Pub Date : 2020-09-15DOI: 10.1109/IC2IE50715.2020.9274566
Abdul Azies Muslim, Avon Budiono, A. Almaarif
Along with the development of the Windows operating system, browser applications to surf the internet are also growing rapidly. The most widely used browsers today are Google Chrome and Mozilla Firefox. Both browsers have a username and password management feature that makes users login to a website easily, but saving usernames and passwords in the browser is quite dangerous because the stored data can be hacked using brute force attacks or read through a program. One way to get a username and password in the browser is to use a program that can read Google Chrome and Mozilla Firefox login data from the computer’s internal storage and then show those data. In this study, an attack will be carried out by implementing Rubber Ducky using BadUSB to run the ChromePass and PasswordFox program and the PowerShell script using the Arduino Pro Micro Leonardo device as a USB Password Stealer. The results obtained from this study are the username and password on Google Chrome and Mozilla Firefox successfully obtained when the USB is connected to the target device, the average time of the attack is 14 seconds then sending it to the author’s email.
随着Windows操作系统的发展，用于上网的浏览器应用程序也在迅速增长。目前使用最广泛的浏览器是Google Chrome和Mozilla Firefox。这两款浏览器都有用户名和密码管理功能，使用户可以轻松登录网站，但将用户名和密码保存在浏览器中是相当危险的，因为存储的数据可能被暴力破解或通过程序读取。在浏览器中获取用户名和密码的一种方法是使用一个程序，该程序可以从计算机的内部存储读取谷歌Chrome和Mozilla Firefox登录数据，然后显示这些数据。在本研究中，将通过使用BadUSB实现Rubber Ducky来运行ChromePass和PasswordFox程序以及使用Arduino Pro Micro Leonardo设备作为USB密码窃取器的PowerShell脚本来进行攻击。从这项研究中获得的结果是，当USB连接到目标设备时，成功获得了Google Chrome和Mozilla Firefox上的用户名和密码，攻击的平均时间为14秒，然后将其发送到作者的电子邮件。
{"title":"Implementation and Analysis of USB based Password Stealer using PowerShell in Google Chrome and Mozilla Firefox","authors":"Abdul Azies Muslim, Avon Budiono, A. Almaarif","doi":"10.1109/IC2IE50715.2020.9274566","DOIUrl":"https://doi.org/10.1109/IC2IE50715.2020.9274566","url":null,"abstract":"Along with the development of the Windows operating system, browser applications to surf the internet are also growing rapidly. The most widely used browsers today are Google Chrome and Mozilla Firefox. Both browsers have a username and password management feature that makes users login to a website easily, but saving usernames and passwords in the browser is quite dangerous because the stored data can be hacked using brute force attacks or read through a program. One way to get a username and password in the browser is to use a program that can read Google Chrome and Mozilla Firefox login data from the computer’s internal storage and then show those data. In this study, an attack will be carried out by implementing Rubber Ducky using BadUSB to run the ChromePass and PasswordFox program and the PowerShell script using the Arduino Pro Micro Leonardo device as a USB Password Stealer. The results obtained from this study are the username and password on Google Chrome and Mozilla Firefox successfully obtained when the USB is connected to the target device, the average time of the attack is 14 seconds then sending it to the author’s email.","PeriodicalId":211983,"journal":{"name":"2020 3rd International Conference on Computer and Informatics Engineering (IC2IE)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124177835","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 : 2020-09-15DOI: 10.1109/IC2IE50715.2020.9274574
Fitri Wijayanti, D. I. Sensuse, A. Putera, Andy Syahrizal
The DRC of the Ministry XYZ has suffered from a system breach. The DRC's problem will lead to a lack of system information security, availability, and an increasing threat to the whole system of Ministry XYZ. In 2019, the KAMI Index assessment of the Ministry XYZ stated that the level of maturity and completeness of the application of ISO 27001 standards of the XYZ Ministry were at the level of fulfillment of the basic framework. There is a gap between the assessment result and the operational problem within the DRC of Ministry XYZ due to the lack of an information security management system. Therefore, this study conducts the same KAMI Index assessment within the scope of the DRC only and aims to offer a recommendation based on ISO 27001 as the basis of the KAMI Index assessment. This study used discussion, observation, and KAMI Index assessment tools for collecting data and analyze the result. The assessment result of the DRC showed that the maturity level of the ISO 27001 standard on the DRC is on the application of the basic framework. The suggested recommendations to improve the information security management system of the DRC were mostly in the aspect of the information security framework and assets management.
{"title":"Assessment of Information Security Management System: A Case Study of Data Recovery Center in Ministry XYZ","authors":"Fitri Wijayanti, D. I. Sensuse, A. Putera, Andy Syahrizal","doi":"10.1109/IC2IE50715.2020.9274574","DOIUrl":"https://doi.org/10.1109/IC2IE50715.2020.9274574","url":null,"abstract":"The DRC of the Ministry XYZ has suffered from a system breach. The DRC's problem will lead to a lack of system information security, availability, and an increasing threat to the whole system of Ministry XYZ. In 2019, the KAMI Index assessment of the Ministry XYZ stated that the level of maturity and completeness of the application of ISO 27001 standards of the XYZ Ministry were at the level of fulfillment of the basic framework. There is a gap between the assessment result and the operational problem within the DRC of Ministry XYZ due to the lack of an information security management system. Therefore, this study conducts the same KAMI Index assessment within the scope of the DRC only and aims to offer a recommendation based on ISO 27001 as the basis of the KAMI Index assessment. This study used discussion, observation, and KAMI Index assessment tools for collecting data and analyze the result. The assessment result of the DRC showed that the maturity level of the ISO 27001 standard on the DRC is on the application of the basic framework. The suggested recommendations to improve the information security management system of the DRC were mostly in the aspect of the information security framework and assets management.","PeriodicalId":211983,"journal":{"name":"2020 3rd International Conference on Computer and Informatics Engineering (IC2IE)","volume":"115 6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128394159","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 : 2020-09-15DOI: 10.1109/IC2IE50715.2020.9274581
Khan Md Hasib, Md. Imran Hossain Showrov, Anik Das
Nowadays road accident in Bangladesh is a buzzword due to its lack of carefulness of the driver of the vehicle where some parameter exists. The traffic safety of the roadway is an essential concern not only for transportation governing agencies but also for citizens of our country. For safe driving suggestions, the important thing is to find the variables that are tensed to relate to the fatal accidents that are occurring often. In this paper, we create a model using a machine learning approach on the countrywide traffic accident dataset of Bangladesh as an aim to address this problem. The model also helps out to find the diversity of the data by grouping similar objects together to find the accident-prone areas in the country concerning different accident factors as well as detects the cooperation between these factors and causalities.
{"title":"Accidental Prone Area Detection in Bangladesh using Machine Learning Model","authors":"Khan Md Hasib, Md. Imran Hossain Showrov, Anik Das","doi":"10.1109/IC2IE50715.2020.9274581","DOIUrl":"https://doi.org/10.1109/IC2IE50715.2020.9274581","url":null,"abstract":"Nowadays road accident in Bangladesh is a buzzword due to its lack of carefulness of the driver of the vehicle where some parameter exists. The traffic safety of the roadway is an essential concern not only for transportation governing agencies but also for citizens of our country. For safe driving suggestions, the important thing is to find the variables that are tensed to relate to the fatal accidents that are occurring often. In this paper, we create a model using a machine learning approach on the countrywide traffic accident dataset of Bangladesh as an aim to address this problem. The model also helps out to find the diversity of the data by grouping similar objects together to find the accident-prone areas in the country concerning different accident factors as well as detects the cooperation between these factors and causalities.","PeriodicalId":211983,"journal":{"name":"2020 3rd International Conference on Computer and Informatics Engineering (IC2IE)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133749986","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 : 2020-09-15DOI: 10.1109/IC2IE50715.2020.9274644
H. Widiputra, Adele Mailangkay, Elliana Gautama
It has been found that the existence of outliers, particularly in time-series data, can be significantly influenced the modelling and analysis results that are conducted on the data, which is further may lead to improper decision making. Nevertheless, the task of time-series outlier detection can be quite challenging when dealing with collection of data that retain non-linear trends over time as the progression of series may shifted and would be infer as possible outliers. In this study, an algorithm for time-series outlier detection that makes use of a clustering approach on time-series data to construct a set of localized trend models that is capable to identify anomalous data in a collection of non-linear trends is proposed. Decisively, results from conducted experiments confirm that the procedure performs prompt, incremental valuation of information as soon as it becomes accessible, able to handle significant amount of data, and does not need any pre-classification of anomalies. Furthermore, trials with real-world data from insurance field confirm that the proposed method is able to correctly identify abnormal data and can be of help to increase decision making process.
{"title":"Time-Series Outliers Detection Algorithm with Clustering Approach on Non-Linear Trends","authors":"H. Widiputra, Adele Mailangkay, Elliana Gautama","doi":"10.1109/IC2IE50715.2020.9274644","DOIUrl":"https://doi.org/10.1109/IC2IE50715.2020.9274644","url":null,"abstract":"It has been found that the existence of outliers, particularly in time-series data, can be significantly influenced the modelling and analysis results that are conducted on the data, which is further may lead to improper decision making. Nevertheless, the task of time-series outlier detection can be quite challenging when dealing with collection of data that retain non-linear trends over time as the progression of series may shifted and would be infer as possible outliers. In this study, an algorithm for time-series outlier detection that makes use of a clustering approach on time-series data to construct a set of localized trend models that is capable to identify anomalous data in a collection of non-linear trends is proposed. Decisively, results from conducted experiments confirm that the procedure performs prompt, incremental valuation of information as soon as it becomes accessible, able to handle significant amount of data, and does not need any pre-classification of anomalies. Furthermore, trials with real-world data from insurance field confirm that the proposed method is able to correctly identify abnormal data and can be of help to increase decision making process.","PeriodicalId":211983,"journal":{"name":"2020 3rd International Conference on Computer and Informatics Engineering (IC2IE)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115081894","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 : 2020-09-15DOI: 10.1109/ic2ie50715.2020.9274611
{"title":"IC2IE 2020 TOC","authors":"","doi":"10.1109/ic2ie50715.2020.9274611","DOIUrl":"https://doi.org/10.1109/ic2ie50715.2020.9274611","url":null,"abstract":"","PeriodicalId":211983,"journal":{"name":"2020 3rd International Conference on Computer and Informatics Engineering (IC2IE)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114478850","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 : 2020-09-15DOI: 10.1109/IC2IE50715.2020.9274617
Uus Khusni, H. I. Dewangkoro, A. M. Arymurthy
Change detection is one of the hot issues related to world observation and has been extensively studied in recent decades. The application of remote sensing technology provides inputs to systems for urban change detection primarily focus on the urban data user environment. Urban change detection refers to the general problem of monitoring the urban system and discerning changes that are occurring within that system that use to urban planners, managers, and researchers. Current methods based on a simple mechanism for independently encoding bi-temporal images to get their representation vectors. In fact, these methods do not make full use of the rich information between bi-temporal images. We propose to combine deep learning methods such as Convolutional Neural Network (U-Net) for feature extraction and Recurrent Neural Network (BiLSTM) temporal modeling. Our developed model while the validation phase gets 97.418% overall accuracy on the Onera Satellite Change Detection (OSCD) Sentinel-2 bi-temporal dataset.
{"title":"Urban Area Change Detection with Combining CNN and RNN from Sentinel-2 Multispectral Remote Sensing Data","authors":"Uus Khusni, H. I. Dewangkoro, A. M. Arymurthy","doi":"10.1109/IC2IE50715.2020.9274617","DOIUrl":"https://doi.org/10.1109/IC2IE50715.2020.9274617","url":null,"abstract":"Change detection is one of the hot issues related to world observation and has been extensively studied in recent decades. The application of remote sensing technology provides inputs to systems for urban change detection primarily focus on the urban data user environment. Urban change detection refers to the general problem of monitoring the urban system and discerning changes that are occurring within that system that use to urban planners, managers, and researchers. Current methods based on a simple mechanism for independently encoding bi-temporal images to get their representation vectors. In fact, these methods do not make full use of the rich information between bi-temporal images. We propose to combine deep learning methods such as Convolutional Neural Network (U-Net) for feature extraction and Recurrent Neural Network (BiLSTM) temporal modeling. Our developed model while the validation phase gets 97.418% overall accuracy on the Onera Satellite Change Detection (OSCD) Sentinel-2 bi-temporal dataset.","PeriodicalId":211983,"journal":{"name":"2020 3rd International Conference on Computer and Informatics Engineering (IC2IE)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117123481","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 : 2020-09-15DOI: 10.1109/IC2IE50715.2020.9274629
Muhammad Yusup Zakaria, E. C. Djamal, Fikri Nugraha, Fatan Kasyidi
Social, affective communication in recent years shows significant developments, especially in the verbal understanding of emotions. Human connection naturally adjusts to their responses based on the actions of their interlocutor in a particular matter. Previous research has shown that the use of neural network architecture can identify emotions based on speech, but the results of accuracy are not good due to the imbalance of data and problems with the design of the classification system. This study uses Linear Predictive Coding (LPC). LPC can represent the pronunciation of one’s dialogue. From 16 coefficient LPC is used as a vector feature as input for voice emotion identification using Recurrent Neural Network (RNN). Long Short Term Memory (LSTM) or Gated Recurrent Unit (GRU) architecture is used to overcome vanishing or exploding gradient. At the identification stage, that uses forward propagation with a softmax activation function. We have conducted a simulation using RNN as a method for making emotional identification. The results of this study RNNGRU using Adam optimization model with a learning rate of 0.001 get an accuracy of 90.93% and a losses value of 0.216. In comparison, the RNN-LSTM got an accuracy of 87.50% and losses value of 0.262. The experimental results show that the best model is achieved when using the RNN-GRU with the Adam optimization method. The F-Measure value obtained is 0.91.
{"title":"Speech Emotion Identification Using Linear Predictive Coding and Recurrent Neural","authors":"Muhammad Yusup Zakaria, E. C. Djamal, Fikri Nugraha, Fatan Kasyidi","doi":"10.1109/IC2IE50715.2020.9274629","DOIUrl":"https://doi.org/10.1109/IC2IE50715.2020.9274629","url":null,"abstract":"Social, affective communication in recent years shows significant developments, especially in the verbal understanding of emotions. Human connection naturally adjusts to their responses based on the actions of their interlocutor in a particular matter. Previous research has shown that the use of neural network architecture can identify emotions based on speech, but the results of accuracy are not good due to the imbalance of data and problems with the design of the classification system. This study uses Linear Predictive Coding (LPC). LPC can represent the pronunciation of one’s dialogue. From 16 coefficient LPC is used as a vector feature as input for voice emotion identification using Recurrent Neural Network (RNN). Long Short Term Memory (LSTM) or Gated Recurrent Unit (GRU) architecture is used to overcome vanishing or exploding gradient. At the identification stage, that uses forward propagation with a softmax activation function. We have conducted a simulation using RNN as a method for making emotional identification. The results of this study RNNGRU using Adam optimization model with a learning rate of 0.001 get an accuracy of 90.93% and a losses value of 0.216. In comparison, the RNN-LSTM got an accuracy of 87.50% and losses value of 0.262. The experimental results show that the best model is achieved when using the RNN-GRU with the Adam optimization method. The F-Measure value obtained is 0.91.","PeriodicalId":211983,"journal":{"name":"2020 3rd International Conference on Computer and Informatics Engineering (IC2IE)","volume":"131 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123687488","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 : 2020-09-15DOI: 10.1109/IC2IE50715.2020.9274626
Kevin Fitzgerald Senewe, M. Suryanegara
This paper aims to propose an Internet of Things (IoT)LoRa based system that determines radio refractivity in real-time. Our system consists of temperature and humidity sensor, LoRa gateway, LoRa shield, and Arduino UNO. ThingSpeak IoT platform is used to aggregate, visualize, and analyze live data streams using a numerical computing platform in the cloud. Real-time radio refractivity measurements will help network engineers when designing communication systems because the data obtained by this system are data specifically for that location, not the overall area. We have conducted the testing of our system for two days in the city of Sawangan, Indonesia and it succeded in obtaining real-time refractivity data.
{"title":"Innovative Design of Internet of Things LoRa to Determine Radio Refractivity in Real-Time","authors":"Kevin Fitzgerald Senewe, M. Suryanegara","doi":"10.1109/IC2IE50715.2020.9274626","DOIUrl":"https://doi.org/10.1109/IC2IE50715.2020.9274626","url":null,"abstract":"This paper aims to propose an Internet of Things (IoT)LoRa based system that determines radio refractivity in real-time. Our system consists of temperature and humidity sensor, LoRa gateway, LoRa shield, and Arduino UNO. ThingSpeak IoT platform is used to aggregate, visualize, and analyze live data streams using a numerical computing platform in the cloud. Real-time radio refractivity measurements will help network engineers when designing communication systems because the data obtained by this system are data specifically for that location, not the overall area. We have conducted the testing of our system for two days in the city of Sawangan, Indonesia and it succeded in obtaining real-time refractivity data.","PeriodicalId":211983,"journal":{"name":"2020 3rd International Conference on Computer and Informatics Engineering (IC2IE)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126505737","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 : 2020-09-15DOI: 10.1109/IC2IE50715.2020.9274565
Tonny Hidayat, Ika Asti Astuti
The general concept in marker-based Augmented Reality is to add virtual objects in the real world using markers as object tracking. In its development AR devices can detect 3D real objects as object tracking (3D Object Tracking) so as to allow interaction between virtual objects and real objects. However, the application of AR for general devices such as Android smartphones that do not have depth sensors, virtual objects are added without having depth information from the real world so that virtual content is always displayed in front of or on top of real objects and causes Occlusion problems. Occlusion refers to the problem when real objects that are closer to the user are covered by more distant virtual objects. This research formulates the handling of the Occlusion problem using the Model-Based Occlusion method in which the geometry information of the model from real objects must be known and registered in advance to the system. To maintain the suitability of the model’s geometry information with its real object, Tracking is needed. In this case using 3D Object Tracking by utilizing real objects that have been registered in the ModelBased Occlusion. The results of this study are the virtual objects that appear can occupy the correct position in the Augmented Reality application. In this case using 3D Object Tracking by utilizing real objects that have been registered in the ModelBased Occlusion. The results of this study are the virtual objects that appear can occupy the correct position in the Augmented Reality application. In this case using 3D Object Tracking by utilizing real objects that have been registered in the ModelBased Occlusion. The results of this study are the virtual objects that appear can occupy the correct position in the Augmented Reality application.
基于标记的增强现实的一般概念是使用标记作为对象跟踪在现实世界中添加虚拟对象。在其发展过程中，AR设备可以检测三维真实物体进行物体跟踪(3D object tracking)，从而实现虚拟物体与真实物体的交互。然而，AR应用于一般设备，如Android智能手机，没有深度传感器，虚拟物体被添加，没有来自现实世界的深度信息，虚拟内容总是显示在真实物体的前面或上面，造成遮挡问题。遮挡是指距离用户较近的真实物体被较远的虚拟物体覆盖的问题。本研究提出了基于模型的遮挡(model - based Occlusion)方法处理遮挡问题，该方法必须事先知道来自真实物体的模型几何信息并将其注册到系统中。为了保持模型的几何信息与其真实对象的适用性，需要进行跟踪。在这种情况下，使用3D对象跟踪，利用已经在基于模型的遮挡中注册的真实对象。研究结果表明，在增强现实应用中出现的虚拟物体能够占据正确的位置。在这种情况下，使用3D对象跟踪，利用已经在基于模型的遮挡中注册的真实对象。研究结果表明，在增强现实应用中出现的虚拟物体能够占据正确的位置。在这种情况下，使用3D对象跟踪，利用已经在基于模型的遮挡中注册的真实对象。研究结果表明，在增强现实应用中出现的虚拟物体能够占据正确的位置。
{"title":"Interactive Augmented Reality For The Depth Of An Object Using The Model-Based Occlusion Method","authors":"Tonny Hidayat, Ika Asti Astuti","doi":"10.1109/IC2IE50715.2020.9274565","DOIUrl":"https://doi.org/10.1109/IC2IE50715.2020.9274565","url":null,"abstract":"The general concept in marker-based Augmented Reality is to add virtual objects in the real world using markers as object tracking. In its development AR devices can detect 3D real objects as object tracking (3D Object Tracking) so as to allow interaction between virtual objects and real objects. However, the application of AR for general devices such as Android smartphones that do not have depth sensors, virtual objects are added without having depth information from the real world so that virtual content is always displayed in front of or on top of real objects and causes Occlusion problems. Occlusion refers to the problem when real objects that are closer to the user are covered by more distant virtual objects. This research formulates the handling of the Occlusion problem using the Model-Based Occlusion method in which the geometry information of the model from real objects must be known and registered in advance to the system. To maintain the suitability of the model’s geometry information with its real object, Tracking is needed. In this case using 3D Object Tracking by utilizing real objects that have been registered in the ModelBased Occlusion. The results of this study are the virtual objects that appear can occupy the correct position in the Augmented Reality application. In this case using 3D Object Tracking by utilizing real objects that have been registered in the ModelBased Occlusion. The results of this study are the virtual objects that appear can occupy the correct position in the Augmented Reality application. In this case using 3D Object Tracking by utilizing real objects that have been registered in the ModelBased Occlusion. The results of this study are the virtual objects that appear can occupy the correct position in the Augmented Reality application.","PeriodicalId":211983,"journal":{"name":"2020 3rd International Conference on Computer and Informatics Engineering (IC2IE)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130008803","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 : 2020-09-15DOI: 10.1109/ic2ie50715.2020.9274584
{"title":"IC2IE 2020 List Reviewer Page","authors":"","doi":"10.1109/ic2ie50715.2020.9274584","DOIUrl":"https://doi.org/10.1109/ic2ie50715.2020.9274584","url":null,"abstract":"","PeriodicalId":211983,"journal":{"name":"2020 3rd International Conference on Computer and Informatics Engineering (IC2IE)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127674190","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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