Pub Date : 2023-02-16DOI: 10.1109/ICCoSITE57641.2023.10127730
Danh Khoi Nguyen, Hoang Pham, Quoc Vinh Nguyen, Tuong Nguyen Huynh, Trang Hong Son
A loyalty program brings benefits to both companies and customers. In this paper, we consider the use of loyalty program integration in blockchain technology, one of the most promising advanced technologies, where trust is of prime significance and customer identification will no longer require a physical certificate. Based on the essential characteristics of the loyalty program, 4 possible approaches that integrate into a blockchain platform are identified. An analysis through several observations helps to determine the most suitable one to create a loyalty program that benefits all stakeholders. The performance of the proposed system related to the most suitable approach is evaluated according to the criteria that consider the feasibility of practical usability.
{"title":"Blockchain-based loyalty system: feasibility testing","authors":"Danh Khoi Nguyen, Hoang Pham, Quoc Vinh Nguyen, Tuong Nguyen Huynh, Trang Hong Son","doi":"10.1109/ICCoSITE57641.2023.10127730","DOIUrl":"https://doi.org/10.1109/ICCoSITE57641.2023.10127730","url":null,"abstract":"A loyalty program brings benefits to both companies and customers. In this paper, we consider the use of loyalty program integration in blockchain technology, one of the most promising advanced technologies, where trust is of prime significance and customer identification will no longer require a physical certificate. Based on the essential characteristics of the loyalty program, 4 possible approaches that integrate into a blockchain platform are identified. An analysis through several observations helps to determine the most suitable one to create a loyalty program that benefits all stakeholders. The performance of the proposed system related to the most suitable approach is evaluated according to the criteria that consider the feasibility of practical usability.","PeriodicalId":256184,"journal":{"name":"2023 International Conference on Computer Science, Information Technology and Engineering (ICCoSITE)","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122999368","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-02-16DOI: 10.1109/ICCoSITE57641.2023.10127722
Boby Siswanto, Haryono Soeparno, N. F. Sianipar, W. Budiharto
Cardiovascular disease is one of the dangerous non-communicable disorders or diseases that has become one of the causes of death worldwide. Various studies have been conducted to prevent cardiovascular disease in the world. This study analyzed cardiovascular disease medical record data from the Kaggle public dataset by implementing correlational analysis combined with association rule mining to identify variables that are the predominant cause of cardiovascular disease. Correlational analysis can analyze the interrelationships between variables in a dataset, but not in depth. Association rule mining can identify the interrelationships of variables in the form of frequent item sets, which can be calculated for their support and confidence values. The result of this study is a combination of correlation analysis with association rule mining that can identify predominant variables to cause cardiovascular disease. Found that the variable gender=woman, height=short (<165 cm), and age=middle (45-60 years) are more likely to be affected by cardiovascular disease. The variable gender=woman with height=short indicates a 76.07% probability of developing cardiovascular disease.
{"title":"Cardiovascular Disease Analysis Using Correlational Analysis and Association Rules Mining for In-depth Analysis to Identify Predominant Variables","authors":"Boby Siswanto, Haryono Soeparno, N. F. Sianipar, W. Budiharto","doi":"10.1109/ICCoSITE57641.2023.10127722","DOIUrl":"https://doi.org/10.1109/ICCoSITE57641.2023.10127722","url":null,"abstract":"Cardiovascular disease is one of the dangerous non-communicable disorders or diseases that has become one of the causes of death worldwide. Various studies have been conducted to prevent cardiovascular disease in the world. This study analyzed cardiovascular disease medical record data from the Kaggle public dataset by implementing correlational analysis combined with association rule mining to identify variables that are the predominant cause of cardiovascular disease. Correlational analysis can analyze the interrelationships between variables in a dataset, but not in depth. Association rule mining can identify the interrelationships of variables in the form of frequent item sets, which can be calculated for their support and confidence values. The result of this study is a combination of correlation analysis with association rule mining that can identify predominant variables to cause cardiovascular disease. Found that the variable gender=woman, height=short (<165 cm), and age=middle (45-60 years) are more likely to be affected by cardiovascular disease. The variable gender=woman with height=short indicates a 76.07% probability of developing cardiovascular disease.","PeriodicalId":256184,"journal":{"name":"2023 International Conference on Computer Science, Information Technology and Engineering (ICCoSITE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115721090","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-02-16DOI: 10.1109/ICCoSITE57641.2023.10127848
Surjandy Surjandy, Akbar Yusuf, Radiano Arran Harum, Jelita Gultom
Online shopping is now a trend, especially during the COVID pandemic. The rapid advancement of technology causes changes in consumer purchasing behavior. One of them is making purchases online, such as shopping on e-commerce platforms. The purpose of this study is to determine the factors that influence short video advertising in social electronic commerce shops. The structural equation model with partial least squares (SEM-PLS) technique as the quantitative research method. 505 respondents who had made purchases from the Tiktok Shop responded to questionnaires distributed via Google Forms using snowball sampling techniques to collect the research data. After examining the outliers, 30 outliers were found that could not be used, so the data used was 475. It can be concluded from the results of this study that several factors have a significant influence, such as entertainment on brand awareness, entertainment on brand loyalty, the value on brand awareness, the value on brand image, trendiness on brand image, trendiness on brand loyalty, vividness on brand awareness, vividness on brand image, vividness on brand loyalty, consistent on brand awareness, consistent on brand loyalty, accuracy on brand awareness, accuracy on brand loyalty, brand awareness on purchase intention, brand image on purchase intention, the brand image on customer loyalty, brand loyalty on purchase intention, brand loyalty on customer loyalty, and purchase intention on customer loyalty. Hopefully, this research can be useful for industries that sell their products using video advertising content to increase sales.
{"title":"The Influence Factors of Short Video Advertising in Social Electronic Commerce Shop Based on Customer Brand Engagement Model","authors":"Surjandy Surjandy, Akbar Yusuf, Radiano Arran Harum, Jelita Gultom","doi":"10.1109/ICCoSITE57641.2023.10127848","DOIUrl":"https://doi.org/10.1109/ICCoSITE57641.2023.10127848","url":null,"abstract":"Online shopping is now a trend, especially during the COVID pandemic. The rapid advancement of technology causes changes in consumer purchasing behavior. One of them is making purchases online, such as shopping on e-commerce platforms. The purpose of this study is to determine the factors that influence short video advertising in social electronic commerce shops. The structural equation model with partial least squares (SEM-PLS) technique as the quantitative research method. 505 respondents who had made purchases from the Tiktok Shop responded to questionnaires distributed via Google Forms using snowball sampling techniques to collect the research data. After examining the outliers, 30 outliers were found that could not be used, so the data used was 475. It can be concluded from the results of this study that several factors have a significant influence, such as entertainment on brand awareness, entertainment on brand loyalty, the value on brand awareness, the value on brand image, trendiness on brand image, trendiness on brand loyalty, vividness on brand awareness, vividness on brand image, vividness on brand loyalty, consistent on brand awareness, consistent on brand loyalty, accuracy on brand awareness, accuracy on brand loyalty, brand awareness on purchase intention, brand image on purchase intention, the brand image on customer loyalty, brand loyalty on purchase intention, brand loyalty on customer loyalty, and purchase intention on customer loyalty. Hopefully, this research can be useful for industries that sell their products using video advertising content to increase sales.","PeriodicalId":256184,"journal":{"name":"2023 International Conference on Computer Science, Information Technology and Engineering (ICCoSITE)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123734768","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-02-16DOI: 10.1109/ICCoSITE57641.2023.10127681
Sechan Al Farisi, Fauziah, Rima Tamara Aldisa
Manual attendance systems are generally inefficient and can waste time calling individually. The lecture attendance system is one of the most important elements in education. Attendance is part of the evaluation process between lecturers and students. This affects the final grades received by students. Problems that arise are often found in class, namely false attendance and often cheating by students related to absence so that they can achieve a minimum level of attendance in teaching and learning activities. Then an application is made using two algorithms that can produce solutions to reduce problems such as cheating with the method used, namely the haversine formula algorithm to measure the distance between students and campus buildings and the sequential search algorithm to search data. The results of the calculation of the haversine formula algorithm get an accuracy of 99.5969% from 100 student data to campus buildings and the data search process in sequential search gets an average run time of 19.0634 seconds.
{"title":"A Combination of the Haversine Formula Algorithm and the Sequential Searching Algorithm in Web based Online Attendance","authors":"Sechan Al Farisi, Fauziah, Rima Tamara Aldisa","doi":"10.1109/ICCoSITE57641.2023.10127681","DOIUrl":"https://doi.org/10.1109/ICCoSITE57641.2023.10127681","url":null,"abstract":"Manual attendance systems are generally inefficient and can waste time calling individually. The lecture attendance system is one of the most important elements in education. Attendance is part of the evaluation process between lecturers and students. This affects the final grades received by students. Problems that arise are often found in class, namely false attendance and often cheating by students related to absence so that they can achieve a minimum level of attendance in teaching and learning activities. Then an application is made using two algorithms that can produce solutions to reduce problems such as cheating with the method used, namely the haversine formula algorithm to measure the distance between students and campus buildings and the sequential search algorithm to search data. The results of the calculation of the haversine formula algorithm get an accuracy of 99.5969% from 100 student data to campus buildings and the data search process in sequential search gets an average run time of 19.0634 seconds.","PeriodicalId":256184,"journal":{"name":"2023 International Conference on Computer Science, Information Technology and Engineering (ICCoSITE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130206127","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-02-16DOI: 10.1109/ICCoSITE57641.2023.10127847
Bambang Sulistio, H. Warnars, F. Gaol, B. Soewito
Nowadays, many people are starting to care about early investment. One of the most popular investments lately, especially for millennials, is a stock investment. In investing, there are advantages and risks of loss. One way to reduce the risk of loss is by using price predictions before investing in stocks. This paper proposes the use of deep learning in making stock predictions. We conducted research by calculating the performance of six deep-learning algorithms to predict stock closing prices. The application of the CNN-LSTM-GRU hybrid algorithm combination produces the best performance compared to other methods, based on the value: Root Mean Squared Error (RMSE) decreased by 1.100 by 14%, Mean Absolute Error (MAE) was successfully reduced by 0.798 by 13.4%, and R Square increased by 0.957 by 3.9%. In predicting stock prices on the Indonesian Stock Exchange, especially in the energy sector, CNN-LSTM-GRU is more appropriate for investors than using a single algorithm to make decisions in investing in stocks..
{"title":"Energy Sector Stock Price Prediction Using The CNN, GRU & LSTM Hybrid Algorithm","authors":"Bambang Sulistio, H. Warnars, F. Gaol, B. Soewito","doi":"10.1109/ICCoSITE57641.2023.10127847","DOIUrl":"https://doi.org/10.1109/ICCoSITE57641.2023.10127847","url":null,"abstract":"Nowadays, many people are starting to care about early investment. One of the most popular investments lately, especially for millennials, is a stock investment. In investing, there are advantages and risks of loss. One way to reduce the risk of loss is by using price predictions before investing in stocks. This paper proposes the use of deep learning in making stock predictions. We conducted research by calculating the performance of six deep-learning algorithms to predict stock closing prices. The application of the CNN-LSTM-GRU hybrid algorithm combination produces the best performance compared to other methods, based on the value: Root Mean Squared Error (RMSE) decreased by 1.100 by 14%, Mean Absolute Error (MAE) was successfully reduced by 0.798 by 13.4%, and R Square increased by 0.957 by 3.9%. In predicting stock prices on the Indonesian Stock Exchange, especially in the energy sector, CNN-LSTM-GRU is more appropriate for investors than using a single algorithm to make decisions in investing in stocks..","PeriodicalId":256184,"journal":{"name":"2023 International Conference on Computer Science, Information Technology and Engineering (ICCoSITE)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125348005","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-02-16DOI: 10.1109/ICCoSITE57641.2023.10127850
Gunawan, Wresti Andriani, H. Purnomo, I. Sembiring, Ade Iriani
Earthquakes are a major obstacle to sustainable development, hindering social and economic growth. This study uses a model to predict the magnitude of earthquakes that occur from the Sunda Strait to Sumbawa Island. Earthquake prediction is important to take preventive measures and predict damage accurately. Several Earthquake Prediction (EQP) approaches have been proposed; however, these approaches only identify anomalies without distinguishing noise, thereby reducing the accuracy of predicting the probability of an earthquake occurring. The proposed model is a Neural Network (NN) optimized using evolutionary parameters to produce a lower and better error rate. Evolutionary Parameter Optimization was chosen because this parameter is superior in hyperparameter selection to achieve more optimal accuracy compared to other parameter models. Evolutionary Parameter Optimization was chosen because this parameter is superior in hyperparameter selection to achieve more optimal accuracy compared to other parameter models. This research aims to determine the best hyperparameter model to increase the accuracy of the Neural Network. The results of this study obtained the Root Mean Square Error (RMSE) value of the M 8 windowing combination using the Neural Network algorithm of 0.823. After increasing accuracy by optimizing using evolutionary parameters, the RMSE results obtained are 0.822. In this study, an increase in accuracy was obtained with a decrease in the RMSE value obtained by 0.001. Optimizing the Neural Network's evolutionary parameters improves the RMSE accuracy value so that the proposed model is better.
{"title":"Evolutionary Parameter Optimization on Neural Network Models for Earthquake Prediction","authors":"Gunawan, Wresti Andriani, H. Purnomo, I. Sembiring, Ade Iriani","doi":"10.1109/ICCoSITE57641.2023.10127850","DOIUrl":"https://doi.org/10.1109/ICCoSITE57641.2023.10127850","url":null,"abstract":"Earthquakes are a major obstacle to sustainable development, hindering social and economic growth. This study uses a model to predict the magnitude of earthquakes that occur from the Sunda Strait to Sumbawa Island. Earthquake prediction is important to take preventive measures and predict damage accurately. Several Earthquake Prediction (EQP) approaches have been proposed; however, these approaches only identify anomalies without distinguishing noise, thereby reducing the accuracy of predicting the probability of an earthquake occurring. The proposed model is a Neural Network (NN) optimized using evolutionary parameters to produce a lower and better error rate. Evolutionary Parameter Optimization was chosen because this parameter is superior in hyperparameter selection to achieve more optimal accuracy compared to other parameter models. Evolutionary Parameter Optimization was chosen because this parameter is superior in hyperparameter selection to achieve more optimal accuracy compared to other parameter models. This research aims to determine the best hyperparameter model to increase the accuracy of the Neural Network. The results of this study obtained the Root Mean Square Error (RMSE) value of the M 8 windowing combination using the Neural Network algorithm of 0.823. After increasing accuracy by optimizing using evolutionary parameters, the RMSE results obtained are 0.822. In this study, an increase in accuracy was obtained with a decrease in the RMSE value obtained by 0.001. Optimizing the Neural Network's evolutionary parameters improves the RMSE accuracy value so that the proposed model is better.","PeriodicalId":256184,"journal":{"name":"2023 International Conference on Computer Science, Information Technology and Engineering (ICCoSITE)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124972577","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-02-16DOI: 10.1109/ICCoSITE57641.2023.10127766
Muhammad Rifqi Wiliatama, Reza Septiawan, I. Kurniawan
A drug is a mixture of substances that can prevent, reduce and cure disease. Besides being able to prevent disease, drugs can cause side effects. It is the fourth leading cause of death in America and causes as many as 100,000 deaths each year. Many researchers identify drugs by combining compounds (receptors and enzymes), to produce predictions of drug side effects. But traditional experimentation and drug development are time-consuming and expensive. In vitro use is more difficult because biochemical tests must test cellular compounds, but many drugs target proteins that have not been described. In silico method is considered quite effective due to its ability to produce good predictions and new insights about how drugs work and the mechanism of side effects. In this study, a prediction model for drug side effects was developed using the Gravitational Search Algorithm (GSA) for feature selection and the ensemble method for building a prediction model with the aim of drug discovery in a case study of hepatobiliary disorders. with three methods, namely Random Forest, Adaptive Boosting (AdaBoost), and Extreme Gradient Boosting (XGBoost). The best model was obtained from Random Forest model with accuracy and F1 scores of 0.68 and 0.77, respectively.
{"title":"Implementation of Gravitational Search Algorithm - Ensemble in Predicting of Drug Side Effect: Case Study Hepatobiliary Disorders","authors":"Muhammad Rifqi Wiliatama, Reza Septiawan, I. Kurniawan","doi":"10.1109/ICCoSITE57641.2023.10127766","DOIUrl":"https://doi.org/10.1109/ICCoSITE57641.2023.10127766","url":null,"abstract":"A drug is a mixture of substances that can prevent, reduce and cure disease. Besides being able to prevent disease, drugs can cause side effects. It is the fourth leading cause of death in America and causes as many as 100,000 deaths each year. Many researchers identify drugs by combining compounds (receptors and enzymes), to produce predictions of drug side effects. But traditional experimentation and drug development are time-consuming and expensive. In vitro use is more difficult because biochemical tests must test cellular compounds, but many drugs target proteins that have not been described. In silico method is considered quite effective due to its ability to produce good predictions and new insights about how drugs work and the mechanism of side effects. In this study, a prediction model for drug side effects was developed using the Gravitational Search Algorithm (GSA) for feature selection and the ensemble method for building a prediction model with the aim of drug discovery in a case study of hepatobiliary disorders. with three methods, namely Random Forest, Adaptive Boosting (AdaBoost), and Extreme Gradient Boosting (XGBoost). The best model was obtained from Random Forest model with accuracy and F1 scores of 0.68 and 0.77, respectively.","PeriodicalId":256184,"journal":{"name":"2023 International Conference on Computer Science, Information Technology and Engineering (ICCoSITE)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117035257","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-02-16DOI: 10.1109/ICCoSITE57641.2023.10127685
Angela Lorraine C. Alarde, C. Y. N. Kato, Cynthia Jane P. Maralit, Juliane T. Bernardo, Renz Louie M. Bagsit, Geefrey Victor F. Salamat, Rommel M. Anacan, Josephine L. Bagay, Nelor Jane L. Agustin, Cayetano D. Hiwatig, Marjorie B. Villanueva
The unnecessary antenna tilting poses a significant challenge to mobile network operators with thousands of cell sites and continues to expand over the years. The rapid increase in mobile users causes a growing demand for data rate, reliable and broader coverage, and efficient transmission for mobile network operators since wireless communication is essential to everyday life. Thus, efficient monitoring and detecting antenna tilting are vital. The authors proposed the Design of a Wired Transmission in an Antenna Misalignment Detection System as a solution to immediately assess and detect unnecessary antenna tilting caused by natural phenomena. The device has an MPU 6050 sensor/s that detects any excessive movement/tilting of the antenna and a 4G LTE module that sends the collected data to a cloud, where operators view it via computer, laptop or mobile app to notify the telecommunications headquarters on the level of how the antenna shifted from its designated position.
{"title":"Design of a Wired Transmission in an Antenna Misalignment Detection System","authors":"Angela Lorraine C. Alarde, C. Y. N. Kato, Cynthia Jane P. Maralit, Juliane T. Bernardo, Renz Louie M. Bagsit, Geefrey Victor F. Salamat, Rommel M. Anacan, Josephine L. Bagay, Nelor Jane L. Agustin, Cayetano D. Hiwatig, Marjorie B. Villanueva","doi":"10.1109/ICCoSITE57641.2023.10127685","DOIUrl":"https://doi.org/10.1109/ICCoSITE57641.2023.10127685","url":null,"abstract":"The unnecessary antenna tilting poses a significant challenge to mobile network operators with thousands of cell sites and continues to expand over the years. The rapid increase in mobile users causes a growing demand for data rate, reliable and broader coverage, and efficient transmission for mobile network operators since wireless communication is essential to everyday life. Thus, efficient monitoring and detecting antenna tilting are vital. The authors proposed the Design of a Wired Transmission in an Antenna Misalignment Detection System as a solution to immediately assess and detect unnecessary antenna tilting caused by natural phenomena. The device has an MPU 6050 sensor/s that detects any excessive movement/tilting of the antenna and a 4G LTE module that sends the collected data to a cloud, where operators view it via computer, laptop or mobile app to notify the telecommunications headquarters on the level of how the antenna shifted from its designated position.","PeriodicalId":256184,"journal":{"name":"2023 International Conference on Computer Science, Information Technology and Engineering (ICCoSITE)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121532731","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-02-16DOI: 10.1109/ICCoSITE57641.2023.10127830
Ari Satmoko, E. Kosasih, A. R. Antariksawan, Irfan Dzaky, Hairul Abrar, Andril Arafat
Most of the inverse problems are ill conditions in which the numerical solution has the potential to become unstable. This paper discusses the Inverse Heat Conduction Problem for 2D thin plate structures. By using the temperature measurement data, the Levenberg-Marquardt Method is applied to predict the heat flux. The efficacy of this method was tested using synthetic data where the temperature measurement error was assumed to be small. The evaluation gives the result that whatever the initial values of the computational parameters (flux guess, damping coefficient and finite difference step) have no significant effect on the final results. The solution tends to be stable. The deviation of the calculation results is satisfying, less than 1% compared to the ideal heat flux. Experimentally, the Levenberg-Marquardt Method has also been applied to predict flux at 3 different heater flux levels. For fluxes with a nominal power of 6, 17 and 37 Watts, the errors are 5.2%, 0.8% and 6.1%, respectively, compared to experimental reference values. These errors are still acceptable.
{"title":"Evaluation of Computational Parameters of the Levenberg-Marquardt Method for Solving Inverse Heat Conduction Problems in Heat Flux Prediction","authors":"Ari Satmoko, E. Kosasih, A. R. Antariksawan, Irfan Dzaky, Hairul Abrar, Andril Arafat","doi":"10.1109/ICCoSITE57641.2023.10127830","DOIUrl":"https://doi.org/10.1109/ICCoSITE57641.2023.10127830","url":null,"abstract":"Most of the inverse problems are ill conditions in which the numerical solution has the potential to become unstable. This paper discusses the Inverse Heat Conduction Problem for 2D thin plate structures. By using the temperature measurement data, the Levenberg-Marquardt Method is applied to predict the heat flux. The efficacy of this method was tested using synthetic data where the temperature measurement error was assumed to be small. The evaluation gives the result that whatever the initial values of the computational parameters (flux guess, damping coefficient and finite difference step) have no significant effect on the final results. The solution tends to be stable. The deviation of the calculation results is satisfying, less than 1% compared to the ideal heat flux. Experimentally, the Levenberg-Marquardt Method has also been applied to predict flux at 3 different heater flux levels. For fluxes with a nominal power of 6, 17 and 37 Watts, the errors are 5.2%, 0.8% and 6.1%, respectively, compared to experimental reference values. These errors are still acceptable.","PeriodicalId":256184,"journal":{"name":"2023 International Conference on Computer Science, Information Technology and Engineering (ICCoSITE)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116230781","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-02-16DOI: 10.1109/ICCoSITE57641.2023.10127824
Christopher Dylan Kusen, Nicholas Leonardo Hermanto Trima, Enrico Theodore Vernatha, A. Gui, Y. Ganesan, M. S. Shaharudin
This study aims to investigate the factors affecting the usage of e-wallet services among customers in Greater Jakarta, Indonesia. With the rapid growth of e-wallet usage in Indonesia, it is important to enhance these services to meet customer expectations. To gather data, a total of 105 respondents were surveyed through various social media platforms (Line, Discord, Telegram, and WhatsApp) from October 4, 2022 to November 19, 2022. The results show that perceived usefulness, perceived ease of use, sales promotion, subjective norms, and attitudes have a positive effect on user's attitude towards using E-Wallet services for making transactions. As for security, it has a negative effect on affecting the attitude of the user towards using the E-wallet service, meaning that user is not influenced enough by the security factor of E-wallet application. The results and insights from this study may be able to help Indonesian E-wallet service providers to establish guidelines for more efficient E-wallet service to their customers. In addition, limitations of the study are included to provide a deeper insight for future research.
{"title":"The factors affecting the intensity of customers in making transactions using E-wallet","authors":"Christopher Dylan Kusen, Nicholas Leonardo Hermanto Trima, Enrico Theodore Vernatha, A. Gui, Y. Ganesan, M. S. Shaharudin","doi":"10.1109/ICCoSITE57641.2023.10127824","DOIUrl":"https://doi.org/10.1109/ICCoSITE57641.2023.10127824","url":null,"abstract":"This study aims to investigate the factors affecting the usage of e-wallet services among customers in Greater Jakarta, Indonesia. With the rapid growth of e-wallet usage in Indonesia, it is important to enhance these services to meet customer expectations. To gather data, a total of 105 respondents were surveyed through various social media platforms (Line, Discord, Telegram, and WhatsApp) from October 4, 2022 to November 19, 2022. The results show that perceived usefulness, perceived ease of use, sales promotion, subjective norms, and attitudes have a positive effect on user's attitude towards using E-Wallet services for making transactions. As for security, it has a negative effect on affecting the attitude of the user towards using the E-wallet service, meaning that user is not influenced enough by the security factor of E-wallet application. The results and insights from this study may be able to help Indonesian E-wallet service providers to establish guidelines for more efficient E-wallet service to their customers. In addition, limitations of the study are included to provide a deeper insight for future research.","PeriodicalId":256184,"journal":{"name":"2023 International Conference on Computer Science, Information Technology and Engineering (ICCoSITE)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127384523","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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