Pub Date : 2020-09-07DOI: 10.1109/ICST50505.2020.9732829
Phounsiri Sihakhom, S. Sulistyo, I. Mustika
At the present time, we discuss the human behavior of driving and death rates due to an accident on the road around the world. Hence, the real-time response of notification about the risk on road is insufficient. Moreover, the most problem is people's lack of knowledge for driving, especially people careless while driving that may lead to an accident. Driver's behavior classification is required in order to prevent unfortunate accidents on the road. Many previous studies, researchers focused on simulation driver and limited road pattern to collect data for classification. However, the main problem is the data is inadequate and the driver's data should be collected from the driver's daily life to get an effective classification. This work deals with an efficient supervised learning procedure to predict driver's behavior by comparison from five classifiers and vote the highest score to predict data. All data are collected from sensors embedded in the vehicle's in Indonesia. Throughout the dataset over one million records, DBC which classify Aggressive and Non-aggressive, the result show F1-score is 86% of twenty thousand labels.
{"title":"Classification Driver's Behaviour Using Supervised Algorithm","authors":"Phounsiri Sihakhom, S. Sulistyo, I. Mustika","doi":"10.1109/ICST50505.2020.9732829","DOIUrl":"https://doi.org/10.1109/ICST50505.2020.9732829","url":null,"abstract":"At the present time, we discuss the human behavior of driving and death rates due to an accident on the road around the world. Hence, the real-time response of notification about the risk on road is insufficient. Moreover, the most problem is people's lack of knowledge for driving, especially people careless while driving that may lead to an accident. Driver's behavior classification is required in order to prevent unfortunate accidents on the road. Many previous studies, researchers focused on simulation driver and limited road pattern to collect data for classification. However, the main problem is the data is inadequate and the driver's data should be collected from the driver's daily life to get an effective classification. This work deals with an efficient supervised learning procedure to predict driver's behavior by comparison from five classifiers and vote the highest score to predict data. All data are collected from sensors embedded in the vehicle's in Indonesia. Throughout the dataset over one million records, DBC which classify Aggressive and Non-aggressive, the result show F1-score is 86% of twenty thousand labels.","PeriodicalId":125807,"journal":{"name":"2020 6th International Conference on Science and Technology (ICST)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131380699","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 : 2020-09-07DOI: 10.1109/ICST50505.2020.9732880
Aloysius Gonzaga Pradnya Sidhawara, S. Wibirama, T. B. Adji, Sri Kusrohmaniah
Multimedia learning is defined as building mental representations from words and pictures. In multimedia learning, the difference in cognitive style indicates different learning strategies. The cognitive styles of visual and verbal exert influence on behavior, preferences, and even learning outcomes. On the other hand, eye-tracking has been used to study cognitive aspects during multimedia learning. Unfortu-nately, previous studies on the identification of cognitive styles were limited to statistical descriptive analysis. The use of eye-tracking was limited merely for validation purposes. In addition, previous studies have yet to apply automatic classification of cognitive style based on eye-tracking data. Hence, this study proposes a method to automatically classify visual-verbal cogni-tive styles based on eye-tracking metrics. We implemented three shallow classifiers: K-Nearest Neighbors, Random Forest, and Support Vector Machine. Based on our experimental results, Random Forest—enhanced with two selected features from SelectKBest-gained 78% of classification accuracy. Our study has been the first investigation that reveals the possibility of implementing machine learning for automatic classification of cognitive styles based on eye-tracking data.
{"title":"Classification of Visual-Verbal Cognitive Style in Multimedia Learning using Eye-Tracking and Machine Learning","authors":"Aloysius Gonzaga Pradnya Sidhawara, S. Wibirama, T. B. Adji, Sri Kusrohmaniah","doi":"10.1109/ICST50505.2020.9732880","DOIUrl":"https://doi.org/10.1109/ICST50505.2020.9732880","url":null,"abstract":"Multimedia learning is defined as building mental representations from words and pictures. In multimedia learning, the difference in cognitive style indicates different learning strategies. The cognitive styles of visual and verbal exert influence on behavior, preferences, and even learning outcomes. On the other hand, eye-tracking has been used to study cognitive aspects during multimedia learning. Unfortu-nately, previous studies on the identification of cognitive styles were limited to statistical descriptive analysis. The use of eye-tracking was limited merely for validation purposes. In addition, previous studies have yet to apply automatic classification of cognitive style based on eye-tracking data. Hence, this study proposes a method to automatically classify visual-verbal cogni-tive styles based on eye-tracking metrics. We implemented three shallow classifiers: K-Nearest Neighbors, Random Forest, and Support Vector Machine. Based on our experimental results, Random Forest—enhanced with two selected features from SelectKBest-gained 78% of classification accuracy. Our study has been the first investigation that reveals the possibility of implementing machine learning for automatic classification of cognitive styles based on eye-tracking data.","PeriodicalId":125807,"journal":{"name":"2020 6th International Conference on Science and Technology (ICST)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131393563","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 : 2020-09-07DOI: 10.1109/ICST50505.2020.9732790
Z. F. Azzahra, R. Andreswari, M. A. Hasibuan
Today, there are many applications available on the Google Play Store, especially the online hotel booking application. In Indonesia, 2 out of 3 people book hotels online and users also rely on digital reviews for travel inspiration as well as research and bookings. Users can find out user satisfaction by looking at reviews from previous users, but it is very problematic if we read the reviews of this application one by one because it takes a very long time. Measuring the level of user satisfaction of an application can be done by knowing how the sentiment from the public. This paper provides an approach to analyzing sentiments for online hotel booking applications based on user reviews on the Google Play Store using the Naive Bayes algorithm. The process starts with data collection using web-scraping, text preprocessing using python, data labeling using SentiStrength, classification with the Naive Bayes algorithm, and website development using Django Web Framework. This website provides information support for users in choosing an online hotel booking application. From this study, the highest accuracy value obtained was 94%.
今天，b谷歌Play Store上有很多应用程序，尤其是在线酒店预订应用程序。在印度尼西亚，三分之二的人在网上预订酒店，用户也会通过数字评论获取旅游灵感、研究和预订。用户可以通过查看以前用户的评论来发现用户满意度，但如果我们一个一个地阅读这个应用程序的评论，这是非常有问题的，因为它需要很长时间。衡量用户对应用程序的满意程度可以通过了解公众的情绪来完成。本文提供了一种基于b谷歌Play Store用户评论的在线酒店预订应用情感分析方法，该方法使用朴素贝叶斯算法。这个过程从使用Web抓取收集数据开始，使用python进行文本预处理，使用SentiStrength进行数据标记，使用朴素贝叶斯算法进行分类，使用Django Web Framework进行网站开发。本网站为用户选择网上酒店预订应用程序提供信息支持。从本研究中，获得的最高准确率值为94%。
{"title":"Sentiment Analysis Website of Online Hotel Booking Application Reviews Using the Naive Bayes Algorithm","authors":"Z. F. Azzahra, R. Andreswari, M. A. Hasibuan","doi":"10.1109/ICST50505.2020.9732790","DOIUrl":"https://doi.org/10.1109/ICST50505.2020.9732790","url":null,"abstract":"Today, there are many applications available on the Google Play Store, especially the online hotel booking application. In Indonesia, 2 out of 3 people book hotels online and users also rely on digital reviews for travel inspiration as well as research and bookings. Users can find out user satisfaction by looking at reviews from previous users, but it is very problematic if we read the reviews of this application one by one because it takes a very long time. Measuring the level of user satisfaction of an application can be done by knowing how the sentiment from the public. This paper provides an approach to analyzing sentiments for online hotel booking applications based on user reviews on the Google Play Store using the Naive Bayes algorithm. The process starts with data collection using web-scraping, text preprocessing using python, data labeling using SentiStrength, classification with the Naive Bayes algorithm, and website development using Django Web Framework. This website provides information support for users in choosing an online hotel booking application. From this study, the highest accuracy value obtained was 94%.","PeriodicalId":125807,"journal":{"name":"2020 6th International Conference on Science and Technology (ICST)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133289598","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 : 2020-09-07DOI: 10.1109/ICST50505.2020.9732784
A. N. Hakim, I. E. Putro
This paper presents a design analysis of rocket RCX1H-1 developed by LAPAN. The analysis will be conducted to analyze its behavior, including trajectory estimation and flight attitude prediction. RCX1H-1 uses a liquid rocket engine for the propulsion system to generate thrust to accomplish trajectory mission. The engine uses kerosene and nitric acid as its fuel and oxidizer, delivered by pressurized helium gas. Estimation of rocket trajectory will be governed by an ordinary differential equation from Newton-Euler law in the form of 6 degrees of freedom on Matlab/Simulink software. Missile DATCOM is used to predict the rocket's attitude in longitudinal mode and lateral-directional mode by defining the rocket body configuration and set the flight condition where the rocket will be flown. The results show that RCX1H-1 has stable characteristics both in longitudinal and lateral-directional modes even though the rocket has struggled in roll motion for the angle of attack greater than 4 degrees.
{"title":"Performance Analysis of Liquid Rocket Attitude and Trajectory Estimation","authors":"A. N. Hakim, I. E. Putro","doi":"10.1109/ICST50505.2020.9732784","DOIUrl":"https://doi.org/10.1109/ICST50505.2020.9732784","url":null,"abstract":"This paper presents a design analysis of rocket RCX1H-1 developed by LAPAN. The analysis will be conducted to analyze its behavior, including trajectory estimation and flight attitude prediction. RCX1H-1 uses a liquid rocket engine for the propulsion system to generate thrust to accomplish trajectory mission. The engine uses kerosene and nitric acid as its fuel and oxidizer, delivered by pressurized helium gas. Estimation of rocket trajectory will be governed by an ordinary differential equation from Newton-Euler law in the form of 6 degrees of freedom on Matlab/Simulink software. Missile DATCOM is used to predict the rocket's attitude in longitudinal mode and lateral-directional mode by defining the rocket body configuration and set the flight condition where the rocket will be flown. The results show that RCX1H-1 has stable characteristics both in longitudinal and lateral-directional modes even though the rocket has struggled in roll motion for the angle of attack greater than 4 degrees.","PeriodicalId":125807,"journal":{"name":"2020 6th International Conference on Science and Technology (ICST)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116372039","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 : 2020-09-07DOI: 10.1109/ICST50505.2020.9732797
R. Jayadi, Adrianus Kelvin, Jery, Pravasta Rifyansyah, Muhammad Mufarih, Hafizh Maulana Firmantyo
In an Indonesian insurance company, since 2015, the value of the fire insurance policy portfolio increases from year to year in an Indonesia insurance company. However, the retention rate of their consumers who extend their insurance policies showing a downward trend. In this study, we showcase the application of the Decision Tree model and the Naïve Bayes model to predict loyal or disloyal customers on their insurance subscription. The decision tree model produces better accuracy of 92.4 percent compared with Naïve Bayes model accuracy 82.9 percent. These predictions model help the company to create a more effective marketing strategy by accurately predicting its consumer churn behavior.
{"title":"Predicting Customer Churn of Fire Insurance Policy: A Case Study in an Indonesian Insurance Company","authors":"R. Jayadi, Adrianus Kelvin, Jery, Pravasta Rifyansyah, Muhammad Mufarih, Hafizh Maulana Firmantyo","doi":"10.1109/ICST50505.2020.9732797","DOIUrl":"https://doi.org/10.1109/ICST50505.2020.9732797","url":null,"abstract":"In an Indonesian insurance company, since 2015, the value of the fire insurance policy portfolio increases from year to year in an Indonesia insurance company. However, the retention rate of their consumers who extend their insurance policies showing a downward trend. In this study, we showcase the application of the Decision Tree model and the Naïve Bayes model to predict loyal or disloyal customers on their insurance subscription. The decision tree model produces better accuracy of 92.4 percent compared with Naïve Bayes model accuracy 82.9 percent. These predictions model help the company to create a more effective marketing strategy by accurately predicting its consumer churn behavior.","PeriodicalId":125807,"journal":{"name":"2020 6th International Conference on Science and Technology (ICST)","volume":"68 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120899401","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 : 2020-09-07DOI: 10.1109/ICST50505.2020.9732850
Andri Dwi Utomo, Z. Zainuddin, Syafruddin Syarif
The purpose of this study is to create a media for teaching staff in early childhood education schools, which is one of the features of the education robot answering system. Speech is used as input and output data from the system. The question answering system is a conversation data in the domain of early childhood education that is collected. Preprocessing stages are performed in the dataset to produce data that can be processed by the system. The question answering system uses the RNN algorithm with the Seq2Seq model. The highest results of the training process are 89.5% accuracy, precision 99.02%, and 70.5% recall. The response generation test also obtained an accuracy of 75%. The results of testing the response to the Questions according to the dataset produce maximum value.
{"title":"Question Answering System in the Domain of Early Childhood Education in Bahasa Indonesia","authors":"Andri Dwi Utomo, Z. Zainuddin, Syafruddin Syarif","doi":"10.1109/ICST50505.2020.9732850","DOIUrl":"https://doi.org/10.1109/ICST50505.2020.9732850","url":null,"abstract":"The purpose of this study is to create a media for teaching staff in early childhood education schools, which is one of the features of the education robot answering system. Speech is used as input and output data from the system. The question answering system is a conversation data in the domain of early childhood education that is collected. Preprocessing stages are performed in the dataset to produce data that can be processed by the system. The question answering system uses the RNN algorithm with the Seq2Seq model. The highest results of the training process are 89.5% accuracy, precision 99.02%, and 70.5% recall. The response generation test also obtained an accuracy of 75%. The results of testing the response to the Questions according to the dataset produce maximum value.","PeriodicalId":125807,"journal":{"name":"2020 6th International Conference on Science and Technology (ICST)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129740210","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 : 2020-09-07DOI: 10.1109/ICST50505.2020.9732879
Muhammad Aulia Rahman, I. Pranoto
As one of the most prominent parts of an electric vehicle, Li-ion battery has been widely used as the main power source of the vehicle. However, this battery is very sensitive to the working temperature. Some thermal issues can occur when the temperature of the battery exceeds the maximum allowable working temperature of a battery. Thus, a proper battery thermal management system is required in order to support electric vehicle performance. In this paper, some problems which can occur during overheating are explained. Then, the current development of the battery thermal management system based on the cooling mechanism as well as the cooling mode is reviewed together with the merits and demerits of each model. Lastly, brief comparisons between the systems are explained as the conclusion of the most promising battery thermal management system in the future.
{"title":"Review on Current Thermal Issue and Cooling Technology Development on Electric Vehicles Battery","authors":"Muhammad Aulia Rahman, I. Pranoto","doi":"10.1109/ICST50505.2020.9732879","DOIUrl":"https://doi.org/10.1109/ICST50505.2020.9732879","url":null,"abstract":"As one of the most prominent parts of an electric vehicle, Li-ion battery has been widely used as the main power source of the vehicle. However, this battery is very sensitive to the working temperature. Some thermal issues can occur when the temperature of the battery exceeds the maximum allowable working temperature of a battery. Thus, a proper battery thermal management system is required in order to support electric vehicle performance. In this paper, some problems which can occur during overheating are explained. Then, the current development of the battery thermal management system based on the cooling mechanism as well as the cooling mode is reviewed together with the merits and demerits of each model. Lastly, brief comparisons between the systems are explained as the conclusion of the most promising battery thermal management system in the future.","PeriodicalId":125807,"journal":{"name":"2020 6th International Conference on Science and Technology (ICST)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120947864","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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