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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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Nonlinear Value Function Approximation Method with Easy Hyperparameter Tuning and Convergence Guarantee

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IEEE Access 2025年 13卷 30117-30126页

作者： Watabe, Yuuya Shibuya, Takeshi University of Tsukuba Master's Program in Intelligent and Mechanical Interaction Systems Ibaraki305-8577 Japan University of Tsukuba Institute of Systems and Information Engineering Ibaraki305-8577 Japan

Reinforcement Learning is a branch of machine learning to learn control strategies that achieve a given objective through trial-And-error in the environment. Because this can be applicable even when the state transition function of the control object is unknown or it is difficult to create its model properly, this can reduce the designer's burden. Reinforcement learning repeats evaluating a policy and improving it based on that evaluation. An algorithm called value function approximation is used in this evaluation. Value function approximation is a general term for algorithms of approximating a function called value function, which maps an arbitrary state to how much reward can be obtained in the future when acting according to a policy from that state. Value function approximation should have high approximation accuracy and hyperparameter easy to tune. Although various value function approximation methods have been proposed so far, some of them does not have convergent guarantee and the others' hyperparameter tuning is difficult. The purpose of this paper is to propose a value function approximation method that have convergence guarantee even with nonlinear function approximator and whose hyperparameter tuning is easy and to verify its performance through numerical experiments. To this end, we focused on GTD2 method, which is one of the value function approximation methods that has convergence guarantee even with a nonlinear function approximator. GTD2 method has the drawback that its hyperparameter is difficult to tune appropriately so that the approximation does not diverge during learning. Therefore, we firstly clarified the cause of the divergence of GTD2 method. Secondly, based on this cause, we proposed Normalized and Regularized GTD2 (NRGTD2) method, that incorporates a method for suppressing divergence into GTD2 method. Finally, through numerical experiments, it was clarified that the proposed method suppresses the divergence and can achieve better approxim

关键词： Reinforcement learning

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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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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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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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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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Optimizing Customer Retention in Urban Laundry Services: A Comparative Analysis of Machine Learning Algorithms in Jakarta's MSME Sector 9

Optimizing Customer Retention in Urban Laundry Services: A C...

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

作者： Putri, Rhisa Adika Mauritsius, Tuga Ananda, Bryan Jayadi, Riyanto BINUS Graduate Program - Master of Information Systems Management Bina Nusantara University Information Systems Management Department Jakarta11480 Indonesia

ISBN: (纸本)9798350390025

In the rapidly urbanizing context of major cities such as Jakarta, the demand for efficient and rapid laundry services poses a considerable challenge, particularly within residential settings like boarding houses or dormitories in Central Jakarta, where access to communal laundry facilities is markedly constrained. Against this backdrop, laundry enterprises categorized under Micro, Small, and Medium Enterprises (MSMEs) in the service sector have witnessed significant growth. These services typically encompass the collection, washing, drying, folding, and delivery of laundry to the customer's domicile. Nevertheless, the burgeoning proliferation of laundry businesses has precipitated intensified competition within the sector, compelling these enterprises to devise strategies for customer retention and revenue augmentation. This research endeavors to delineate effective strategies and solutions to navigate the competitive landscape of the laundry industry. The study undertakes a meticulous evaluation of various machine learning algorithms, including Decision Tree, Logistic Regression, Naïve Bayes, Support Vector Machine, Random Forest, XGBoost, LightGBM, and CatBoost, based on their performance with unseen data. This evaluation employs a comprehensive set of metrics such as precision, recall, Fl-score, and ROC AUC to determine the efficacy of each model. The findings reveal that the CatBoost Classifier algorithm outperforms others, achieving a training accuracy of 95.9% and a testing accuracy of 85.7%. The novelty of this study lies in its comprehensive comparison of multiple machine learning algorithms in the context of MSMEs in Jakarta's laundry sector, highlighting the superior performance of the CatBoost algorithm. Furthermore, the study contributes by demonstrating the practical application of these algorithms to improve customer retention strategies, enhance service quality, and optimize marketing tactics. Based on these results, the study recommends the implemen

关键词： Decision trees

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Royalty Management by Using Blockchain Network: A Multiple Case Study 9

Royalty Management by Using Blockchain Network: A Multiple C...

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

作者： Nathaniela, Thalea Christy Princes, Elfindah Wang, Gunawan BINUS Graduate Program - Master of Information Systems Management Bina Nusantara University Information Systems Management Department Jakarta11480 Indonesia

ISBN: (纸本)9798350390025

The rise of music streaming services has led to significant challenges in royalty management, particularly affecting independent musicians who face poor royalty payments and a lack of transparency in royalty calculations. This research explores the application of blockchain technology to address these issues, focusing on its potential to revolutionize royalty management within the music industry. The study provides a conceptual framework for understanding music streaming services, digital music piracy, and royalty transparency, analyzing the impact of blockchain on revenue generation in the Indonesian music industry. Utilizing a literature review, case studies, and a theoretical framework, the research investigates the effectiveness of blockchain in enhancing transparency, and fairness in revenue distribution, and rights management. Through purposive sampling and the analysis of BitTorrent as a case study, the results highlight the trans formative potential of blockchain in the music industry, particularly in addressing the 'value gap' in unallocated royalties and improving revenue generation for musicians. The findings underscore the significant implications of blockchain technology for the future of royalty management and the overall music ecosystem. © 2024 IEEE.

关键词： blockchain music streaming services Royalty management

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Sentiment Analysis of Digital Customer Service Helpdesk Tickets: A Comparison between BERT and SVM

Sentiment Analysis of Digital Customer Service Helpdesk Tick...

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2024 International Conference on Computer Engineering, Network and Intelligent Multimedia, CENIM 2024

作者： Jayusman Jayadi, Riyanto Bina Nusantara University BINUS Graduate Program - Master of Information Systems Management Information Systems Management Department Jakarta11480 Indonesia

ISBN: (纸本)9798350368802

This research aimed to perform a comparative analysis of the effectiveness of customer support systems and the management of helpdesk tickets, which play a pivotal role in maintaining customer satisfaction and loyalty in the current digital age. As helpdesk tickets surge in number, leveraging automation to accurately gauge and address customer emotions becomes essential. This research introduces the application of Bidirectional Encoder Representations from Transformers (BERT), a sophisticated natural language processing (NLP) technique, for analyzing the sentiments expressed in helpdesk tickets of digital products. A BERT model was crafted and trained to categorize the sentiments of these tickets as either positive, negative, or neutral. This endeavor aims to equip businesses with deeper insights, enabling them to improve their approach to customer service. The model underwent training with a collection of helpdesk tickets from a variety of digital products, such as software, mobile applications, and online services, featuring a total of 8,240 entries with a distribution of 991 negative, 6,613 neutral, and 636 positive comments. Findings reveal that the BERT model outshines the Support Vector Machine in accuracy, achieving a 90% success rate versus the latter's 88%. © 2024 IEEE.

关键词： Customer satisfaction

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