Pub Date : 2022-10-01DOI: 10.1109/IWBIS56557.2022.9924843
A. Alamsyah, D. P. Ramadhani, Syifa Afina Ekaputri
Financial institutions currently use credit history to determine whether to grant creditors credit. However, companies such as P2P Lending has a data shortage, especially credit history data, so innovative credit models emerge to improve the ability to assess creditors. Along with technology development, we have the opportunity to extract data from social media. This study uses social media data to create models for assessing creditworthiness. We collect data from social media and then process it using the credit scoring scorecard, linear correlation formula, credit scoring model weight composition, and threshold according to expert judgments. We find that by using a greater weight of the demographic attributes, we receive more data in the good credit category. This research on establishing model combinations contributes to assisting and making it easier for lenders to assess creditors using available data in a more practical way.
{"title":"Modeling Person’s Creditworthiness over Their Demography and Personality Appearance in Social Media","authors":"A. Alamsyah, D. P. Ramadhani, Syifa Afina Ekaputri","doi":"10.1109/IWBIS56557.2022.9924843","DOIUrl":"https://doi.org/10.1109/IWBIS56557.2022.9924843","url":null,"abstract":"Financial institutions currently use credit history to determine whether to grant creditors credit. However, companies such as P2P Lending has a data shortage, especially credit history data, so innovative credit models emerge to improve the ability to assess creditors. Along with technology development, we have the opportunity to extract data from social media. This study uses social media data to create models for assessing creditworthiness. We collect data from social media and then process it using the credit scoring scorecard, linear correlation formula, credit scoring model weight composition, and threshold according to expert judgments. We find that by using a greater weight of the demographic attributes, we receive more data in the good credit category. This research on establishing model combinations contributes to assisting and making it easier for lenders to assess creditors using available data in a more practical way.","PeriodicalId":348371,"journal":{"name":"2022 7th International Workshop on Big Data and Information Security (IWBIS)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122552607","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 : 2022-10-01DOI: 10.1109/IWBIS56557.2022.9924902
H. T. Y. Achsan, D. Kurniawan, Diki Gita Purnama, Quintin Kurnia Dikara Barcah, Yuri Yusyana Astoria
The new capital city (IKN) of the Republic of Indonesia has been ratified and inaugurated by President Joko Widodo since January 2022. Unfortunately, there are still many Indonesian citizens who do not understand all the information regarding the determination of the new capital city. Even though the Indonesian Government has created an official website regarding the new capital city (www.ikn.go.id) the information is still not optimal because web page visitors are still unable to interact actively with the required information. Therefore, the development of the Chatting Robot (Chatbot) application is deemed necessary to become an interactive component in obtaining information needed by users related to new capital city. In this study, a chatbot application was developed by applying Natural Language Processing (NLP) using the Term Frequency-Inverse Document Frequency (TF-IDF) method for term weighting and the Cosine-Similarity algorithm to calculate the similarity of the questions asked by the user. The research successfully designed and developed a chatbot application using the Cosine-Similarity algorithm. The testing phase of the chatbot model uses several scenarios related to the points of NLP implementation. The test results show that all scenarios of questions asked can be responded well by the chatbot.
{"title":"Application of Natural Language Processing Using Cosine-Similarity Algorithm in Making Chatbot Information on the New Capital City of the Republic of Indonesia","authors":"H. T. Y. Achsan, D. Kurniawan, Diki Gita Purnama, Quintin Kurnia Dikara Barcah, Yuri Yusyana Astoria","doi":"10.1109/IWBIS56557.2022.9924902","DOIUrl":"https://doi.org/10.1109/IWBIS56557.2022.9924902","url":null,"abstract":"The new capital city (IKN) of the Republic of Indonesia has been ratified and inaugurated by President Joko Widodo since January 2022. Unfortunately, there are still many Indonesian citizens who do not understand all the information regarding the determination of the new capital city. Even though the Indonesian Government has created an official website regarding the new capital city (www.ikn.go.id) the information is still not optimal because web page visitors are still unable to interact actively with the required information. Therefore, the development of the Chatting Robot (Chatbot) application is deemed necessary to become an interactive component in obtaining information needed by users related to new capital city. In this study, a chatbot application was developed by applying Natural Language Processing (NLP) using the Term Frequency-Inverse Document Frequency (TF-IDF) method for term weighting and the Cosine-Similarity algorithm to calculate the similarity of the questions asked by the user. The research successfully designed and developed a chatbot application using the Cosine-Similarity algorithm. The testing phase of the chatbot model uses several scenarios related to the points of NLP implementation. The test results show that all scenarios of questions asked can be responded well by the chatbot.","PeriodicalId":348371,"journal":{"name":"2022 7th International Workshop on Big Data and Information Security (IWBIS)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130589946","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 : 2022-10-01DOI: 10.1109/IWBIS56557.2022.9924891
A. P. W. Aldryani, Hendrana Tjahjadi, I. A. Dahlan, I. Kholis, Reza Istoni, Angelita Friskilla Bangun, Anry Christiano Tambunan, Jhon Kristel Sabathino Pigome
Indonesia is a vast archipelago and a large sea area. Quoting from the Preamble of the 1945 Constitution states that the purpose of the Government of the Republic of Indonesia is to protect the entire Indonesian nation and its homeland. Therefore, defensive aspects need to be taken into special account. The Sea Defense System requires an agile unmanned mini-sea boat maneuvering capability which is able to secure the sea area according to its function and detect foreign ships. Indonesia needs to be more vigilant and detect its underwater defenses so that intruders do not attempt to violate the sovereignty of the Republic of Indonesia. This research is intended as a solution by designing a mini marine prototype to protect and strengthen Indonesian sea border. The system is developed using OpenCV and YOLO (You Only Look Once) method to detect ship. It is developed on an laptop which run on Linux. This research yields the results of the ship detection system by percentage of precision confidence level range 54–96% and several factors of undetectable condition, namely camouflaged ship, half body of ship image, and sunset condition.
印度尼西亚是一个幅员辽阔的群岛和广阔的海域。引用1945年《宪法》的序言指出，印度尼西亚共和国政府的宗旨是保护整个印度尼西亚民族及其家园。因此，防御方面需要特别考虑。海上防御系统需要一种灵活的无人小型海上艇机动能力，能够根据其功能保护该海域并探测外国船只。印度尼西亚需要提高警惕，探测其水下防御，使入侵者不会试图侵犯印度尼西亚共和国的主权。本研究旨在通过设计一个小型海洋原型来保护和加强印度尼西亚的海上边界。该系统采用OpenCV和YOLO (You Only Look Once)方法对船舶进行检测。它是在运行Linux的笔记本电脑上开发的。本研究以精度置信度百分比在54-96%的范围内，结合伪装船舶、半船身图像、日落条件等几个不可检测条件，得出船舶检测系统的结果。
{"title":"Development of Ship Detection Using OPENCV YOLO Method on Unmanned Prototype Boat for Monitoring National Sea","authors":"A. P. W. Aldryani, Hendrana Tjahjadi, I. A. Dahlan, I. Kholis, Reza Istoni, Angelita Friskilla Bangun, Anry Christiano Tambunan, Jhon Kristel Sabathino Pigome","doi":"10.1109/IWBIS56557.2022.9924891","DOIUrl":"https://doi.org/10.1109/IWBIS56557.2022.9924891","url":null,"abstract":"Indonesia is a vast archipelago and a large sea area. Quoting from the Preamble of the 1945 Constitution states that the purpose of the Government of the Republic of Indonesia is to protect the entire Indonesian nation and its homeland. Therefore, defensive aspects need to be taken into special account. The Sea Defense System requires an agile unmanned mini-sea boat maneuvering capability which is able to secure the sea area according to its function and detect foreign ships. Indonesia needs to be more vigilant and detect its underwater defenses so that intruders do not attempt to violate the sovereignty of the Republic of Indonesia. This research is intended as a solution by designing a mini marine prototype to protect and strengthen Indonesian sea border. The system is developed using OpenCV and YOLO (You Only Look Once) method to detect ship. It is developed on an laptop which run on Linux. This research yields the results of the ship detection system by percentage of precision confidence level range 54–96% and several factors of undetectable condition, namely camouflaged ship, half body of ship image, and sunset condition.","PeriodicalId":348371,"journal":{"name":"2022 7th International Workshop on Big Data and Information Security (IWBIS)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131819224","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 : 2022-10-01DOI: 10.1109/IWBIS56557.2022.9924722
Manan, Gathut Imam Gunadi, G. R. Deksino
The use of data visualization in defense is very important. In the application of data visualization can use GIS applications. GIS can help a country in maintaining the integrity of the country. This study aims to show how data visualization using GIS is used in defense. Methodology This study uses a qualitative research methodology. The results of this study are that by knowing visualization data assisted by GIS applications, you can find out the Hazard and Vulnerability of the terrain in the country. With data visualization, it can also provide the supply chain needed in the defense industry as well as in times of war. And everyone who needs this visualization data can better plan the network plan and urban plan that will be used.
{"title":"Implementation of Data Visualization in Defense","authors":"Manan, Gathut Imam Gunadi, G. R. Deksino","doi":"10.1109/IWBIS56557.2022.9924722","DOIUrl":"https://doi.org/10.1109/IWBIS56557.2022.9924722","url":null,"abstract":"The use of data visualization in defense is very important. In the application of data visualization can use GIS applications. GIS can help a country in maintaining the integrity of the country. This study aims to show how data visualization using GIS is used in defense. Methodology This study uses a qualitative research methodology. The results of this study are that by knowing visualization data assisted by GIS applications, you can find out the Hazard and Vulnerability of the terrain in the country. With data visualization, it can also provide the supply chain needed in the defense industry as well as in times of war. And everyone who needs this visualization data can better plan the network plan and urban plan that will be used.","PeriodicalId":348371,"journal":{"name":"2022 7th International Workshop on Big Data and Information Security (IWBIS)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126779307","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 : 2022-10-01DOI: 10.1109/IWBIS56557.2022.9924937
O. Khalifa, M. Z. Ahmed, R. Saeed, Saleh Hussaini, A. H. Hashim, Elmahdi A. El-Khazmi
Network of vehicles using Internet of Things (IoT) frameworks have efficient characteristics of modern intelligent transportation system with a few challenges in vehicular ad-hoc networks (VANETs). However, its security framework is required to manage trust management by preserving user privacy. Wireless mobile communication (5G) system is regarded as an outstanding technology that provide ultra-reliable with limited latency wireless communication services. By extension, integrating Software Defined Network (SDN) with 5G-VANET enhances global information gathering and network control. Therefore, real-time IoT application for monitoring transport services is efficiently supported. These ensures vehicular security on this framework. This paper provides a technical solution to a self-confidential framework for a smart transport system. This process exploiting IoT for vehicle communication by incorporating SDN and 5G technology. Due to some features of blockchain, this framework has been implemented to provide various alternative support for vehicular smart services. This involves real-time access to cloud to stream video information and protection management to vehicular network. The implemented framework presents a promising technique and reliable vehicular IoT environment while ensuring user privacy. Results of simulation presents that vehicular nodes/messages (malicious) and overhead is detected and the impact on network performance are satisfactory when deployed in large-scale network scenarios.
{"title":"Blockchain Security for 5G Network using Internet of Things Devices","authors":"O. Khalifa, M. Z. Ahmed, R. Saeed, Saleh Hussaini, A. H. Hashim, Elmahdi A. El-Khazmi","doi":"10.1109/IWBIS56557.2022.9924937","DOIUrl":"https://doi.org/10.1109/IWBIS56557.2022.9924937","url":null,"abstract":"Network of vehicles using Internet of Things (IoT) frameworks have efficient characteristics of modern intelligent transportation system with a few challenges in vehicular ad-hoc networks (VANETs). However, its security framework is required to manage trust management by preserving user privacy. Wireless mobile communication (5G) system is regarded as an outstanding technology that provide ultra-reliable with limited latency wireless communication services. By extension, integrating Software Defined Network (SDN) with 5G-VANET enhances global information gathering and network control. Therefore, real-time IoT application for monitoring transport services is efficiently supported. These ensures vehicular security on this framework. This paper provides a technical solution to a self-confidential framework for a smart transport system. This process exploiting IoT for vehicle communication by incorporating SDN and 5G technology. Due to some features of blockchain, this framework has been implemented to provide various alternative support for vehicular smart services. This involves real-time access to cloud to stream video information and protection management to vehicular network. The implemented framework presents a promising technique and reliable vehicular IoT environment while ensuring user privacy. Results of simulation presents that vehicular nodes/messages (malicious) and overhead is detected and the impact on network performance are satisfactory when deployed in large-scale network scenarios.","PeriodicalId":348371,"journal":{"name":"2022 7th International Workshop on Big Data and Information Security (IWBIS)","volume":"106 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124176477","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 : 2022-10-01DOI: 10.1109/IWBIS56557.2022.9924801
Girinoto, Dimas Febriyan Priambodo, Tiyas Yulita, R. K. A. M. Zulkham, A. Rifqi, A. S. Putri
One of the most common types of social engineering attacks is phishing. This technique uses psychological manipulation of the target to unknowingly hand over the information the attacker wants. Our research tries to find out how a phishing attack can be executed by first pretexting the OmeTV video chat application. The target of our attack is OmeTV players from Indonesia who are over 18 years old. The proposed attack methodology is Social Engineering Session (SES). This study also aims to provide an overview of what kind of information can be extracted from the target during an attack. The results show that the pretexting phishing attack on the OmeTV video chat application was successfully carried out to obtain some of the target’s personal information including: full name, date of birth or age, address, educational status, hobbies, Instagram account, and phone number.
{"title":"OmeTV Pretexting Phishing Attacks: A Case Study of Social Engineering","authors":"Girinoto, Dimas Febriyan Priambodo, Tiyas Yulita, R. K. A. M. Zulkham, A. Rifqi, A. S. Putri","doi":"10.1109/IWBIS56557.2022.9924801","DOIUrl":"https://doi.org/10.1109/IWBIS56557.2022.9924801","url":null,"abstract":"One of the most common types of social engineering attacks is phishing. This technique uses psychological manipulation of the target to unknowingly hand over the information the attacker wants. Our research tries to find out how a phishing attack can be executed by first pretexting the OmeTV video chat application. The target of our attack is OmeTV players from Indonesia who are over 18 years old. The proposed attack methodology is Social Engineering Session (SES). This study also aims to provide an overview of what kind of information can be extracted from the target during an attack. The results show that the pretexting phishing attack on the OmeTV video chat application was successfully carried out to obtain some of the target’s personal information including: full name, date of birth or age, address, educational status, hobbies, Instagram account, and phone number.","PeriodicalId":348371,"journal":{"name":"2022 7th International Workshop on Big Data and Information Security (IWBIS)","volume":"120 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133976405","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 : 2022-10-01DOI: 10.1109/IWBIS56557.2022.9924844
Inigo Ramli, A. Krisnadhi, Radityo Eko Prasojo
Pretrained language models posses an ability to learn the structural representation of a natural language by processing unstructured textual data. However, the current language model design lacks the ability to learn factual knowledge from knowledge graphs. Several attempts have been made to address this issue, such as the development of KEPLER. KEPLER combines the BERT language model and TransE knowledge embedding method to achieve a language model that can incorporate knowledge graphs as training data. Unfortunately, such knowledge enhanced language model is not yet available for the Indonesian language. In this experiment, we propose IndoKEPLER: a language model trained usingWikipedia Bahasa Indonesia andWikidata. We also create a new knowledge probing benchmark named IndoLAMA to test the ability of a language model to recall factual knowledge. The benchmark is based on LAMA, which is designed to test the suitability of our language model to be used as a knowledge base. IndoLAMA tests a language model by giving cloze style question and compare the prediction of the model to the factually correct answer. This experiment shows that IndoKEPLER increases the ability of a normal DistilBERT model to recall factual knowledge by 0.8%. Moreover, the most significant increase happens when dealing with many-to-one relationships, where IndoKEPLER outperforms it’s original text encoder model by 3%.
{"title":"IndoKEPLER, IndoWiki, and IndoLAMA: A Knowledge-enhanced Language Model, Dataset, and Benchmark for the Indonesian Language","authors":"Inigo Ramli, A. Krisnadhi, Radityo Eko Prasojo","doi":"10.1109/IWBIS56557.2022.9924844","DOIUrl":"https://doi.org/10.1109/IWBIS56557.2022.9924844","url":null,"abstract":"Pretrained language models posses an ability to learn the structural representation of a natural language by processing unstructured textual data. However, the current language model design lacks the ability to learn factual knowledge from knowledge graphs. Several attempts have been made to address this issue, such as the development of KEPLER. KEPLER combines the BERT language model and TransE knowledge embedding method to achieve a language model that can incorporate knowledge graphs as training data. Unfortunately, such knowledge enhanced language model is not yet available for the Indonesian language. In this experiment, we propose IndoKEPLER: a language model trained usingWikipedia Bahasa Indonesia andWikidata. We also create a new knowledge probing benchmark named IndoLAMA to test the ability of a language model to recall factual knowledge. The benchmark is based on LAMA, which is designed to test the suitability of our language model to be used as a knowledge base. IndoLAMA tests a language model by giving cloze style question and compare the prediction of the model to the factually correct answer. This experiment shows that IndoKEPLER increases the ability of a normal DistilBERT model to recall factual knowledge by 0.8%. Moreover, the most significant increase happens when dealing with many-to-one relationships, where IndoKEPLER outperforms it’s original text encoder model by 3%.","PeriodicalId":348371,"journal":{"name":"2022 7th International Workshop on Big Data and Information Security (IWBIS)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132267701","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 : 2022-10-01DOI: 10.1109/IWBIS56557.2022.9924988
Naufal Muhammad Hirzi, M. A. Ma'sum, Mahardhika Pratama, W. Jatmiko
3D geometric modelling of urban areas has the potential for further development, not only for 3D urban visualization. 3D point cloud, as 3D data commonly used in 3D urban geometry modelling, is needed to extract objects from point clouds to analyze urban landscapes. An automated method to analyze objects from the 3D point cloud can be achieved by using the semantic segmentation method. Unlike other segmentation tasks in 3D point cloud data, 3D urban point cloud segmentation has the challenge of segmenting different object sizes on various types of landscape contours with imbalanced distribution of the object. Therefore, this study modified 3D U-Net Sparse CNN by adding Atrous Spatial Pyramid Pooling (ASPP) as one of the modules in this model, called 3D U-Net ASPP Sparse CNN. The use of ASPP aims to get the contextual multi-scale information of the input feature map from the encoder part of U-Net. Furthermore, 3D U-Net ASPP Sparse CNN is implemented by using weighted dice loss as the loss function. The experiment result shows 3D U-Net ASPP Sparse CNN with weighted dice loss has achieved the best evaluation score in our experiment, with OA = 96.53 and mIoU = 63.59.
{"title":"Large-scale 3D Point Cloud Semantic Segmentation with 3D U-Net ASPP Sparse CNN","authors":"Naufal Muhammad Hirzi, M. A. Ma'sum, Mahardhika Pratama, W. Jatmiko","doi":"10.1109/IWBIS56557.2022.9924988","DOIUrl":"https://doi.org/10.1109/IWBIS56557.2022.9924988","url":null,"abstract":"3D geometric modelling of urban areas has the potential for further development, not only for 3D urban visualization. 3D point cloud, as 3D data commonly used in 3D urban geometry modelling, is needed to extract objects from point clouds to analyze urban landscapes. An automated method to analyze objects from the 3D point cloud can be achieved by using the semantic segmentation method. Unlike other segmentation tasks in 3D point cloud data, 3D urban point cloud segmentation has the challenge of segmenting different object sizes on various types of landscape contours with imbalanced distribution of the object. Therefore, this study modified 3D U-Net Sparse CNN by adding Atrous Spatial Pyramid Pooling (ASPP) as one of the modules in this model, called 3D U-Net ASPP Sparse CNN. The use of ASPP aims to get the contextual multi-scale information of the input feature map from the encoder part of U-Net. Furthermore, 3D U-Net ASPP Sparse CNN is implemented by using weighted dice loss as the loss function. The experiment result shows 3D U-Net ASPP Sparse CNN with weighted dice loss has achieved the best evaluation score in our experiment, with OA = 96.53 and mIoU = 63.59.","PeriodicalId":348371,"journal":{"name":"2022 7th International Workshop on Big Data and Information Security (IWBIS)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122308124","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 : 2022-10-01DOI: 10.1109/IWBIS56557.2022.9924685
Fityan Azizi, Akbar Fathur Sani, R. Priambodo, Wisma Chaerul Karunianto, M. M. L. Ramadhan, M. F. Rachmadi, W. Jatmiko
An accurate assessment of heart function is crucial in diagnosing the cardiovascular disease. One way to evaluate or detect the disease can use echocardiography, by detecting systolic and diastolic volumes. However, manual human assessments can be time-consuming and error-prone due to the low resolution of the image. One way to detect heart failure on echocardiogram is by segmenting the left ventricle on the echocardiogram using deep learning. In this study, we modified the MultiResUNet model for left ventricle segmentation in echocardiography images by adding Atrous Spatial Pyramid Pooling block and Attention block. The use of multires blocks from MultiResUnet is able to overcome the problem of multi-resolution segmentation objects, where the segmentation objects have different sizes. This problem has similar characteristics to echocardiographic images, where the systole and diastole segmentation objects have different sizes from each other. Performance measure were evaluated using Echonet-Dynamic dataset. The proposed model achieves dice coefficient of 92%, giving an additional 2% performance result compared to the MultiResUNet.
{"title":"Modified MultiResUNet for Left Ventricle Segmentation from Echocardiographic Images","authors":"Fityan Azizi, Akbar Fathur Sani, R. Priambodo, Wisma Chaerul Karunianto, M. M. L. Ramadhan, M. F. Rachmadi, W. Jatmiko","doi":"10.1109/IWBIS56557.2022.9924685","DOIUrl":"https://doi.org/10.1109/IWBIS56557.2022.9924685","url":null,"abstract":"An accurate assessment of heart function is crucial in diagnosing the cardiovascular disease. One way to evaluate or detect the disease can use echocardiography, by detecting systolic and diastolic volumes. However, manual human assessments can be time-consuming and error-prone due to the low resolution of the image. One way to detect heart failure on echocardiogram is by segmenting the left ventricle on the echocardiogram using deep learning. In this study, we modified the MultiResUNet model for left ventricle segmentation in echocardiography images by adding Atrous Spatial Pyramid Pooling block and Attention block. The use of multires blocks from MultiResUnet is able to overcome the problem of multi-resolution segmentation objects, where the segmentation objects have different sizes. This problem has similar characteristics to echocardiographic images, where the systole and diastole segmentation objects have different sizes from each other. Performance measure were evaluated using Echonet-Dynamic dataset. The proposed model achieves dice coefficient of 92%, giving an additional 2% performance result compared to the MultiResUNet.","PeriodicalId":348371,"journal":{"name":"2022 7th International Workshop on Big Data and Information Security (IWBIS)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123193761","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 : 2022-10-01DOI: 10.1109/IWBIS56557.2022.9924920
A. Alamsyah, D. P. Ramadhani, Herlambang Septiaji Basuseno
User generated content (UGC) provides abundant tourist information regarding destinations. The textual digital traces bring great opportunity along with great challenges. Text mining approaches including sentiment analysis, multiclass text classification, and network analysis are suitable for extracting the buried pattern under piles of unstructured data. We processed 18.721 reviews from worldwide tourists about Bali’s 15 topmost tourist attractions. This study uncovers the tourist perception through textual data using sentiment analysis to extract the positive and negative perceptions, and multiclass classification to extract the tourist cognitive concern for each destination. We discover the tourist visiting patterns deeper by combining perception tone and cognitive concern results using network analysis to map out the destinations’ popularity, interconnectivity, and major cognitive perception. Most of the tourists disclose positive expressions and give their concerns about Bali’s natural attractions. They feel best for the social setting and environment aspect, and worst for the accessibility. Sacred Monkey Forest Sanctuary is the most favorite destination and a potential point of a visit to other destinations. This research provides insight into the global perception of Bali’s topmost destinations for government and other tourism stakeholders to support the development and improvement of Bali’s tourism.
{"title":"Mining Digital Traces to Uncover Global Perception of Bali’s Topmost Destinations","authors":"A. Alamsyah, D. P. Ramadhani, Herlambang Septiaji Basuseno","doi":"10.1109/IWBIS56557.2022.9924920","DOIUrl":"https://doi.org/10.1109/IWBIS56557.2022.9924920","url":null,"abstract":"User generated content (UGC) provides abundant tourist information regarding destinations. The textual digital traces bring great opportunity along with great challenges. Text mining approaches including sentiment analysis, multiclass text classification, and network analysis are suitable for extracting the buried pattern under piles of unstructured data. We processed 18.721 reviews from worldwide tourists about Bali’s 15 topmost tourist attractions. This study uncovers the tourist perception through textual data using sentiment analysis to extract the positive and negative perceptions, and multiclass classification to extract the tourist cognitive concern for each destination. We discover the tourist visiting patterns deeper by combining perception tone and cognitive concern results using network analysis to map out the destinations’ popularity, interconnectivity, and major cognitive perception. Most of the tourists disclose positive expressions and give their concerns about Bali’s natural attractions. They feel best for the social setting and environment aspect, and worst for the accessibility. Sacred Monkey Forest Sanctuary is the most favorite destination and a potential point of a visit to other destinations. This research provides insight into the global perception of Bali’s topmost destinations for government and other tourism stakeholders to support the development and improvement of Bali’s tourism.","PeriodicalId":348371,"journal":{"name":"2022 7th International Workshop on Big Data and Information Security (IWBIS)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126404431","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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