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Analysis of Community Satisfaction with the Use of PeduliLindung Applications during the Covid-19 Pandemic 5

Analysis of Community Satisfaction with the Use of PeduliLin...

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5th International Seminar on Research of information Technology and Intelligent systems, ISRITI 2022

作者： Oktavia, Tanty Barunastra, Farrel Izza Wicaksono, Arief Prasetyo, Mochamad Rafli Mudji Vernanda, Rafi Sulthan Syahrial, Hadi Bina Nusantara University BINUS Graduate Program - Master of Information System Management Information System Management Department Jakarta11480 Indonesia School of Information Systems Bina Nusantara University Information System Department Jakarta11480 Indonesia

ISBN: (纸本)9781665455121

The Covid-19 virus with a very fast spread requires governments in every country to act quickly to prevent the spread of the coronavirus so that it does not spread quickly. The method that is mostly used is the contact tracing application, Indonesia applies this method by creating the PeduliLindungi mobile application which can track the position of people visiting a place by scanning a QR code. To ensure the successful implementation of the application, a user satisfaction evaluation is needed. This research was conducted by conducting a questionnaire survey to users of the PeduliLindungi application to determine the level of user satisfaction, the survey data was then processed using SmartPLS. The results of this study showed that all variables are positive. Two of them, usability and reliability have a significant effect on user satisfaction. It can be concluded that the user is satisfied with the PeduliLindungi application because the application has good usability and reliability. © 2022 IEEE.

关键词： Coronavirus

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Predictive Analysis of Hypertensive Heart Disease Using a Machine Learning Approach

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

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International Conference on ICT For Smart Society (ICISS)

作者： Fran Siska Tanty Oktavia Information System Management Department BINUS Graduate Program - Master of Information System Management Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798350389654

ISBN: (纸本)9798350389661

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.

关键词： Heart Accuracy Machine learning algorithms Hospitals Predictive models Decision trees Predictive analytics Random forests Medical diagnostic imaging Diseases

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Evaluating Customer Satisfaction on Indonesian Banking Artificial Intelligence-Driven Chatbot Services

Evaluating Customer Satisfaction on Indonesian Banking Artif...

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Intelligent Cybernetics Technology & Applications (ICICyTA), International Conference on

作者： Michael Widjaja Nilo Legowo Information System Management Department BINUS Graduate Program - Master of Information System Management Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331506490

ISBN: (纸本)9798331506506

The rapid evolution of artificial intelligence (AI) has significantly impacted various industries, including the banking sector. AI-powered chatbots have emerged as a promising tool to enhance customer service and satisfaction. This research delves into the factors that influence customer satisfaction with banking chatbot services. Specifically, the study examines the impact of system quality, information quality, service quality, trust, and social presence on customer satisfaction. A purposive sampling technique was employed to collect data from fifty-one respondents who had prior experience utilizing banking chatbot services. Data analysis techniques were used to assess the relationships between the independent variables and the dependent variable, customer satisfaction. The findings in this study revealed that system quality, information quality, service quality, trust, and social presence exhibited positive impacts on customer satisfaction on using chatbot services. The results of this study can contribute to several practical implications for Indonesian banks. By understanding the factors that drive customer satisfaction, banks can optimize their chatbot systems to enhance user experience, build customer loyalty, and gain a competitive advantage. Specifically, banks can focus on improving system reliability, ensuring the accuracy and relevance of information provided by the chatbot, enhancing the chatbot's ability to understand and respond to customer queries, building trust through transparent communication and data privacy practices, and developing chatbots that can engage in natural and empathetic conversations. Furthermore, this research provides a foundation for future studies in the field of AI-powered chatbot services in the Indonesian banking sector.

关键词： Customer satisfaction Banking Oral communication Chatbots Distance measurement Mathematical models Reliability Artificial intelligence Testing information integrity

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Effectiveness Agile Project Management Tools For Managing Software Development Using F-AHP Method

Effectiveness Agile Project Management Tools For Managing So...

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International Seminar on Research of information Technology and Intelligent systems (ISRITI)

作者： Aulia Mahardika Astari Retnowardhani Information System Management Department BINUS Graduate Program – Master Of Information System Management Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331519643

ISBN: (纸本)9798331519650

Software management tools for effectiveness could be focused on supporting software development process. One of the key factors that software management needs to accomplish are using agile project management tools. The main function of using Project Management Tools (PMT) were to increase the productivity of many developers. However, there were issues until a survey of the technology companies for management projects in IT work, where almost 70% failed when there was no proper completion process. The remaining factor that constrains the effectiveness and efficiency is the use of project management tools. Therefore, the root problem of this research is to determine the effectiveness and efficiency of software management through the agile method and assess the application of the two methods between fuzzy logic and AHP (Analytical Hierarchy Process). In addition, the quantitative method will be the basis for the calculation of the ranking factor of efficiency and time delivery projects through the fuzzy AHP method.

关键词： Surveys Seminars Productivity Fuzzy logic Decision support systems Analytic hierarchy process Agile project management Software Intelligent systems information technology

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Designing Personalized Integrated Healthcare Monitoring system through Blockchain and IoT 8

Designing Personalized Integrated Healthcare Monitoring Syst...

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8th International Conference on Computer Science and Computational Intelligence, ICCSCI 2023

作者： Wang, Gunawan Nurcahyo, Aldian Information Systems Management Department BINUS Graduate Program Bina Nusantara University Jakarta11480 Indonesia Information System Department BINUS Online Learning Bina Nusantara University Jakarta11480 Indonesia

Personalised Integrated Healthcare Monitoring system (PIHMS) has received major attention from the healthcare industry. With large population and strong growth of Indonesian middle class, PIHMS is certainly a big market for major healthcare providers. Although Indonesia has strong growth of middle-class, ironically, it is not supported with adequate physician ratio. Indonesian Ministry of Health (MoH) reported in 2020 the ratio of physicians per 1.000 patients is 0,62, far below WHO standard. As consequence, many Indonesian patients prefer to go abroad to have better healthcare services. The article supports the government's initiative to develop PIHM design by implementing IoT devices and blockchain networks. The use of PIHMS is essential to reach large patients that spread in remote areas, that suits to Indonesian condition. The article proposes PIHM based on blockchain solution (MedDApp) that enables to utilise IoT devices to carry out remote monitoring and medical consultation. The article examines the use of blockchain architecture to support developing data interchange between patients, healthcare providers and third parties such insurances and ministry of health (MoH). Data gatherings from respective stakeholders from Data and information Centre division (Pusdatin), MoH and state-own hospitals to support developing smart health care architecture (MedDApp). The MedDApp design is expected to provide secure access between patients with healthcare professionals and third parties (national insurance companies and researchers in MoH). The outcome of the article is expected to be used as a reference for major healthcare providers to provide better services for their large patients. © 2023 The Authors. Published by Elsevier B.V.

关键词： blockchain networks IoT devices Personalised Integrated Healthcare Monitoring (PIHMS)

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Drug Demand Prediction Based on Epidemiology Factors Using Random Forest 15

Drug Demand Prediction Based on Epidemiology Factors Using R...

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15th International Congress on Advanced Applied Informatics Winter, IIAI-AAI-Winter 2023

作者： Emmanuel, Gunadi Arifin, Yulyani Sonata, Ilvico Zarlis, Muhammad Bina Nusantara University Doctor of Computer Science Program Jakarta Indonesia Bina Nusantara University Binus Graduate Program-Doctor of Computer Science Computer Science Department Jakarta Indonesia Binus Graduate Program Bina Nusantara University Information System Management Department Jakarta Indonesia

ISBN: (纸本)9798350383829

The characteristics of the disease that spreads quickly, the number of sufferers, and the severity of sufferers of Coronavirus Disease 2019 are components of uncertainty during the pandemic. In an uncertain situation, prediction models for the need for drugs and medical devices are of great concern to policymakers in government, drug manufacturers, distributors, and pharmaceutical installation managers to maintain drug availability. Drug need prediction models that rely on historical data components on drug use are no longer reliable. Learning from the COVID-19 case, epidemiological variables correlate with predicting drug demand. This research includes data on ten major diseases in private hospital units for 2017-2022 to complete historical data on drug use. This study implements the Random Forest algorithm. The research method uses literature studies and processing field data from pharmaceutical installations. The analysis process uses KNIME software. The level of accuracy in predicting drug demand from historical drug use data was 77.272%, increasing to 81.818% with a model for predicting drug demand based on consumption cycles and classification of drug therapy groups. Furthermore, predictions of drug demand can consider variables recorded in medical records related to the seasonal frequency of diseases. © 2023 IEEE.

关键词： Forecasting

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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)

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

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International Conference on information Technology, information systems and Electrical Engineering (ICITISEE)

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

ISBN: (数字)9798350368970

ISBN: (纸本)9798350368987

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.

关键词： Accuracy Predictive models Turbomachinery Maintenance Machinery information technology Long short term memory Monitoring Context modeling Predictive maintenance

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

Hidden Markov Model Approach to Predict Performance Improvem...

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International Conference on Electrical Engineering, Computer Science and Informatics (EECSI)

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

ISBN: (数字)9798350355314

ISBN: (纸本)9798350355321

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.

关键词： Cellular networks Support vector machines Analytical models Accuracy Key performance indicator Hidden Markov models Predictive models Prediction algorithms Data models Optimization

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Linkedin's Influence on User Success in The Professional World 9

Linkedin's Influence on User Success in The Professional Wor...

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9th International Conference on ICT for Smart Society, ICISS 2022

作者： Oktavia, Tanty Roekman, Faza Fakhriansyah Putera, Muhammad Ikhsan Hidayah, Muhammad Alif Rafi, Muhammad Hosoda, Takaaki Gaol, Ford Lumban BINUS Graduate Program Information System Management Bina Nusantara University Information System Management Department Jakarta Indonesia School of Information Systems Bina Nusantara University Information System Department Jakarta Indonesia Advanced Institute of Industrial Technology Decision Science Department Tokyo Japan BINUS Graduate Program Bina Nusantara University Computer Science Department Jakarta Indonesia

ISBN: (数字)9781665471350

ISBN: (纸本)9781665471350

Social media has now become a place for others to exchange information in the form of writing, photos, videos, and audio. Social media has become a place for humans to interact from all over the world. In the use of social media, attractive professional information is conceptualized as access to relevant information and opening up better career opportunities. User content about work and choosing personal information compilation strategies consistently predict information usefulness. This study discusses the effect of using a linked-in application on the success of its users in the professional world. It is hoped that it can provide insight into the use of this recruitment application, making it easier for users to find work. The research model used in this research is quantitative. The survey was conducted on connected users in Indonesia using an online questionnaire, and the data collected was analyzed using PLS-SEM. Of the 68 respondents who took the survey, it can be said that all research models have a positive relationship with the successful use of the linked-in platform. © 2022 IEEE.

关键词： Surveys

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Analysis of Factors Influencing Decisions to Purchase Airline Tickets Online during the Covid-19 Pandemic

Analysis of Factors Influencing Decisions to Purchase Airlin...

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2023 International Conference on Computer Science, information Technology and Engineering, ICCoSITE 2023

作者： Lie, Yulius Perbangsa, Anzaludin Samsinga Pardamean, Bens Bina Nusantara University School of Information Systems Information System Department Jakarta11480 Indonesia Bina Nusantara University BINUS Graduate Program - Master of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350320954

The background of this research is the condition of the covid-19 pandemic which has an impact on online ticket sales. Meanwhile, when future of covid-19 pandemic is starting to become clearer, we are going to have a lot more infections but hopefully a lot fewer hospitalizations, people began to travel long distances that had been delayed by implementing health protocols. Airfare competition can influence the decision to buy airline tickets. Various things have been done to attract the attention of buyers. This research is a non-experimental quantitative research. Population in this research was people who have purchased plane tickets online during the covid-19 pandemic, and the sample were 113 respondents age between 15 and 64. Sampling technique used was simple random sampling. Analysis of the data used in this study using multicollinearity, heteroscedasticity, multiple linear, T-test, F-test, and coefficient of determination test. It was concluded that price, promotion, and lifestyle variables can influence online purchasing decisions. © 2023 IEEE.

关键词： Sales

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