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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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Parallelization of LSTM-GRU Architectures for Multivariate Prediction of Stock Prices 9

Parallelization of LSTM-GRU Architectures for Multivariate P...

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9th International Conference on Information Management and Technology, ICIMTech 2024

作者： Tarigan, Gabriel Asael Hermawan, Eric Girsang, Abba Suganda Bina Nusantara University BINUS Graduate Program - Master of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350390025

Anticipating stock market movements is challenging, as hasty prediction risks large financial losses. Stock price prediction is complex due to the high volatility and many influencing external factors. Recurrent Neural Network (RNN) models are made to learn time series data making it ideal for stock prediction based on records. Well-known RNN architectures for forecasting problems are Long-Short Term Memory (LSTM) and Gated Recurrent Unit (GRU). However, past studies only explored standalone models and have not generated convincing performance results. By integrating the efficiency of GRU to effectively handle shorter data sequences with LSTM's ability to capture long-term dependencies, our model aims to improve stock price forecasting accuracy by collaborating the strength between both architectures. The proposed model was used to forecast multivariate stock prices and evaluated using Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). We found that parallelization of LSTM-GRU offers better prediction, up to 9.1% improvement compared to sequential LSTM-GRU and Bidirectional LSTM (Bi-LSTM). © 2024 IEEE.

关键词： Mean square error

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Indonesian Tweet Emotion Detection Using IndoBERT 9

Indonesian Tweet Emotion Detection Using IndoBERT

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9th International Conference on Information Management and Technology, ICIMTech 2024

作者： Masaling, Nikita Ananda Putri Siswanto, Ricky Reynardo Girsang, Abba Suganda Bina Nusantara University BINUS Graduate Program - Master of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350390025

This study investigates emotion detection on tweets written in the Indonesian language, using the IndoBERT model that has been trained for 50 epochs. The examination conducted in this study provides a comprehensive analysis of the performance dynamics shown by the model. The assessment results provide an accuracy score of 0.74, indicating the model's proficiency in identifying sentiments within the collection of tweets. By using assessment criteria such as accuracy, recall, and the F1-score, the model's comprehensive analytical ability is validated, emphasizing its effectiveness in deciphering various emotional circumstances. The accuracy, recall, and F1-score of the model, with average values of 0.79, 0.78, and 0.78 respectively, combined demonstrate its resilience in accurately capturing subtle emotional subtleties. This study enhances our comprehension of language model applications in the field of emotional intelligence by exploring the complexities of sentiment analysis. It emphasizes the practical value of these applications in real-world scenarios. © 2024 IEEE.

关键词： Emotion Recognition

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Object Driven Fuzzy Decision Model for Evaluating the Cryptocurrency's Performance 11

Object Driven Fuzzy Decision Model for Evaluating the Crypto...

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

作者： Utama, Ditdit Nugeraha Binus Graduate Program - Master of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350389654

The assessment of cryptocurrency performance has been subject to criticism due to the lack of a standardized approach, leaving the evaluation process without clear guidance. In this research, an innovative object-driven decision model based on fuzzy logic (OFDM) conception was developed to systematically analyze cryptocurrency performance. Object-oriented operated as the main method in analyzing and designing the model. In this context, the model elements deeply observed and functioned to become fundamental thinking for constructing the model. Then, fuzzy logic with curve membership function form was also used as the primary method to avoid the bias value of three key parameters;trend line (and then used to see the slope distance), price consistency, and transaction volume consistency. The constructed model is a novel decision model that was successfully constructed for assessing the determined cryptocurrency performance. Utilizing six months of real data from three major cryptocurrencies (Bitcoin, Ethereum, and Dogecoin), the model conclusively demonstrates that Bitcoin exhibits the strongest performance, scoring 36.39 performance points. © 2024 IEEE.

关键词： Fuzzy logic

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Analyzing the Impacts of Dynamic Game Balancing in a Survival Roguelike Chess Game

Analyzing the Impacts of Dynamic Game Balancing in a Surviva...

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2025 International Conference on Advancement in Data science, E-learning and Information System, ICADEIS 2025

作者： Mario Arifin, Yulyani Bina Nusantara University Binus Graduate Program - Master of Computer Science Computer Science Department Jakarta Indonesia Bina Nusantara University Binus Graduate Program - Doctor of Computer Science Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798331513320

This research investigates the novel application of Dynamic Game Balancing (DGB) techniques in the context of a hybrid chess-survival roguelike game, a unique combination of genres not widely explored in previous studies. The study addresses two primary research questions: the identification of suitable DGB techniques for these genres and the enhancement of user experience through DGB. A game prototype was developed using Unity, where players control a chess knight in a turn-based system, facing increasingly challenging enemy pieces. The DGB system dynamically adjusts the game's difficulty based on player performance, utilizing various inputs such as damage received or dealt, health points, powerup usage, and decision speed. Preliminary results from 14 beta-testers show overall positive feedback, with players reporting higher engagement and enjoyment due to the seamless difficulty adjustments, as reflected by the high mean scores from surveys like the User Experience Questionnaire (UEQ). This research fills a gap in the literature by demonstrating the feasibility and benefits of integrating DGB in a chess-survival roguelike setting. These findings highlight the potential for DGB to significantly improve player experience in hybrid game genres, suggesting that its application can create more adaptive and immersive gaming environments in various other genres as well. © 2025 IEEE.

关键词： Game design

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Machine Learning-enhanced loT and Wireless Sensor Networks for predictive analysis and maintenance in wind turbine systems

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International Journal of Intelligent Networks 2024年第1期5卷 133-144页

作者： Gong, Lei Chen, Yanhui Master Science and Technology of Computer Department PingDingShan Vocational and Technical College Henan Pingdingshan467002 China Master College of Tourism and Planning Pingdingshan University Henan Pingdingshan467002 China

Wind power (WP) represents a Renewable Energy Source (RES) that has noticed substantial development as people continuously search for green energy sources. Utilizing predominantly Predictive Maintenance (PM) of Wind Turbines (WT), this research analyzes the potential benefits that could be generated by Wind Energy (WE) through the use of the Internet of Things (IoT) and Wireless Sensor Networks (WSN). This research recommends an Internet of Things-WSN model for PM comprised of three distinct phases: the primary phase is the collection of data via sensors, the second phase is the transmission of that data through a connection to the Internet, and the final phase is the implementation of data analytics on that data in the context of cloud computing. For PM analytics, this work introduces a Predictive Maintenance Convolutional Long Short-Term Memory (PM-C-LSTM) model that combines the spatial pattern recognition capabilities of a Convolutional Neural Network with the sequential data prowess of LSTM networks. The PM-C-LSTM model combines CNN for recognizing spatial patterns and LSTM networks for analyzing sequential data in a way that doesn't affect the accuracy of WT-PM. A Failure Sample Generator model is also fused into the study to measure soft failure and hard failure factors and improve the predictive accuracy of the Machine Learning (ML) model. Data became available over 16 months while the model was applied to a Wind Farm (WF) positioned on the Qinghai-Tibet Plateau. It has been demonstrated that the PM-C-LSTM model possesses enhanced PM capabilities by comparing its efficiency to other standard models using a selection of performance metrics. The result of the test indicates that there is a probability that the hybrid IoT and ML will improve PM methods in WT, which will subsequently help improve the effectiveness and sustainability of WE generation. © 2024 The Authors

关键词： Long short-term memory

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File Classification Containing Secret Key in Git Repository Using Character Level CNN and Shannon Entropy 4th

File Classification Containing Secret Key in Git Repository ...

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4th International Conference on Advances in Computational science and Engineering, ICACSE 2023

作者： Muhammad, Rafie Musdholifah, Aina Master Program in Computer Science FMIPA UGM Yogyakarta Indonesia Department of Computer Science and Electronics FMIPA UGM Yogyakarta Indonesia

ISBN: (纸本)9789819729760

Current technological developments are directly proportional to the number of information technology projects made. This project is usually in the form of a program formed from a code. To facilitate version control and contributions between developers, git tools are usually used which are placed on an online platform such as Github. If a repository on an online platform contains sensitive information such as secret key variables and the repository can be accessed by the public, then this can be used to harm the company or organization that owns the repository. Several ways have been done before to detect this secret key variable by using the regex method and using machine learning to minimize false positive results. In this research, we will try to build a text or file classification model that contains secret key variables using the character level CNN and Shannon entropy. The Shannon entropy algorithm is used during pre-processing to retrieve the group of words with the largest entropy value and the CNN character level will be used because it is considered suitable for this case since the value of the secret key is abstract and does not require a sequence context for the said secret key in a file. The results of this study were able to obtain an accuracy value of 96.1% for the accuracy value for the classification of files containing secret key. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Character level CNN Git repository Secret key Shannon entropy Text classification

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Human Stress Detection in and Through Sleep Patterns Using Machine Learning Algorithms

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Journal of The Institution of Engineers (India): Series B 2024年第6期105卷 1691-1713页

作者： Geetha, R. Gunanandhini, S. Srikanth, G. Umarani Sujatha, V. Department of Computer Science and Engineering S.A. Engineering College Chennai600077 India Department of Computer Science and Engineering Panimalar Engineering College Chennai India Department of Master of Computer Applications S.A. Engineering College Chennai600077 India

Stress has a remarkable impact on various cognitive functions, demanding timely and effective detection using strategies deployed across interdisciplinary domains. It influences decision-making, attention, learning, and problem-solving abilities. As a result, stress detection and modeling have become important areas of study in both psychology and computer science. This study links the fields of psychology and machine learning to deal with the urgent requirement of accurate stress detection methodologies and highlights sleep patterns as a key indicator for stress detection, discussing a novel approach to understand and determine stress levels. Psychologists use affective states to measure stress, which refers to a sense of feeling an underlying emotional state. However, most stress classification work has been limited to user-dependent models, which new users cannot use without additional training. This can be a significant time burden for new users trying to predict their affective states. Therefore, it is critical to address basic mental health issues in children and adults to prevent them from developing more complex problems on account of undergoing stress. The medical field processes vast amounts of medical data;the machine learning algorithms sift through patterns that might escape the human eye. The machine learning algorithms act as detectives, able to spot correlations and bring out a sense of complex information. The machine learning algorithms reveal fine correlations and patterns, aiding in more precise and prompt diagnoses particularly to focus fundamental mental health issues in individuals of all ages. This research work deploys an enhanced Multilayer Perceptron (MLP), exhibiting an extensive feature analysis for processing medical datasets, resulting in improved effectiveness in predicting stress levels. This helps us to diagnose issues more accurately and swiftly which improves the patient outcomes. The proposed and enhanced MLP model undergoes stri

关键词： Decision trees

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Synonym Replacement Augmentation for Handling Data Imbalance in Personality Classification 8

Synonym Replacement Augmentation for Handling Data Imbalance...

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8th International Conference on Information Technology, Information Systems and Electrical Engineering, ICITISEE 2024

作者： Pradana, Rilo Chandra Suhartono, Derwin Bina Nusantara University Binus Graduate Program Master of Computer Science Computer Science Department Jakarta11480 Indonesia School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350368970

The Myers-Briggs Type Indicator (MBTI) classification is a widely utilized instrument for personality assessment. However, it frequently encounters challenges due to imbalanced data distributions across personality dimensions. It is paramount to address this issue to enhance the accuracy and reliability of personality predictions. Most current research in this field is focused on data balancing techniques, such as oversampling and undersampling, which have been demonstrated to enhance model performance. Nevertheless, there is a dearth of research exploring text augmentation methods, particularly synonym replacement, for this purpose. This study examines the efficacy of synonym replacement as a data augmentation technique for MBTI classification. Experiments are conducted with varying levels of synonym replacement (10%, 30%, 50%, 70%, and 90%) to assess its impact on model accuracy and F1 scores across the four MBTI dimensions. Our findings indicate that low levels of synonym replacement, particularly at 10% to 50%, can enhance the performance of the model in predicting MBTI dimensions. On the other hand, a higher number of replaced words in the synonym replacement augmentation can harm the model's performance. These observations suggest that synonym replacement can effectively address data imbalance in MBTI classification, although its application must be tailored to specific personality dimensions. © 2024 IEEE.

关键词： Data accuracy

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Development of an Automated Scoring Model Using SentenceTransformers for Discussion Forums in Online Learning Environments

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Journal of Computing and Information Technology 2022年第2期30卷 85-99页

作者： Dhini, Bachriah Fatwa Girsang, Abba Suganda Department of Computer Science BINUS Graduate Program-Master of Computer Science Bina Nusantara University Jakarta Indonesia

Due to the limitations of public datasets, research on automatic essay scoring in Indonesian has been restrained and resulted in suboptimal accuracy. In general, the main goal of the essay scoring system is to improve execution time, which is usually done manually with human judgment. This study uses a discussion forum in online learning to generate an assessment between the responses and the lecturer's rubric in the automated essay scoring. A SentenceTransformers pre-trained model that can construct the highest vector embedding was proposed to identify the semantic meaning between the responses and the lecturer's rubric. The effectiveness of monolingual and multilingual models was compared. This research aims to determine the model's effectiveness and the appropriate model for the Automated Essay Scoring (AES) used in paired sentence Natural Language Processing tasks. The distiluse-base-multilingual-cased-v1 model, which employed the Pearson correlation method, obtained the highest performance. Specifically, it obtained a correlation value of 0.63 and a mean absolute error (MAE) score of 0.70. It indicates that the overall prediction result is enhanced when compared to the earlier regression task research. ACM CCS (2012) Classification: Computing methodologies → Modeling and simulation → Model development and analysis → Model verification and validation Applied computing → Education → Distance learning. © 2022, University of Zagreb Faculty of Electrical Engineering and Computing. All rights reserved.

关键词： Semantics

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