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9th IEEE Information Technology International Seminar, ITIS 2023

作者： Amadeo, Adriel Justin Siento, Joyceline Graciella Eikwine, Tesalonika A. Diana Parmonangan, Ivan Halim School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350306835

Bitcoin is one of the cryptocurrencies that have a large number of holders and a high transaction volume. Its price has experienced major fluctuation since its inception, resulting in relatively high price volatility. This has been a challenge for researchers over the past decade in forecasting its future price. In this study, we aim to address this problem by utilizing a neural network model called the Temporal Fusion Transformer (TFT). The TFT model uses the concept of multi-horizon forecasting to learn from historical data and predict future prices based on multiple time steps. Due to the numerous factors influencing Bitcoin's price, our study incorporates additional predictive data, such as Twitter sentiment, trends, and seasonality. The proposed method is trained using the TFT model with historical Bitcoin data from 2014 until 2020, then we performed transfer learning by using different past covariates as inputs. As a result, the proposed method in this study demonstrated the best performance, with a MAE of 0.05, a RMSE of 0.07, a MAPE of 5.92%, and a quantile loss of 0.03, surpassing other approaches. This makes it the state-of-the-art solution for highly volatile forecasting using a neural network model. © 2023 IEEE.

关键词： Forecasting

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Rice Varieties Classification Using Neural Network and Transfer Learning with MobileNetV2 4

Rice Varieties Classification Using Neural Network and Trans...

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4th International Conference on Artificial Intelligence and data sciences, AiDAS 2023

作者： Vania, Vanessa Setyadi, Anthony Widyatama, I Made Danendra Kurniadi, Felix Indra School of Computer Science Bina Nusantara University Computer Science Department Jakarta11530 Indonesia

ISBN: (纸本)9798350318432

Rice plays a vital role in global food security, feeding over 8 billion people worldwide. The classification of rice varieties based on visual characteristics, such as color, shape, and texture, is crucial for quality inspection and efficient farming practices. However, existing classification methods heavily rely on subjective and time-consuming human visual inspection, leading to errors and inefficiencies. This research paper explores the potential of deep learning approaches, specifically neural network and transfer learning with MobileNetV2, for accurate and efficient rice variety classification. The paper builds upon previous studies that have demonstrated the superiority of deep learning over traditional machine learning methods. Experimental results using a publicly available dataset of 75,000 rice grain images, labeled with five rice varieties, show that both the neural network and MobileNetV2 models achieve high accuracy rates. The neural network model is trained using stochastic gradient descent with 250 epochs, while the MobileNetV2 model leverages pretraining on the ImageNet dataset and is trained with the Adam optimizer for 10 epochs. The comparative analysis reveals that the MobileNetV2 model performs slightly better, benefiting from its ability to extract features from images learned from the large-scale ImageNet dataset. The findings highlight the potential of deep learning methods for rice variety classification, offering improved accuracy and efficiency in rice quality inspection. The choice between the neural network and MobileNetV2 models depends on the specific requirements and priorities of the application, with the neural network offering flexibility and transparency, and MobileNetV2 providing high accuracy through pre-trained feature extraction. © 2023 IEEE.

关键词： Textures

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Distance Metric Analysis in Recommendation System Using Content-Based Filtering Method 6

Distance Metric Analysis in Recommendation System Using Cont...

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6th International Conference on Information and Communications Technology, ICOIACT 2023

作者： Christina Sanjaya, Febrian Cahyaningtyas, Valentina Tiara Edbert, Ivan Sebastian Suhartono, Derwin Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350315639

Despite the prevalence of music recommendations in the music industry, the rapid evolution of music and the increasing diversity of musical genres present a formidable challenge in discovering songs that match user's preferences. The content-based filtering method is a common recommendation system utilizing a music feature-based approach to identify user preferences. The K-Nearest Neighbor (KNN) algorithm is utilized to determine the similarity between songs. However, in order to enhance the quality of music recommendations, it is necessary to employ appropriate distance metrics and threshold values. The study collects song data and associated music features from Spotify to experimentally determine the best distance metric and feature selection threshold. The evaluation metrics employed in this study are root mean square error (RMSE) and mean absolute error (MAE). The results show that Cityblock Distance performs best with an average RMSE of 0.2257, and a threshold value of 0.05 achieves optimal performance with an average MAE of 0.124. These findings indicate that choosing suitable distance metrics and implementing feature selection can enhance the accuracy of music recommendation systems. However, limitations in dataset representation warrant further research with larger datasets and additional factors to improve generalization and user experience in music recommendations. © 2023 IEEE.

关键词： Recommender systems

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Sentiments comparison on Twitter about LGBT 7

Sentiments comparison on Twitter about LGBT

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7th International Conference on computer science and Computational Intelligence, CSCI 2022

作者： Aldinata Soesanto, Axell Mondrian Chandra, Vincent Christian Suhartono, Derwin Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

Twitter has become one of the most popular mediums for people to voice their opinions freely on any topic from around the world. Most companies use sentiment analysis from Twitter to see how well their product is. These opinions are categorized into three types which are positive, negative, and neutral sentiments. This paper focuses on Sentiment Analysis of LGBT, which has become a trending and controversial topic for society to talk about. We took these Tweets from fifty states in the United States of America and pre-process them before classifying their sentiment. We test five algorithms to classify the sentiments which are TextBlob PatternAnalyzer, Naive Bayes, Linear Support Vector Machine, Logistic Regression, and XGBoost using both non pre-processed data and pre-processed data. We found that the Logistic Regression without text-preprocessing gives us the best result with a 70.87% F1-score. After applying our sentiment classifier to the US tweets that talked about LGBT, we found that most tweets have a neutral sentiment. © 2022 The Authors. Published by ELSEVIER B.V.

关键词： Sentiment analysis

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Utilization of Augmented Reality for Introducing Tongkonan Toraja Traditional House 8

Utilization of Augmented Reality for Introducing Tongkonan T...

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8th International Conference on Information Management and Technology, ICIMTech 2023

作者： Nadita, Sekar Ayu Maheswari, Eileen Anindya Putri Santoso, Kezia Angeline Cahyono, Mochammad Alfito Dwi Permana, Frihandhika School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350326093

This paper discusses the implementation of Augmented Reality (AR) as a form to introduce the culture of Tongkonan Toraja House that holds cultural and historical values of the Toraja tribe. However, many people have an absence of understanding about Torajan culture. The purpose of this AR is to obtain knowledge and recognition by giving experiences and interactions through virtual representation of the Tongkonan Toraja House. Development uses the Waterfall approach, starting with using good 3D models, designing the layout, implementing the marker, adding interactivity, testing, and maintenance of the application. The analysis of User Experience Questionnaire (UEQ) as a measurement method obtains positive evaluations by entering data from 27 respondents. The positive evaluations were obtained of the Tongkonan Toraja AR, which its values surpass 0,8. Stimulation aspect indicates a good level of excitement from the users with the mean value of 2.204, the highest mean value among other aspects. On the contrary, Efficiency received the lowest mean value of 1.981, despite the fact that its mean value is above 0.8 which indicates positive evaluation. The UEQ dimensions are categorized into attractiveness, pragmatic quality, and hedonic quality. Pragmatic quality received the lowest score, while hedonic quality obtained the highest score, with attractiveness falling in between. Overall, the findings demonstrate positive user evaluations of the Tongkonan Toraja AR across dimensions, with Stimulation receiving particular praise and Efficiency showing potential for enhancement. These results indicate that the AR experience is generally well-received, providing valuable insights for further improvements and development. © 2023 IEEE.

关键词： Augmented reality

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Efficacy Comparison of Augmented Reality and Virtual Reality-based Games for Post-Stroke Rehabilitation: A Systematic Literature Review 9

Efficacy Comparison of Augmented Reality and Virtual Reality...

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9th International HCI and UX Conference in Indonesia, CHIuXiD 2023

作者： Taufiq, Milkhatussyafa'ah Wicaksono, Danang Wahyu School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350304084

The usage of Augmented Reality (AR) and Virtual Reality (VR) games is seen to have the potential to deliver considerable advantages for post-stroke rehabilitation since it can increase healing progress while saving time and money. This study compares the use of AR and VR in games for post-stroke rehabilitation by assessing the game design, measurement technique, and challenges that may be improved for future studies. Using the PRISMA concept to conduct a Systematic Literature Review (SLR) of related research by identifying relevant data sources, evaluating abstracts, determining eligible publications, and analyzing data using the meta-analysis approach. We analyzed 198 publications from multiple database sources during this procedure and identified 24 papers for additional investigation. The study's implications show that the game design and assessment technique that best meets post-stroke rehabilitation assignments is the most important aspect in assessing the capabilities of AR and VR games for post-stroke rehabilitation, which influences future research on AR and VR games. It was also discovered that numerous obstacles to the usage of AR and VR games must be solved in order to maximize the post-stroke recovery process. However, the data sources employed in the study have limitations. To optimize the utilization of AR and VR games in post-stroke rehabilitation, it is required to examine rules and infrastructure, as well as to employ more diverse data sources. © 2023 IEEE.

关键词： Augmented reality

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Rust's Memory Safety Model: An Evaluation of Its Effectiveness in Preventing Common Vulnerabilities 8

Rust's Memory Safety Model: An Evaluation of Its Effectivene...

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8th International Conference on computer science and Computational Intelligence, ICCSCI 2023

作者： Oey, Kevin Andrian Santoso Kwee, Catherine Chua, William Nabiilah, Ghinaa Zain Rojali Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

Ensuring safe program operation is crucial for systems programming, and memory safety is a significant aspect of this. This paper assesses the memory safety strategies used in Rust and C++ programming languages. Rust is a modern language that has become popular due to its focus on safety, performance, and concurrency, whereas C++ is known for its efficiency and low-level programming capabilities. The analysis compares and contrasts the memory safety features of both languages, including Rust's ownership and borrowing model and C++'s manual memory management techniques. Additionally, the study examines the development tools available for both languages and compares ease of use and productivity. The analysis shows that Rust has several memory safety advantages over C++. Rust's ownership and borrowing model enables safe memory management without requiring manual intervention. By enforcing strict rules for memory usage, the compiler in Rust prevents typical memory-related errors like null and dangling pointers. However, in contrast to Rust, C++ provides more low-level hardware control but needs manual memory management, exposing it to memory errors such as buffer overflows and memory leaks. Moreover, Rust's memory safety features simplify the development of secure and dependable code, especially for applications with crucial security requirements. In contrast, C++ demands significant memory management expertise to avoid memory-related errors. In conclusion, Rust offers several memory safety benefits over C++, making it a superior choice for applications with critical safety needs. However, the selection of language ultimately depends on the specific project requirements and the development team's experience and preferences. © 2023 The Authors. Published by Elsevier B.V.

关键词： C++ programming Language Memory Safety Rust programming Language Security Software Systems programming

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Tracking of Food Products from Source to consumption, Enhancing Transparency and Food Safety using Blockchain 7

Tracking of Food Products from Source to consumption, Enhanc...

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Surya Kumar, S. Viswa Sai Avinash, Vanka Gowri, S. Sathyabama Institute of Science And Technology Department of Computer Science India School of Computing Sathyabama Institute of Science And Technology India

ISBN: (纸本)9798350359299

This research discusses about the implementation of blockchain technology to enhance transparency and food safety by tracking food products from source to consumption. With growing concerns about food safety and fraud, there is a need for an efficient and secure system that can trace the journey of food products and provide accurate information to consumers. Blockchain technology offers a decentralized and immutable ledger that can record and verify every transaction in the food supply chain. By utilizing blockchain, all relevant stakeholders including farmers, suppliers, manufacturers, distributors, and retailers can input and access data at each stage, ensuring transparency and real-time visibility. This enables consumers to have access to reliable information about the origin, quality, and safety of the food they consume. Additionally, blockchain can provide a robust mechanism for detecting and preventing food fraud, as any attempt to tamper with the recorded data will be immediately detected. Implementation of blockchain technology in the food industry holds great promise for ensuring food safety and transparency, empowering consumers to make informed choices about the products they purchase and consume. © 2024 IEEE.

关键词： Supply chains

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Collaborative Filtering of Malicious Information from the MULTimedia data Using s 1

Collaborative Filtering of Malicious Information from the MU...

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1st International Conference on computer science and Emerging Technologies, CSET 2023

作者： Parasuraman, Manikandan Abirami, G. Arockia Raj, A. Krishnan, Batri Munnangi, Ashok Kumar Kumaresan, M. School of Computer Science and Engineering Jain Deemed to Be University Bengaluru India Sathyabama Institute of Science and Technology Department of Computer Science Chennai India Madanapalle Institute of Technology and Science Department of Computer Science and Engineering - Data Science Madanapalle India Velagapudi Ramakrishna Siddhartha Engineering College Vijayawada India

ISBN: (纸本)9798350341737

Due to high proliferation of unsolicited information in the text, image and video files, the social media analytics engine suffers from losing its user, user privacy and others. The unsolicited information is been a huge threat to the service provides as it affects the reputations of the services provided and hence it is necessary to develop an intelligent model. Modern machine learning techniques are applied to identify and filter unsolicited information. However, some of the conventional methods are designed in an insecure way while processing all the text file, image and video files. © 2023 IEEE.

关键词： Collaborative filtering

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Electromyography-Based Gesture Recognition With Explainable AI (XAI): Hierarchical Feature Extraction for Enhanced Spatial-Temporal Dynamics

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IEEE Access 2025年 13卷 88930-88951页

作者： Shin, Jungpil Miah, Abu Saleh Musa Konnai, Sota Hoshitaka, Shu Kim, Pankoo The University of Aizu School of Computer Science and Engineering Aizuwakamatsu965-8580 Japan Chosun University Department of Computer Engineering Gwangju61452 Korea Republic of

Hand gesture recognition using multichannel surface electromyography (sEMG) is challenging due to unstable predictions and inefficient time-varying feature enhancement. To overcome the lack of signal-based time-varying feature problems, we propose a lightweight squeeze-excitation deep learning-based multi-stream spatial-temporal dynamics time-varying feature extraction approach to build an effective sEMG-based hand gesture recognition system. Each branch of the proposed model was designed to extract hierarchical features, capturing both global and detailed spatial-temporal relationships to ensure feature effectiveness. The first branch, utilizing a Bidirectional-TCN (Bi-TCN), focuses on capturing long-term temporal dependencies by modelling past and future temporal contexts, providing a holistic view of gesture dynamics. The second branch, incorporating a 1D Convolutional layer, separable CNN, and Squeeze-and-Excitation (SE) block, efficiently extracts spatial-temporal features while emphasizing critical feature channels, enhancing feature relevance. The third branch, combining a Temporal Convolutional Network (TCN) and Bidirectional LSTM (BiLSTM), captures bidirectional temporal relationships and time-varying patterns. Outputs from all branches are fused using concatenation to capture subtle variations in the data and then refined with a channel attention module, selectively focusing on the most informative features while improving computational efficiency. The proposed model was tested on the Ninapro DB2, DB4, and DB5 datasets, achieving accuracy rates of 95.31%, 92.40%, and 93.34%, respectively. Additionally, we visualize the attention maps across various classes. These results demonstrate the system’s capability to handle complex sEMG dynamics, offering advancements in prosthetic limb control and human-machine interface technologies with significant implications for assistive technologies. © 2013 IEEE.

关键词： Deep learning

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