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Hyperparameter optimization for cardiovascular disease data-driven prognostic system

Visual Computing for Industry,Biomedicine,and Art 2023年第1期6卷 218-244页

作者： Jayson Saputra Cindy Lawrencya Jecky Mitra Saini Suharjito Suharjito Industrial Engineering Department BINUS Graduate Program-Master of Industrial EngineeringBina Nusantara UniversityJakarta 11480Indonesia

Prediction and diagnosis of cardiovascular diseases(CVDs)based,among other things,on medical examinations and patient symptoms are the biggest challenges in *** 17.9 million people die from CVDs annually,accounting for 31%of all deaths *** a timely prognosis and thorough consideration of the patient’s medical history and lifestyle,it is possible to predict CVDs and take preventive measures to eliminate or control this life-threatening *** this study,we used various patient datasets from a major hospital in the United States as prognostic factors for *** data was obtained by monitoring a total of 918 patients whose criteria for adults were 28-77 years *** this study,we present a data mining modeling approach to analyze the performance,classification accuracy and number of clusters on Cardiovascular Disease Prognostic datasets in unsupervised machine learning(ML)using the Orange data mining *** techniques are then used to classify the model parameters,such as k-nearest neighbors,support vector machine,random forest,artificial neural network(ANN),naïve bayes,logistic regression,stochastic gradient descent(SGD),and *** determine the number of clusters,various unsupervised ML clustering methods were used,such as k-means,hierarchical,and density-based spatial clustering of applications with noise *** results showed that the best model performance analysis and classification accuracy were SGD and ANN,both of which had a high score of 0.900 on Cardiovascular Disease Prognostic *** on the results of most clustering methods,such as k-means and hierarchical clustering,Cardiovascular Disease Prognostic datasets can be divided into two *** prognostic accuracy of CVD depends on the accuracy of the proposed model in determining the diagnostic *** more accurate the model,the better it can predict which patients are at risk for CVD.

关键词： Cardiovascular disease Data-driven analytics Data mining Hyperparameter optimization Orange data mining software Prognostic system Unsupervised machine learning

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The Influence of Digital Adoption, Absorptive Capacity and Risk Management Implementation on Organizational Resilience: An Empirical Study in The Indonesian Banking Industry 2

The Influence of Digital Adoption, Absorptive Capacity and R...

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2nd IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation, IATMSI 2024

作者： Tjahyono, Boy Prabowo, Harjanto Alamsjah, Firdaus Hamsal, Mohammad Binus Graduate Program Binus Business School Research in Management Jakarta Indonesia Binus Graduate Program Binus Business School Industrial Engineering Jakarta Indonesia

ISBN: (纸本)9798350360523

One of the primary difficulties of Indonesia's banking sector is to ensure that it continues to develop in business, is robust, and can foresee new risks while enhancing governance, risk, and compliance. This study will determine the effect of risk management implementation, absorptive capacity, and digital adoption on organizational resilience. The survey method is being used as a research method, with targeted respondents from senior executives, including C-suites, senior vice presidents, and vice presidents. The results show that digital adoption and absorptive capacity impact risk management implementation and organizational resilience despite the limited research available in the Indonesian banking sector. The study will contribute to understanding how enterprise resilience is affected by risk management. This research has presented an opportunity for both banking management and policymakers to formulate strategies aimed at bolstering organizational resilience within the banking sector. These strategies could aid in navigating unforeseen and disruptive business environments. Furthermore, this study stands to expand the application of concepts such as organizational resilience and dynamic capacity theory, particularly within the banking industry of developing nations. © 2024 IEEE.

关键词： Risk management

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The Impact of Indonesian Managers' Digital Disruptive Skills on Organizational Resilience

The Impact of Indonesian Managers' Digital Disruptive Skills...

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2023 IEEE International Conference on industrial engineering and engineering Management, IEEM 2023

作者： Alamsjah, F. Asrol, M. Sukarta, S.M. Bina Nusantara University Binus Graduate Program - Master of Industrial Engineering Industrial Engineering Department Jakarta11480 Indonesia Binus Creates Jakarta11480 Indonesia

ISBN: (纸本)9798350323153

This article strives to study the direct effects of managers' disruptive skills to survive in a digital revolution assessed indirectly by organizational agility. In addition, the interactions of digital orientation are also assessed. A conceptual framework was devised to investigate the aforementioned correlations. Five hypotheses were developed and tested with data from 100 respondents who were affected by digital transformation and with positions one level above the manager level or higher. SmartPLS is used for analysis and to generate 500 bootstrap samples. This study makes three distinct contributions to the existing literature on digital transformation. First, digital disruptive skills have a direct impact on both organizational agility and organizational resilience. Second, leadership abilities have a strong beneficial impact on organizational agility and resilience across all three digital disruptive talents examined in this study. Finally, digital orientation has little effect on organizational resilience when it comes to digital disruptive talents. © 2023 IEEE.

关键词： Digital Disruptive Skills Digital Orientation Digital Transformation Organizational Agility Organizational Resilience

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Prediction of Powerplant Performance by Machine Learning Technique 8

Prediction of Powerplant Performance by Machine Learning Tec...

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8th IEEE International Conference on Recent Advances and Innovations in engineering, ICRAIE 2023

作者： Gunawan, Fergyanto E. Wulandari, Rahmanda Wahyuningtyas, Dian Asrol, Mohammad Bina Nusantara University BINUS Graduate Program-Master of Industrial Engineering Industrial Engineering Department Jakarta11480 Indonesia Bina Nusantara University BINUS-ASO School of Engineering Business Engineering Program Industrial Engineering Department Jakarta11480 Indonesia

ISBN: (纸本)9798350315516

The IEEE Standard 762-1987 introduced a comprehensive set of 25 indices for reporting power plant reliability, availability, and productivity. The sheer number of indices can be impractical, and an univariate indicator is preferable for real-world applications. Developing a single indicator derived from the 25 indices is the work's main theme. This study addresses this issue using machine learning to consolidate these indices, ultimately classifying power plant performance into "performed"and "unperformed."Monthly performance data from 2020 were collected from 570 power plants in the Java-Sumatra interconnected electric system. These data were obtained from the Generation Availability Information System, a proprietary application owned by the Indonesia National Electric Power Company. Using the k-means clustering method, we classified the performance of these power plants. The clustering process was supplemented with the opinions of seven experts with extensive experience in the system. The comparison between machine learning classifications and expert opinions is presented. © 2023 IEEE.

关键词： IEEE Standards

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Preliminary Study: Electronic Counting in Indonesian National Election Using Computer Answer Sheets and DMR Software 22

Preliminary Study: Electronic Counting in Indonesian Nationa...

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2nd International Conference on engineering and Information Technology for Sustainable Industry, ICONETSI 2022

作者： Norman, Ferly Hadiwijaya, Syarif Gunawan, Fergyanto E. Master Industrial Engineering Binus Graduate Program Bina Nusantara University Indonesia

ISBN: (纸本)9781450397186

The 2019 election is the most expensive, complicated, and the worst of the 12 elections in Indonesian history. There are 894 election officers' death due to overworking load and it cost the nation 25 trillion Rupiah or about 1.8 billion USD, involved 16 parties, 192.2 million voters, 7.6 million officers, and 810,329 polling stations spreading in 83,405 villages throughout Indonesia. The election was held simultaneously for selecting legislative members and the president, utilized five ballots, and was held one day, including for polling, and counting, making the 2019 elections the most complicated in the world. The time for processing five ballots for voting took about 5-7 minutes on average and another five minutes for counting. Moreover, the ballot tabulation process from polling stations to the national level was manual and took 36 calendar days, adding complexity to the post-election process. This work aims to propose an approach to improve the efficiency and accountability of the voting and tabulation process. Propose to use a single ballot like a computer answer sheet, marked with a regular marker, optical scanned, and finally processed by Digital Mark Reader (DMR) software. The proposal can save 280.7 million square meters of paper use nationally, shorten 80% of voting time to no more than one minute, increase pooling station capacity by 930%, and 8 working hours for officers in. And in the Kapuk-Jakarta Administration Area alone, it can save a budget of Rp 3.4 billion and 38,556 working hours. © 2022 ACM.

关键词： Budget control

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Comparison of YOLOv8 and EfficientDet4 Algorithms in Detecting the Ripeness of Oil Palm Fruit Bunch 10

Comparison of YOLOv8 and EfficientDet4 Algorithms in Detecti...

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

作者： Priyadi, Muhammad Reza Azhar Suharjito Bina Nusantara University BINUS Online Learning Computer Science Department Jakarta11480 Indonesia Bina Nusantara University BINUS Graduate Program-Master of Industrial Engineering Industrial Engineering Department Jakarta11480 Indonesia

ISBN: (纸本)9798350339543

Despite Indonesia's leading position in palm oil processing, the inaccurate assessment of oil palm fruit maturity poses challenges in determining the optimal harvest timing and maintaining product quality. With advancements in information technology, including image processing and object detection techniques like YOLO and EfficientDet, there is potential for automated and precise identification of palm fruit maturity. To explore this, the authors conducted research to identify the most suitable algorithm for detecting the ripeness of palm oil fruit. The study utilized a dataset consisting of 8299 images with six maturity levels, employing augmentation techniques to increase image variability. Results show that YOLO V8s outperformed EfficientDet Lite 4 in terms of object detection precision, achieving higher mean average precision (mAP) scores. YOLO V8s showed a 4% improvement in mAP@50 and a 16% improvement in mAP@50-95 compared to EfficientDet Lite 4. YOLO V8s achieved a mAP of 0.978 for mAP@50 and 0.765 for mAP@50-95, surpassing EfficientDet Lite 4. However, EfficientDet Lite 4 had an advantage in terms of class accuracy and bounding box placement, as indicated by smaller box loss and cIs- loss values. YOLO V8s also demonstrated better frames per second (FPS) performance, ranging from 10 to 15 FPS, compared to EfficientDet Lite 4. © 2023 IEEE.

关键词： Object detection

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Improving Supplier Evaluation Model using Ensemble Method-Machine Learning for Food Industry 8

Improving Supplier Evaluation Model using Ensemble Method-Ma...

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8th International Conference on Computer Science and Computational Intelligence, ICCSCI 2023

作者： Asrol, Muhammad Wahyudi, Sofyan Suharjito Harito, Christian Utama, Ditdit N. Syafrudin, Muhammad Industrial Engineering Department BINUS Graduate Program - Master of Industrial Engineering Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia Department of Artificial Intelligence Sejong University Seoul05006 Korea Republic of

Supplier evaluation has a crucial role in maintaining efficiency in the food industry supply chain. Machine learning approaches can be employed to formulate models aimed at analyzing and evaluating supplier performance. Previous research has successfully designed decision tree and neural network models for assessing suppliers in the food industry with accuracies of 84.2% and 92.8% separately. Recognizing the opportunity to improve the model's performance, this study aims to advancing the machine learning models accuracy for analyzing and evaluating suppliers in the food industry. Two main models are proposed to enhance model accuracy: ensemble methods and support vector machine. This research has successfully designed a supplier evaluation model and demonstrated that the ensemble method - gradient boosting model outperforms other ensemble methods and support vector machine which is achieved a accuracy of 93.6% on a cross-validation dataset. The development of a dashboard is required to implement the supplier evaluation model using machine learning, facilitating decision-makers in evaluating and controlling supplier performance. © 2023 The Authors. Published by Elsevier B.V.

关键词： Ensemble Method Food Machine Learning Supply chain Supplier evaluation

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Implementation Graph Database Framework for Credit Card Fraud Detection 8

Implementation Graph Database Framework for Credit Card Frau...

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8th International Conference on Computer Science and Computational Intelligence, ICCSCI 2023

作者： Mauliddiah, Nurrahmi Suharjito Computer Science Department BINUS Online Learning Bina Nusantara University Jakarta11480 Indonesia Industrial Engineering Department BINUS Graduate Program - Master of Industrial Engineering Bina Nusantara University Jakarta11480 Indonesia

Credit card use is becoming more and more commonplace every day. Financial organizations and credit card customers lose a lot of money because of complicated illegal transactions. Fraudsters constantly stay on top of new technology to quickly perpetrate fraud against customer transaction patterns. We analyze credit card transaction networks and identify suspicious patterns, such as transactions connected to multiple accounts or unusual transaction patterns, transactions made at unusual times, and to monitor credit card transactions in real-time and quickly identify suspicious transactions. TigerGraph is used to analyze data, display results on a dashboard, and send notifications via email. One meth'\ Vc 1``13-od commonly used in anomaly detection is to compare data values against the standard deviation. In this research, we explain the use of TigerGraph as a platform for anomaly detection above the standard deviation, as well as the use of the Louvain algorithm in finding merchant communities used by fraudsters. The data used in this study comes from Sparkov simulation data obtained from Kaggle. Our results show that by using TigerGraph, we managed to achieve a very high accuracy rate of 99.77%, precision 82.84%, recall 72.38%, and f1-score 77,26% in predicting transaction fraud on Sparkov simulation data. This is much better than the results reported in a paper that uses the supervised machine learning method with the AdaBoost algorithm which achieves the highest accuracy of 77%. © 2023 The Authors. Published by Elsevier B.V.

关键词： Alert Credit card fraud detection Dashboard TigerGraph

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Random Number Generator for Securing Data Exchange in Smart Factory: Recent Trends and Best Practices 3

Random Number Generator for Securing Data Exchange in Smart ...

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3rd International Conference on Computer Technologies, ICCTech 2024

作者： Sama, Andi Meyliana Heryadi, Yaya Sahroni, Taufik Roni Bina Nusantara University Binus Graduate Program - Computer Science Computer Science Department Jakarta11480 Indonesia Bina Nusantara University School of Information System Information System Department Jakarta11480 Indonesia BINUS Graduate Program-Doctor of Computer Science Jakarta Computer Science Department Jakarta11530 Indonesia Bina Nusantara University BINUS Graduate Program - Master of Industrial Engineering Industrial Engineering Department Jakarta11480 Indonesia

ISBN: (纸本)9798350395419

IT/OT convergence in the form of a Cyber-Physical System (CPS) presents an opportunity to apply recent Information Technology (IT) advancements to Operational Technology (OT). The IT/OT convergence introduces cyber security threats for OT. The OT communication protocols, for example, have limited or no security capabilities for securing data exchange beyond basic authentication. The RNG (Random Number Generator) function is crucial in PLC for producing the encryption keys or the initial seed for security algorithms. The software-based PRNG (Pseudo RNG) is the most common approach for RNG in OT, instead of hardware-based RNG. This study provides a systematic literature review of the most recent evidence in digital transformation to secure data exchange in smart factories, within OT, or across OT and IT settings. It does this by looking at the PRNG on PLC and identifying the most recent trends and best practices. The findings include up-to-date data on author countries' distribution, published journals, research trends, industries, and industry types. The findings also address whether data flow is encrypted or shared in plaintext to ensure secure data transfer between IT and legacy OT environments. This paper concludes that while there is known RNG implementation in the actual embedded system in ICS, there is no evidence in the literature for using RNG in ICS networks for PLC implementation to secure data transfer with encryption. © 2024 IEEE.

关键词： Data transfer

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Tool Tracking System Design Using Quality Function Deployment Method for Vocational Education 8

Tool Tracking System Design Using Quality Function Deploymen...

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

作者： Ferianto, Ibnu Priambada, Dwima Septiar Sahroni, Taufik Roni Afani, Randy Putra Bina Nusantara University Faculty of Engineering Industrial Engineering Department Jakarta11480 Indonesia Binus Graduate Program - Master of Industrial Engineering Bina Nusantara University Industrial Engineering Department Jakarta11480 Indonesia

ISBN: (纸本)9798350326093

Currently the process of lending tools in machining laboratories still uses the traditional method. it is considered inefficient and ineffective. Therefore, implementing a more modern tool lending system is one possible solution. In this study, the design and manufacture of a tool lending system was carried out by applying Radio Frequency Identification (RFID) technology and a website-based information system. The purpose of this research is to simplify the process of lending tools and simplify the process of tracking and collecting data on tools borrowed by students. The method used in this study is the Quality Function Deployment (QFD) method to determine the priority of the user's attribute requirements that will be used in the design and development of the system. System testing is carried out using the System Usability Scale (SUS) analysis. The result of this research was to produce a tool lending system called a tool tracking system that will completely automate the tool tracking procedure. This helps to better manage the fleet of tools efficiently. The system has been successfully developed and approved for use in machining laboratories at vocational education © 2023 IEEE.

关键词： Quality function deployment

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