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The Evaluations of Cross-Cultural User Experience for Indonesian E-Marketplace: Facilitating MSME (Micro, Small, and Medium Enterprises) Export to MERCOSUR

The Evaluations of Cross-Cultural User Experience for Indone...

引用

2024 International Conference on information Technology systems and Innovation, ICITSI 2024

作者： Pratama, Rizaldi Oktavia, Tanty Bina Nusantara Graduate Program Information System Management Jakarta Indonesia

ISBN: (纸本)9798331511470

This research aims to investigate the cross- cultural impact of User Experience (UX) perceptions between Indonesian users and users from MERCOSUR countries (Argentina, Brazil, Paraguay, and Uruguay) on the Indonesian e-marketplace accessible to Micro, Small, and Medium Enterprises (MSMEs). With the rapid growth of the digital economy and the launch of the Indonesia Mercosur Comprehensive Economic Partnership Agreement (IM-CEPA), this study is relevant to comprehend the potential assistance for Indonesian MSMEs in enhancing the value of exports. The research employs the User Experience Questionnaire Plus (UEQ+) method to assess whether the current UX of the Indonesian e-marketplace is acceptable to users with significantly different cultural backgrounds from Indonesia. Additionally, SmartPLS structural equation modeling is utilized to test hypotheses and examine relationships between UX dimensions and user behaviors. The tool is designed to accommodate cultural differences among users, enabling in- depth cross-cultural research. By exploring perceptions, preferences, and reactions of MERCOSUR users to the Indonesian e-marketplace, this research aims to identify factors influencing user acceptance and satisfaction in a cross-cultural context. The findings of this study are expected to provide strategic insights for designers, e-marketplace developers, and other stakeholders. The research can also contribute to optimizing IM-CEPA by aligning the Indonesian e-marketplace platform with the preferences and expectations of MERCOSUR users, strengthening the competitiveness of Indonesian MSMEs in the international market. By understanding the cross-cultural impact on UX, this research lays the foundation for more effective design improvements and marketing strategies, fostering economic growth and the competitiveness of Indonesian MSMEs on the global stage. © 2024 IEEE.

关键词： Marketplaces

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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...

引用

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...

引用

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

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

引用

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

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

引用

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...

引用

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

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

引用

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...

引用

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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Hidden Markov Model Approach to Predict Performance Improvement of 4G Cellular Network 11

Hidden Markov Model Approach to Predict Performance Improvem...

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11th International Conference on Electrical Engineering, Computer Science and Informatics, EECSI 2024

作者： Kosasih, Eka Oktavia, Tanty BINUS Graduate Program Information System Management Department Bina Nusantara University 11480 Indonesia

ISBN: (纸本)9798350355314

The automation of performance optimization in 4G cellular networks remains a significant research challenge. Although advances in artificial intelligence, machine learning, and deep learning have been made, their application in cellular networks is still in its early stages. The evolution of automated learning algorithms in these networks requires continuous optimization to improve performance. This research aims to develop a data analysis model using a Machine Learning approach to predict 4G network Key Performance Indicators (KPIs) and enable automated assessments. The study employs Hidden Markov Models (HMM) to predict KPIs such as eRAB Drop Rate and Setup Success Rate. Data were collected from 1,600 newly deployed sites of a mobile operator in Indonesia over six months, from July 2023 to January 2024. The findings indicate that HMM models can significantly contribute to understanding and enhancing cellular network performance. For instance, the model achieved an accuracy of 99.52% and an F1 score of 99.76% in predicting the eRAB Drop Rate. The Inter-Frequency Handover Success Rate showed 96.15% accuracy and a 97.99%F1 score, while the RRC Connection Setup Success Rate achieved 91.35% accuracy and a 95.29% F1 score. These metrics highlight the model's effectiveness in predicting KPIs. By utilizing HMM models for high-accuracy KPI analysis, network operators can gain deeper insights into network performance and implement targeted improvements. This approach presents a promising avenue for automating and enhancing performance in 4G networks. © 2024 IEEE.

关键词： Hidden Markov models

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Model Predicting Airline Passengers Purchasing Seat Upgrade Ancillaries Products on Indonesian Domestic Routes with Random Forest Machine Learning 4

Model Predicting Airline Passengers Purchasing Seat Upgrade ...

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4th International Conference on Intelligent Cybernetics Technology and Applications, ICICyTA 2024

作者： Mahardika, Anggara Nurul Fajar, Ahmad Binus Graduate Program Bina Nusantara University Information System Management Department Jakarta Indonesia

ISBN: (纸本)9798331506490

To increase profitability, airlines are starting to focus on generating revenue not only from ticket sales, but also from the sale of ancillary products. The research focuses only on ancillary seat upgrade products and aims to create a Machine Learning (ML) model to predict economy class passengers who are likely to accept a seat upgrade offer to business class based on historical data on seat upgrade offers received by airline passengers in the 2023 period. The challenge faced in developing a Machine Learning Prediction model is that the number of passengers who take the upgrade offer and those who do not take it is very unbalanced. In previous literature, most investigations covered European, United Kingdom, China routes, most of which use hub and spoke route networks, while in this research try to fill this gap, especially for airlines operating in Indonesia that have domestic routes and use a point to point route network model. The background of domestic routes is the object of research because 80% of the routes flown are domestic routes between cities in Indonesia, and the top 10 routes that contribute the most to ancillary seat upgrade sales are also domestic routes. To build the model, the author uses machine learning for classification, which is combined with Feature Engineering, Data Balancing and Data Dimension Reduction Techniques. Based on an empirical analysis of a historical dataset of passengers who upgraded their seats, it was found that the best combination was provided by Random Forest, PCA, and Stratified Sampling to predict Indonesian passengers who would receive a seat upgrade with 95.6% accuracy. © 2024 IEEE.

关键词： Sales

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