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2nd International Conference on Computer, Science, Engineering, and technology, ICComSET 2019

作者： Henderi Sunarya, Abas Zakaria Nurmika, Sri Linggawati Asmainah, Nur Master of Information Technology Study Program Faculty of Science and Technology Raharja University Tangerang Indonesia

The progress of students learning outcomes have to evaluated and make monitor. Student learning outcomes were indicated by score of the exam. Educational institutions can determine the ability of students' knowledge and competences based on student exam scores. For this reason, the goals of this research is develops an evaluation model of student learning outcomes. The model was developed using k-means algorithm and it has functions to analyze the student learning outcomes. The model was tested by conducted using 50 data exam of students, rapid manner software, and has implemented by the Python 3.0 programming language. The testing the evaluation model of student learning outcomes that were developed resulted: there are 3 clusters of student learning outcomes that are good, satisfying, and lacking. The tested results also showed that 40% of students had good learning outcomes, 44% of students were satisfactory, and 16% of other students had poor learning outcomes. The results of testing the student learning evaluation model can be used by the parties concerned to make decisions related to work programs or formulate steps that need to be taken to improve student learning outcomes. © Published under licence by IOP Publishing Ltd.

关键词： Students

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Finding the Best Techniques for Predicting Term Deposit Subscriptions (Case study UCI Machine Learning Dataset) 4

Finding the Best Techniques for Predicting Term Deposit Subs...

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4th International Conference on Sustainable Engineering and Creative Computing, ICSEC 2022

作者： Setiyani, Lila Indahsari, Ayu Rosalina Wansen, Tjong President University Master of Science Information Technology Study Program Bekasi Indonesia President University Informatics Study Program Bekasi Indonesia

ISBN: (纸本)9798350322002

Knowing a lot about attributes, such as bank client data, marketing campaigns, social and economic context, and so on, is necessary to predict which bank customers are likely to sign up for time deposits. The direct marketing campaigns dataset for the bank institution was made available by the UCI machine learning repository. This dataset had previously been used in studies to forecast customers who would sign up for time deposits, but the outcomes of the two studies were different. The purpose of this study is to determine which machine learning methods are most effective for predicting customers who will sign up for time deposits. In this study, researchers used machine learning methods like random forests, K-nearest neighbors, decision trees, naive bayes, logistic regression, SVM, and XGBoost to uncover the truth. Results showed that XGBoost had the highest accuracy (91.73%), followed by the DT classifier (88.79%), LR classifier (91.2%), NB classifier (84.78%), KNN classifier (90.6%), SVM (89.6%), and XGBoost (91.44%). © 2022 IEEE.

关键词： Forecasting

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Envisioning the Future of Collaboration for Banking and FinTech Industry 5

Envisioning the Future of Collaboration for Banking and FinT...

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5th IEEE International Conference on Computer and Informatics Engineering, IC2IE 2022

作者： Legowo, Mercurius Broto Sorongan, Fangky Antoneous Subanidja, Steph Perbanas Institute Information System Study Program Faculty of Information Technology Jakarta Indonesia Perbanas Institute Accounting Study Program Faculty of Economic and Business Jakarta Indonesia Post Graduate School Perbanas Institute Master of Management Program Jakarta Indonesia

ISBN: (数字)9781665453387

ISBN: (纸本)9781665453387

This study aims to envision the future of banking and the FinTech industry collaboration by the triggers for change based on trends watching in current conditions. Envisioning is the ability to describe the impact of changes in the business environment caused by the various trigger changes observed in trend watching. The current conditions and the triggers of change based on trend-watching are issues and considerations in envisioning collaboration between banking and the Fintech industry. The research methodology uses a descriptive method with a qualitative approach. The study surveyed 100 respondents, each representing the banking and FinTech sectors, who understand the issues related to the collaboration of the two industries. In addition, to complement the survey data, this study also conducted Focus Group Discussions and In-depth Interviews as a qualitative approach. The study's results by analyzing current conditions and triggers of change based on trend watching will represent a picture of the future of banking and FinTech industry collaboration. This research will expect a significant contribution to government regulations/policies and a positive contribution and support on the IT/IS side, especially in applied technology and architecture/business models for collaboration in the future of collaboration banking and the FinTech Industry that follow. © 2022 IEEE.

关键词： Laws and legislation

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Risk Management of Bank and FinTech Collaboration: A Phenomenological Research 6

Risk Management of Bank and FinTech Collaboration: A Phenome...

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6th International Conference on Computer and Informatics Engineering, IC2IE 2023

作者： Legowo, Mercurius Broto Sorongan, Fangky Antoneus Subanidja, Steph Institute of Perbanas Study Program of Information System Faculty of Information Technology Jakarta Indonesia Institute of Perbanas Study Program of Accounting Faculty of Economic and Business Jakarta Indonesia Post Graduate School Institute of Perbanas Program of Master of Management Jakarta Indonesia

ISBN: (纸本)9798350345162

Banking and Fintech are encouraged to collaborate to fill each other's deficiencies and further grow the digital economy. This collaboration will create benefits for both banks, Fintech, and customers. However, this collaboration also comes with risks. The Financial Services Authority has stated that several risk phenomena can occur in the collaboration of banks and Fintech. First, no law guarantees the protection and exchange of customer personal data. Second, the risk of customer data leakage. Third, the risk of cyberattacks. The fourth is the risk of outsourcing. This research aims to analyze the risk phenomenon of bank and fintech collaboration through the Enterprise Risk Management Framework approach. This study uses a phenomenological research approach, which is a study to study how individuals subjectively experience and give meaning to the phenomenon. Data analysis based on this phenomenon data uses qualitative methods involving banking and fintech practitioners. The results of this study describe the phenomenon of risks that occur in banking-fintech collaboration, as approached by the Enterprise Risk Management framework. Proper risk management will minimize risks when the Bank collaborates with FinTech. This study hopefully contributes to making brief policy recommendations on bank and fintech collaboration risk management for Indonesian financial regulators. © 2023 IEEE.

关键词： bank collaboration ERM framework fintech risk management

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Model of Sustainable Development Based on FinTech in Financial and Banking Industry: A Mixed-Method Research 3

Model of Sustainable Development Based on FinTech in Financi...

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3rd International Conference on Computer and Informatics Engineering, IC2IE 2020

作者： Legowo, Mercurius Broto Subanidja, Steph Sorongan, Fanky Antoneus Information Technology Faculty Institute of Perbanas Information System Study Program Jakarta Indonesia Post Graduate School Institute of Perbanas Master of Management Program Jakarta Indonesia Economic and Business Faculty Institute of Perbanas Accounting Study Program Jakarta Indonesia

ISBN: (纸本)9781728182476

In the past decade, it stated that the discourse on Sustainable Development and the Financial technology(FinTech) role in the financial and banking industries, has attention on researchers, practitioners, regulators, and policymakers. The real problem arises from the discourse on whether FinTech has an effect on sustainable development in the financial and banking industry in Indonesia. This discourse needed to be understood better and more deeply. For credible research results, it is necessary to use a mixedmethod research approach. This research aims to develop a model that will help uncover the phenomenon of FinTech to sustainable development in the Financial and Banking Industry in Indonesia, using mixed methods research. In this study, Exploratory Sequential Mixed Method used as a type of mixedmethod approach. The main results of this study reveal that FinTech, which is influenced by business drivers, has a significant role in sustainable development in the Financial and Banking Industry in Indonesia. The contribution of this research can provide insight and understanding for researchers and practitioners in the financial and banking industry who want to implement a sustainable development model based on FinTech in more depth. © 2020 IEEE.

关键词： Sustainable development

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Integration of Renewable Energy technology in Waste Recycling Utilization

Integration of Renewable Energy Technology in Waste Recyclin...

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2022 IEEE Creative Communication and Innovative technology, ICCIT 2022

作者： Alwiyah Padeli Yusuf, Maulana Kusumah, Hendra Suwandi Wiraraja Madura University Accounting Study Program Sumenep Indonesia University of Raharja Master of Information Technology Indonesia University of Raharja Department of Information System Indonesia University of Catur Insan Cendikia Department of Information Technology Indonesia

ISBN: (纸本)9781665468763

The increase in the production of municipal solid waste around the world will be expected to continue to increase. Even though developed countries already have recycling technology to convert waste into products that can be reused, such as generating electricity, heat energy, composting fertilizers, and biofuels, there are still many challenges created by waste because in developing countries the countermeasures themselves are still so things are difficult starting from the inadequate collection, transportation, and disposal of waste. This research will solve the problem of evaluate various waste-to-energy technologies that have been developed since the invention that was able to convert recycled waste into renewable energy until now. This research can be divided into four groups. Namely starting from waste treatment thermally, biologically, biorefinery, and utilization of landfill gas. The novelty of this research for waste-to-energy technology with an integrated waste management system, it has been safely understood and tested feasible for energy renewal which can already show several examples from around the world. The method used to solve the unresolved waste problem in this scientific paper is to use WTE and ISWM-S technology which can certainly be a brilliant solution in converting urban solid waste into renewable energy for a clean world from waste to waste and also provides a future. Where people no longer need to worry about the limitations of limited natural resources to be processed into energy. © 2022 IEEE.

关键词： Municipal solid waste

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Decision Support System in Determining the Achievement of Widyaiswara Value Using WASPAS and TOPSIS Algorithm 1

Decision Support System in Determining the Achievement of Wi...

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1st International Conference on technology Innovation and Its Applications, ICTIIA 2022

作者： Pratama, Yudhistira Adhitya Iswara, Bagus Ajie Nababan, Adli Abdillah Information System Studies Program Pelita Harapan University Medan Campus Faculty Of Computer Science Medan Indonesia Master's Study Program Informatics Engineering Universitas Sumatera Utara Faculty Of Computer Science And Information Technology Medan Indonesia Digital Business Study Program Stmik Pelita Nusantara Medan Indonesia

ISBN: (数字)9781665488266

ISBN: (纸本)9781665488266

Widyaiswara is required to show the best performance to fulfill his duties and obligations. Therefore, it is very important to measure the performance of the Widyaiswara, so that it can be used as evaluation material by the BPSDM PROVSU. However, the assessment process that occurs is still not precise and time-consuming. Therefore, to minimize this, a decision support system is needed that can minimize time errors and data processing in decision-making so that the assessment runs well and accurately. This system was built by applying the WASPAS method and the TOPSIS method. Based on the results of tests carried out five times, the results of the WASPAS method processing time resulted in an average processing time of 0.00029791 ms and an average memory usage value of 2.836.44 kb, while the results of the TOPSIS method processing time test resulted in an average processing time - an average of 0.000296428 ms and an average memory usage value of 2.862.34 kb. © 2022 IEEE.

关键词： Decision support systems

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Finding the Best Techniques for Predicting Term Deposit Subscriptions (Case study UCI Machine Learning Dataset)

Finding the Best Techniques for Predicting Term Deposit Subs...

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International Conference on Sustainable Engineering and Creative Computing (ICSECC)

作者： Lila Setiyani Ayu Indahsari Rosalina Tjong Wansen Master of Science Information Technology study program President University Bekasi Indonesia Informatics Study Program President University Bekasi Indonesia

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Bloom-epistemic and sentiment analysis hierarchical classification in course discussion forums

arXiv

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arXiv 2024年

作者： Toba, Hapnes Hernita, Yolanda Trixie Ayub, Mewati Wijanto, Maresha Caroline Master of Computer Science Study Program Faculty of Information Technology Maranatha Christian University Bandung Indonesia Bachelor of Informatics Study Program Faculty of Information Technology Maranatha Christian University Bandung Indonesia

Online discussion forums are widely used for active textual interaction between lecturers and students, and to see how the students have progressed in a learning process. The objective of this study is to compare appropriate machine-learning models to assess sentiments and Bloom's epistemic taxonomy based on textual comments in educational discussion forums. The proposed method is called the hierarchical approach of Bloom-Epistemic and Sentiment Analysis (BE-Sent). The research methodology consists of three main steps. The first step is the data collection from the internal discussion forum and YouTube comments of a Web programming channel. The next step is text preprocessing to annotate the text and clear unimportant words. Furthermore, with the text dataset that has been successfully cleaned, sentiment analysis and epistemic categorization will be done in each sentence of the text. Sentiment analysis is divided into three categories: positive, negative, and neutral. Bloom's epistemic is divided into six categories: remembering, understanding, applying, analyzing, evaluating, and creating. This research has succeeded in producing a course learning subsystem that assesses opinions based on text reviews of discussion forums according to the category of sentiment and epistemic analysis. © 2024, CC BY-SA.

关键词： Sentiment analysis

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Risk Management of Bank and FinTech Collaboration: A Phenomenological Research

Risk Management of Bank and FinTech Collaboration: A Phenome...

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International Conference of Computer and Informatics Engineering (IC2IE)

作者： Mercurius Broto Legowo Fangky Antoneus Sorongan Steph Subanidja Study Program of Information System Faculty of Information Technology Institute of Perbanas Jakarta Indonesia Study Program of Accounting Faculty of Economic and Business Institute of Perbanas Jakarta Indonesia Program of Master of Management Post Graduate School Institute of Perbanas Jakarta Indonesia

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