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Determining the Main Symptoms of Lung Cancer with Machine Learning Methods 10

Determining the Main Symptoms of Lung Cancer with Machine Le...

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10th International Conference on ICT for Smart Society, ICISS 2023

作者： Meylia, Belinda Christina Immanuel, David Gabriel Faustine, Regina Dharma, Surya Anggreany, Maria Susan Parmonangan, Ivan Halim School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Bina Nusantara University BINUS Graduate Program Doctor of Computer Science Computer Science Department Jakarta11530 Indonesia

ISBN: (纸本)9798350339543

The ability to identify cancer at an early stage is critical to increase the likelihood of effective treatment or stopping the progression of the disease in the body. Lung cancer is one of the most common deadly diseases and quickly kills the patient. The number of deaths caused by lung cancer surpasses those of colon, rectal, breast, and prostate cancers combined. Unfortunately, only two percent of patients with advanced lung cancer survive for five years or more. However, the survival rates are better, with 49 % of the patients surviving for five years or more, if the disease is detected early. On the other hand, in this modern era, machine learning has become one of the most reliable tools in the world for healthcare, as machine learning can learn from the data obtained and process the data that will later be used to help complete certain tasks. Therefore, in this research, machine learning or especially classification algorithms such as K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Random Forest, Logistic Regression, and XGBoost are used to identify three main symptoms that serve as markers for early detection of lung cancer. Determining the three main symptoms is done by combining the results of the feature importance score on each model using the rank-averaging method. The result is an average ranking of each feature based on its combined importance with an accuracy of 93.5 %. © 2023 IEEE.

关键词： Support vector machines

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Predicting Stock Market Movements Using Long Short-Term Memory (LSTM) 4

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

引用

4th International Conference on Artificial Intelligence and Data sciences, AiDAS 2023

作者： Fauzan, Achmad Susananggreainy, Maria Nathaniel, Nicholas Kurniawan, Afdhal School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program Doctor of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350318432

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%. © 2023 IEEE.

关键词： Long short-term memory

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Exploring the Potential of Emotional Intelligence Robots in Mitigating Depression in Indonesian Youth: A Systematic Literature Review

Exploring the Potential of Emotional Intelligence Robots in ...

引用

2023 International Conference on Networking, Electrical Engineering, computer science, and Technology, IConNECT 2023

作者： Woge, Patricia Laurencia Etania Saputra, Karen Gunawan, Aprilia Jocelyne Agung Santoso Gunawan, Alexander Bina Nusantara University School of Computer Science Computer Science Department Malang65154 Indonesia Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350331172

Depression among young people is a growing concern in Indonesia, exacerbated by the rapid pace of technological change. However, this same pace of change has also led to the development of social robots, which have been shown to improve mental health outcomes, including reducing depression. This study draws upon Social Cognitive Theory, Self-Determination Theories, Cognitive Behavioral Theories, and Technology Acceptance Models to assess the ability of social robots to mitigate depression in Indonesian youth. Social robots have the potential to provide social support, promote autonomy and connectivity, enhance cognitive processes, and increase technology acceptance, all of which can contribute to mitigating depression in youth. To explore this potential, this study conducts a literature review of relevant studies. The aim is to analyze the potential ability of emotional intelligence robots in reducing depression among young people in Indonesia. The result of this study is to show that emotional intelligence robots can be a support tool to address the growing problem of depression among Indonesian youth. The findings of this study will be valuable to researchers, practitioners, and policymakers in the field of mental health, as well as technology developers seeking to design innovative solutions to address societal problems. © 2023 IEEE.

关键词： Intelligent robots

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Method For Traffic Violation Detection Using Deep Learning

Method For Traffic Violation Detection Using Deep Learning

引用

2023 International Conference on Informatics, Multimedia, Cyber and Information Systems, ICIMCIS 2023

作者： Tangamus, Darren Thai, Nathanael Alvaro Shan, Jung Sihotang, Erna Fransisca Angela Irwansyah, Edy Bina Nusantara University School of Computer Science Computer Science Department Jakarta Indonesia Bina Nusantara University School of Computer Science Statistics Department Jakarta Indonesia

ISBN: (纸本)9798350307306

Urban areas commonly encounter traffic congestion. One of its primary causes is motorists violating traffic regulations, such as travelling at excessive speeds. Besides risking the safety of other road users, speeding exacerbates traffic capacity. Artificial Intelligence will detect instances where vehicles exceed the maximum speed limit on a thoroughfare to address this issue. The YOLOv8 algorithm, a commonly utilised Convolutional Neural Network Architecture in computer Vision for object detection and classification, is employed in this study to detect instances of speeding vehicles in live video footage. The research model can accurately identify and categorise vehicles in real-time CCTV footage, showcasing superior bounding box prediction performance. However, the model's low Recall rate and Mean Average Precision scores at both IoU thresholds warrant further exploration. Issues such as misidentification of objects remain prevalent in the model, which may stem from inaccurate classification of object categories, computational restrictions, and partiality in the data. Issues such as misidentification of objects remain prevalent in the model, which may stem from inaccurate classification of object categories, computational restrictions, and partiality in the data. Nonetheless, this study reveals multiple possibilities for enhancing the model, which includes employing new data sets and incorporating supplementary detections. © 2023 IEEE.

关键词： computer vision

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The Impact of Artificial Intelligence on Art - A Systematic Literature Review 9

The Impact of Artificial Intelligence on Art - A Systematic ...

引用

9th IEEE Information Technology International Seminar, ITIS 2023

作者： Then, Christian Soewandi, Erickar Jeffry Danial, Muhammad Fakhri Achmad, Said Sutoyo, Rhio School of Computer Science Bina Nusantara University Computer Science Department Indonesia

ISBN: (纸本)9798350306835

The Impact of Artificial Intelligence (AI) on the world of art, especially its integration with computer science, is the focus of our research, which uses the Systematic Literature Review (SLR) methodology in conducting its research. We started by identifying sources in Google Scholar and finding approximately 821 related research papers. Then, we carried out a screening process of these papers, finally getting the 30 most relevant papers. These results show how AI significantly impacts art, enabling artists to produce more avant-garde and immersive works of art using tools such as computer vision and generative adversarial networks (GANs). AI offers new tools for creating works of art and increases audience interaction and participation in artistic experiences. This study highlights how AI has the potential to enrich and revolutionize the process of art creation as well as audience participation. © 2023 IEEE.

关键词： computer vision

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Authorship Attribution in Bahasa Indonesia Using Twitter Dataset on Political Topic 4

Authorship Attribution in Bahasa Indonesia Using Twitter Dat...

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4th International Conference on Artificial Intelligence and Data sciences, AiDAS 2023

作者： Muliono, Yohan Gaol, Ford Lumban Chowanda, Andry Budiharto, Widodo Bina Nusantara University Binus Graduate Program-Doctor of Computer Science Computer Science Department Jakarta11480 Indonesia School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350318432

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. © 2023 IEEE.

关键词： Curricula

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AI Chatbot Technology to Predict Disease: A Systematic Literature Review 4

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

引用

4th International Conference on Artificial Intelligence and Data sciences, AiDAS 2023

作者： Velasco, Vincent Dedy Setiawan, Kevin Robert Sanjaya, Renaldo Susan Anggreainy, Maria Kurniawan, Afdhal School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program Doctor of Computer Science Computer Science Department Jakarta11530 Indonesia

ISBN: (纸本)9798350318432

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. © 2023 IEEE.

关键词： Diagnosis

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Impact Analysis of Insufficient Destination Resources on VoIP Service Quality: Case Study 9th

Impact Analysis of Insufficient Destination Resources on VoI...

引用

9th International Conference on computer science and Computational Intelligence, ICCSCI 2024

作者： Iskandar, Mhd Indra Gunawan, Alexander Agung Santoso Engineering Faculty Bina Nusantara University Jakarta11480 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

This research investigates the disruption caused by insufficient destination resources in Voice over Internet Protocol (VoIP) networks. The study is based on a case study of service disruptions at a Public Service Company (PSC) where the public experienced dropped calls. The research employs Ribbon Analytics, a big data analytics platform, to measure the concurrent session connection on the Session Initiation Protocol (SIP). The observation of the VoIP network followed a specific topology with 22 Session Border Controller (SBC) nodes, 2 Call Detail Record (CDR) servers, and 2 Ribbon Analytics (RA) devices. The results indicate that the call failure occurred because the number of sessions had reached the maximum limit. The solution proposed is to increase the number of sessions;however, this cannot be done immediately due to contractual constraints between the telecom operators and the PSC. An alternative solution is to provide a call answering machine managed by AI. Future work could also use text message in the form of chat prompts from AI. The study highlights the importance of big data analysis in identifying and resolving service disruptions in VoIP networks. © 2024 The Authors.

关键词： Voice/data communication systems

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Air Pollution Level Prediction in Jakarta Using Artificial Neural Network 4

Air Pollution Level Prediction in Jakarta Using Artificial N...

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4th International Conference on Artificial Intelligence and Data sciences, AiDAS 2023

作者： Ferdinan, Roger Margareta, Krestella Christyan, Stevan Anggreainy, Maria Susan Kurniawan, Afdhal School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program Doctor of Computer Science Computer Science Department Jakarta11530 Indonesia

ISBN: (纸本)9798350318432

Air pollution has emerged as a critical issue in several major urban areas, including Jakarta. To address this problem, the study explores the utilization of the Artificial Neural Network (ANN) method. Three distinct approaches are investigated in this research: the Support Vector Classifier (SVC), Deep Artificial Neural Network (Deep ANN), and Long Short-Term Memory (LSTM). The available Air Quality data were utilized to train and assess the performance of the proposed ANN models. The research findings reveal that the Deep ANN model surpasses the other methods, achieving an impressive accuracy of approximately 96.57% and a cross-entropy value of 0.1103. In predicting air quality in Jakarta, Deep ANN has demonstrated superior performance when compared to SVC and LSTM. These results highlight the significant potential of deploying Deep Artificial Neural Network (ANN) methodologies for air quality prediction. Such an approach could play a pivotal role in the development of monitoring and early warning systems aimed at effectively addressing air quality issues in the Jakarta region. © 2023 IEEE.

关键词： Long short-term memory

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Fostering Digital Innovation in Indonesia: The Role of STEM-AI Skills in Overcoming Talent Deficits and Gender Stereotypes 4

Fostering Digital Innovation in Indonesia: The Role of STEM-...

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4th International Conference on Intelligent Cybernetics Technology and Applications, ICICyTA 2024

作者： Halim, Erwin Zahra, Amalia Maryani Wulandhari, Lili Ayu Fitrianah, Devi Puspita, Virienia School of Information Systems Bina Nusantara University Information Systems Department Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program Computer Science Department Jakarta11480 Indonesia School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program Communication Department Jakarta11480 Indonesia

ISBN: (纸本)9798331506490

This research explores the optimization of digital talent in advanced industries, particularly in the context of rapid digital transformation. Despite the increasing importance of digital talent for gaining a competitive edge, several challenges persist. In Indonesia, for example, there is a significant shortage of digitally skilled workers, with the World Bank reporting a gap of 9 million skilled individuals over the past 15 years. Additionally, artificial intelligence (AI) is expected to impact 9.5 million jobs, further exacerbating the demand for digital skills. Furthermore, the employability of graduates remains low, largely due to inadequate training, poor language proficiency, and limited cultural sensitivity. Many companies face difficulties in finding qualified individuals, quickly upskilling their workforce, and fostering an innovative organizational culture. This study seeks to evaluate the factors influencing digital talent performance in the context of these challenges. It examines seven key variables: digital readiness, digital technology adoption, relevance of AI, digital skills, individual performance, problem-solving ability, and overall digital talent performance. Hypotheses were tested using Partial Least Squares Structural Equation Modeling (PLS-SEM). Data was collected from 378 participants, primarily students with STEM backgrounds, in the Jabodetabek area of Indonesia in November 2024. The findings reveal that all hypotheses are significant, with results indicating that user satisfaction and the perceived value of digital talent in Indonesia have a notable impact on continued usage intentions in digital roles. © 2024 IEEE.

关键词： digital readiness digital talent performances digital technology individual performance STEM-AI

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