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Boosting Team Spirit: The Impact of Ambidextrous LMS Usage on Indonesian Academics 4

Boosting Team Spirit: The Impact of Ambidextrous LMS Usage o...

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4th International Conference on Innovative Research in Applied science, Engineering and Technology, IRASET 2024

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

ISBN: (纸本)9798350309508

The study explores the role of Learning Management Systems (LMS) in enhancing academic teamwork and ambidextrous learning in Indonesian higher education. It examines how different uses of LMS, both explorative and exploitative, affect academics' morale and teamwork. The study employs Structural Equation Modeling to analyse questionnaire data from academics in Greater Jakarta, Indonesia. Key findings reveal significant correlations between the exploration and exploitation of LMS, ambidextrous learning, and the spirit of teamwork. The research contributes to understanding how LMS fosters collaborative learning and innovation in academic settings, highlighting its importance in modern education. © 2024 IEEE.

关键词： Human resource management

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Metrics and Red Teaming in Cyber Resilience and Effectiveness: A Systematic Literature Review 29

Metrics and Red Teaming in Cyber Resilience and Effectivenes...

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29th International Conference on Telecommunications, ICT 2023

作者： Yulianto, Semi Soewito, Benfano Gaol, Ford Lumban Kurniawan, Aditiya Binus Graduate Program Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Binus Graduate Program Master of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Bina Nusantara University Doctor of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350361100

Rising cyber risks have compelled organizations to adopt better cyber-protection measures. This study focused on discovering crucial security metrics and assessing the function of red teaming in enhancing cybersecurity defenses against novel cyber hazards. The PRISMA standard considered nine core research works issued between 2014 and 2023. The inclusion of red teaming best practices can significantly enhance cybersecurity architecture. Accurate simulations of cyber threats during red teaming exercises help identify vulnerabilities, and actively embracing red teaming can amplify an organization's capacity to repel future cyber assaults. Researchers and practitioners can utilize the study's insights to pioneer novel security solutions. Combining red teaming methodologies with relevant metrics is essential for enhancing cybersecurity posture. The study's discoveries grant companies a priceless benefit in navigating the rapidly changing cyber threat environment and reinforcing their cyber protection mechanisms. © 2023 IEEE.

关键词： Cybersecurity

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Self-Attention Approach for Inter-class Similarities of Grocery Product Classification 7

Self-Attention Approach for Inter-class Similarities of Groc...

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7th International Conference on Informatics and Computational sciences, ICICoS 2024

作者： Kenardi, Marvelius Putra The, Steven Rahmania, Rissa Bina Nusantara University School of Computer Science Computer Science Department Bandung Campus Jakarta11480 Indonesia

ISBN: (纸本)9798350375886

Artificial Intelligence plays a crucial role throughout the ages, specifically in smart retail. However, it is still flawed when differentiating similar patterns in various products. This paper aims to classify and analyze grocery products using ResNet50 alongside a self-mechanism algorithm. The dataset is developed internally and focuses on 1 brand of a product with 8 variations representing the product's flavor. There are a total of 4000 images which are split into train, validation, and test. An experiment is then carried out by implementing a self-attention block after the backbone of the ResNet50 architecture. The performance result is evaluated by accuracy, loss, and confusion matrix. The results of the experiment yield an accuracy value of 96.8% and loss of 2%. This indicates that the combination of deep learning has the potential in supporting classify similar products in smart retail. © 2024 IEEE.

关键词： Contrastive Learning

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Predicting Anger Proneness Using Deep Learning Techniques 9

Predicting Anger Proneness Using Deep Learning Techniques

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9th IEEE International Conference on Computing, Engineering and Design, ICCED 2023

作者： Adhinegoro, Ammar Yaafi Adiputra, Ida Bagus Gede Purwa Manik Tionando, Vincent Colin Setiawan, Karli Eka Hasani, Muhammad Fikri Bina Nusantara University Computer Science Department Jakarta Indonesia Bina Nusantara University School of Computer Science Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798350370126

High levels of anger proneness can affect the individuals and others around them in the society. Anger proneness or the likelihood of experiencing anger in response to certain stimuli or situations is influenced by a variety of factors, including personality traits, life experiences, and environmental factors. Accurately predicting the level of anger proneness in individuals can help mitigate a variety of risks, including relationship and mental health problems. In this paper, we tried to create a model that is capable of accurately predicting levels of anger proneness based on one's answers to a personality questionnaire. To do so, we utilized a deep learning algorithm namely SAINT (Self-Attention and Intersample Attention Transformer), alongside with other three machine learning and deep learning algorithms as the comparisons, LSTM (Long Short-Term Memory), GRU (Gated Recurrent Unit), and XGBoost (EXtreme Gradient Boosting). The four models were trained with the same dataset and the same data preprocessing methods. All the four algorithms couldn't manage to produce any models with satisfactory performances, with the highest performing model produced using the state-of-the-art machine learning classifier XGBoost only achieving 55.38% accuracy. Multiple data preprocessing methods such as resampling and feature selection with the help of XGBoost were applied to the dataset with negligible effects to the models' performances. The results lead us to the conclusion that the dataset contains too many inconsistencies in its rows, as can be fairly expected from a dataset based on individuals' responses to an online personality questionnaire. The inconsistencies affected all the models' ability to find the patterns in between rows and columns and compromise their resulting efficacies to do the inferences. © 2023 IEEE.

关键词： Long short-term memory

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Investigation of player emotion and instinct on racing video game using 6-11 framework (case study: split second (2010) single player modes) 9th

Investigation of player emotion and instinct on racing video...

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9th International Conference on computer science and Computational Intelligence, ICCSCI 2024

作者： Pratama, Galih Dea Novika, Andien Dwi Ardiyanto, Elshad Ryan Game Application & Technology Program Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Binus Graduate Program Bina Nusantara University Jakarta11480 Indonesia

Racing is one of the most prominent genres in the modern video game industry, where the games within the genre enable players to use any vehicle of their choice and win through doing series of action within gameplay. Thus, it made the genre popular over the years, spawning many famous titles such as Split Second back in 2010. Like every other video game title, Split Second also invokes the player emotion and instinct during its gameplay session. To understand the emotion and instinct aspect present in players of the game, we will use 6-11 framework which focuses on the two aspects. As part of the conclusion, Split Second game involves instincts such as Competition, Collecting, and Survival, with emotions including Fear, Pride, Joy, and Excitement, which can be found from its vast single player game modes. © 2024 The Authors.

关键词： 6-11 framework game analysis racing video games video games

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Classification of Chili Varieties using Convolutional Neural Network Model 6

Classification of Chili Varieties using Convolutional Neural...

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

作者： Rangkuti, Abdul Haris Wardana, Lie Melinda Putri Quincy, Angelina Pohan, Husni Iskandar Maheswari, Fakhira Shafa Computer Science Department School of Computer Science Bina Nusantara University Bandung Campus Jakarta11480 Indonesia

ISBN: (纸本)9798331539603

The diversity of chili types poses a challenge in accurately identifying chili varieties, hindering the selection of superior varieties by farmers and industry. To address this issue, our research focuses on leveraging machine learning, specifically Convolutional Neural Network (CNN) models, to classify visual images of chili varieties. We collected a dataset comprising 13 classes with a total of 1835 images, representing various species and varieties of chili. The dataset was divided into 70% for training, 20% for validation, and 10% for testing. The training process involved the use of deep learning with several CNN models to evaluate their effectiveness in accurately classifying chili varieties based on visual characteristics. Through refining the model architecture and parameters, our aim is to achieve a classification accuracy above 0.9, demonstrating the potential of CNN in overcoming current challenges in chili variety identification. In the training data experiment, models such as ResNet 50V2, NasNetMobileV2, Densenet169, MobileNetV2, Xception, and InceptionV3 demonstrated accuracy, precision, and recall of more than 92%. Subsequently, during testing data processing, the highest accuracy was achieved by the DenseNet 169 model at 95%, followed by MobileNetV2 at 95.9%, Xception at 94.3%, and InceptionV3 at 91.3%. Overall, our research has successfully classified 13 different varieties of chili peppers. However, further research is warranted to address more diverse image characteristics in other types of images. © 2024 IEEE.

关键词： Pepper

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Playing Away the Rage: A Systematic Literature Review of Games in Anger Management Interventions

Playing Away the Rage: A Systematic Literature Review of Gam...

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2023 International Conference on Networking, Electrical Engineering, computer science, and Technology, IConNECT 2023

作者： Marcelino, Nico Kusuma, Samuel Triwijaya Setiadi, Tan Albertus Agung Santoso Gunawan, Alexander Saputra, Karen Etania Bina Nusantara University School of Computer Science Computer Science Department Bandung40181 Indonesia Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350331172

There are very few studies on the benefits of video games regarding emotional regulation. Video games have been demonstrated to have the capacity to influence the player's emotions. A player may experience delight after a win, sadness after a tragic story, or rage after a defeat. This rage might express as yelling, slamming the controller, or even breaking the screen. Consequently, violent conduct tends to be associated with video games. Despite that, further research has shown the potential of video games in reducing anger. As a result, the purpose of this study is to look at the feasibility of video games in anger management and the mechanisms that lead to such results. The authors of this paper performed a Systematic Literature Review (SLR) on video games and their potential as a form of anger management treatment in this study. Several sorts of video games from diverse genres have been reviewed in past studies, and each has its own set of advantages and disadvantages. In conclusion, video games are considered to be an appealing option for anger management therapies as well as other mental health disorders, but additional research is needed to uncover more of the pastime's latent benefits. © 2023 IEEE.

关键词： Human computer interaction

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Understanding Fun in Cooking: Preliminary Study of Serious Games on Cooking Domain

Understanding Fun in Cooking: Preliminary Study of Serious G...

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2024 International Conference on Information Technology Systems and Innovation, ICITSI 2024

作者： Halim, Steven Andrew Rumi, Jalaluddin Pratama, Galih Dea Ramdhan, Dimas Bina Nusantara University School of Computer Science Game Application & Technology Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798331511470

Cooking is an important life skill to master. Despite the importance, a study suggests that there is a decline in cooking skills between generations. In accordance with that, serious games emerge as a form of solution which has purposes to solve the problems in an entertaining way. Serious games are often presented to solve numerous real-life problems, such as education, medical, and even culinary. This literature review studies a few of the many benefits of serious games that may help solve problems in the realm of cooking. The study focuses primarily on cooking serious games due to their capabilities to solve problems in an entertaining manner. The result indicates that there are positive benefits in utilizing serious games to solve certain problems not only in cooking, but also other issues such as cognitive skills. The findings from this research are intended to be the preliminary in developing serious game to compensate the problem from cooking domain. © 2024 IEEE.

关键词： cooking preliminary study serious game systematic literature review

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The Learning Outcomes Achievement and User Experience Analysis of a Cryptography Education Web Game 10

The Learning Outcomes Achievement and User Experience Analys...

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10th International HCI and UX Conference in Indonesia, CHIuXiD 2024

作者： Susanto, Helmi Yusuf Arifin, Yulyani Bina Nusantara University Binus Graduate Program - Master of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350380439

The rising threat of cybercrimes in Indonesia, including a significant breach of the national data center, reveals the urgent need for effective cybersecurity education. Cryptography, a key component of cybersecurity, is often difficult for beginners to grasp. To address this, we developed an interactive web game that blends puzzle-solving with a visual novel to make cryptographic concepts more accessible. This study analyzes the game's user experience (UX) using the ARCS model - Attention, Relevance, Confidence, Satisfaction - and assesses learning outcomes by comparing pre-test and post-test results. The findings show significant improvements in participants' understanding and high levels of engagement, demonstrating a strong correlation between positive user experience and enhanced learning outcomes. However, some aspects of the educational content still need refinement. Future updates will offer multilingual support and personalized learning paths, with pre-and post-tests targeting specific cryptography and cybersecurity sub concepts, and analysis of UX features through targeted surveys and in-game analytics to enhance learning experiences. © 2024 IEEE.

关键词： ARCS Model cryptography cybersecurity educational games puzzle games user experience Visual Novels web games

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The Impact of Emotion Recognition Models Towards Believability Factor of Chatbots 15

The Impact of Emotion Recognition Models Towards Believabili...

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15th International Congress on Advanced Applied Informatics Winter, IIAI-AAI-Winter 2023

作者： Sutoyo, Rhio Warnars, Harco Leslie Hendric Spits Muhamad Isa, Sani Budiharto, Widodo Bina Nusantara University Binus Graduate Program - Doctor of Computer Science Computer Science Department Jakarta11480 Indonesia School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350383829

Emotionally-aware chatbots are chatbots that are equipped with emotional intelligence. Based on the literature, using emotional elements in chatbots can improve user engagement and believability factors. This study attempts to make a novel contribution by empirically evaluating the impact of emotion recognition models on the believability factor of chatbots. This study examines the impact of the emotions model and avatar on chatbot interactions through three implementations. Thirty-one participants volunteered to evaluate emotionally aware chatbots. The participants evaluated the interaction with the chatbot using the Godspeed Questionnaire Series (GQS). The questionnaire results are utilized to measure the effect of the emotions model on the chatbot's believability factor. The evaluation results of the experiment show that implementing the emotion recognition model on the chatbot increases its believability factors. On average, the believability measures in interaction type B (with an emotion model) are enhanced 1.71 times compared to interaction type A (a basic model). Furthermore, the believability measures in interaction type C (with an emotion model and an avatar) are enhanced 2 times more than in interaction type A (a basic model). The believability factor is also heightened by integrating a chatbot avatar into the interaction system. Using avatars in chatbots increases the believability variables of the system by 1.17 times if compared with not using avatars. © 2023 IEEE.

关键词： Emotion Recognition

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