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Lockdown strategy worth lives: The SEIRD modelling in COVID-19 outbreak in Indonesia 2

Lockdown strategy worth lives: The SEIRD modelling in COVID-...

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2nd International Conference on Biospheric Harmony Advanced Research, ICOBAR 2020

作者： Nurlaila, I. Hidayat, A.A. Pardamean, B. Information Systems Department BINUS Online Learning Bina Nusantara University Jakarta11480 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program-Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia

Lockdowns, despite their conflicting restrictions and consequences they might offer when enforced as a national strategy, are deemed to be suggestive for a prompt conquer to the Coronavirus Disease-19 (COVID-19) outbreak. There have been some success stories such as in China, South Korea and our ASEAN member fellow, Vietnam, which exhibited extremely fallen numbers of COVID-19 cases post the enforcement of lockdown. Indonesia, however, remains in the crux of dispute whether or not the lockdown is opted to force COVID-19 transmission down under control. We, in this respect, employ the most popular model which has been broadly applied in the field of epidemiology, referred to as SEIRD (Susceptible, Exposed, Infectious, Recovered, and Death), the extension form of an age-structured SEIR, where the Death (D) is included to provide more factual situation. We modify the I (Infectious) fraction as symp-tomatic (Is) and asymptomatic (Ia) infectives. There are three lockdown scenarios simulated in our modified SEIRD with the starting date are 26 April 2020 when the immediate lockdown was enacted-and a-week and two-week-gaps, respectively. We figured out that by stipulating the lockdown without delay (26 April 2020), the new cases could be kept below 10,000. A week delay escalates case number to 5,000 and delay for one more week results in beyond 20,000 new cases. Furthermore, when the intervention is delayed following the delayed lockdown, normalization would demand a longer period within which dealing with more critical and dying patients is unavoidable and should be more anticipated. © Published under licence by IOP Publishing Ltd.

关键词： Coronavirus

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Exploring The Significance of E-Learning Quality, Usefulness, and Effectiveness on Student Intention

Exploring The Significance of E-Learning Quality, Usefulness...

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IoT, Communication and Automation Technology (ICICAT), International Conference on

作者： Satrio Matin Utomo Doni Purnama Alamsyah Heni Rohaeni Boby Siswanto Management Program BINUS Business School Undergraduate Program Bina Nusantara University Jakarta Indonesia Entrepreneurship Department BINUS Business School Undergraduate Program Bina Nusantara University Jakarta Indonesia Management Program Faculty of Economy and Management Universitas Bhayangkara Jakarta Raya Jakarta Indonesia Computer Science Study Program School of Computer Science Bina Nusantara University Jakarta Indonesia

This study's purpose is to look at techniques and models that can increase students at universities' willingness to continue with e-learning while considering the most recent technological advances and data analysis. The study assesses the variables that can facilitate the continuance intention, focusing on the measurable aspects related to the quality, usefulness, and effectiveness of online learning. A quantitative survey was conducted to acquire relevant data, and questionnaires were completed by an intake of 491 individuals studying in several colleges in Bandung (Indonesia). The evaluation of the data demonstrates that online learning quality, usefulness, and effectiveness have a beneficial association with the intention to use online learning. This can reveal that participants who believe online learning is of good quality, useful, and effective are more likely to indicate a desire to use it in the decades to come. The study also highlights the significance of the three factors of user behavior in interactions with online learning in influencing the strength of learner intention. Using the most recent technological breakthroughs and data analysis, the research model produced in this study reveals the antecedent elements that enable the development of user intention to maintain online studies. The study's findings provide valuable insights for universities to develop effective strategies and indicators that can enhance the sustainable utilization of online learning in the future. It is essential to modify technology for e-learning to better support the dissemination of knowledge at universities, and this study can help guide universities in this endeavor.

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A Systematic Literature Review of Gamification in Massive Online Open Course 5

A Systematic Literature Review of Gamification in Massive On...

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5th IEEE International Conference on Information Technology, Information Systems and Electrical Engineering, ICITISEE 2021

作者： Bangkit Saputra, Jeffri Prayitno Nizar Hidayanto, Achmad Prabowo, Harjanto Meyliana Universitas Amikom Purwokerto Doctor Of Computer Science Graduate Program Bina Nusantara University Faculty Of Computer Science Jakarta Indonesia Universitas Indonesia Faculty Of Computer Science Depok Indonesia Bina Nusantara University Computer Science Graduate Program Jakarta Indonesia Bina Nusantara University School Of Information Systems Information Systems Department Jakarta Indonesia

ISBN: (纸本)9781665401968

The development of information and communication technology makes it easy for anyone to be able to take part in online-based learning. Currently, online-based learning has been offered by many institutions or commercial industries. Gamification is one of the strategies to increase students' learning motivation. In this study, we focus on examining the role of gamification and game elements are contained in gamification-based learning. From the results of our research, it shows that gamification in the online-based learning process is growing. Gamification is proven to have a positive impact on students' learning motivation, although it is still dependent on various factors and conditions. © 2021 IEEE.

关键词： Motivation

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Trends, Opportunities, and Challenges in Detecting Depressive Disorders Through Mobile Devices: A Review

Trends, Opportunities, and Challenges in Detecting Depressiv...

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computer System, Information Technology, and Electrical Engineering (COSITE), International Conference on

作者： Gregorius Natanael Elwirehardja Mahmud Isnan Anzaludin Samsinga Perbangsa Kahlil Muchtar Bens Pardamean Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Information System Department School of Information Systems Bina Nusantara University Jakarta Indonesia Department of Electrical and Computer Engineering Universitas Syiah Kuala Banda Aceh Indonesia Computer Science Department BINUS Graduate Program -Master of Computer Science Bina Nusantara University Jakarta Indonesia

Depressive Disorders (DD) is one of the most prevalent mental disorders in the world that may lead to suicide cases. To prevent the latter, ubiquitous early detection systems may be effective. Recent studies have since researched the development of such systems by exploiting several forms of data, including video, audio, Ecological Momentary Assessments (EMA), and passive sensing data using sensors embedded in mobile devices. To summarize the trends, opportunities, and existing challenges in this field, this study reviewed 15 papers to answer four research questions. EMA was the most popular data to be used in this task, but other approaches, such as using video, audio, and typing behaviors, may be considered due to the subjectivity of EMA. These data were typically recorded using smartphones and analyzed using Machine Learning (ML). However, most of the developed systems had yet to be implemented. Overall, it was concluded that further studies may need to explore usages of more objective data in multimodal approaches as well as consider using Mobile Cloud Computing (MCC) to deploy these systems to provide more effective and efficient diagnoses. Future studies must also take into account the existing challenges of the data and infrastructures, such as the weaknesses of several data types, limitations of mobile devices, as well as the challenges of diagnosis approaches.

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Society with Trust: A Scientometrics Review of Zero-Knowledge Proof Advanced Applications in Preserving Digital Privacy for Society 5.0 1

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

作者： Dominic, Nicholas Pratama, Naufal Rizqi Cornelius, Kenny Senewe, Shavellin Herliman Pardamean, Bens Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Information System Department School of Information Systems Bina Nusantara University Jakarta11480 Indonesia Department of Computer Science Universitas Multimedia Nusantara Tangerang15111 Indonesia Psychology Department Faculty of Humanities Bina Nusantara University Jakarta11480 Indonesia BINUS Graduate Program Bina Nusantara University Jakarta11480 Indonesia

ISBN: (数字)9783031290787

ISBN: (纸本)9783031290770

Society 5.0 focuses on human productivity in the midst of advanced technological services. While the concept has human trust at its core, technology development is now leading to zero-trust architecture. In this scientometrics review, 107 selected papers were analyzed to obtain the nature of the Zero-knowledge Proof (ZKP) theory and its advanced applications to favor digital privacy in greater society. With the common literature review strategy, it was found that the most citations in this field appeared in 2018, with IEEE, Springer, and Elsevier as the top three publishers. By affiliations, China and United States are both leading countries where the researcher comes from. And as the positive trend emerged, the ZKP mechanism will keep being leveraged as one of the cryptographic toolsets in the cloud services, Internet of Things (IoT), smart contracts, healthcare, electronic systems, and many other industrial needs. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Smart contract

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Individual Performance of Students in Online Learning Environments: Study of User Satisfaction

Individual Performance of Students in Online Learning Enviro...

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International Conference on Information Technology, computer and Electrical Engineering (ICITACEE)

作者： Doni Purnama Alamsyah Indriana Satrio Matin Utomo Boby Siswanto Leni Susanti Doni Morika Entrepreneurship Department BINUS Business School Bina Nusantara University Jakarta Indonesia Management Program BINUS Business School Bina Nusantara University Jakarta Indonesia Computer Science Study Program School of Computer Science Bina Nusantara University Jakarta Indonesia Interior Design Study Program School of Design Bina Nusantara University Jakarta Indonesia

Investigation on e-learning is important, especially user satisfaction in support of student academic performance. Evaluation of user satisfaction can be measured by evaluating perceived quality on the user’s side, so this research aims to investigate the role of perceived quality over online learning on User satisfaction. Users of online learning targeted are students because students have experience and knowledge of the implementation of online learning at the university. Collected data and information from students related to satisfaction with online learning through online questionnaires. 615 students filled out questionnaires, then questionnaire data was processed quantitatively through SPSS with statistical testing based on research hypotheses. Three factors essentially increase user satisfaction in online learning, regarding the system, service, and information. Student satisfaction can be improved when supported by information on the platform. The role of a platform regarding information in the online learning context is important, a measure that explains the quality of information among other things useful, understandable, interesting, and reliable. Research findings provide important information for universities as organizers of education, aimed at attaining information quality related to online learning.

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The Deep Learning Approach For American Sign Language Detection

The Deep Learning Approach For American Sign Language Detect...

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Creative Communication and Innovative Technology (ICCIT), IEEE

作者： Gusti Pangestu Yaya Heryadi Lukas Wayan Suparta Yulyani Arifin Computer Science Department BINUS Graduate Program Computer Science. Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program Computer Science Bina Nusantara University Jakarta Indonesia Cognitive Engineering Research (CERG) Faculty of Engineering Universitas Katolik Indonesia Atma Jaya Jakarta Indonesia Department of Electrical Engineering Faculty of Industrial Technology Institut Teknologi Nasional Yogyakarta Yogyakarta Indonesia

Sign language has importance rule to deal with communication process especially with impairments hearing people. Sign language detection also attract lot of researchers to join the challenge of research to detect and recognize the sign language in the field of computer science. Hence, there is still no any standard approach and method to recognize the meaning in every pose of sign language. This research proposed a mechanism to detect Alphabet American Sign Language by utilizing Convolutional Neural Network (CNN) process. The CNN approach was chosen based on the ability and capability to recognize image. In this research, MNIST dataset is used for traning and testing process. The proposed CNN architecture produced 97% of accuracy that outperform the previous research using the same dataset which made this architecture promising.

关键词： Deep learning computer science Image recognition Gesture recognition computer architecture Auditory system Assistive technologies

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Factors Affecting the Willingness to Share Data in Mobile Health (mHealth) Apps in Indonesia

Factors Affecting the Willingness to Share Data in Mobile He...

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International Seminar on Intelligent Technology and Its Applications (ISITIA)

作者： Fairuz Iqbal Maulana Dian Lestari Anita Rahayu Puput Dani Prasetyo Adi Chasandra Puspitasari Satria Fadil Persada Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Faculty of Medicine Surabaya Indonesia Research Center for Telecommunication National Research and Innovation Agency (BRIN) Bandung Indonesia Entrepreneurship Department BINUS Business School Undergraduate Program Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798350378573

ISBN: (纸本)9798350378580

This study investigates the factors influencing user willingness to share data in mobile health (m-health) applications in Indonesia, integrating the Technology Acceptance Model, Theory of Planned Behavior, and Privacy Calculus Theory through Structural Equation Modeling (SEM). Data were collected via an online questionnaire from 103 Indonesian active users of m-health platforms, aged 15-55, focusing on applications that provide online consultations and health-related information. The SEM analysis, validated by indices including the Goodness of Fit Index and Root Mean Square Error of Approximation, reveals a strong model fit. The findings highlight that perceived benefits, such as improved health outcomes and personalized advice, are the primary drivers for users' willingness to share data, significantly outweighing concerns over privacy risks. Perceived usefulness and trust in the application also significantly affect data-sharing behavior, while ease of use and social influences have a moderate to minor influence, respectively. These insights are critical for application developers and policymakers in enhancing app utility and user engagement while addressing privacy concerns. The study's limitations include its demographic scope and reliance on self-administered questionnaires, pointing to directions for future research to broaden demographic representation and deepen qualitative insights.

关键词： Data privacy Analytical models Privacy Technology acceptance model Numerical analysis Information sharing Focusing

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The Development of Breast Cancer Classification Methods: Healthcare Professionals Early Detection

The Development of Breast Cancer Classification Methods: Hea...

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International Conference on Smart Technologies For Smart Nation (SmartTechCon)

作者： Doni Purnama Alamsyah Yudi Ramdhani Leni Susanti Entrepreneurship Department BINUS Business School Bina Nusantara University Jakarta Indonesia Computer Science Program Universitas Adhirajasa Reswara Sanjaya Bandung Indonesia

The aim of this research is to improve the classification performance of the Random Forest algorithm in breast cancer diagnosis through the application of feature optimization techniques. By focusing on the Breast Cancer Coimbra dataset, the study conducts experiments to assess the classification accuracy before and after feature optimization. The findings demonstrate that the Random Forest algorithm achieves an accuracy of 75.91% without feature optimization. However, after implementing feature optimization using the genetic algorithm, the accuracy significantly improves to 81.29%. A comparative analysis with other feature optimization methods reveals the superiority of the genetic algorithm in enhancing classification accuracy. These results highlight the effectiveness of the proposed optimization approach in improving the Random Forest algorithm's performance for breast cancer classification. The research contributes to the advancement of breast cancer diagnosis methods, offering valuable insights for healthcare professionals in early disease detection. Furthermore, the application of data mining concepts aids in understanding the factors influencing breast cancer, providing a basis for future investigations into the utilization of genetic algorithms in classifying other diseases.

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Twitter Data Sentiment Analysis of COVID-19 Vaccination using Machine Learning 5

Twitter Data Sentiment Analysis of COVID-19 Vaccination usin...

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5th International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2022

作者： Maulana, Fairuz Iqbal Adi, Puput Dani Prasetyo Lestari, Dian Purnomo, Agung Prihatin, Sukeipah Yuli School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Telecommunication Research Center National Research and Innovation Agency Jakarta11480 Indonesia Airlangga University Faculty of Medicine Surabaya60132 Indonesia BINUS Business School Undergraduate Program Bina Nusantara University Entrepreneurship Department Jakarta11480 Indonesia Informatics Study Program Politeknik Kota Malang Malang Indonesia

ISBN: (纸本)9781665455121

The expansion of the web is accelerating, which helps encourage the creation of fresh ideas. In today's internet era, we must suggest techniques to filter out various information. Social media sentiment analysis based on Twitter data can monitor the real-Time monitoring of the COVID-19 vaccine. In this way, relevant organizations or governments can take proactive steps to address misinformation and inappropriate behaviour around the COVID-19 vaccine, which threatens the success of the national vaccination campaign. The purpose of this research is to determine if there is a link between how people feel about the COVID-19 vaccine on Twitter and how many people actually get vaccinated against it. This study uses the COVID-19 All Vaccines Tweet dataset sourced from Kaggle. This research Identifies public sentiment, emotion, word usage, and trend of all filtered tweets. The results show that there are 31% positive tweets, 10% negative tweets, and 58% neutral tweets. Tweets with neutral subjective valence tend to cluster in the middle of the polarity scale (between-1 and +1), whereas tweets with strong subjective valence are spread across the scale. © 2022 IEEE.

关键词： COVID-19

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