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computer science, Information Technology and Engineering (ICCoSITE), International Conference on

作者： Bambang Sulistio Harco Leslie Hendric Spits Warnars Ford Lumban Gaol Benfano Soewito Computer Science Department Binus Graduate Program – Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

Nowadays, many people are starting to care about early investment. One of the most popular investments lately, especially for millennials, is a stock investment. In investing, there are advantages and risks of loss. One way to reduce the risk of loss is by using price predictions before investing in stocks. This paper proposes the use of deep learning in making stock predictions. We conducted research by calculating the performance of six deep-learning algorithms to predict stock closing prices. The application of the CNN-LSTM-GRU hybrid algorithm combination produces the best performance compared to other methods, based on the value: Root Mean Squared Error (RMSE) decreased by 1.100 by 14%, Mean Absolute Error (MAE) was successfully reduced by 0.798 by 13.4%, and R Square increased by 0.957 by 3.9%. In predicting stock prices on the Indonesian Stock Exchange, especially in the energy sector, CNN-LSTM-GRU is more appropriate for investors than using a single algorithm to make decisions in investing in stocks..

关键词： Deep learning computer science Recurrent neural networks Sociology Prediction methods Prediction algorithms Convolutional neural networks

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Fostering AI Synergy in the Workplace: The Mediating Role of Indonesian Employees Well-Being in Authentic Leadership and AI Experience

Fostering AI Synergy in the Workplace: The Mediating Role of...

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Innovative Research in Applied science, Engineering and Technology (IRASET),Conference on

作者： Daniella Tjung Justin Axela Darmanto Jason William Kelanajaya Maria Grace Herlina Karto Iskandar Management Department BINUS Business School Undergraduate Program Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331532970

ISBN: (纸本)9798331532987

The present study explores into the relationships between authentic leadership, employee well-being, and AI experience among white-collar workers in Greater Jakarta, Indonesia. The study employs a cross-sectional survey and partial least squares structural equation modeling (PLS-SEM) to reveal that authentic leadership significantly enhances employee well-being, subsequently leading to positive interactions between employees and AI systems. The relationship between authentic leadership and AI experience is partially mediated by employee well-being, demonstrating the significance of leadership qualities like transparency, integrity, and self-awareness in creating a supportive organizational culture. The findings highlight the importance of initiatives focused on employees, such as mental health support and work-life balance, in facilitating AI adoption. However, the study also highlights certain limitations, including geographic and methodological constraints. Future investigations should broaden their scope to encompass various contexts, utilize longitudinal methodologies, and integrate supplementary elements such as technological infrastructure and user perspectives to enhance comprehension. These insights contribute to the growing discourse on leadership, well-being, and technology integration in modern workplaces.

关键词： Training Surveys Industries Leadership Technological innovation Employment Organizations Mental health Mathematical models Artificial intelligence

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Ensuring Success in Quick Commerce by Evaluating Its Application Development Capability Maturity Using COBIT 2019

Ensuring Success in Quick Commerce by Evaluating Its Applica...

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International Conference on Information Management and Technology (ICIMTech)

作者： Chandra Hermawan Heruatmadja Ford Lumban Gaol Suhono Harso Supangkat Benny Ranti Computer Science Department BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Faculty of Digital Communication and Hotel & Tourism Institute of Technology Bandung Bandung Indonesia Computer Science Department Faculty of Computer Science Universitas Indonesia Depok Indonesia

In the digital era, the demand for quick access to goods (q-Commerce) has driven retail companies to develop online shopping applications, aiming to attract more consumers. To cater to the demands of q-Commerce, specialized mobile applications that prioritize fast delivery and user-friendly experiences are crucial. However, developing and maintaining these applications present challenges in terms of speed of development and security. This research focuses on a retail company, PT X, with a q-Commerce application serving 3.5 million active users monthly. The research aims to assess PT X's capability maturity level in application development governance using the COBIT 2019 Capability Maturity Model. Additionally, the study investigates the differences in assessment methods between COBIT 2019 and previous versions. The findings contribute to understanding in which level of capability maturity that companies can achieve in developing quick commerce applications with a large user base. Findings of this research provides an overview for PT X to initiate the necessary initial steps to comply with the good application development according to COBIT 2019 standard. This research also highlights to future researchers that COBIT 2019 is primarily a framework for enterprise-level assessment and need to be adjusted to be suitable for measurements below the enterprise level.

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Analyzing Company’s Share Purchase Value Based on Financial Reports Using Artificial Neural Network (ANN)

Analyzing Company’s Share Purchase Value Based on Financial...

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International Conference on Computing, Engineering, and Design (ICCED)

作者： Jeremy Shawn Jonathan Gunawan Chen Zi Hao Maria Susan Anggreainy Afdhal Kurniawan 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

Because financial time series forecasting is sensitive to political, economic, and social factors, it is not a simple task. As a result, those who make investments in currency exchange and financial markets typically search for reliable models that can guarantee they will maximize their profile and minimize their losses. Fortunately, many studies have used a method from Artificial Neural Networks (ANNs) called Backpropagation, could improve the predictive accuracy of the behavior of the financial data over time. This paper aims to forecast stock share prediction from closing value of PT. Bank Central Asia Tbk, and PT. Bank Maybank Indonesia Tbk. The results show that the using Backpropagation gives the closest result. And for the rating of judgement for cast accuracy, it exceeded 10% accuracy, which means high accurate from the prediction. For further checking, comparing the results of research from Victor’s results, it almost hits the same accuracy percentage. Which means, these prediction are accurate enough to do time series forecasting.

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Machine Learning Methods for Predicting the Necessity of Caesareans Section of Childbirth 5

Machine Learning Methods for Predicting the Necessity of Cae...

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

作者： Sunge, Aswan Supriyadi Heryadi, Yaya Abdurachman, Edi Kartowisatro, Iman H. Bina Nusantara University BINUS Graduate Program- Computer Science Computer Science Department Jakarta11480 Indonesia Bina Nusantara University Faculty Of Engineering Computer Science Departement - BINUS Graduate Program Computer Science Computer Engineering Department Jakarta11480 Indonesia

ISBN: (纸本)9781665401968

In the process of delivery usually the baby comes out of the vagina but under some circumstances a cesarean section is performed. Caesarean section, on the one hand can have short-term and long-term effects for the mother but on the other hand it is a life-saving procedure for both mother and child. The purpose of this study is to predict whether or not a caesarean section is necessary for a pregnant mother using a machine learning classifier model and some features from medical records as input data. In this study, some machine models were explored including Logistic Regression, K-Nearest Neighbor, Random Forest, Decision Tree, Gradient Boosting and Support Vector Machine. The experiment found that Logistic Regression model achieved the highest performance as measured by average accuracy 95%, average precision 86%, average recall 95%, and average F1 86%. © 2021 IEEE.

关键词： Forecasting

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Exploring the Effect of Activation Function on Transformer Model Performance for Official Announcement Translator from Indonesian to Sundanese Languages

Exploring the Effect of Activation Function on Transformer M...

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computer science, Information Technology and Engineering (ICCoSITE), International Conference on

作者： Bambang Dwi Wijanarko Dina Fitria Murad Yaya Heryadi Cuk Tho Kiyota Hashimoto Computer Science Department BINUS Online Learning Jakarta Indonesia Information Systems Department BINUS Online Learning Jakarta Indonesia Computer Science Department BINUS Graduate Program-Doctor of Computer Science Jakarta Indonesia Interdisciplinary Graduate School or Earth System Science and Andaman Natural Disaster Management Prince of Songkla University Phuket Campus Phuket Thailand

Automated language translation involving low-resource language has gained wide interest from many research communities in the past decade. One lesson learned from the past COVID-19 pandemic, particularly in Indonesia, is that many local Governments have to release regular public announcements to keep people following health protocol especially when they are in public areas. Many studies showed some evidence that rural people in Indonesia which covers a large proportion of Indonesia’s population, feel more convenience receiving official announcements in their local language. However, translating official announcement from the national language to many local languages in Indonesia require many experienced bilingual translators and time. This paper presents exploration results in developing an automated language translator model to translate texts in Bahasa Indonesia to the Sundanese language. In particular, this study aims to explore the effect of ReLU, Sigmoid, and Tanh activation functions of the Vanilla Transformer Model on its translation performance. The experiment results showed that the activation function under study gives similar training accuracy (0.98). However, ReLU achieves better performance than Tanh in terms of validation accuracy, training loss, and validation loss.

关键词： Training COVID-19 computer science Protocols Pandemics Local government Sociology

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Mobile Application as Community Social Media

Mobile Application as Community Social Media

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International Conference on Inventive Computation Technologies (ICICT)

作者： Teuku Ryan Pahlevi Johannes Haryanto Rahmat Adha Bayu Alfian Harco Leslie Hendric Spits Warnars Rahayu Widiono Computer Science Department School of Computer Science. Bina Nusantara University Jakarta Indonesia Information Systems department School of Information Systems Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department Binus Online Learning Bina Nusantara University Jakarta Indonesia

To day's society is different from the past, where the speed of the internet is getting more sophisticated, and the existing gadget technology is getting faster, and of course, this is also changing the lifestyle of today's people, which of course almost half of their daily life is spent in front of gadgets. On the one hand, this is undoubtedly very good and supports humans in carrying out their daily life activities, but on the other hand, this raises new problems where humans start to lack time to communicate and socialize with each other and often making them stressed and anxious because they are already tired and do not know how to socialize anymore. This paper begins with a model and implementation of a very simple mobile application that behaves as a social media inspired by *** as a social media for academics. The design of this mobile application is done by using use case diagrams to describe existing processes and class diagrams to describe class relationships.

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Sentiment Analysis in Indonesian Trading using Lexicon-based and Support Vector Machine

Sentiment Analysis in Indonesian Trading using Lexicon-based...

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computer science, Information Technology and Engineering (ICCoSITE), International Conference on

作者： Raymond Sunardi Oetama Yanfi Yanfi Masagus M. Ikhsan Assiddiq U.P Sani Muhamad Isa Information System DepartmentFaculty of Engineering and Informatics Universitas Multimedia Nusantara Jakarta Indonesia Computer Science Department BINUS Graduate Program Computer Science School of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program Computer Science Bina Nusantara University Jakarta Indonesia

Many individuals now trade online utilizing trading software in the digital world. Binomo is one of Indonesia's most popular trading platforms. This is because some influencers made several promises to Binomo customers. Since many customers were deceived, this case became quite popular. This study was executed to see how Indonesians felt about the Binomo application after the case went viral. The solution taken was in the form of sentiment analysis because there had been no previous research on sentiment analysis that discussed the Binomo case. The data was scanned using Netlytic tools, a cloud-based text and social network analyzer capable of identifying any talks on social media sites such as Twitter. The sentiment analysis of Binomo trading tweets by using the Multi-Perspective Question Answering lexicon utilized the KNIME tool. But unfortunately, the accuracy of sentiment analysis results is low. Furthermore, the Support Vector Machine technique is also being conducted. The Term Frequency-Inverse Document Frequency method is applied to perform feature extraction whilst the chi-square approach is utilized to identify features that are thought to be useful for inclusion in the classification process and to exclude features that are irrelevant to the target class. The obtained accuracy is 86%. The study proposes that words from the algorithm's outputs can be utilized to improve the quality of sentiment analysis using the lexicon. As an outcome of the algorithm, positive and negative terms are added to the lexicon, increasing the accuracy of sentiment analysis using the new vocabulary from 58.984% to 71.146%.

关键词： Support vector machines Training Sentiment analysis Vocabulary Dictionaries Social networking (online) Blogs

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Transfer Learning Method for Malaria Identification

Transfer Learning Method for Malaria Identification

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International Conference on Informatics and Computing (ICIC)

作者： Cecilia Audrey Herli Daniel Zerge Wijaya Hansen Michelle Angela Zakaria Berlam Pragusma Ajeng Wulandari Maria Susan Anggreainy 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

Malaria is a severe disease caused by parasites of the genus Plasmodium, which are transmitted to humans through the bite of an infected female Anopheles mosquito. Symptoms of malaria begin to appear at least within 10 to 15 days. If malaria is not treated immediately, it is feared that it will cause respiratory problems, shortness of breath, and death. To avoid the occurrence of these events, the idea arose to create an AI (Artificial Intelligence) project that can recognize the presence of malaria parasites in blood cells. Thus, the main objective of this project is to find out how to create a Machine Learning model that can efficiently identify malaria parasites in the human body. The AI project uses CNN (Convolutional Neural network) as an algorithm to recognize the presence or absence of parasites in blood cell images that will be inputted by the user. Process of implementing CNN, using VGG19 which is an advanced CNN that has pre-trained layers and a good understanding of describing an image, both the shape, color, and structure of the image. After implementing the Transfer Learning algorithm on the dataset, the result is a Transfer Learning algorithm that can detect the presence of Malaria parasites in blood cells with an accuracy rate of 92 percent a specificity of 95 percent, and a sensitivity of 89 percent. The accuracy can still increase depending on the diversity of the data provided. The more often we train and input test data as train data, the accuracy of AI will also increase.

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Decision Theory and Risk Simulation Analysis for Optimizing Profit in PayLater Services

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Procedia computer science 2023年 219卷 60-67页

作者： Nicholas Dominic Bens Pardamean Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program Master of Computer Science Program Bina Nusantara University Jakarta Indonesia 11480

The projected increase in PayLater utilization reaches up to five million people by 2025. To optimize the yearly profit from their PayLater service, fintech companies must examine all possible risks before a unanimous decision is taken. Therefore, we proposed a unified decision framework derived from decision theory and the Monte Carlo simulation technique. Two schemes were coined: (1) a decision-making scheme, and (2) a risk simulation scheme. Throughout experiments, the framework was able to estimate several alternative decisions and their impacts, analyze the causes of failure and delays in the development of the PayLater service, and execute Monte Carlo simulations in up to 10,000 trials. Outputs of this study will benefit decision-makers in the fintech initiative before launching their PayLater products.

关键词： PayLater decision-making Bayesian analysis fault tree analysis critical path method Monte Carlo simulation

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