reliable DeepFake detection model. For comparison, three experiments using three different models were done. The three models include EfficientNet-B0 with Multi-task Cascaded Convo-lutional Networks (MTCNN), EfficientNet-B0 with MTCNN and Face-CutOut, and EfficientNet-B0 with MTCNN and a common data augmentation method. The experiments showed that The EfficientNet-B0and MTCNN model yielded the best results, reaching above the 90% mark on the Accuracy, AUC-ROC, and Fl-Score. When face augmentation was implemented, the results faced a slight decrease of around 1% on the Accuracy, AUC-ROC, and Fl-Score compared to the model using only EfficientNet-B0 and MTCNN. However, the EfficientNet-B0 and MTCNN model implemented with Face-Cutout has the best AUC-ROC score, above the model using only EfficientNet-B0 and MTCNN, which has a score of 97.82%. The results demonstrate that data augmentation is effective in enhancing model generalization. © 2024 IEEE.

关键词： computer crime

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Integration of Land and Tax Geospatial Data with Artificial Intelligence (AI)-Based Clustering 11

Integration of Land and Tax Geospatial Data with Artificial ...

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11th International Conference on ICT for Smart Society, ICISS 2024

作者： Chandra, Billy Daniel Willyanto, Samuel Irwansyah, Edy School of Computer Science Bina Nusantara University Department of Computer Science Jakarta Indonesia

ISBN: (纸本)9798350389654

Integrating Land Geospatial Data and Tax Geospa-tial Data is challenging due to differences in file formats and attributes. This research addresses these challenges and seeks to effectively combine the datasets. Initially, the study identifies a suitable file format that can handle the various attributes of both datasets without losing data quality. A systematic approach is then employed to align the different attributes. The research focuses on data from East Jakarta, Indonesia. The land data includes files such as. shp,. shx,. dbf, and. prj, representing the shapes and attributes of land parcels. The tax data includes sim-ilar files containing detailed tax information. Since the datasets lack common attributes, spatial relationships are used to join them using QGIS software. The process begins by simplifying the data structures and clipping them to the relevant areas. Spatial joins are performed based on where the data overlaps the most. Post-integration, the dataset is cleaned to remove any duplicate or unnecessary information. K-Means clustering with Scikit-Iearn is then used to analyze the data and identify patterns. The results show that land and tax data can be combined spatially, but not by their attributes. When joined, the tax data, which may be divided among multiple owners, follows the land parcel data, resulting in a single ownership representation. This method ensures that the datasets are effectively merged, enabling better analysis and decision-making. The approach demonstrates a robust methodology for integrating disparate geospatial datasets, enhancing data accessibility and utility. © 2024 IEEE.

关键词： clustering geospatial integration land data tax data

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Leveraging Deep Learning for Early Detection of Stomach Cancer: A Convolutional Neural Network Approach 6

Leveraging Deep Learning for Early Detection of Stomach Canc...

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

作者： Arya Maulana Bratajaya, A. Nabiilah, Ghinaa Zain Nurimaba, Defara Putra Moniaga, Jurike V. Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Dept. Computer Science School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

ISBN: (纸本)9798331539603

Early detection is crucial in improving patient outcomes and survival rates for stomach cancer. This research introduces a deep learning system employing Convolutional Neural Networks (CNN) to aid in the early detection of stomach cancer. Our methodology involves training CNN models on medical images, including endoscopy images and histopathological slides, to identify early-stage precursors of the disease. We conducted experiments using medical image data from a clinical study, which included diagnoses for various stages of stomach cancer. The results demonstrate that our CNN-based framework achieves high accuracy in recognizing early-stage stomach cancer, suggesting its potential as a valuable tool for early diagnosis in clinical settings. This framework could significantly enhance the ability of healthcare providers to diagnose stomach cancer at its earliest stages, ultimately improving treatment outcomes and patient survival rates. © 2024 IEEE.

关键词： Convolutional neural networks

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