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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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Viral Melodies: Exploring the Factors Influencing Music Virality in TikTok Engagement

Viral Melodies: Exploring the Factors Influencing Music Vira...

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

作者： Evelyn Zefanya Rahardjo Jasmine Mutia Alifa Sharon Zefanya Setiawan 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

In the digital era and the evolution of social media platforms like TikTok, understanding the factors influencing content virality has become increasingly crucial. Therefore, this research aims to delve into the musical variables contributing to the popularity of content on TikTok, including aspects such as artist, beat of music, and the number of shares. Focusing on content that successfully garnered over $\mathbf{5, 0 0 0}$ likes, this study employed a data analysis approach involving the librosa library for music tempo extraction and logistic regression to identify relationships among these variables. The research findings indicate that the most significant variable influencing the virality of music content on TikTok is the number of shares. This highlights that the phenomenon of virality is not solely dependent on musical characteristics like artist or beat but is more influenced by social interactions through video sharing among users. The findings suggest that when someone shares a video with others who may have similar music preferences, the video is likely to receive likes, even if it does not appear on the user’s For You Page (FYP). Thus, the virality of content on TikTok can be explained by the existence of social connections among friends that trigger the viral spread of the video. These results provide valuable insights into the mechanisms behind the success of conten/t on this platform.

关键词： Logistic regression Video on demand Social networking (online) Refining Music Focusing Libraries Web sites data mining Information technology

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Interpretable Machine Learning in Leveraging Customer Information for Success Direct Marketing Campaigns

Interpretable Machine Learning in Leveraging Customer Inform...

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International Conference on Smart Computing & Communications (ICSCC)

作者： Clarissa Octavia Tjandra Grace Shirley Cam 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)9798350363104

ISBN: (纸本)9798350363111

A bank's marketing campaign execution is very important. Undoubtedly, a well-crafted marketing plan will contribute to the bank's increased revenue. Marketing is crucial because it can be used to connect with consumers and inform them of the advantages of making a deposit with the bank, which will increase the number of deposits the bank receives. Thus, the goal of this research is to determine the best customer information that affects their decision on subscribing a term deposit and providing the best model for predicting the outcome of future marketing campaign. By determining the significance of each feature, this research constructed machine learning models using methods like Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF) and Logistic Regression (LR) as our research contribution. According to the research's findings, out of the three machine learning models, Random Forest (RF) has the highest accuracy scores. Additionally, the feature importance model reveals that a customer's decision to make a deposit or not is greatly influenced by factors such as the age of the customer, how much does the customer has in their bank account, did the bank have contacts with longer call durations, and did it has a positive outcomes on the previous marketing campaigns. The customers who are contacted more frequently and have subscribed before are more likely to subscribe in the future marketing campaign.

关键词： Support vector machines Radio frequency Accuracy Computational modeling Oral communication Predictive models data models Real-time systems Random forests Testing

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An Exploratory Computational Approach for Elucidating a Nonstructural Protein Interaction in Dengue Virus 2 9th

An Exploratory Computational Approach for Elucidating a Nons...

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

作者： Hidayat, Alam Ahmad Nirwantono, Rudi Pardamean, Bens Mathematics Department School of Computer Science Bina Nusantara University 11480 Indonesia Biotechnology Department Faculty of Engineering Bina Nusantara University 11480 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program Bina Nusantara University Jakarta11480 Indonesia

As the dengue infection still impacts hundreds of millions of people globally, unprecedented efforts in dengue drug development have been more progressive in recent decades. Computational methods provide a fast, sustainable, and efficient screening of active compounds and newly created drug molecules, including those specifically targeting nonstructural proteins (NS) of dengue viruses. In this work, protein modeling for the NS proteins of DENV-2/16681 strain was performed using a template-based homology modeling for the NS3 protein and an Artificial Intelligence (AI)-based prediction via AlphaFold for the NS4B protein. Moreover, the protein-protein interaction between the two structures was predicted using the HADDOCK server, which employs information about active and passive residues of the interaction interface to guide the docking process. After the modeling and its respective refinement process, the predicted structures of NS3 and NS4B improved their steric clashing scoring from MolProbity assessment. The refined models were then docked, and the resulting docking pose was analyzed to extract the interacting residues based on the polar contacts within the interface of the two proteins. Our result presents a preliminary study to create a dataset related to in silico molecular interactions of the NS3-NS4B interaction of different DENV types. It is helpful for building a computational pipeline for elucidating protein-ligand problems in dengue drug screenings. © 2024 The Authors.

关键词： Molecular docking

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Leveraging XGBoost and Feature Importance for NBA Salary Classification

Leveraging XGBoost and Feature Importance for NBA Salary Cla...

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

作者： Musyaffa Ayman Rafif Rayhan Prawira Daksa Gede Sanjaya Indrajaya Alexander A S 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

Focusing on feature importance, this study utilized the XGBoostClassifier to predict NBA player salaries within categorized salary bands. Oversampling techniques ensured balanced class representation, while feature importance analysis identified Minutes Played, Age, Points Scored, Games Started, and Turnovers as key factors influencing model predictions. The XGBoostClassifier achieved a balanced accuracy of 83% across all salary tiers, demonstrating its ability to navigate NBA player valuation complexities. Focusing on these key features further boosted average accuracy to 85%, highlighting the potential for optimization through prioritizing impactful attributes. Our findings suggest valuable avenues for future research, including leveraging feature importance for model refinement, expanding the dataset size, incorporating more relevant features, and investigating outliers for deeper insights into the multifaceted dynamics of NBA player salary classification.

关键词： Analytical models Technological innovation Accuracy Navigation Focusing Games Predictive models Remuneration Optimization Cost accounting

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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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database Design for Pre-Eclampsia Case Administration 8

Database Design for Pre-Eclampsia Case Administration

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8th International Conference on Information Technology and Digital Applications, ICITDA 2023

作者： Asadi, Faisal Elwirehardja, Gregorius Natanael Trinugroho, Joko Pebrianto Rahutomo, Reza Pardamean, Bens School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Bina Nusantara University Bioinformatics and Data Science Research Center Jakarta11480 Indonesia School of Information Systems Bina Nusantara University Information System Department Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program Master of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350344691

The Ministry of Health of Indonesia has referred to pre-eclampsia as one of the most severe diseases affecting women. As an urgency, it is crucial to administrate pre-eclampsia cases for disease prevention as a long-term national healthcare strategy. Regarding health science, case data was significant in developing research and innovation. However, the main problem regarding pre-eclampsia case administration is data handling, recording, and management incompetence. Hence, this research proposed a conceptual design of a database for pre-eclampsia case administration. The proposed design covered conceptual, logical, and physical design. We elaborate the concept into three concepts of pre-eclampsia disease: pre-treatment, treatment, and post-treatment. This study proposed a solution to gain more data and study pre-eclampsia disease in Indonesia. © 2023 IEEE.

关键词： Medical informatics

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Machine Learning Implementations in Childhood Stunting Research: A Systematic Literature Review 8

Machine Learning Implementations in Childhood Stunting Resea...

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8th International Conference on Information Management and Technology, ICIMTech 2023

作者： Rahutomo, Reza Elwirehardja, Gregorius Natanael Isnan, Mahmud Asadi, Faisal Pardamean, Bens School of Information Systems Bina Nusantara University Information Systems Department Jakarta11480 Indonesia School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Binus Graduate Program - Master of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350326093

Childhood stunting is a condition anticipated to affect the growth potential of children under the age of five. With numerous stunting researches that have been conducted, stunting datasets are now widely available to facilitate stunting research. This provides an opportunity to implement machine learning (ML) principles to produce a broader insight or a novel technique in stunting prediction. A systematic literature review is necessary to discover the landscape of machine learning implementation in the application domain as a preliminary study for creating an effective research roadmap. This paper presents a systematic literature review (SLR) of 22 curated manuscripts that focuses on identifying the ML models applied in stunting research, as well as the datasets used in such studies that were published during 2017-2022. The SLR process found that ML principles have been applied in stunting research since 2017, and the diversity of ML implementation has become more varied in 2021-2022. In terms of ML models, XGBoost and Random Forest are recognized as the two most utilized models, and stunting prediction is the most common ML implementation. The majority of stunting research utilizing ML has been conducted in Indonesia. Although national survey data has been the most commonly utilized dataset in stunting research, researchers in Indonesia have shown a preference for utilizing data from regional or independent surveys. This study will be followed by developing a classifier model for stunted children using XGBoost and Random Forest algorithms. The model will be trained on a dataset generated from StuntingDB. © 2023 IEEE.

关键词： Machine learning

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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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HIV Inhibitory Activity Classification with Morgan Fingerprint and Integrated Graph-Based Deep Learning

HIV Inhibitory Activity Classification with Morgan Fingerpri...

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International Conference of Information and Communication Technology (ICoICT)

作者： Jocelyn Verna Siswanto Caroline Angelina Sunarya Agnes Calista Alexander Agung Santoso Gunawan Karli Eka Setiawan Computer Science Department Data Science Program School of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798350363432

ISBN: (纸本)9798350363449

This research offers a new perspective on predicting the activity of the HIV virus from the Drug Therapeutics program (DTP) Antiviral Screen by using the molecular data represented in SMILES notation. The topic has significance as it focuses on a major global health issue using modern computational approaches and has the potential to uncover new antiviral drug candidates, which could eventually save lives and improve public health outcomes. The study addresses the data imbalance between two classes, active and inactive, and employs the Morgan Fingerprint method for feature extraction, along with the Graph Convolutional Network (GCN) and Graph Attention Network (GAT) as the baseline architectures and the fusion of GCN's and GAT's main features as the proposed architecture. The random oversampling technique is applied to alleviate dataset imbalances. However, even though it improved the training process, the performance of the model flopped when the test set was fed into the model. Combining the main features in GCN and GAT, the proposed model was able to do the classification task more accurately. The attention mechanism from GAT allows the model to focus more on the parts that are more relevant and ignore the irrelevant ones. It managed to outperform the baseline models. Despite a high overall accuracy of 94%, the fusion model exhibits significant disparities in precision, recall, and f1-score metrics, potentially due to class imbalance. Random oversampling led to improved training but compromised model performance on the test set.

关键词： Training Measurement Drugs Attention mechanisms Computational modeling computer architecture Fingerprint recognition Predictive models Feature extraction data models

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