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International Conference on Artificial Intelligence and Data sciences (AiDAS)

作者： Yohan Muliono Ford Lumban Gaol Andry Chowanda Widodo Budiharto Computer Science Department BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

Fake news so called made-up news intended to cause misinformation is identified as Hoax. In Indonesia, hoaxes could not be ignored, fairly low literacy rate is the cause of hoaxes could spread quickly in Indonesia, to make it even worse, there are so many people would believe in hoaxes without any intention to make sure of the integrity of the news. even the media in Indonesia are competing with one another to provide content with provocative names in the hopes of increasing their rating and reaching as many people as possible. In the course of this research, a method for identifying authors on the basis of their writings will be developed to reduce the spread of hoaxes and encourage people to re-evaluate their decision to create harmful content and hoax news in the future. In this investigation, a transformer-based methodology focusing on IndoBERTbase, IndoBERTbase-uncased, and IndoBERTtweet will be employed in conjunction with the self-collected Indonesian tweet data that will be crawled from Indonesian Social Actor Focused on politicians in Twitter and will be combined with another topic in the future study. This research has shown a promising 98,93% in Training Accuracy using IndoBERTtweet.

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The Impact of Emotion Recognition Models Towards Believability Factor of Chatbots

The Impact of Emotion Recognition Models Towards Believabili...

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Advanced Applied Informatics Winter (IIAI-AAI-Winter), International Congress on

作者： Rhio Sutoyo Harco Leslie Hendric Spits Warnars Sani Muhamad Isa Widodo Budiharto Computer Science Department BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

Emotionally-aware chatbots are chatbots that are equipped with emotional intelligence. Based on the literature, using emotional elements in chatbots can improve user engagement and believability factors. This study attempts to make a novel contribution by empirically evaluating the impact of emotion recognition models on the believability factor of chatbots. This study examines the impact of the emotions model and avatar on chatbot interactions through three implementations. Thirty-one participants volunteered to evaluate emotionally aware chatbots. The participants evaluated the interaction with the chatbot using the Godspeed Questionnaire Series (GQS). The questionnaire results are utilized to measure the effect of the emotions model on the chatbot's believability factor. The evaluation results of the experiment show that implementing the emotion recognition model on the chatbot increases its believability factors. On average, the believability measures in interaction type B (with an emotion model) are enhanced 1.71 times compared to interaction type A (a basic model). Furthermore, the believability measures in interaction type C (with an emotion model and an avatar) are enhanced 2 times more than in interaction type A (a basic model). The believability factor is also heightened by integrating a chatbot avatar into the interaction system. Using avatars in chatbots increases the believability variables of the system by 1.17 times if compared with not using avatars.

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Street View Object Detection for Autonomous Car Steering Angle Prediction Using Convolutional Neural Network 1

Street View Object Detection for Autonomous Car Steering Ang...

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1st International Conference on computer science and Artificial Intelligence, ICCSAI 2021

作者： Sonata, Ilvico Heryadi, Yaya Budiharto, Widodo Wibowo, Antoni Bina Nusantara University BINUS Graduate Program-Doctor of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9781728133331

Autonomous car research is currently developing rapidly to find optimal and accurate steering angle and speed control. Various sensors such as cameras, LIDAR, and RADAR are used to recognize the surrounding environment to determine the correct steering angle prediction in avoiding obstacles. In addition to being expensive, LIDAR and RADAR have several drawbacks such as the level of accuracy that depends on the weather and the ability to detect adjacent objects. This paper will propose the prediction of steering angle and speed control in autonomous cars based on the detection of street view objects such as cars in front, traffic signs, pedestrians, and lane lines. The process of object detection and prediction of steering angle, as well as prediction of speed control using a convolutional neural network (CNN) through video captured using a single camera. In this method, other sensors such as LIDAR and RADAR are no longer needed so that the costs required are lower and the weaknesses found in LIDAR and RADAR can be eliminated. The results obtained are very good with 92% accuracy for steering angle prediction and 85% for speed control prediction. The autonomous car can run well in the simulator environment through the video taken on the real road. © 2021 IEEE.

关键词： Object recognition

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Analyzing Microservices and Monolithic Systems: Key Factors in Architecture, Development, and Operations

Analyzing Microservices and Monolithic Systems: Key Factors ...

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International Conference of computer and Informatics Engineering (IC2IE)

作者： Juan Christian Steven Afdhal Kurniawan Maria Susan Anggreainy Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program Doctor of Computer Science Bina Nusantara University Jakarta Indonesia

Microservices and monolithic systems are two prevalent architectural approaches in software development. This study provides a complete review and analysis of the key components involved in design, development, and operation in software development. A systematic review of the literature was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. This review examined various sources debating monolithic systems vs microservices, highlighting their benefits, drawbacks, and implementation in enterprises. The paper addresses important research questions, aiming to further analyze this architectural approach. Findings show that microservices offer benefits such as scalability, flexibility, and independent deployment, while monolithic systems provide simplicity and ease of development. However, challenges related to network communication, data consistency, and operational complexity were also found with microservices. This research focuses on discussing the trade-offs and factors to consider when deciding between monolithic systems and microservices, which provides in-depth information for practitioners in decision-making for software development. This research aims to help readers understand the effects of using monolithic or microservice-based systems in software development.

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AI Chatbot Technology to Predict Disease: A Systematic Literature Review

AI Chatbot Technology to Predict Disease: A Systematic Liter...

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International Conference on Artificial Intelligence and Data sciences (AiDAS)

作者： Vincent Velasco Kevin Dedy Setiawan Renaldo Robert Sanjaya Maria Susan Anggreainy Afdhal Kurniawan Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program Doctor of Computer Science Bina Nusantara University Jakarta Indonesia

Artificial Intelligence (AI) and chatbot technology have emerged as promising solutions to improve healthcare services. AI chatbots can mimic human-like interactions and assist with tasks such as triaging patients and providing medical advice. This review focuses on the use of AI chatbots in predicting diseases, aiming to explore their effectiveness and potential for early intervention and treatment. The purpose of this systematic literature review is to analyze studies related to AI chatbot technology in disease prediction. A systematic literature review was conducted, analyzing a total of 24 selected journals based on predefined inclusion and exclusion criteria. The review protocol involved examining studies published from various years, with a particular emphasis on articles from 2020. The findings indicate that AI chatbots have the potential to play a significant role in predicting diseases and assisting healthcare professionals in making informed decisions. AI chatbot technology shows tremendous potential in disease prediction. By utilizing machine learning algorithms and techniques, chatbots can enhance the accuracy and quickness of disease diagnosis. Continued research and development efforts are necessary to refine AI chatbots' capabilities and revolutionize healthcare delivery for improved patient outcomes and disease management.

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Predicting Stock Market Movements Using Long Short-Term Memory (LSTM)

Predicting Stock Market Movements Using Long Short-Term Memo...

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International Conference on Artificial Intelligence and Data sciences (AiDAS)

作者： Achmad Fauzan Maria SusanAnggreainy Nicholas Nathaniel Afdhal Kurniawan Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program Doctor of Computer Science Bina Nusantara University Jakarta Indonesia

This paper explores using artificial intelligence (AI) to predict stock market movements and build optimal portfolios. The research methodology involves using LSTM networks to predict stock performance. The study aims to combine AI with human expertise to develop an intelligent trading system. The findings emphasize the importance of selecting appropriate AI approaches for accurate predictions and optimal portfolio management. The results of this study state that with LSTM, we can predict stock prices that are very close to their real prices. The average LSTM method in predicting stock prices is about 97.2938%. Average error obtained when using LSTM was 2.3223%.

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Analysis of Factors Influencing Decisions to Purchase Airline Tickets Online During the Covid-19 Pandemic

Analysis of Factors Influencing Decisions to Purchase Airlin...

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

作者： Yulius Lie Anzaludin Samsinga Perbangsa Bens Pardamean Information System Department School of Information Systems Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia

The background of this research is the condition of the covid-19 pandemic which has an impact on online ticket sales. Meanwhile, when future of covid-19 pandemic is starting to become clearer, we are going to have a lot more infections but hopefully a lot fewer hospitalizations, people began to travel long distances that had been delayed by implementing health protocols. Airfare competition can influence the decision to buy airline tickets. Various things have been done to attract the attention of buyers. This research is a non-experimental quantitative research. Population in this research was people who have purchased plane tickets online during the covid-19 pandemic, and the sample were 113 respondents age between 15 and 64. Sampling technique used was simple random sampling. Analysis of the data used in this study using multicollinearity, heteroscedasticity, multiple linear, T-test, F-test, and coefficient of determination test. It was concluded that price, promotion, and lifestyle variables can influence online purchasing decisions.

关键词： COVID-19 computer science Protocols Pandemics Sociology Statistics Information technology

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Drug Demand Prediction Based on Epidemiology Factors Using Random Forest

Drug Demand Prediction Based on Epidemiology Factors Using R...

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Advanced Applied Informatics Winter (IIAI-AAI-Winter), International Congress on

作者： Gunadi Emmanuel Yulyani Arifin Ilvico Sonata Muhammad Zarlis Doctor of Computer Science Program Bina Nusantara University Jakarta Indonesia Computer Science Department Binus Graduate Program-Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Information System Management Department Binus Graduate Program Bina Nusantara University Jakarta Indonesia

The characteristics of the disease that spreads quickly, the number of sufferers, and the severity of sufferers of Coronavirus Disease 2019 are components of uncertainty during the pandemic. In an uncertain situation, prediction models for the need for drugs and medical devices are of great concern to policymakers in government, drug manufacturers, distributors, and pharmaceutical installation managers to maintain drug availability. Drug need prediction models that rely on historical data components on drug use are no longer reliable. Learning from the COVID-19 case, epidemiological variables correlate with predicting drug demand. This research includes data on ten major diseases in private hospital units for 2017–2022 to complete historical data on drug use. This study implements the Random Forest algorithm. The research method uses literature studies and processing field data from pharmaceutical installations. The analysis process uses KNIME software. The level of accuracy in predicting drug demand from historical drug use data was 77.272%, increasing to 81.818% with a model for predicting drug demand based on consumption cycles and classification of drug therapy groups. Furthermore, predictions of drug demand can consider variables recorded in medical records related to the seasonal frequency of diseases.

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Security and Privacy Issues in Cloud-Based Databases: A Literature Review

Security and Privacy Issues in Cloud-Based Databases: A Lite...

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International Conference on ICT For Smart Society (ICISS)

作者： Oliver Sebastian Marpaung David Adriel Alvyn Vincent William Maria Susan Anggereainy Afdhal Kurniawan Computer Science Department School Of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program Doctor of Computer Science Bina Nusantara University Jakarta Indonesia

Nowadays, technology is getting more sophisticated. It can be seen with the emergence of Cloud Computing. One application of Cloud Computing is Cloud databases. In a cloud environment, there will be privacy and security issues. From the literature results, data leaks and attacks from outsiders are still found when using cloud databases. Cloud service providers already have the technology to maintain data privacy and security. Three procedures can be carried out to maintain data privacy and security: encryption, access control, and third-party audits. Data privacy and security will be maintained with cloud service providers and procedures technology.

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Developing an online information portal for enhancing society awareness of RPTRA (A case study RPTRA MAYA ASRI 13)

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Procedia computer science 2023年 216卷 144-150页

作者： Yulyani Arifin Elizabeth Paskahlia Gunawan Margaretha Ohyver Computer Science Departement BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University Jakarta Indonesia. Computer Science Department School of Computer Science Bina Nusantara University Jakarta 11480 Indonesia. Statistics Department School of Computer Science Bina Nusantara University Jakarta 11480 Indonesia.

The long COVID-19 pandemic has limited the activities of Ruang Publik Terpadu Ramah Anak (RPTRA) such as environment cleaning, repair of RPTRA infrastructure, learning and others, as well as the lack of public awareness about the existence and importance of RPTRA to the public and foreign tourists encouraging the RPTRA to make changes in the dissemination of information about the activities carried out in the RPTRA through the use of technology to survive and grow even during the Covid-19 Pandemic. The methodology used in this research is Group Discussion Forum with the administrators of the RPTRA Maya ASRI 13 to learn about the needs and expectations of the RPTRA administrators regarding the web portal to be built. Then, the implementation of the conceptual model, development, and evaluation of the effectiveness of the RPTRA information web portal was carried out. The contribution in this research is to implement a conceptual model on the information portal built on the platform of the Website with the PHP framework, which is then evaluated on the results of the pre-test and post-test about RPTRA the Wilcoxon-Signed Rank Test to test the effectiveness of the information web portal for general users. The results obtained from these tests indicate that the RPTRA Information Web Portal can help the public learn more about the existence of RPTRA Maya Asri 13.

关键词： PHP RPTRA The Covid-19 Pandemic Web Portal Web Technology

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