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

作者： Nurcahyo, Aldian Soeparno, Haryono Gaol, Ford Lumban Arifin, Yulyani Bina Nusantara University BINUS Graduate Program-Doctor of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350339543

This study discusses the development of smart precision farming systems using big data and cloud-based intelligent decision support systems. Big data plays an important role in collecting, storing, and analyzing large amounts of data from various sources related to agriculture, including data from weather stations, soil sensors, satellite imagery, crop yield records, pest and disease reports, and other sources. This study highlights the differences between smart farming and precision farming. This study describes key techniques and system architecture, including data collection, processing, analysis, and decision support components. Utilizing a cloud platform enables scalability and optimized performance, which lowers costs and makes it safer and easier to manage. The integration of big data and Alibaba cloud computing in smart precision farming can improve farming productivity by providing timely information and recommendations to farmers for better decision-making. Finally, the system produces smart precision farming, which provides cost-effective real-Time monitoring and predictive analytics to increase agricultural production and sustainability. © 2023 IEEE.

关键词： Decision support systems

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Emotion Recognition Model using Product Review from Indonesia Marketplace 2

Emotion Recognition Model using Product Review from Indonesi...

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2nd International Conference on computer System, Information Technology, and Electrical Engineering, COSITE 2023

作者： Richard Andres, Jomari Rasheed Soetandar, Jason Pratama Sutoyo, Rhio Riza, Hammam Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Jakarta Indonesia

ISBN: (纸本)9798350343069

Online marketplace platforms create opportunities for vendors to sell their products to consumers online. During the early phase of the COVID pandemic, the adoption of online shopping in Indonesia is rapidly increasing. This rising state of online marketplace usage produces abundant data and allows researchers to conduct studies and explore. Product review data in the online marketplace contain rich information for emotion analysis tasks. This research proposes an emotion recognition model using the PRDECT-ID dataset. The TF-IDF approach is utilized as the baseline model for this research. Then, the authors focus on fine-tuning IndoBERT to outperform the baseline model. For the evaluation, the F1-Score was used to evaluate the models’ performance. This research found that the best configuration model is fine-tuned IndoBERT-base-p1 with 3e-6 as the learning rate, 32 for the batch size, and training with four epochs. The F1-Score of fine-tuned IndoBERT is 71%, surpassing the TF-IDF model by 9%. © 2023 IEEE.

关键词： Emotion Recognition

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Neural Network and Resampling to Handle Imbalance Class on Estimate Software Defect 15

Neural Network and Resampling to Handle Imbalance Class on E...

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15th International Congress on Advanced Applied Informatics Winter, IIAI-AAI-Winter 2023

作者： Setiawan, Tri Agus Soeparno, Haryono Gaol, Ford Lumban Arifin, Yulyani Bina Nusantara University Binus Graduate Program - Doctor of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350383829

Rapid progress in the field of artificial intelligence has created opportunities for extensive applications in software development. One area that receives attention is the evaluation of code quality using machine learning techniques. In this investigation, we examined the possible application of machine learning to predict the likelihood of defects in computer code. We employ NASA archival data as case studies. Machine learning models employ neural network algorithms. Our exploration involves partitioning the dataset into training data and test data for performance evaluation. The findings indicate that the Neural Organize technique with resampling yields a high level of accuracy in predicting software defects. Our simulated neural network is capable of identifying intricate patterns in the data and providing precise measurements of the size and intensity of defects. These results have significant implications in the software business, enabling developers to promptly identify possible vulnerabilities and take preventive measures before product release. © 2023 IEEE.

关键词： Machine learning

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Predicting Coral Reef Bleaching through Machine Learning 13

Predicting Coral Reef Bleaching through Machine Learning

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13th IEEE International Conference on Communication, Networks and Satellite, COMNETSAT 2024

作者： Maulidina, Alysha Puti Mazel, Kimberly Manuaba, Ida Bagus Kerthyayana Bina Nusantara University School of Computing and Creative Arts Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798350368086

Coral reef bleaching is a growing, ongoing issue that has yet to be tackled completely. One way to prevent these events is to make data-driven decisions, which can be done through data science. This paper uses machine learning to potentially predict future coral bleaching so that actions can be taken before the event's occurrence. Three machine learning algorithms are used, namely Support Vector Machines (SVM), Decision Trees, and Naïve Bayes. In this paper, we use both numerical and categorical datasets from BCO-DMO. The performances of the models, such as SVM, Decision Tree, and Naïve Bayes are evaluated to reveal which algorithm performed best in this application. The findings reveal that SVM had the best overall performance in predicting coral bleaching events, with an accuracy score of 93 percent for the BCO-DMO dataset. Further research can be done to expand and improve the reliability of these results. © 2024 IEEE.

关键词： Adversarial machine learning

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Developing an Exam Assessment Website Based Application for Assurance of Learning at One of Universities in Indonesia 3

Developing an Exam Assessment Website Based Application for ...

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3rd International Conference on Smart Cities, Automation and Intelligent Computing Systems, ICON-SONICS 2023

作者： Sari, Azani Cempaka Deodat, Christopher Susanto, Jasmine Tanti Amartya, Natashya Nurhakim, Muhammad Ihsan Arief Maryani Bina Nusantara University School of Computer Science Computer Science Department Tangerang Indonesia Bina Nusantara University School of Information System Information Systems Department Jakarta Indonesia

ISBN: (纸本)9781509062805

Assessment is essential in the education system as it helps determining students' performance as well as evaluation. There are different types of assessment and one of them is exam. The creation of exams may differ from one education institute to another. Observation and research were done at one of Indonesian universities to find out that the process of creating exams is still being done manually up to this date. This research uses waterfall development method and gathers data through literature research, questionnaires, and interviews. Laravel framework and MySQL database are used in the development of the application. The Eight Golden Rules, Five Human Factors Evaluation, Black Box Testing, and Paired T-Test Testing are used to evaluate the application developed. The result indicates that the application has a significant impact in simplifying the process of creating exams. © 2023 IEEE.

关键词： Websites

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Multivariate stock market forecasting using lstm on Indonesian banking stock market

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Procedia computer science 2024年 245卷 1047-1056页

作者： Kenrick Steffano Claus Ivancio Elisabeth Isabellini Canggih Gelar Setyo Adhi Anang Prasetyo Computer Science Computer Science Department School of Computer Science Universitas Bina Nusantara Semarang Campus Indonesia Computer Science Computer Science Department School of Computer Science Universitas Bina Nusantara Jakarta Indonesia

Forecasting stock market prices and trends can be a major challenge for investors and traders because of its volatility and various factors that affects it. Because of that, it is crucial for investors to be able to forecast the stock market alongside its trends as accurately as possible to minimize risk and increase profit, given the complexities and uncertainties inherent in the market. This research aims in digging the potential of LSTM models to forecast the open and close price of the 6 Indonesia bank stock market which consist of 3 national bank (BNI, BRI, and Mandiri) and 3 private bank (CIMB, BCA, and OCBC) in which it also helps in enhancing investment strategies in the banking sector, making it a very helpful tools for investors to gain more profit and minimize loss while also test and evaluate its error rate and accuracy while also evaluate the LSTM performance based on its accuracy and error rates. The historical stock data from April 30, 2019, to April 30, 2024, that's used in this study as the main dataset was acquired from Yahoo Finance. The provided model average accuracy and error rate when forecasting the open and close prices can be seen with an exceptional accuracy and very low error rates which can be proven with the predicted open's MSE: 0.000303, RMSE: 0.018430, MAE: 0.013380, and R²: 0.967834, as well as predicted close's MSE: 0.000511, RMSE: 0.022322, MAE: 0.017260, and R²: 0.942172, making it considered as a robust, reliable, and accurate model for investors and traders to forecast the close and open price of the stock market in the future. The LSTM model performance highlights its capabilities to capture important multivariate patterns of the tested bank's stock market data, which offer more advantages over traditional methods.

关键词： Forecasting volatility stock market trends RNN multivariate LSTM bank accuracy error rate open price close price

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Examining the impact of attitude, availability, price, and quality of service on broadband Internet customer satisfaction 9

Examining the impact of attitude, availability, price, and q...

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9th IEEE International Conference on Computing, Engineering and Design, ICCED 2023

作者： Arin, Ikrar Adinata Murad, Dina Fitria Warnars, Harco Leslie Hendric Spits Meyliana Bina Nusantara University Information System Department School of Information Systems Computer Science Department BINUS Graduate Program Computer Science Jakarta Indonesia Bina Nusantara University Information Systems Department BINUS Online Learning Jakarta Indonesia Bina Nusantara University Computer Science Department BINUS Graduate Program Computer Science Jakarta Indonesia Bina Nusantara University Information System Department School of Information Systems Jakarta Indonesia

ISBN: (纸本)9798350370126

After the COVID-19 pandemic, broadband internet utilization is predicted to continue to grow and experience a significant increase. The pandemic has changed how people work, study, communicate, shop, and carry out many aspects of daily life. Research reported that since 2018, research on broadband internet has continued to increase until now. This quantitative study examines the impact of the four critical variables on the link between attitude, availability, pricing, and quality of service (QoS) on broadband internet customer satisfaction using the Structural Equation Model approach and the Partial Lease Square technique. Three hundred and sixty-one citizen respondents in DKI Jakarta Province were collected using the snowball sampling method facilitated by Google Forms. At the same time, the COVID-19 post-pandemic was still ongoing in Indonesia. Through rigorous statistical analysis, the results show that customer satisfaction with broadband internet services is influenced by all factors, from attitude towards broadband Internet, broadband Internet availability, broadband Internet price, and service quality. The implications of this research are significant for service providers. They provide actionable insights for developing effective strategies to improve customer satisfaction and increase broadband internet subscriptions. © 2023 IEEE.

关键词： Quality of service

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Development of Detection Lung Disease Stand Alone System Based on Convolutional Neural Network (CNN) 7

Development of Detection Lung Disease Stand Alone System Bas...

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7th International Conference of computer and Informatics Engineering, IC2IE 2024

作者： Liewarnata, Edgar Caesar Mariana, Yosica Tan, Sofyan Liawatimena, Suryadiputra Astuti, Winda Bina Nusantara University Binus Aso School of Engineering Automotive and Robotics Program Computer Engineering Department Jakarta11480 Indonesia Bina Nusantara University Binus Aso School of Engineering Product Design Program Industrial Engineering Department Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program - Master of Computer Science Automotive and Robotics Program Computer Engineering Department Binus Aso School of Engineering Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798331505530

Lung disease, especially Tuberculosis (TBC), placed the highest death rate in Indonesia. Tuberculosis (TB) in Indonesia is ranked second after India. Therefore, it is important to reduce or early detection of the lung disease, to prevent this disease and speed up handling. The system can recognize the disease lung identification, and the system applied as standalone system. In this work, the Convolutional Neural Network (CNN) approach for identifying diseases lung identification is proposed. The Mel Frequency Cepstral Coefficient (MFCC) applied to process the stethoscope sounds which will used as input to the CNN. The performance of the proposed system has been investigated and resulted. The accuracy of 99% and 98%, for training and testing accuracy respectively. Furthermore, the system accurately detects lung diseases identification. © 2024 IEEE.

关键词： Lung cancer

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Analyzing Genshin Impact Review Scores Using BERT Model 11

Analyzing Genshin Impact Review Scores Using BERT Model

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11th International Conference on ICT for Smart Society, ICISS 2024

作者： Widjaja, Tiffany Wilson, Jocelin Federico, Benedictus Filbert Phangandy, Daniel Eric Yessenbayev, Zhandos School of Computing and Creative Arts Bina Nusantara University Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798350389654

Genshin Impact, a critically acclaimed ongoing multiplayer RPG, has amassed 4.4 million reviews on Google Play, reflecting its widespread popularity across mobile and PC platforms. Developers face the challenge of handling a growing amount of user feedback, which slows down the timely integration of valuable insights into the game. To address this, sentiment analysis is conducted on 48,000 English reviews using the BERT method, providing a comprehensive overview for developers. This study delves into hyperparameter adjustments for the BertForSequenceClassification model in predicting sentiment for Genshin Impact reviews. The best accuracy achieved by our fine-tuned model is 70 %, with a Matthews coefficient score of 55 %, utilizing the bert-base-uncased model (4 epochs, batch size 32, learning rate 0.00002). While successfully constructing a BERT model for sentiment prediction, the study recognizes room for improvement and recommends further fine-tuning and increasing the dataset size to exceed the accuracy obtained by this study. © 2024 IEEE.

关键词： Sentiment analysis

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Enhancing Tourism Innovation Through Disruptive Technology and Strategic Alliances: Evidence From Popular Destinations In West Java 6

Enhancing Tourism Innovation Through Disruptive Technology a...

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6th International Conference on Cybernetics and Intelligent System, ICORIS 2024

作者： Yustian, Okky Rizkia Ratnapuri, Chyntia Ika Indrawati, Desi Entrepreneurship Department Binus Business School Undergraduate Program Bina Nusantara University Indonesia Language Center Computer Science Department Faculty of Humanities Bina Nusantara University Indonesia

ISBN: (纸本)9798331539603

The tourism industry significantly contributes to Indonesia’s economy, particularly in West Java, where it plays a crucial role in social interaction and preserving local culture. This study aims to explore the impact of disruptive technology and strategic alliances on tourism innovation in popular destinations in West Java, incorporating local cultural engagement as a moderator. Using a quantitative, explanatory survey design, the study involved a sample of 172 businesspersons from 310 popular destinations. The results reveal that disruptive technology has a substantial direct and indirect effect on tourism innovation, with a significant positive role as the mediator. Additionally, local cultural engagement significantly moderates the relationships between both disruptive technology and strategic alliances with tourism innovation. These findings imply that integrating local culture into strategic partnerships and technological advancements can enhance innovation in tourism, offering new insights into effective practices in the industry. © 2024 IEEE.

关键词： Leisure industry

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