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检索条件"机构=Graduate Program in Systems"

共 2550 条记录，以下是131-140 订阅

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Mapping Fishing Behavior: Machine Learning Implementation on VMS Data

Mapping Fishing Behavior: Machine Learning Implementation on...

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

作者： Kanda Mahendra Tanty Oktavia Information Systems Management Department BINUS Graduate Program Master of Information Systems Management Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331519643

ISBN: (纸本)9798331519650

The persistent challenge of Illegal, Unreported, and Unregulated (IUU) fishing necessitates advanced technological solutions in maritime surveillance and fisheries management. This study introduces a sophisticated analytical framework for detecting and preventing IUU fishing through the systematic implementation of machine learning algorithms on Vessel Monitoring System (VMS) data. The methodology encompasses a novel hybrid model that integrates Hidden Markov Model (HMM) with three distinct classification algorithms: Naive Bayes, Gradient Boosting Machine, and Support Vector Machine. This integration enables precise classification of vessel behaviors into three distinct states: steaming, hauling, and fishing. The empirical analysis utilizes comprehensive VMS data collected from January 1, 2023, to December 31, 2023, within Indonesian waters, encompassing 6,068 vessel movement patterns from a single vessel's annual activity. The findings demonstrate that the integration of HMM with Gradient Boosting Machine achieves superior detection accuracy of 88%, with a precision rate of 90% and recall rate of 85%, representing a significant improvement over conventional methodologies. This research makes a substantial contribution to maritime surveillance literature by establishing a more reliable and automated approach to IUU fishing detection, while simultaneously offering practical implications for reducing monitoring costs and enhancing the operational efficiency of maritime law enforcement agencies.

关键词： Support vector machines Seminars Systematics Machine learning algorithms Law enforcement Surveillance Hidden Markov models Boosting Bayes methods Reliability

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

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

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International Conference on Information Management and Technology (ICIMTech)

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

ISBN: (数字)9798350390025

ISBN: (纸本)9798350390032

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 impl

关键词： Support vector machines Machine learning algorithms Accuracy Boosting Prediction algorithms Real-time systems Classification algorithms Random forests Business Testing

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How Privacy and Security Concerns Impact the Intention to Engage with Personalization Using Cookies and Collected Data

How Privacy and Security Concerns Impact the Intention to En...

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

作者： Jason Matthew Wahyu Sardjono Information Systems Management Department Binus Graduate Program - Master of Information Systems Management Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331506490

ISBN: (纸本)9798331506506

The ubiquitous nature of online platforms has fostered the proliferation of personalization technologies. These technologies, powered by the collection and analysis of user data through cookies, are employed by organizations to enhance user experience by tailoring content and recommendations. While personalization offers the potential to improve user satisfaction and engagement, it also raises significant privacy and security concerns. This research delves into the intricate relationship between perceived privacy and security risks and user intentions to engage with personalized services. Drawing upon the dual-factor theory, this study posits that individuals' perceptions of privacy and security risks associated with data collection act as inhibitors, while the perceived benefits of personalization serve as enablers. To empirically examine these relationships, a comprehensive survey was conducted. The findings underscore the significant impact of privacy and security concerns on users' willingness to embrace personalized experiences. As individuals become increasingly aware of the potential risks associated with data sharing, organizations must adopt strategies to mitigate these concerns and foster trust. The implications of this research are far-reaching. By understanding the factors that influence users' decisions to engage with personalized services, organizations can develop effective strategies to balance the benefits of personalization with the need to protect user privacy and security. This research contributes to the growing body of knowledge on the intersection of technology, privacy, and consumer behavior, providing valuable insights for both academic researchers and industry practitioners.

关键词： Surveys Industries Privacy Data privacy Consumer behavior Inhibitors Organizations Data collection User experience Security

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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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International Conference on Computer Engineering, Network and Intelligent Multimedia (CENIM)

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

ISBN: (数字)9798350368802

ISBN: (纸本)9798350368819

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%.

关键词： Support vector machines Sentiment analysis Accuracy Customer services Computational modeling Customer satisfaction Bidirectional control Transformers Encoding Context modeling

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Digital Twin (DT) Smart City for Air Quality Management 8

Digital Twin (DT) Smart City for Air Quality Management

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

作者： Ariansyah, Dedy Isnan, Mahmud Rahutomo, Reza Pardamean, Bens Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Information Systems Department School of Information Systems Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program-Master of Computer Science Program Bina Nusantara University Jakarta11480 Indonesia

Air pollution is a pressing issue in cities, and managing air quality poses a challenge for urban designers and decision-makers. This study proposes a Digital Twin (DT) Smart City integrated with Mixed Reality technology to enhance visualization and collaboration for addressing urban air pollution. The research adopts an applied research approach, with a focus on developing a DT framework. A use case of DT development for Jakarta, the capital of Indonesia, is presented. By integrating air quality data, meteorological information, traffic patterns, and urban infrastructure data, the DT provides a comprehensive understanding of air pollution dynamics. The visualization capabilities of the DT, utilizing Mixed Reality technology, facilitate effective decision-making and the identification of strategies for managing air quality. However, further research is needed to address data management challenges to build a DT for Smart City at scale. © 2023 The Authors. Published by Elsevier B.V.

关键词： Augmented Reality Digital Twin Mixed Reality Smart City

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Adoption of Artificial Intelligence in Response to Industry 4.0 in the Mining Industry 1

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International Conference on Innovative Technologies in Intelligent systems and Industrial Applications, CITISIA 2022

作者： Sardjono, Wahyu Perdana, Widhilaga Gia Information Systems Management Department Master of Information Systems Management BINUS Graduate Program Bina Nusantara University Jl. K. H. Syahdan No. 9 Kemanggisan Palmerah Jakarta11480 Indonesia Post Graduate Program of Management Science State University of Jakarta Jl. R.Mangun Muka Raya No.11 Jakarta Timur Jakarta13220 Indonesia

ISBN: (数字)9783031290787

ISBN: (纸本)9783031290770

Nowadays The Industrial Revolution 4.0 is identified by the growing implementation of technology in every business, one of the technologies it called Artificial Intelligence (AI). AI gives a solution for a company to manage efficient operations by finding solutions, relearning data insight, and implementing action to reduce inefficiencies in Operations. The confluence of information technology with industrial process automation and routine jobs provides for better operations, lower costs, and higher process quality. With the ability to extract and control data in real-time connected home and commercial equipment, technologies like big data, the internet of things, information technologies, and robotic operating systems have become important partners and pillars of both industry and society 4.0. PT Delta Dunia Makmur TBK (BUMA), Optimizes its efficient production cost by using Artificial Intelligence in order to help the company to manage its fuel consumption, however, AI has weaknesses due to high dependency on data integrity the company should improve those issues. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Artificial intelligence

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Minimizing the Number of Stunting Prevalence Using the Euclid Algorithm Clustering Approach 7

Minimizing the Number of Stunting Prevalence Using the Eucli...

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7th IEEE International Conference of Computer Science and Information Technology, ICOSNIKOM 2023

作者： Zarlis, Muhammad Oktavia, Tanty Buaton, Relita Ernawan, Ferda Andrian, Kevin Bina Nusantara University Information System Management Department BINUS Graduate Program - Master of Information System Management Jakarta11480 Indonesia STMIK Kaputama Informatics Engineering Binjai20714 Indonesia Universiti Malaysia Pahang Al-Sultan Abdullah Faculty of Computing Pekan26600 Malaysia BINUS Graduate Program-Master of Information Systems Management Bina Nusantara University Department of Information Systems Management Jakarta Indonesia

ISBN: (纸本)9798350360752

Superior and quality human resources are based on healthy human resources with indicators of adequate nutritional intake according to age development. However, the world still faces the problem of hunger and malnutrition today. According to a UNICEF report, the number of people suffering from malnutrition in the world will reach 767.9 million people in 2021. The World Health Organization (WHO) said that malnutrition is a dangerous threat to the health of the world's population. Stunting also has an impact in Indonesia, the prevalence of toddlers experiencing stunting in Indonesia is 24.4% in 2021. The solution created is to classify and cluster the prevalence of stunting to produce a pattern that can be used as best practice to be transmitted to other affected areas. The algorithm used is Euclid. The Euclid algorithm can cluster stunting prevalence data into 4 clusters with the very little category at 79%, the little category at 67%, the many categories at 51%, and the very much category at 21%. The results of the classification and clustering of the best stunting prevalence in cluster one with a very small number, can be used as a source of accurate and updated information that can be used by the government in its efforts to optimize stunting handling in each district/city based on artificial intelligence which can provide handling and optimization patterns. stunting in every district/city. © 2023 IEEE.

关键词： Classification (of information)

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Microservices Architecture As a Data Governance Tools in Decentralized E-Government

Microservices Architecture As a Data Governance Tools in Dec...

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2022 International Conference on Science and Technology, ICOSTECH 2022

作者： Paramita, Adi Suryaputra Gaol, Ford Lomban Ranti, Benny Supangkat, Suhono Harso Graduate Program Universitas Ciputra Surabaya/ Bina Nusantara University Information Systems Department/Doctor of Computer Science Jakarta Surabaya Indonesia Bina Nusantara University Doctor of Computer Science Graduate Program Jakarta Indonesia Institute of Technology Bandung Systems and Information Technology Department Bandung Indonesia

ISBN: (纸本)9781665479851

Electronic government, or e-Government, is the use of information and communication technology by the public sector (ICT). Additionally, it can be viewed as a paradigm shift in terms of how governments operate. The government protects public values, while citizens act as problem solvers, co-creators, and active participants in value creation. On the other hand, decentralized systems have grown in popularity over the last few years. Data has become a significant issue in decentralized E-Government;each local entity has their own data, which is private. A suitable data governance tool is required to ensure that data governance is properly implemented and to prevent the organizational entity from leaking information. The purpose of this research is to examine the data governance tools mechanism utilizing microservices. © 2022 IEEE.

关键词： Information use

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Artificial Intelligence as a New Competitive Advantage in Digital Marketing in the Banking Industry 1

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International Conference on Innovative Technologies in Intelligent systems and Industrial Applications, CITISIA 2022

作者： Sardjono, Wahyu Perdana, Widhilaga Gia Information Systems Management Department BINUS Graduate Program—Master of Information Systems Management Bina Nusantara University Jl. K. H. Syahdan No. 9 Kemanggisan Palmerah Jakarta11480 Indonesia Post Graduate Program of Management Science State University of Jakarta Jl. R.Mangun Muka Raya No. 11 Jakarta Timur Jakarta13220 Indonesia

ISBN: (数字)9783031290787

ISBN: (纸本)9783031290770

Digital disruption has entered the banking industry, especially since the era of the COVID-19 pandemic. To remain competitive, every bank must adjust to the digital age. Reputation has a direct impact on a bank’s performance, capacity to draw in new clients, and capacity to keep hold of current ones. Making choices about how to address the difficulties of adopting artificial intelligence must consider these issues (AI). The financial sector as a whole now has a way to meet client expectations for more creative, practical, and secure solutions thanks to artificial intelligence (AI). The bank can swiftly decide which channel to use at what time and what material to target consumers with by utilizing AI. Of course, this is very important considering the shift in customer behavior in the digital era, so AI is expected to assist the Bank in providing products and services that are right on target according to customer needs and create a new competitive advantage. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Artificial intelligence

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Navigating Digital Transformation Challenges: An Exploration of Employee Attitudes, Expectations, and Support 18

Navigating Digital Transformation Challenges: An Exploration...

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18th International Conference on Ubiquitous Information Management and Communication, IMCOM 2024

作者： Princes, Elfindah Hidayat, Desman Eni, Yuli Kesuma, Adidharma Ekaputra Information Systems Management Department Bina Nusantara University BINUS Graduate Program Jakarta11480 Indonesia Bina Nusantara University BINUS Entrepreneurship Center Management Department Jakarta11480 Indonesia Bina Nusantara University BINUS Business School Undergraduate Program Management Department Jakarta11480 Indonesia

ISBN: (纸本)9798350331011

In today's rapidly evolving business landscape, digital transformation has become a strategic imperative for organizations seeking to maintain competitiveness. This case study delves into the intricacies of digital transformation, highlighting challenges that can hinder its successful implementation. The central problem explored is the emergence of obstacles during the transformation, stemming from employee attitudes, knowledge, and technical proficiency. This study addresses critical research questions to understand the human dimension of digital transformation. It investigates the relationships between employees' expectations, well-being aspirations, attitudes toward change, and technical support accessibility. By examining these issues, the study provides valuable insights into the role of human factors in digital transformation. The analysis, conducted using PLS-SEM at an 85% confidence level, reveals a significant connection. Employees' performance expectations shape their attitudes, influencing their intention to support digital transformation. Qualitative analysis underscores the importance of planning, training, participation, and support in digital transformation. Employees engaged in the process, with a deep understanding and positive performance expectations, exhibit favorable attitudes and are less resistant to change. Overall, this study contributes valuable guidance for organizations embarking on digital transformation journeys, offering strategies to overcome potential obstacles. © 2024 IEEE.

关键词： Competition

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