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2nd International Conference on Big Data Engineering and Technology, BDET 2020

作者： Pane, Murty Magda Siregar, Christian Rumeser, Johannes A. A. Character Building Development Center Computer Science School of Computer Science Bina Nusantara University Palmerah Jakarta Barat Indonesia Character Building Development Center Mass Communication Program Communication Department Bina Nusantara University Palmerah Jakarta Barat Indonesia Psychology Faculty of Humanities Bina Nusantara University Palmerah Jakarta Barat Indonesia

ISBN: (纸本)9781450376839

The study aimed to gain the performance of students' participations and perceptions in interactive online learning and to understand the role of big data in enhancing it. The study adapted the quantitative method. The respondents amounted 201 students of more than five departments with a different range of semesters. The study measured the participations and perceptions of the respondents when doing teamwork virtually using the online learning management system with the Likert scale on the scale of 1 to 5 with corrected item-total correlations 0.472, and α 0.613 (> 0.5). The performance of it is moderate to low for most of respondents. © 2020 ACM.

关键词： Big data

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Exploration of Technology Home-based Learning Guidance for Parents and Students during the Covid-19 Pandemic 9

Exploration of Technology Home-based Learning Guidance for P...

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9th International Congress on Advanced Applied Informatics, IIAI-AAI 2020

作者： Suzanna, Suzanna Sasmoko, Sasmoko Lumban Gaol, Ford Oktavia, Tanty Matsuo, Tokuro Bina Nusantara University Binus Graduate Program-Doctor of Computer Science Computer Science Department Jakarta11480 Indonesia Advanced Institute of Industrial Technology Information System Department Tokyo140-0011 Japan

ISBN: (纸本)9781728173979

With the sudden attack of the Corona Virus Disease 2019 (COVID-19), some 300 million children in the world have been forced to study at home because of the mass closure of schools that began on March 18, 2020. This has caused many obstacles in the teaching and learning process. Every effort is made to minimize the COVID-19 pandemic while ensuring the teaching and learning process is not interrupted. If the school closures too long and the child is trapped inside the house this will have a negative impact on the physical and mental health of the children. This situation makes parents become worried and look for alternative education in addition to formal schooling for their children. Of the various types of education offers that exist homeschooling or commonly known as home-based learning provides a way out for the current situation. Now children are quarantined in their homes and they will return to school once this epidemic fades. But with this disaster, parents' views on homeschooling or home-based learning can be different from before. They may still be marked if home-based learning can be a long-term education. With the existence of COVID-19, most parents might finally realize that education without formal schooling is not a crisis but an opportunity to take their children to a better future. This research was conducted by utilizing a descriptive qualitative method and within the descriptive qualitative method, the concept of document analysis would be utilized the most. It was expected that the results of this research would explore what type of technology parents could prepare for their children to use to initiate home-based learning. © 2020 IEEE.

关键词： Viruses

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Improved LSTM hyperparameters alongside sentiment walk-forward validation for time series prediction

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Journal of Open Innovation: Technology, Market, and Complexity 2025年第1期11卷

作者： Wahyuddin, Eko Putra Caraka, Rezzy Eko Kurniawan, Robert Caesarendra, Wahyu Gio, Prana Ugiana Pardamean, Bens Department of Statistical Computing Politeknik Statistika STIS Jakarta 13330 Indonesia Statistics Indonesia (BPS) Jl. Dr Sutomo 6-8 Jakarta Indonesia School of Economics and Business Telkom University Bandung 40257 Indonesia Research Center for Data and Information Sciences Research Organization for Electronics and Informatics National Research and Innovation Agency (BRIN) Bandung 40135 Indonesia Faculty of Integrated Technologies Universiti Brunei Darussalam Gadong BE1410 Brunei Darussalam Department of Mathematics Universitas Sumatera Utara Medan 20155 Indonesia Bioinformatics and Data Science Research Centre Bina Nusantara University DKI Jakarta 11480 Indonesia Computer Science Department BINUS Graduate Program Master of Computer Science Program Bina Nusantara University DKI Jakarta 11480 Indonesia

This study aims to address the common issue of biased estimation errors in time series modeling by analyzing the error in locating ideal hyperparameters and defining appropriate validation methods. Specifically, it focuses on predicting the stock price of Bank Rakyat Indonesia using a combination of historical stock prices, technical indicators, exchange rates, and news sentiment data, while determining the optimal variables for deep learning models. Employing a deep learning-based Long-Short Term Memory (LSTM) model, the study optimizes hyperparameters alongside walk-forward validation for time series prediction. It explores different combinations of variables and adapts the sliding window approach to the context of the data. The results highlight the importance of optimizing hyperparameters and utilizing walk-forward validation for accurate time series prediction. The model incorporating historical stock prices and sentiment scores outperforms others, achieving an RMSE of 96.61 and MAE of 86.97. Incorporating sentiment scores reduces RMSE by 39.55 % compared to models using only historical stock prices, while adding technical indicators does not yield improvement. This study contributes to the field by addressing the issue of biased estimation errors in time series modeling, offering insights into the optimization of hyperparameters and validation methods for accurate predictions. It also underscores the significance of incorporating sentiment analysis alongside historical stock prices for improved forecasting accuracy. © 2024 The Authors

关键词： LSTM Sentiment analysis Stock price prediction Time series modeling Validation methods

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Daily rainfall modeling using Neural Network 28

Daily rainfall modeling using Neural Network

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28th Simposium Kebangsaan Sains Matematik, SKSM 2021

作者： Permai, S.D. Ohyver, M. Aziz, M.K.B.M. Statistics Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Centre for Mathematical Sciences College of Computing and Applied Sciences Universiti Malaysia Pahang Lebuhraya Tun Razak Pahang Gambang Kuantan26300 Malaysia

In the early 2020, Indonesia experienced flooding in several areas. This disaster caused a lot of damage and losses. One of the causes of flooding in Indonesia is due to high rainfall. This was not anticipated beforehand so there was a flood. Therefore, research on rainfall in Indonesia is very important to anticipate floods. If it is predicted that rainfall is very high and conditions do not allow it to accommodate, the government can prepare watersheds so that rainwater can flow and not be trapped. In this research, the rainfall data were obtained from Meteorological, Climatological, and Geophysical Agency (BMKG Indonesia), then the analysis of rainfall data in Indonesia was carried out. There are several statistical methods that can be used. There are ARIMA and Neural Network. In this research, the results of ARIMA model are used as input variables in the Neural Network model. Then there are several numbers of hidden layer in the Neural Network model that are compared. The results of ARIMA model and Neural Network model showed that Neural Network model is better than ARIMA model, because the mean square error (MSE) value of Neural Network model is smaller than ARIMA model. © Published under licence by IOP Publishing Ltd.

关键词： Neural network models

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Investigating the human resource management practices and their impact on companies’ performance in culinary business in Jakarta, Indonesia

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AIP Conference Proceedings 2023年第1期2594卷

作者： T. Nurianna H. Marko W. M. Devin Burhanudin International Business Program Management Department BINUS Business School International Undergraduate Program Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480

This research aims to explore the human resource management (HRM) practices and their impact on the performance of micro, small, medium enterprises (MSMEs) in the foodservice industry in Jakarta, Indonesia. This topic is crucial to be explored since one of Indonesia's most competitive MSMEs industries is the food and beverage sector. During the pandemic Covid-19, it is to be one of the most important sectors aiding the economy. Meanwhile, the application of HRM practices is often being ignored by MSMEs in Indonesia. This research applied an exploratory, qualitative-based study, using an inductive confirmatory approach. The data were gathered face-to-face and internet-mediated in-depth semi-structured interviews with 12 business owners and managers. The transcripts were then thematically analyzed. The outcome of this research reveals that nine out of ten HRM practices components have impacts on the performance of the MSMEs in the foodservice sector. The ten HRM practices components include human resource planning, recruiting and selection process, orientation, training and development, human resource administration, performance appraisals, wage and compensation, occupational health and safety, disciplinary action, work termination, and career development. Each HRM practices component differs in significance yet serves specific purposes for the MSME and would help the businesses to function better.

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A greener AI-Based crowd counting via efficient deep learning

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AIP Conference Proceedings 2023年第1期2594卷

作者： A. Chrysler R. Gunarso T. Puteri T. W. Cenggoro Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Bioinformatics & Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480

Crowd counting is one of the important AI implementations, which is defined as an AI model that can automatically count humans from CCTV footage. Similar to other implementations of Artificial Intelligence (AI), a crowd counting AI development tends to produce larger models over time. It is caused by the tendency of larger models to have better performance. This trend negatively impacts the environment because the development of larger AI models generates more CO2 that causes global warming. This study instead focuses on developing crowd counting AI that has a small size with competitive performance. Our proposed model achieves competitive performance compared to other state-of-the-art crowd counting models. Currently, our proposed model is also the 3rd best model in terms of performance-size tradeoff among all crowd counting AI with known model size.

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Deep Learning Based Facial Emotion Recognition using Multiple Layers Model

Deep Learning Based Facial Emotion Recognition using Multipl...

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International Conference on Advanced Mechatronics, Intelligent Manufacture, and Industrial Automation (ICAMIMIA)

作者： Lidia Sandra Yaya Heryadi Lukas Wayan Suparta Antoni Wibowo Psychology Department Krida Wacana Christian University Jakarta Indonesia Computer Science Department BINUS Graduate Program-Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Cognitive Engineering Research Group (CERG) Faculty of Engineering Universitas Katolik Indonesia Atma Jaya Jakarta Indonesia Computer Science Department BINUS Graduate Program Doctor of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9781665499705

A Facial Emotion Recognition (FER) system is an important tool to be implemented in any psychology academic field and beyond. This paper aims to show a system of Facial Emotion Recognition that can be done using the multiple layers model of ResN et50 which is an ANN. The dataset is an array that consists of 7 different emotions represented by the numbers 0–6. The emotion included in the dataset is “anger”, “disgust”, “fear”, “happiness”, “sadness”, “shock”, and “neutrality”. This research found that the highest accuracy in the application of the model was recorded at 65% and the test was 60% to recognize facial emotion from a graphical input.

关键词： Deep learning Emotion recognition Mechatronics Automation Face recognition Electric shock Psychology

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Machine learning's model-agnostic interpretability on the prediction of students' academic performance in video-conference-assisted online learning during the covid-19 pandemic

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computers and Education: Artificial Intelligence 2024年 7卷

作者： Eka Miranda Mediana Aryuni Mia Ika Rahmawati Siti Elda Hiererra Albert Verasius Dian Sano Information Systems Department School of Information Systems Bina Nusantara University Jakarta Indonesia Sekolah Tinggi Ilmu Ekonomi Indonesia (STIESIA) Surabaya Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

Background COVID-19 prompted a global shift to online learning, including video conference-assisted online learning (VCAOL), which necessitated educators understanding students' perspectives. Objective This study aims to develop machine learning (ML) model-agnostic interpretability that could predict students' academic performance in VCAOL. Material and methods Synthetic Minority Over-sampling Technique (SMOTE) and data augmentation were used to handle imbalanced data from small-scale datasets. The prediction model was developed using Random Forest (RF), Support Vector Machine (SVM), and Gaussian Naive Bayes (GNB). SHAP model-agnostic interpretability was used to interpret and comprehend prediction findings. The data was gathered from September 2022 to January 2023, resulting in 361 records. The research variables included students' academic performance as the dependent variable, and the video conference application (VC), learning material (LM), internet connection (IC), students' ability to learn (SL), and student knowledge (SK) as independent variables, which were mapped into 28 attributes. Result The SMOTE improved the performance of three algorithms, with RF outperforming SVM and GNB in almost all tests, achieving an accuracy of 79.45%, precision of 75.71%, and recall of 79.45%. SHAP bar plots ranked attributes by importance demonstrated that “Performance,” “Frequency Constraint,” and “Increase Value” had a significant impact on prediction results. When we mapped the three attributes to our study perspective, we determined that SK and SL were the most important views for students to perform well in VCAOL. SHAP's beeswarm revealed students' performance in VCAOL was positively correlated with “Performance”, “Increase Value”, “Completing Project”, “Adequate Method”, “User Interface”, and “Feature”. As we mapped the three attributes to our study perspective, we found that SK, LM, SL, and VC were positively related to students' performance in VCAOL. Conclusion The st

关键词： Students' academic performance Video-conference-assisted online learning (VCAOL) Machine learning Model-agnostic

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Smart Guard Towns: Multi-Drone Systems with Deep Learning for Situational Awareness in Urban Management and Security

Smart Guard Towns: Multi-Drone Systems with Deep Learning fo...

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International Conference on ICT For Smart Society (ICISS)

作者： Davy Ronald Hermanus Suhono Harso Supangkat Fadhil Hidayat Sasmoko Yasinta Indrianti Computer Science Department Research Interest Group in Educational Technology Bina Nusantara University Bandung Indonesia School of Electric and Informatic Institut Teknologi Bandung Bandung Indonesia School of Engineering and Informatics Institut Teknologi Bandung Bandung Indonesia Primary Teacher Education Department Faculty of Humanities RIG Group in Educational Technology Bina Nusantara University Jakarta Indonesia Entrepreneurship Department Universitas Agung Podomoro Jakarta Indonesia

The design of multi-drone systems with deep learning capabilities in Smart Guard Towns is examined in this research study to improve situational awareness for urban management and security. The study's main objective is to increase real-time understanding and monitoring of the urban environment through the deployment of cutting-edge technologies. The system intends to give comprehensive situational awareness, enabling improved choice-making and resource allocation, by utilizing multiple drones and the power of deep learning algorithms. The research explores the advantages of this method for managing urban environments, boosting security, and addressing the unique difficulties that Smart Guard Towns face. The report also explores possible future advances and ethical issues related to the use of multi-drone systems with deep learning in these situations. With the contribution of this research, lawmakers, urban planners, and technology developers may better apply such systems and progress the development of Smart Guard Towns to create safe and sustainable urban environments.

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Digital Technology in Health Education Trend Mapping through Bibliometrics Analysis

Digital Technology in Health Education Trend Mapping through...

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International Conference on Informatics, Multimedia, Cyber and Information System (ICIMCIS)

作者： Fairuz Iqbal Maulana Dian Lestari Vandha Pradwiyasma Widartha Nirwana Haidar Wahyu Dirgantara Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Faculty of Medicine Airlangga University Surabaya Indonesia Information System of Industrial Engineering Faculty Telkom University Bandung Indonesia Informatics Engineering Study Program University of Madura Pamekasan Indonesia Department of Electrical Engineering University of Merdeka Malang Malang Indonesia

ISBN: (纸本)9781665473286

This study examines the mapping of research data on digital technology in the field of health education using bibliometric analysis method. Data was collected by identifying keywords in the Scopus database and sorting them out to sort the studies from the last 10 years (2012–2021). Through this step, a total of 1482 documents were obtained (articles, journals, proceedings, books, and others). The data is then processed using the VOSViewer instrument to obtain a visualization of the mapping analysis. This study also analyzes the network types of authors and co-authors through the VOSViewer instrument. The result of this study indicates that the most document types published within ten years are Articles (66.5%), Review (22.9 % ), Conference Paper (4.2 % ), and others. The most studied subjects are Medicine (55.7%), Nursing (9.0%), Health Professions (8.2%), Social sciences (7.8%), Engineering (3.9%), computer science (2.6 % ), Environmental science (2.6 % ), Biochemistry-Genetics and Molecular Biology (2.2%), Psychology (1.6%), and Dentistry (1.3%). This study offers a written communication process and the nature and direction of developing descriptive means of counting and analyzing the various phases of communication as well as recognizing the authorship and direction of its symptoms in documents on the subject of digital technology in the health sector.

关键词： Training Pediatrics Instruments Education Bibliometrics Transportation Psychology

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