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HighTech and Innovation Journal 2025年第1期6卷 21-41页

作者： Oktavia, Tanty Wijaya, Eric Information Systems Management Department BINUS Graduate Program Master of Information Systems Management Bina Nusantara University Jakarta11480 Indonesia

In today's data-driven business landscape, robust metadata and data documentation practices are essential for enterprises aiming to maximize their data assets. When integrated with Business Intelligence (BI) systems, this architecture empowers data democratization, allowing widespread utilization by stakeholders across the organization. This research explores the critical role of metadata in shaping Business Intelligence (BI) systems and organizational effectiveness within today's data-driven business landscape. Through a systematic literature review, a preliminary study, a quantitative survey with 318 responses, and a focus group discussion, the study identifies key metadata components influencing BI systems effectiveness and organizational outcomes. Findings indicate a direct and positive impact of BI systems effectiveness on organizational effectiveness. Certain metadata components exhibit direct positive effects on both BI systems and organizational effectiveness. The research underscores the importance of strategic metadata implementation for enterprises seeking to optimize data-driven decision-making processes. Overall, the study provides practical implications for organizations and contributes valuable insights to the understanding of metadata's role in enhancing enterprise effectiveness. © Authors.

关键词： Data assimilation

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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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Product Backlog Recommendations Using Sentiment Analysis with Topic Modelling and Text Summarization in the XYZ E-Wallet Application 29

Product Backlog Recommendations Using Sentiment Analysis wit...

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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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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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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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Predictive Analysis of Hypertensive Heart Disease Using a Machine Learning Approach 11

Predictive Analysis of Hypertensive Heart Disease Using a Ma...

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

作者： Siska, Fran Oktavia, Tanty Bina Nusantara University Binus Graduate Program - Master of Information System Management Information System Management Department Jakarta11480 Indonesia

ISBN: (纸本)9798350389654

Hypertensive Heart Disease has become one of the contributing factors of heart failure disease, responsible for approximately 8 million deaths every year. and can lead to severe complications, including stroke, coronary heart disease, diabetes, kidney failure and blindness. This study aims to build a Machine Learning model by using CRISP-DM framework to predict suspected Hypertensive Heart Disease, and to explore the relationship of Hypertensive Heart Disease and blood cholesterol levels. Data were collected between January 2021 to June 2023 from a private hospital in Medan. Total data were 32,905 patients with hypertension, and 271 patients were diagnosed with Hypertensive Heart Disease. The data were processed using the RapidMiner. In this study, the algorithm with the highest accuracy and the lowest RMSE was selected for performance evaluation, and for the results showed that K-NN with Accuracy 89.65% and RMSE 0.280, had better performance than the Random Forest, Naive Bayes and Decision Tree algorithms. In summary, the implementation of Machine Learning can capture early detection and may help healthcare professionals to take necessary treatment regarding Hypertensive Heart Disease to improve patient prognosis and reduce the experiencing symptoms. Moreover, the hospitals and healthcare institutions can optimize their resource allocation for appropriate treatment and actions, leading to cost efficiency. © 2024 IEEE.

关键词： Patient treatment

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Augmenting the Internet-based Machinery Health Monitoring system (MHMS) Through Integration of Long Short-Term Memory (LSTM) Model for Enhanced Prediction of Remaining Useful Lifetime (RUL) 8

Augmenting the Internet-based Machinery Health Monitoring Sy...

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8th International Conference on information Technology, information systems and Electrical Engineering, ICITISEE 2024

作者： Mardianto, Frederikus D. Mauritsius, Tuga Binus Graduate Program. Master of Information System Management Bina Nusantara University Department of Information System Management Jakarta11530 Indonesia

ISBN: (纸本)9798350368970

Extensive research has been conducted to estimate the Remaining Useful Lifecycle (R UL) of components utilizing various approaches and models, including Machine Learning - Deep Learning (DL) techniques. This study aims to assess the efficacy of preselected DL methodologies in achieving superior prediction accuracy compared to conventional approaches when applied within the context of Predictive Maintenance (PdM) for turbomachinery equipment. Experimental findings reveal that the conventional model yields a Mean Absolute Percentage Error (MAPE) of 17.81%, whereas all preselected DL models consistently demonstrate improved prediction accuracy, with the lowest MAPE averaging 7.02%. Notably, the Long Short-Term Memory (LSTM) model emerges as the most accurate predictor, surpassing the naive model by 19.93 % at a 21-day horizon. Consequently, the LSTM model exhibits potential for enhancing the online Machinery Health Monitoring system (MHMS), showcasing its robust data analytic capabilities over extended forecasting horizons. Furthermore, it can provide recommendations for R UL estimation in turbo machinery components, facilitating PdM-based activities. © 2024 IEEE.

关键词： Prediction models

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Assessing the Usability and User Acceptance of an E-Commerce Application Through a Combined SUMI and TAM Framework 5

Assessing the Usability and User Acceptance of an E-Commerce...

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5th International Conference on Advances in Electrical, Electronics and Computing Technology, EECT 2025

作者： Immanuel, Jason Thoyyibah, T. Information Systems Management Department BINUS Graduate Program-Master of Information System Management Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9798331541545

In this study, a combined approach using the Software Usability Measurement Inventory (SUMI) and the Technology Acceptance Model (TAM) is employed to evaluate an e-commerce application's usability and user acceptance. Usability plays a significant role in shaping consumer satisfaction, while user acceptance is crucial for the application's success in a competitive market. This research explores how users interact with the platform, aiming to identify usability issues and factors influencing user acceptance. Key usability criteria, including visual appeal, information clarity, and ease of navigation, are analyzed alongside TAM constructs such as perceived usefulness and ease of use. The study's primary objective is to uncover the platform's strengths and weaknesses, providing insights into functionality, design, and user acceptance. By addressing identified usability concerns and understanding the factors driving user adoption, the research aims to enhance customer satisfaction and engagement. Ultimately, the findings are expected to guide the development of a more user-friendly interface, improving the e-commerce platform's overall effectiveness, user experience, and market competitiveness. © 2025 IEEE.

关键词： User experience

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Innovation in Education: Analyzing the Dynamics of Digital Transformation, and Teaching Practices on School Performance through Teacher Engagement 11

Innovation in Education: Analyzing the Dynamics of Digital T...

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11th International Conference on Computer, Control, Informatics and its Applications, IC3INA 2024

作者： Sayekti, Any Arief, Mts Utami, Viany Tjhin Management Department BINUS Business School Doctor of Research in Management Bina Nusantara University Jakarta Indonesia Information System Management Department BINUS Graduate Program Master of Information System Management Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9798331542313

Digital transformation has become an important phenomenon in modern education, rapidly changing the learning landscape. This study aims to investigate the impact of digital transformation, teaching practices, and teacher engagement on school performance. Through comprehensive analysis, this research aims to explore how the adoption of technology influences the way teachers teach and how this influences the level of teacher involvement thereby impacting school performance. The research method for this study uses a quantitative method through a survey. The scale used is a Likert scale. The sample consisted of 324 respondents who were representatives of teachers and school leaders. The instrument was tested for reliability and validity using Cronbach's Alpha, Average Variance Extracted (Pvc) and Composite Reliability (Pc). Data analysis for this study used SEM with Smartpls 3.0. The study provides results that the variables of digital transformation, teaching practices, and teacher engagement have a positive and significant effect on school performance. Based on the Path Coefficient value in the results, the higher the digital transformation, teaching practices and teacher engagement, the higher the school performance will be. © 2024 IEEE.

关键词： Digital Transformation Innovation School Performance Teacher Engagement Teaching Practices

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