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29th International Conference on information Technology, IT 2025

作者： Susanto, Giovan Meidy Mauritsius, Tuga BINUS Graduate Program-Master of Information System Management Information Systems Management Department Jakarta11480 Indonesia

ISBN: (纸本)9798331517649

This research investigates sentiment analysis of user reviews for the XYZ e-wallet application on the Google Play Store, which currently holds a low rating of 2.7 out of 5. A preliminary study, including a focus group discussion with key stakeholders, revealed that this rating is primarily due to insufficient consideration of user feedback during the Software Development Life Cycle (SDLC). This study employs word2vec and Long Short-Term Memory (LSTM) models to analyze user sentiments, using a dataset comprising reviews collected between July 1, 2023, and June 30, 2024. The developed sentiment analysis model achieved an accuracy of 91.40%. Subsequently, the model was applied to predict sentiments from new review data spanning July 1, 2024, to July 31, 2024. The results were visualized using BERTopic for topic modeling and BART for text summarization. Insights from these visualizations informed recommendations for application improvement, particularly addressing frequent issues with balance top-ups that fail to update, which were incorporated into the product backlog to enhance user satisfaction. This study is limited to a single sentiment analysis model, topic modeling technique, and summarization method. Future research should explore applying the proposed model to other applications and evaluating alternative algorithms for improved performance. © 2025 IEEE.

关键词： Software design

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Assessing the Potential of Robo-Advisors in Transforming Indonesia's Insurance Sector 29

Assessing the Potential of Robo-Advisors in Transforming Ind...

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29th International Conference on information Technology, IT 2025

作者： Arbi, Daniel Yosia Gui, Anderes BINUS Graduate Program-Master of Information System Management Information Systems Management Department Jakarta11480 Indonesia

ISBN: (纸本)9798331517649

This study aims to investigate the adoption of robo-advisors in the Indonesian insurance sector, focusing on key factors influencing their acceptance, such as trust, anxiety, performance expectancy, preference for human advisors, attitude, and perceived privacy. The research employed a quantitative approach using a structural equation modeling (SEM) technique through Partial Least Squares (PLS). Data were collected via a Google Forms survey distributed to 442 valid respondents using purposive sampling, with the study conducted in Jakarta, Indonesia. The findings revealed that performance expectancy, anxiety, preference for human advisors, and perceived privacy significantly impact the intention to use robo-advisors, whereas trust and attitude showed limited influence. The study concludes that while robo-advisors hold potential in enhancing efficiency and personalization, concerns such as user privacy and the need for human interaction remain critical. Insurance companies should prioritize robust data privacy measures and consider hybrid models combining robo-advisors with human advisors to enhance user trust and satisfaction. Implications for practice include fostering user trust through transparent data handling policies and providing educational resources to mitigate technology-related anxiety. Limitations of this research include its focus on respondents in a single geographic region and its reliance on self-reported data, which may introduce biases. Future research should explore the application of robo-advisors in broader insurance markets and examine their long-term impact on user behavior and financial decision-making. © 2025 IEEE.

关键词： Differential privacy

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Evaluation User Experience of Interest in Using Digital Insurance Platforms in Indonesia 19

Evaluation User Experience of Interest in Using Digital Insu...

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19th International Conference on Ubiquitous information Management and Communication, IMCOM 2025

作者： Windari, Jessicania Oktavia, Tanty Information Systems Management Department BINUS Graduate Program-Master of Information System Management Jakarta11480 Indonesia

ISBN: (纸本)9798331507817

This study looks on the factors that influence 452 Indonesian customers' adoption of digital insurance. The study investigates the impact of psychological, social, and technical aspects on adoption intentions using a modified UTAUT2 model. The results highlight the importance of psychological and social factors over technical ones in influencing the adoption of digital insurance. These factors include perceived benefits, perceived risk, price value, social influence, facilitating conditions, trust, behavioral intention, effort expectancy, and performance expectancy. These findings offer the insurance sector invaluable direction for creating digital products that suit consumer preferences and efficient marketing plans. Purposive sampling was used in the data gathering procedure, and Smart PLS 4 was the statistical tool of choice for structural equation modeling, which was used for hypothesis testing. The results indicate that attitude has a major influence on behavioral intention. © 2025 IEEE.

关键词： Behavioral Intention Digital Insurance Indonesia Trust User Experience

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Analysis of Factors Affecting the Adoption of Digital Insurance in Jakarta 29

Analysis of Factors Affecting the Adoption of Digital Insura...

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29th International Conference on information Technology, IT 2025

作者： Windari, Jessicania Oktavia, Tanty BINUS Graduate Program-Master of Information System Management Information Systems Management Department Jakarta11480 Indonesia

ISBN: (纸本)9798331517649

This study related phenomena to the adoption of digital insurance services in Jakarta. Analysis of 403 respondents reveals that several factors have no significant impact on individual behavioral intention to use digital insurance, although facilitating conditions and performance expectations do not have a significant effect on behavioral intention. This research suggests a unique behavior among digital insurance consumers in Jakarta that requires further understanding. Utilizing SmartPLS 4.0 software for Structural Equation Modeling (SEM-PLS) analysis, the study delves beyond the technical aspects of using digital insurance applications. It needs to highlight to consider psychological and social factors such as perceived benefit, perceived risk, and trust when designing the method. This research makes a significant contribution to identifying the factors influencing digital insurance adoption in Jakarta. By using the UTAUT2 model with modification, the study successfully identifies a gap between theoretical expectations and the market in Jakarta. These results open avenues for further research to delve deeper into context-specific factors in different Indonesian big cities. Future study suggests that need for developing more comprehensive models to predict consumer behavior in the context of financial technology adoption. Insurance industry can design more targeted strategies to enhance the penetration of digital insurance service in Indonesia. © 2025 IEEE.

关键词： and User Experience Behavioral Intention Digital Insurance Jakarta Trust

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Analyzing Twitter Sentiment on Stock Using the Bidirectional Encoder Representations (BERT) Based on the Transformer Model 29

Analyzing Twitter Sentiment on Stock Using the Bidirectional...

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29th International Conference on information Technology, IT 2025

作者： Putri, Rhisa Adika Oktavia, Tanty BINUS Graduate Program-Master of Information System Management Information Systems Management Department Jakarta11480 Indonesia

ISBN: (纸本)9798331517649

This study explores sentiment analysis on Indonesian social media data related to stock investments, focused on GOTO stocks. Using the Knowledge Discovery in Database (KDD) methodology, data was collected from the social media platform X (formerly Twitter) during the period from January 2023 to December 2024, preprocessed, and manually labeled into positive, neutral, and negative categories. A deep learning model, Bidirectional Encoder Representations from Transformers (BERT), was employed to analyze the data. The model demonstrated strong performance, achieving a weighted average precision, recall, and F1-score of 0.95 on the test set, with precision scores of 0.99 for positive sentiments and recall scores of 0.97 for neutral sentiments. However, the F1-score for negative sentiments was comparatively lower at 0.87, indicating an area for improvement. These results highlight the effectiveness of sentiment analysis for monitoring stock market trends and public opinion. However, the study is limited by its reliance on data from a single platform (Twitter) and the relatively small dataset size. Future research should incorporate data from multiple platforms, such as Instagram and Facebook, and explore alternative models alongside BERT to enhance performance and generalizability. © 2025 IEEE.

关键词： Tweets

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A deep learning framework for automatic assessment of presence in virtual reality using multimodal behavioral cues

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Neural Computing and Applications 2025年第8期37卷 6283-6303页

作者： Pannattee, Peerawat Shimada, Shogo Yem, Vibol Nishiuchi, Nobuyuki Graduate School of Systems Design Tokyo Metropolitan University Tokyo191-0065 Japan Graduate School of Science and Technology Degree Program in Systems and Information Engineering University of Tsukuba Ibaraki3058573 Japan

Effective development of virtual reality (VR) applications is heavily reliant on the evaluation of user experience (UX). However, traditional methods such as questionnaires have inherent limitations which hinder their ability to capture nuanced behavioral responses and impede the agility of VR content creation: They are time-consuming, burdensome for users, and require significant human effort for interpretation. This study introduces an automated framework aimed at addressing the limitations of questionnaire-based evaluations to assess UX in VR. Our primary focus is to validate the concept of this framework through assessment of the sense of presence (SOP), a crucial psychological perception with significant impact on VR UX. Our proposed framework utilizes a deep neural network (DNN) to analyze patterns in multimodal behavioral cues, including facial expressions, head movements, and hand movements, to predict scores from the Igroup Presence Questionnaire (IPQ). Additionally, we introduce two statistical profiles: the Visual Entropy Profile (VEP), which offers insights into visual complexity by depicting scene entropy, and the Experiential Presence Profile (EPP), which is designed to capture users’ historical SOP levels to enable personalized baseline and sensitivity estimation. The proposed framework achieves a significant correlation between actual and predicted IPQ scores, with a Spearman’s rank correlation coefficient of 0.7303, showcasing the potential of DNNs in analyzing complex behavioral signals and automating SOP assessment. This study represents a pioneering effort in leveraging DNNs for the automatic assessment of SOP and paves the way for future advances in automatically assessing VR UX and unlocking new opportunities in the field. © The Author(s) 2025.

关键词： Virtual environments

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Neural Networks and Explainable Artificial Intelligence for Breast Cancer Prediction and Classification 16

Neural Networks and Explainable Artificial Intelligence for ...

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16th International Conference on ENTERprise information Systems, CENTERIS 2024 - 12th International Conference on Project MANagement, ProjMAN 2024 - 14th International Conference on Health and Social Care information Systems and Technologies, HCist 2024

作者： Yahata, Erika Santos, Pablo Deoclecia Dos De Souza Pires, Maria Marlene Suyama, Ricardo Simoes, Priscyla Waleska Graduate Program in Information Engineering Federal University of ABC SP Santo André Brazil Graduate Program in Biomedical Engineering Federal University of ABC São Bernardo Do Campo Brazil Pediatrics Department Federal University of Santa Catarina Santa Catarina Brazil

Solutions based on Artificial Intelligence techniques have been proposed in the healthcare sector, however the lack of understanding of the results has been a cause for concern and one of the main barriers to their effective use. To fill the gap in understanding these models considered black boxes, the Explainable Artificial Intelligence was proposed to explain the relationship between input data and the prediction results made by the Artificial Intelligence models. In this study, we present the use of explainability techniques for a black box machine learning model in breast cancer classification. Breast cancer is the type of cancer that causes the most deaths among women in the world and early diagnosis is essential to increase the chances of survival. The study evaluated the explainability of breast tumor predictions based on the Multilayer Perceptron artificial neural networks algorithm, considering a sample that contained data from 164 female patients undergoing Core Biopsy in the southern region of Brazil. The SHAP, LIME, PDP and ICE methods were used for the global and local explainability of the predictions. The results showed that in the global assessment, the BI-RADS® 5 ultrasound and mammography attributes were considered the most important for predicting the malignant tumor. Nodule size greater than 2 cm, presence of a family history and palpable nodule were also considered important for the prediction. In the local evaluation, it was found that the model correctly classified the tumors considering different characteristics of the patients. Through the results presented, the methods of explicability show evidence that the predictive model for breast cancer can be interpreted and understood, overcoming the barrier of lack understanding of the results of a black box model. © 2025 Elsevier B.V.. All rights reserved.

关键词： Prediction models

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Product Backlog Recommendations Using Sentiment Analysis with Topic Modelling and Text Summarization in the XYZ E-Wallet Application

Product Backlog Recommendations Using Sentiment Analysis wit...

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International Scientific-Professional Conference on information Technology (IT)

作者： Giovan Meidy Susanto Tuga Mauritsius Information Systems Management Department BINUS Graduate Program-Master of Information System Management Jakarta Indonesia

ISBN: (数字)9798331517649

ISBN: (纸本)9798331517656

关键词： Analytical models Sentiment analysis Reviews Online banking Text summarization Data visualization Predictive models Stakeholders Long short term memory Software development management

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Evaluation User Experience of Interest in Using Digital Insurance Platforms in Indonesia

Evaluation User Experience of Interest in Using Digital Insu...

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International Conference on Ubiquitous information Management and Communication (IMCOM)

作者： Jessicania Windari Tanty Oktavia Information Systems Management Department BINUS Graduate Program-Master of Information System Management Jakarta Indonesia

ISBN: (数字)9798331507817

ISBN: (纸本)9798331507824

关键词： Insurance Psychology Mathematical models User experience Social factors Data models information management Testing

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Analyzing Twitter Sentiment on Stock Using the Bidirectional Encoder Representations (BERT) Based on the Transformer Model

Analyzing Twitter Sentiment on Stock Using the Bidirectional...

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International Scientific-Professional Conference on information Technology (IT)

作者： Rhisa Adika Putri Tanty Oktavia Information Systems Management Department BINUS Graduate Program-Master of Information System Management Jakarta Indonesia

ISBN: (数字)9798331517649

ISBN: (纸本)9798331517656

关键词： Sentiment analysis Social networking (online) Databases Blogs Bidirectional control Transformers Knowledge discovery Encoding Data models Investment

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