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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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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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The Influence of Social Media on Human Behavior in Adolescents (Case Study of bina nusantara University Students) 2020

The Influence of Social Media on Human Behavior in Adolescen...

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

作者： Kusumajati, Dian Anggraini Chairiyani, Rina Patriana Martoredjo, Nikodemus Thomas Character Building Development Center Information System Department School of Information Systems Bina Nusantara University Jakarta Indonesia Character Building Development Center Primary Teacher Education Department Faculty of Humanities Bina Nusantara University Jakarta Indonesia Character Building Development Center Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9781450376839

The use of social media at this time cannot be separated from the needs of the community, in this case, they are teenagers. The use of social networks by students generates positive and negative behavioral changes. This study measures the effect of social networks on the behavior of human values in adolescents. The research subjects were active students studying at bina nusantara University, Jakarta. The purpose of this study was to determine the effect of social media on human behavior in *** research uses non-experimental quantitative methods. Data obtained from social media and human behavior measurement tools and have been tested on 473 students. The population in this study were active students at bina nusantara University. The results showed that there was an influence between social media and human behavior in social aspects and diversity. This means that the higher the use of social media by students increases the influence in social activities together with other people from various aspects of the background. © 2020 ACM.

关键词： Students

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User Preference Modelling from Movie Database 7

User Preference Modelling from Movie Database

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7th International Conference on ICT for Smart Society, ICISS 2020

作者： Qomariyah, Nunung Nurul Kazakov, Dimitar Fajar, Ahmad Nurul Bina Nusantara University Faculty of Computing and Media Computer Science Department Jakarta11480 Indonesia University of York Computer Science Department York United Kingdom Bina Nusantara University BINUS Graduate Program-Master of Information Systems Management Information Systems Management Department Jakarta11480 Indonesia

ISBN: (纸本)9781665404228

Film industry becomes one of the largest amongst the other economic sectors worldwide nowadays. This industry also has a significant impact in the global economic market. Many movies have been produced each year. However, some movies still fail to reach break-even points every year. Players in this industry need to think carefully when spending most of their money to make a great and successful movie in the market. Several attempts also need to be made to estimate the success performance before a movie can be released. On the audience side, there is an indicator that we can use to decide that a movie is good by looking to its ratings. The industry can use this rating systems as a factor to be considered when evaluating the past movie performances to make better movie for the next production. This study aims to predict the movie ratings and find the most important features which correlate to the ratings. We performed an experiment on IMDb dataset which contains 5,043 movie metadata on four models of machine learning, Linear Regression (LR), Decision Tree (DT), Random Forest (RF) and K-Nearest Neighbor (KNN). From the experiment, Random Forest achieves the best performance amongst the four models. © 2020 IEEE.

关键词： Decision trees

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Predicting User Preferences with XGBoost Learning to Rank Method 3

Predicting User Preferences with XGBoost Learning to Rank Me...

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3rd International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2020

作者： Qomariyah, Nunung Nurul Kazakov, Dimitar Fajar, Ahmad Nurul Bina Nusantara University Faculty of Computing and Media Computer Science Department Jakarta11480 Indonesia University of York Computer Science Department York United Kingdom Binus Graduate Program-Master of Information Systems Management Bina Nusantara University Information Systems Management Department Jakarta11480 Indonesia

ISBN: (纸本)9781728184067

Learning user preferences become very important as the personalization systems grow rapidly in this current era. Offering special and personal services can be an added value for the companies to maintain their customer loyalty. Building a personalized recommendation requires a good machine learning model to understand the individual preferences. Every user can be presented with a list of items sorted by its score learned from the individual preferences. So the first couple items shown will be the most liked items by the user. We can borrow the Learning to Rank algorithm from Information Retrieval to solve this problem. In this paper, we present the implementation of user preferences learning by using XGBoost Learning to Rank method in movie domain. We show the evaluation of three different approaches in Learning to Rank according to their Normalized Discounted Cumulative Gain (NDCG) score. We can conclude that in our case study, the pairwise approach appears to be the best solution to produce a personalized list of recommendation. © 2020 IEEE.

关键词： Recommender systems

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Air Quality Prediction Based on Air Pollution Emissions in the City Environment Using XGBoost with SMOTE

Air Quality Prediction Based on Air Pollution Emissions in t...

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International Conference of computer science and Information Technology

作者： Adli A Nababan Sutarman Muhammad Zarlis Erna B Nababan Department of Computer Science Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia Department of Mathematics Faculty of Mathematics and Natural Sciences Universitas Sumatera Utara Medan Indonesia Department of Information Systems Management BINUS Graduate Program-Master of Information Systems Management Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9798350399080

Air pollution makes it worse in populated regions. Indonesia's major cities are also impacted by air pollution. Due to rising traffic, material consumption by vehicles, industrial growth, land burning, and garbage collection, air quality has altered significantly. Accurate measurement and classification of air quality are required. Results of accurate classification aid in forming governmental regulations. To reach the criteria for livable air quality, we intend to manage controls. Some of the characteristics of air pollution include particulate (PM10) or PM25, sulfides (in the form of SO2), carbon monoxide (CO), ozone (O3), and nitrites (NO2). Using the XGBoost algorithm and synthetic minority oversampling (SMOTE) methods based on the Air Pollution Standard Index (ISPU) category, this study seeks to uncover factors that affect air quality. XGBoost, an ensemble machine learning approach built on a decision tree and applying a gradient reinforcement framework, is the classification algorithm in use. Air quality categorization has been tested and shown to work with the XGBoost algorithm. The dataset used in this study is air quality in 2021 which was collected by the Jakarta Environmental Ministry The proposed classification model will be evaluated using the Repeated k-fold cross-validation method for objective results. The results of the study prove that SMOTE and XGBoost have better performance than using only the XGBoost method in predicting air quality, where the overall accuracy value of SMOTE with XGBoost is 99.60%, The overall precision value is 99.60%, the overall recall value is 99.60% and the overall f1-score is 99.60% and the overall ROC AUC value is 99.96%. The overall SMOTE method with the proposed XGBoost has given better performance in air pollution prediction.

关键词： Machine learning algorithms Urban areas Air pollution Prediction algorithms Particle measurements Regulation Classification algorithms

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A Comparison: Prediction of Death and Infected COVID-19 Cases in Indonesia Using Time Series Smoothing and LSTM Neural Network

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Procedia computer science 2021年 179卷 982-988页

作者： Zulfany Erlisa Rasjid Reina Setiawan Andy Effendi Computer Science Department School of Computer Science Bina Nusantara University Jl. K.H. Syahdan No. 9 Jakarta 11480 Indonesia. Information Systems Department School of Information Systems Bina Nusantara University Jl. K.H. Syahdan No. 9 Jakarta 11480 Indonesia.

COVID-19 is a virus causing pneumonia, also known as Corona Virus Disease. The first outbreak was found in Wuhan, China, in the province of Hubei on December 2019. The objective of this paper is to predict the death and infected COVID-19 in Indonesia using Savitzky Golay Smoothing and Long Short Term Memory Neural Network model (LSTM-NN). The dataset is obtained from Humanitarian Data Exchange (HDX), containing daily information on death and infected due to COVID-19. In Indonesia, the total data collected ranges from 2 March 2020 and by 26 July 2020, with a total of 147 records. The results of these two models are compared to determine the best fitted model. The curve of LSTM-NN shows an increase in death and infected cases and the Time Series also increases, however the smoothing shows a tendency to decrease. In conclusion, LSTM-NN prediction produce better result than the Savitzky Golay Smoothing. The LSTM-NN prediction shows a distinct rise and align with the actual Time Series data.

关键词： Covid-19 Long Short Term Memory Neural Network model Prediction Savitzky Golay Smoothing

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