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International Conference on Smart Computing & Communications (ICSCC)

作者： Rafael Nicholas Tanaja Andrew Widjaya Johnny Alexander Agung Santoso Gunawan Karli Eka Setiawan Data Science Program Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798350363104

ISBN: (纸本)9798350363111

As social media significantly shapes societal norms and ethical paradigms, understanding real-time public sentiment provides valuable insights for political parties to evaluate their candidates, key issues, and campaign strategies. This study aims to gauge each presidential candidate's public sentiment from August 1, 2023, until October 31, 2023, using Twitter data. In this study, the authors would like to conduct an IndoBERT with a Fully Connected Layer to predict the sentiment analysis of Indonesian public sentiment toward the 2024 upcoming presidential election. The paper concluded that the IndoBERT model achieved an accuracy of 84.7%. In conclusion, the findings of this study lay the groundwork for more extensive analyses, enriching our comprehension of the intricate dynamics of public opinion in the political sphere. Furthermore, Sentiment Analysis is dynamic and will change as significant events occur. As we look to the future, integrating cutting-edge language models like IndoBERT with real-time social media data emerges as a transformative force in reshaping political analysis.

关键词： Sentiment analysis Analytical models Accuracy Social networking (online) Shape Voting Dynamics Blogs Data models Real-time systems

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Image-Based Natural Disaster Classification with Deep Learning: A Comparative Analysis

Image-Based Natural Disaster Classification with Deep Learni...

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IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT)

作者： Samuel Eka Alamsyah Simen Ferdinand Djamhari Salomo Hendrian Sudjono Alexander A S Gunawan Karli Eka Setiawan Data Science Program Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798350353464

ISBN: (纸本)9798350353471

Natural disasters, including earthquakes, cyclones, floods, and wildfires, cause significant environmental damage and have emerged as a major global issue. These events can result in loss of life and disrupt communities and economy. Effective disaster management relies on timely and accurate information. With social media becoming a primary source of real-time information during such events, many images of these disasters are shared, though some may not be accurately labeled. Therefore, the purpose of this study is classifying natural disaster images, mainly cyclone, wildfire, flood, earthquake from social media images. This paper is to conduct a comparative analysis over 4 models namely VGG16, VGG19, EfficientNetB0, and ResNet-50. This paper uses a dataset that contains 4428 human-annotated images including cyclones, earthquakes, floods, and wildfires. Results concluded that ResNet-50 and EfficientNetB0 give the best result with both got over 96% average accuracy. ResNet-50 delivers more stable and more convergent results compared to EfficientNetB0, but it delivers slightly less classification accuracy. VGG16 and VGG19 are both outclassed in terms of accuracy, but better in training time, with VGG19 having a more convergent result.

关键词： Training Accuracy Wildfires Disasters Computational modeling Biological system modeling Earthquakes

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Automatic essay scoring for discussion forum in online learning based on semantic and keyword similarities

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Asian Association of Open Universities Journal 2023年第3期18卷 262-278页

作者： Dhini, Bachriah Fatwa Girsang, Abba Suganda Sufandi, Unggul Utan Kurniawati, Heny Department of Multimedia Teaching Material Production Center Universitas Terbuka Tangerang Selatan Indonesia Computer Science Department BINUS Graduate Program Master of Computer Science Bina Nusantara University Jakarta Indonesia Faculty of Sains and Technology Universitas Terbuka Tangerang Selatan Indonesia

Purpose: The authors constructed an automatic essay scoring (AES) model in a discussion forum where the result was compared with scores given by human evaluators. This research proposes essay scoring, which is conducted through two parameters, semantic and keyword similarities, using a SentenceTransformers pre-trained model that can construct the highest vector embedding. Combining these models is used to optimize the model with increasing accuracy. Design/methodology/approach: The development of the model in the study is divided into seven stages: (1) data collection, (2) pre-processing data, (3) selected pre-trained SentenceTransformers model, (4) semantic similarity (sentence pair), (5) keyword similarity, (6) calculate final score and (7) evaluating model. Findings: The multilingual paraphrase-multilingual-MiniLM-L12-v2 and distilbert-base-multilingual-cased-v1 models got the highest scores from comparisons of 11 pre-trained multilingual models of SentenceTransformers with Indonesian data (Dhini and Girsang, 2023). Both multilingual models were adopted in this study. A combination of two parameters is obtained by comparing the response of the keyword extraction responses with the rubric keywords. Based on the experimental results, proposing a combination can increase the evaluation results by 0.2. Originality/value: This study uses discussion forum data from the general biology course in online learning at the open university for the 2020.2 and 2021.2 semesters. Forum discussion ratings are still manual. In this survey, the authors created a model that automatically calculates the value of discussion forums, which are essays based on the lecturer's answers moreover rubrics. © 2023, Bachriah Fatwa Dhini, Abba Suganda Girsang, Unggul Utan Sufandi and Heny Kurniawati.

关键词： Discussion forum Essay scoring Keyword similarity Semantic similarity

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Robot Finder v2: Search and Ingestion of Educational Robotics Data from Youtube 16

Robot Finder v2: Search and Ingestion of Educational Robotic...

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16th Brazilian Symposium on Robotics and 15th Workshop on Robotics in Education, SBR-WRE 2024

作者： Urbano Lopes, Gabriel Batistuta de Santana Costa, Ryllari Raianne Marques Alves Filho, Sebastião Emidio B.Sc. in Computer Science Rio Grande do Norte State University RN Mossoró Brazil Graduate Program in Computer Science - PPgCC UERN/UFERSA Rio Grande do Norte State University Federal University of the Semi-Arid Region RN Mossoró Brazil

ISBN: (纸本)9798350391084

Educational Robotics offers an interactive learning approach where students can turn abstract theories into concrete projects. With the exponential growth in content creation on internet platforms, finding specific, high-quality learning objects about robotics in education can be challenging. Platforms like YouTube, for example, have a vast collection of educational videos that cover various topics related to educational robotics. However, they are scattered among a massive amount of available data, making it difficult to have more assertive searches and identify the best resources. This work aims to present Robot Finder v2, a software that uses an automated approach to feed RepositORE, a repository of Learning Objects in Educational Robotics. The Robot Finder v2 searches and stores data from YouTube videos to make the search for this content simpler and more effective. The collected data feeds the RepostiORE, where these objects can be adapted and researched by users looking to acquire skills in educational robotics. For the objects to be found accurately, the software uses the YouTube Data API v3, which has a set of metadata in addition to the descriptions of the videos provided by their creators. The processing done in the Robot Finder optimizes the search and registration of information by mapping API endpoints and seeking correlations of metadata between RepositORE objects and YouTube videos. ©2024 IEEE.

关键词： Educational robots

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Dual Word Embedding Framework for Gender-Based Writing Style Analysis Using Word2Vec and BERT

Dual Word Embedding Framework for Gender-Based Writing Style...

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Informatics International Conference (BTS-I2C), Beyond Technology Summit on

作者： Aditya Riyanto Amalia Zahra Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331508579

ISBN: (纸本)9798331508586

Social media has evolved into a vast and multifaceted data repository, presenting valuable opportunities to investigate gender-based variations in writing styles. This study aims to enhance the precision of gender classification on Twitter text by leveraging a combined word embedding approach. The paper proposes a framework that utilizes two distinct word embedding models - a combination of GloVe and BERT, as well as GloVe and Word2Vec - to capture both global and contextual linguistic nuances within the text. The dataset comprises a comprehensive collection of tweets and user profile descriptions harvested from Twitter, which underwent meticulous preprocessing and word representation steps before being further processed using the robust Random Forest algorithm. The experimental findings indicate that the combination of GloVe and BERT outperforms the other combination of GloVe and Word2Vec, and also surpasses the performance of single embedding models, with 62.93% accuracy being the highest achieved result. This study provides valuable insights into the potential of combined word embedding techniques for enhancing the accuracy of gender classification in social media text analysis.

关键词： Biometrics Text analysis Accuracy Social networking (online) Blogs Supervised learning Writing Linguistics Natural language processing Random forests

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An Object-Oriented Fuzzy Decision Model for Determining the Fittest Depot to React a Natural Disaster Emergency

An Object-Oriented Fuzzy Decision Model for Determining the ...

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Technology & Engineering Management Conference - Asia Pacific (TEMSCON-ASPAC), IEEE

作者： Ditdit Nugeraha Utama Ayu Kristianty Ferina Computer Science Department BINUS Graduate Program Master of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331515881

ISBN: (纸本)9798331515898

The domain of natural disaster procedure and management is highly challenging. The Indonesian government has proposed various strategies to address this issue. One of the strategies is making depots in every province to treat a natural disaster. This study aims to develop an object-driven decision model to identify the most suitable depot for mobilization to disaster areas. The model design and construction utilize objectoriented and fuzzy logic approaches. Ultimately, the constructed model can simulate 35 depots in Indonesia and identifies depot number 3, with a decision value of 50.40, as the most suitable for mobilization to the natural disaster in Pasaman, West Sumatra Province, which occurred on February 25, 2022.

关键词： Fuzzy logic Disasters Object oriented modeling Engineering management Government Asia Object recognition

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

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

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International Conference on Information Management and Technology (ICIMTech)

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

ISBN: (数字)9798350390025

ISBN: (纸本)9798350390032

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).

关键词： Analytical models Sentiment analysis Accuracy Time series analysis Predictive models Logic gates Data models Information management Forecasting Stock markets

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Drowsiness Detection in Low-Light Conditions Using YOLO and CLAHE Augmentation for Real-Time Applications

Drowsiness Detection in Low-Light Conditions Using YOLO and ...

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International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)

作者： Sella Joanita Nur Aprilia Devi Fitrianah Computer Science Department Binus Graduate Program–Master of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331519643

ISBN: (纸本)9798331519650

In the era of advancing technology, deep learning, particularly within the realm of computer vision, has emerged as a critical tool for enhancing human safety. This research focuses on developing a drowsiness detection system for car drivers using the YOLOv5 algorithm, aimed at mitigating the risks associated with microsleep, a temporary loss of attention that poses a significant threat on the roads. The prevalence of traffic accidents attributed to drowsy driving is alarming, with statistics indicating that over 9.5% of accidents in Indonesia during late 2019 were linked to drowsiness. To address this pressing issue, our study employs the YOLOv5 model to classify driver states into ‘Drowsy’ and ‘Awake’ categories, enhanced by the Contrast Limited Adaptive Histogram Equalization (CLAHE) augmentation technique, which optimizes performance in low-light conditions. The drowsiness detection system is designed as a user-friendly application that provides real-time alerts to drivers upon detecting signs of drowsiness. The implementation process includes data collection, preprocessing, and model training, leading to the evaluation of the system’s accuracy. Evaluation results demonstrate a detection accuracy of 94%, with precision and recall both reaching 93%. These findings underscore the efficacy of integrating YOLO and CLAHE in developing a reliable drowsiness detection system, contributing to road safety by reducing the risk of accidents related to driver fatigue.

关键词： YOLO Deep learning Training Adaptation models Accuracy Real-time systems Sensor systems Intelligent sensors Vehicles Accidents

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A Decision Model for Smart Village in Measuring Socioeconomic Inequality

A Decision Model for Smart Village in Measuring Socioeconomi...

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International Conference on ICT For Smart Society (ICISS)

作者： Ditdit Nugeraha Utama Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798350389654

ISBN: (纸本)9798350389661

Making socio-economic decisions is critical as they profoundly impact communities. It is imperative to approach these decisions with academic rigour and objectivity to effectively address prevalent community-wide issues. One such challenge often encountered by communities is socio-economic inequality. This study attempts to tackle this challenge by creating a decision model to assess socio-economic inequality in a region. The research was performed via four crucial stages, leading to the creation of a fuzzy decision model based on the Williamson Index (WI) method. This carefully developed model was designed and constructed using an object-oriented approach. The simulation results indicated that the integrated inequality index value for Sawah, an urban village in Ciputat, Indonesia, is 0.65. This em-pirical result provides valuable insights, enabling policymakers and stakeholders (as decision-makers) to design targeted policies and programs to reduce inequality and enhance community prosperity.

关键词： Object oriented modeling Simulation Indexes Stakeholders Socioeconomics

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

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

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International Conference on ICT For Smart Society (ICISS)

作者： Ditdit Nugeraha Utama Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798350389654

ISBN: (纸本)9798350389661

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.

关键词： Fuzzy logic Analytical models Object oriented modeling OFDM Bitcoin Market research Data models Context modeling

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