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IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT)

作者： Ferry Irwanto Muhammad Farrell Hafizh Sasongko Stevanus Febrian Alexander Agung Santoso Gunawan Karli Eka Setiawan Data Science Program Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798350353464

ISBN: (纸本)9798350353471

This research delves to predict PT Vale Indonesia Tbk stock price as an experiment on Indonesian stock using three models: naïve, LSTM, and 1D-CNN. Our analysis emphasizes the importance of matching model architectures to data properties. We compare models' performance with fiveday window for predict one-day prediction output. Interestingly, the single-layer LSTM outperforms the 1D-CNN even with similar hyperparameters, showcasing its strength in capturing long-term temporal dependencies crucial for nickel prices. While the 1D-CNN excels at identifying short-term patterns, its limited receptive field hinders long-term dependence. Recognizing the potential of both models, we encourage exploring hybrid architectures combining LSTM and CNN strengths for further improvement in financial forecasting. The experiment result shows single-layer LSTM outperforms a 1D-CNN with similar settings.

关键词： Analytical models Predictive models Nickel Data models Communications technology Fourth Industrial Revolution Convolutional neural networks

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Classification of Health Insurance Fraud Risk with Machine Learning

Classification of Health Insurance Fraud Risk with Machine L...

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Information Technology Research and Innovation (ICITRI), International Conference on

作者： Muhammad Kent Al-Ghazi Ryan Bertrand Muhammad Dzul Qarrnayn Destra Alexander Agung Santoso Gunawan Karli Eka Setiawan Data Science Program Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798350376210

ISBN: (纸本)9798350376227

Health insurance is a useful service that can help its users gain lifesaving medical aid when they are in need. However, health insurance is also exploitable to insurance fraud through the falsification of information to increase the amount of reimbursement and cause massive loss of funds to the insurance provider. We propose the usage of machine learning to accurately determine potential health insurance fraud. The objective of conducting this research is to determine which features are the most important to determine healthcare insurance fraud. This research used a dataset provided in Kaggle titled Healthcare Provider Fraud Detection Analysis using Random Forest Classifier and Logistic Regression. The best-performing model in this test, the Logistic Regression, is then used to which features are the most important for the classification. Our research shows that the most important feature in detecting health insurance fraud is the amount of money reimbursed associated with a provider. The Logistic Regression model achieved an accuracy of 0.90, precision of 0.93, recall of 0.91, and an F1 Score of 0.90, outperforming the Random Forest model in comparative analysis.

关键词： Logistic regression Analytical models Technological innovation Accuracy Insurance Medical services Feature extraction Fraud Information technology Random forests

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Serverless utilization in microservice e-learning platform

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Procedia computer science 2023年 216卷 204-212页

作者： Bryan Andreas Nday Gede Putra Kusuma Renaldy Fredyan Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480

Since cloud computing becoming the trend, the way servers being implemented slowly moves to the cloud. Companies did not need to buy a physical server machine to deploy an app. Having a private server on cloud infrastructure indeed already reduce some cost for on-premise server maintenance. However, there is still a cost for usage when the server is inactive or having low to no traffic at all. Serverless deployment offer function as a service where application is deployed as a function and cost is billed per function call. This paper proposed a solution where there are two deployment that works in turn between infrastructure as a service and function as a service deployment. This dual deployment offered the system to use the virtual private server or deployed instance on active hours, and switch to serverless functions on inactive hours. Switching to serverless on low traffic hours will cut the usage and cost of the microservice app by the least 25%, while having performance slightly comparable to microservice app deployed to instances.

关键词： Cloud Computing E-Learning Function as a Service Hybrid Deployment REST API

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Leveraging BERT with Extractive Summarization for Depression Detection on Social Media 23

Leveraging BERT with Extractive Summarization for Depression...

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23rd International Seminar on Intelligent Technology and Its Applications, ISITIA 2022

作者： William, David Achmad, Said Suhartono, Derwin Gema, Aryo Pradipta Bina Nusantara University BINUS Graduate Program Master of Computer Science Computer Science Department Jakarta11480 Indonesia Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia Epigene Labs United States

ISBN: (数字)9781665460811

ISBN: (纸本)9781665460811

In our current time, the well-being of a person is not only determined by the physical health, but also by their mental health. A lot of focus and effort have been spent into raising the awareness of this issue. One such effort comes from the field of computer science by utilizing data from social media to provide additional information in detecting these mental disorders. In this research, authors proposed the Bidirectional Encoder Representations from Transformers (BERT) with extractive summarization to preprocess data obtained from popular social media platform such as Reddit and Twitter. The proposed method evaluated by compare it to several other methods in text classification to Figure out which method is best classifying whether someone is depressed or not. This research also contributes to evaluate whether applying extractive summarization to a document prior to feeding the data into BERT will contribute to an increase in performance when compared to the current method of data preprocessing, by measuring the accuracy, precision, recall and F1-score of each model. The evaluation result found that our proposed method by applying extractive summarization to reduce the sequence length achieved a better result compared to BERT base model. © 2022 IEEE.

关键词： Text processing

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An Improvement of A Lightweight NFC Authentication Algorithm Based on Modified Hash Function 14

An Improvement of A Lightweight NFC Authentication Algorithm...

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14th International Conference on Information and Communication Technology Convergence, ICTC 2023

作者： Hwang, Min-Shiang Li, Hou-Wen Yang, Cheng-Ying Asia University Department of Computer Science and Information Engineering Taichung354 Taiwan Asia University The Ph.D. Program in Artificial Intelligence Taichung354 Taiwan University of Taipei Department of Computer Science Taipei Taiwan

ISBN: (纸本)9798350313277

In NFC applications, user privacy information must be protected first. Cao and Liu recently proposed a lightweight NFC authentication scheme based on an improved hash function to ensure that the user's private information will not be leaked. Although their method is highly efficient and has Mutual Authentication, Forward Security, Backward Security, and security against attacks such as Replay, Location, and Fake, once out of synchronization occurs, the method must reestablish synchronization data. Therefore, this article will propose a lightweight synchronization method. © 2023 IEEE.

关键词： authentication algorithm hash function near field communication privacy

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Applying ant colony optimization algorithm to solve flying probe inspection problem

Applying ant colony optimization algorithm to solve flying p...

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2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023

作者： Chen, Chih-Yung Chang, Yu-Ming National Pingtung University of Science and Technology Program in Artificial Intelligence and Mechatronics Pingtung Taiwan Shu-Te University Department of Computer Science and Information Engineering Kaohsiung Taiwan

ISBN: (纸本)9798350324174

This paper applies ant colony optimization (ACO) algorithm for the dual-pin flying probe circuit board inspection optimal path searching problem. First, the proposed approach creates a representation for circuit inspection path, which groups and encodes the measurement endpoints into a sequence. Moreover, the ant colony algorithm with the sequence can be used to solve the complex circuit optimal problem. The experimental results show that the best solutions of ACO are performed better than other algorithms. © 2023 IEEE.

关键词： Inspection

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Enhancing Machine Failure Detection with Ensemble Learning and Oversampling Techniques

Enhancing Machine Failure Detection with Ensemble Learning a...

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International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)

作者： Wilson Gregory Pribadi Maria Linneke Adjie Karli Eka Setiawan Computer Science Department School of Computer Science Data Science Program Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331519643

ISBN: (纸本)9798331519650

In modern manufacturing, predictive maintenance plays a vital role in minimizing unexpected machine failures, thereby reducing downtime and maintenance expenses. This study explores the implementation of ensemble learning models, including Bagging, AdaBoost, Gradient Boosting, Random Forest, and XGBoost, to predict machine failures based on operational characteristics such as air temperature, process temperature, rotational speed, torque, and tool wear. To address the class imbalance commonly present in failure datasets, oversampling techniques like SMOTE (Synthetic Minority Over-sampling Technique) and ADASYN (Adaptive Synthetic Sampling) were used to improve the model’s performance. However, a couple of experiments shows that ensemble models without oversampling is better than those that uses these techniques. The Gradient Boosting model using just the oversampling performed best with an F1-score of 0.78 for class 1 and overall accuracy of 0.99 while specifically, the version without any oversampling made it to an F1 Score of even as high as 0. XGBoost and Bagging were the next best models with F1-scores of 0.76 and 0.75, respectively since then. In contrast, the F1-score of the model proposed in this study and the models using any kind of oversampling technique were lower than 0.70 of failure class. This is proof of the power of ensemble methods (especially Gradient Boosting), that as result can be deployed to forecast machine failures and encourage right preventive maintenance actions ensuring less interruptions and smoother operations.

关键词： Adaptation models Accuracy Temperature Torque Atmospheric modeling Velocity control Predictive models Boosting Ensemble learning Bagging

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A Comparative Analysis of User Experience and Educational Outcomes in Math Memory Games

A Comparative Analysis of User Experience and Educational Ou...

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International Conference on computer Engineering, Network and Intelligent Multimedia (CENIM)

作者： Nicholas Martin Raffel Nathanael Doni Jeriyanto Eric Savero Hermawan Janshen Fung Francisco Maruli Panggabean Computer Science Department Game Application & Technology Program School of Computer Science Bina Nusantara University Jakart Indonesia Master of Computer Science Binus Graduate Program Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798350368802

ISBN: (纸本)9798350368819

This research explores multiple varieties of effective and child-friendly learning approaches for mathematics and memorization among young children in today's increasingly digital age considering their specific developmental needs. This type of approach includes in-person teaching, engaging online videos, interactive textbooks, and innovative educational games. This research employs a detailed comparative analysis, considering both ‘User Experience’ and measurable learning outcome factors, and spans a range of age groups from preschoolers to elementary school children. Through highlighting the strengths and weaknesses of each method, this research aims to inspire the adoption of efficient, child-centric, and novel teaching techniques by educators and institutions, thereby advancing the quality of education in Indonesia. The results show that using serious game in education presents high usefulness score alongside positive user experience, suggesting its viability for educational purposes.

关键词： Measurement Video games Education Games Educational technology Information age User experience Mathematics Usability Systematic literature review

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Efficient Node Reduction Heuristic for GNN-Based Traffic Speed Forecasting 11

Efficient Node Reduction Heuristic for GNN-Based Traffic Spe...

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11th IEEE/ACM International Conference on Big Data Computing, Applications and Technologies, BDCAT 2024

作者： Inokuchi, Yuto Gonzalez, Pedro Henrique Aritsugi, Masayoshi Mendonca, Israel Kumamoto University Graduate School of Science and Technology Kumamoto Japan Federal University of Rio de Janeiro System Engineering and Computer Science Program Rio de Janeiro Brazil Kumamoto University Faculty of Advanced Science and Technology Kumamoto Japan

ISBN: (纸本)9798350367300

In this paper, we aim to reduce the number of nodes from Graph Neural Networks (GNNs), thereby simplifying models and reducing computational costs. GNNs are highly effective for various tasks, such as prediction, classification, and clustering, due to their ability to learn node and edge attributes and relationships, and they have been utilized for intelligent transportation systems recently by converting sensor networks into graph structures. Deep spatio-temporal neural networks, including Spatio-Temporal Graph Convolutional Networks (STGCNs), capture spatial and temporal dependencies, making them suitable for traffic speed forecasting, traffic demand prediction, and travel time estimation. Despite their success, GNNs face challenges in industrial applications due to significant memory usage and time consumption. In this paper, we propose a new approach to node reduction that outperforms existing methods in computational efficiency. Our experiments on two real-world traffic datasets demonstrate that using the heuristic and edge information to reduce nodes can cut computation time of optimization up to 95% and, by eliminating noise, can even enhance prediction accuracy. © 2024 IEEE.

关键词： Graph neural networks

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Enhancing Online Learning Experience: An Ensemble Approach for Classifying Student Adaptivity Level

Enhancing Online Learning Experience: An Ensemble Approach f...

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science and Information Technology in Smart Administration (ICSINTESA), International Conference of

作者： Belinda Mutiara Florencia Gabrielle Felicia Ariyanto Alexander Agung Santoso Gunawan Karli Eka Setiawan Computer Science Department School of Computer Science Data Science Program Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798350376111

ISBN: (纸本)9798350376128

The study investigates the increasing demand of online learning as a means of addressing education issues in the context of the COVID-19 epidemic. Online learning requires several adaptations for teaching methods, learning methodologies, and devices needs. The flexibility of both teachers and students is essential to these adaptations. This research uses dataset collected from a survey about student adaptability level in online education. Carrying out the preprocessing is a challenge as the data used in this research has imbalanced value on the target category. Based on this problem, the aim of this research was created, namely to categorize students’ adaptivity levels in online learning and also focuses on finding out approaches that can provide solutions to overcome imbalanced values in the dataset. The model uses ensemble methods - Bagging, Boosting, and Voting with machine learning algorithms. There are two models that stand out, with Soft Voting obtaining the best performance with $\mathbf{9 0 \%}$ accuracy.

关键词： Surveys Adaptation models Accuracy Machine learning algorithms Education Neural networks Boosting Ensemble learning Bagging Random forests

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