Pub Date : 2021-10-20DOI: 10.23919/eecsi53397.2021.9624298
Nizar Alsharif
Technologies, applications and services of Internet of Things (IoT) are growing tremendously. This IoT blast provides an extensive choice of opportunities for consumers and manufacturer, but at the same time carriages major risks with regards to security. As more appliances and sensors become interconnected, securing them will be the major challenge. In order to make IoT objects work efficiently, hardware, software and connectivity require being secured. Less consideration on security for IoT, the connected objects may degrade the performance of services provided by the IoT network. One significant type of attack is denial of service attack (DoS) caused by manipulating handshake Transmission Control Protocol (TCP) mechanism, i.e.: TCP SYN flooding. To solve the DoS attack on IoT networks, ones use Intrusion detection system (IDS) as a potential solution. This paper proposes IDS by combining principle component analysis (PCA) feature selection technique with 3 classifier algorithms, i.e.: Random Tree (RT), K-Means, and Naïve Bayes (NB). Experimental results on IoT tesbed networks traffic dataset show that the proposed IDS using Random Tree classifier achieves the best performance in term of accuracy and energy consumption.
{"title":"Ensembling PCA-based Feature Selection with Random Tree Classifier for Intrusion Detection on IoT Network","authors":"Nizar Alsharif","doi":"10.23919/eecsi53397.2021.9624298","DOIUrl":"https://doi.org/10.23919/eecsi53397.2021.9624298","url":null,"abstract":"Technologies, applications and services of Internet of Things (IoT) are growing tremendously. This IoT blast provides an extensive choice of opportunities for consumers and manufacturer, but at the same time carriages major risks with regards to security. As more appliances and sensors become interconnected, securing them will be the major challenge. In order to make IoT objects work efficiently, hardware, software and connectivity require being secured. Less consideration on security for IoT, the connected objects may degrade the performance of services provided by the IoT network. One significant type of attack is denial of service attack (DoS) caused by manipulating handshake Transmission Control Protocol (TCP) mechanism, i.e.: TCP SYN flooding. To solve the DoS attack on IoT networks, ones use Intrusion detection system (IDS) as a potential solution. This paper proposes IDS by combining principle component analysis (PCA) feature selection technique with 3 classifier algorithms, i.e.: Random Tree (RT), K-Means, and Naïve Bayes (NB). Experimental results on IoT tesbed networks traffic dataset show that the proposed IDS using Random Tree classifier achieves the best performance in term of accuracy and energy consumption.","PeriodicalId":259450,"journal":{"name":"2021 8th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116891570","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 : 2021-10-20DOI: 10.23919/eecsi53397.2021.9624286
Csaba Simon, M. Máté, M. Maliosz
The Deterministic Networking (DetNet) working group of the Internet Engineering Task Force (IETF) is developing methods for building large networks with bounded latency, zero packet loss, and high reliability out of existing networking technologies. To provide strong end-to-end Quality of Service guarantees across multiple network domains, DetNet has to perform joint layer 3 and layer 2 resource reservation. The prime candidate for layer 2 technology in DetNet is Ethernet with Time-Sensitive Networking (TSN) extensions, but it is too new, and not yet fully standardized. In this paper we explore the possibilities of using Audio-Video Bridging (AVB), the precursor of TSN, as the layer 2 medium in DetNet by integrating the AVB resource reservation protocol with layer 3 reservations across multi-domain networks. We show how to match the flow identifiers and the QoS descriptors across the domains, and review the signaling steps needed to establish the connection.
{"title":"Resource Reservation in DetNet with AVB","authors":"Csaba Simon, M. Máté, M. Maliosz","doi":"10.23919/eecsi53397.2021.9624286","DOIUrl":"https://doi.org/10.23919/eecsi53397.2021.9624286","url":null,"abstract":"The Deterministic Networking (DetNet) working group of the Internet Engineering Task Force (IETF) is developing methods for building large networks with bounded latency, zero packet loss, and high reliability out of existing networking technologies. To provide strong end-to-end Quality of Service guarantees across multiple network domains, DetNet has to perform joint layer 3 and layer 2 resource reservation. The prime candidate for layer 2 technology in DetNet is Ethernet with Time-Sensitive Networking (TSN) extensions, but it is too new, and not yet fully standardized. In this paper we explore the possibilities of using Audio-Video Bridging (AVB), the precursor of TSN, as the layer 2 medium in DetNet by integrating the AVB resource reservation protocol with layer 3 reservations across multi-domain networks. We show how to match the flow identifiers and the QoS descriptors across the domains, and review the signaling steps needed to establish the connection.","PeriodicalId":259450,"journal":{"name":"2021 8th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117118710","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 : 2021-10-20DOI: 10.23919/eecsi53397.2021.9624288
N. Akbar, E. C. Djamal
Monitoring the number of people is essential to estimate the level of crowds in a public area, especially during this Covid19 pandemic. CCTV recording needs to process for counting the number of people in a crowd at a specific time. However, counting people on CCTV is not easy. It can be approached by detecting a specific object from a compilation of frames with a certain size that makes up the image. This study proposed the Faster Region-Convolutional Neural Networks (Faster R-CNN) method with ResNet50 to count the number of people in a crowd from the low-resolution image from CCTV. The research gave that crowd counting with the Faster RCNN needs consideration to choose appropriate architecture. ResNet50 architecture provided an accuracy of 97.20% in detecting the number of people in the crowd image. It was compared to other detectors based on previous studies with the same dataset and gave the highest accuracy. Region Proposal Networks makes Faster RCNN robust. Although the various number of people in the crowd image, quality of the dataset, and anchor aspect ratio values provide good results improve accuracy. Besides, the appropriate learning parameters make the method performance more optimal. This configuration can be applied to real-time testing so that it gave the best results of 86% using Faster RCNN and ResNet50.
{"title":"Crowd Counting Using Region Convolutional Neural Networks","authors":"N. Akbar, E. C. Djamal","doi":"10.23919/eecsi53397.2021.9624288","DOIUrl":"https://doi.org/10.23919/eecsi53397.2021.9624288","url":null,"abstract":"Monitoring the number of people is essential to estimate the level of crowds in a public area, especially during this Covid19 pandemic. CCTV recording needs to process for counting the number of people in a crowd at a specific time. However, counting people on CCTV is not easy. It can be approached by detecting a specific object from a compilation of frames with a certain size that makes up the image. This study proposed the Faster Region-Convolutional Neural Networks (Faster R-CNN) method with ResNet50 to count the number of people in a crowd from the low-resolution image from CCTV. The research gave that crowd counting with the Faster RCNN needs consideration to choose appropriate architecture. ResNet50 architecture provided an accuracy of 97.20% in detecting the number of people in the crowd image. It was compared to other detectors based on previous studies with the same dataset and gave the highest accuracy. Region Proposal Networks makes Faster RCNN robust. Although the various number of people in the crowd image, quality of the dataset, and anchor aspect ratio values provide good results improve accuracy. Besides, the appropriate learning parameters make the method performance more optimal. This configuration can be applied to real-time testing so that it gave the best results of 86% using Faster RCNN and ResNet50.","PeriodicalId":259450,"journal":{"name":"2021 8th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126598912","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 : 2021-10-20DOI: 10.23919/eecsi53397.2021.9624241
R. Giuliano
Advances in multimedia enable new services for humans and smart objects. By the year 2030, the forthcoming network will implement new capabilities for richer and more interactive applications. In addition to broadband and low latency services, precise communications and qualitative communications will be supported by the next generation network 2030. This allows providing composed services such as Holographic Type Communications, Multisensorial Communications, Emergency Communications and Collaborative Robots (or Cobots). Complexity and application software should not be confined only to external network nodes (i.e., user terminals and servers) but internal nodes should support the communication and the synchronization among sub-flows coming from different sources in order to guarantee the precise data delivery at a given point. Based on generic applications (e.g., industrial automation, health, automotive, entertainment and education), the network 2030 will provide specific applications such as remote surgery, machine collaboration and virtual laboratories. Finally, the main envisaged enabling technologies are increasing the service bandwidth and the number of access points, adopting the artificial intelligence at any level and any segment of the network, deploying Reconfigurable Intelligent Meta-surfaces and exploiting all potentials of each given kind of architecture such as terrestrial, aerial and space.
{"title":"The Next Generation Network in 2030: Applications, Services, and Enabling Technologies","authors":"R. Giuliano","doi":"10.23919/eecsi53397.2021.9624241","DOIUrl":"https://doi.org/10.23919/eecsi53397.2021.9624241","url":null,"abstract":"Advances in multimedia enable new services for humans and smart objects. By the year 2030, the forthcoming network will implement new capabilities for richer and more interactive applications. In addition to broadband and low latency services, precise communications and qualitative communications will be supported by the next generation network 2030. This allows providing composed services such as Holographic Type Communications, Multisensorial Communications, Emergency Communications and Collaborative Robots (or Cobots). Complexity and application software should not be confined only to external network nodes (i.e., user terminals and servers) but internal nodes should support the communication and the synchronization among sub-flows coming from different sources in order to guarantee the precise data delivery at a given point. Based on generic applications (e.g., industrial automation, health, automotive, entertainment and education), the network 2030 will provide specific applications such as remote surgery, machine collaboration and virtual laboratories. Finally, the main envisaged enabling technologies are increasing the service bandwidth and the number of access points, adopting the artificial intelligence at any level and any segment of the network, deploying Reconfigurable Intelligent Meta-surfaces and exploiting all potentials of each given kind of architecture such as terrestrial, aerial and space.","PeriodicalId":259450,"journal":{"name":"2021 8th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124553026","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 : 2021-10-20DOI: 10.23919/eecsi53397.2021.9624246
M. Sultan, M. I. Nashiruddin, M. Nugraha
Currently, energy monitoring activities in Indonesia are still carried out manually for gas, electricity, and water. However, there is a weakness in manual monitoring; the data cannot be processed in real-time, unlike Advanced Metering Infrastructure (AMI). This study aims to prepare a techno-economic analysis of AMI's network planning using Narrow Band Internet of Things (NB-IoT) technology with its characteristics of low-frequency bands and low costs in one of the highest traffic and populated urban area and the capital city of West Sumatra, namely Padang city, Indonesia. Initially, the preparation of the NB-IoT network on AMI's services will be carried out for the next ten years using coverage and capacity calculations. The obtained sites were seven for capacity and three for coverage. Then the results are simulated using Atoll software and later accompanied by the techno-economic analysis for NB-IoT planning in Padang city. It is found that the signal received is −105 dBm, with throughput 295.45 kbps and Signal Noise Ratio (SNR) of −1 dB. Meanwhile, the obtained Net Present Value (NPV) is 3, 964, 863 USD, Internal Rate of Return (IRR) of 66.05 percent, Payback Period (PP) within four years, and Profitability Index (PI) of 3.395. This means that through the obtained techno-economic results, implementing NB-IoT on AMI's for smart metering services in Padang city is feasible and profitable in the long run.
{"title":"Techno-Economic Analysis of the NB-IoT Network Planning for Smart Metering Services in Urban Area","authors":"M. Sultan, M. I. Nashiruddin, M. Nugraha","doi":"10.23919/eecsi53397.2021.9624246","DOIUrl":"https://doi.org/10.23919/eecsi53397.2021.9624246","url":null,"abstract":"Currently, energy monitoring activities in Indonesia are still carried out manually for gas, electricity, and water. However, there is a weakness in manual monitoring; the data cannot be processed in real-time, unlike Advanced Metering Infrastructure (AMI). This study aims to prepare a techno-economic analysis of AMI's network planning using Narrow Band Internet of Things (NB-IoT) technology with its characteristics of low-frequency bands and low costs in one of the highest traffic and populated urban area and the capital city of West Sumatra, namely Padang city, Indonesia. Initially, the preparation of the NB-IoT network on AMI's services will be carried out for the next ten years using coverage and capacity calculations. The obtained sites were seven for capacity and three for coverage. Then the results are simulated using Atoll software and later accompanied by the techno-economic analysis for NB-IoT planning in Padang city. It is found that the signal received is −105 dBm, with throughput 295.45 kbps and Signal Noise Ratio (SNR) of −1 dB. Meanwhile, the obtained Net Present Value (NPV) is 3, 964, 863 USD, Internal Rate of Return (IRR) of 66.05 percent, Payback Period (PP) within four years, and Profitability Index (PI) of 3.395. This means that through the obtained techno-economic results, implementing NB-IoT on AMI's for smart metering services in Padang city is feasible and profitable in the long run.","PeriodicalId":259450,"journal":{"name":"2021 8th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI)","volume":"152 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114194145","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}
The Covid-19 coronavirus has turned into a serious, life-threatening disease that is prevalent worldwide as it is most likely to infect. An automated protocol system is a compelling idea to stop the spread of covid19. This article aims at a deep learning model supported by a convolutional neural network (CNN) to facilitate automatic diagnosis from chest X-rays. A collection of 2875 covid19 images and 10293 X-ray pictures to recognize covid19 counts is being used as the data set for the drafting. From the experimental results, it can be seen that the proposed structure achieves 96% specificity, 97% AUC 96% accuracy, 96 % sensitivity, and 96 % F1-score. Therefore, the results of the proposed system will help clinicians and researchers discover COVID-19 patients and facilitate the treatment of COVID-19 patients.
{"title":"Deep Viewing for Covid-19 Detection from X-Ray Using CNN Based Architecture","authors":"Partho Ghose, U. Acharjee, Md. Amirul Islam, Selina Sharmin, Md. Ashraf Uddin","doi":"10.23919/eecsi53397.2021.9624257","DOIUrl":"https://doi.org/10.23919/eecsi53397.2021.9624257","url":null,"abstract":"The Covid-19 coronavirus has turned into a serious, life-threatening disease that is prevalent worldwide as it is most likely to infect. An automated protocol system is a compelling idea to stop the spread of covid19. This article aims at a deep learning model supported by a convolutional neural network (CNN) to facilitate automatic diagnosis from chest X-rays. A collection of 2875 covid19 images and 10293 X-ray pictures to recognize covid19 counts is being used as the data set for the drafting. From the experimental results, it can be seen that the proposed structure achieves 96% specificity, 97% AUC 96% accuracy, 96 % sensitivity, and 96 % F1-score. Therefore, the results of the proposed system will help clinicians and researchers discover COVID-19 patients and facilitate the treatment of COVID-19 patients.","PeriodicalId":259450,"journal":{"name":"2021 8th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124035505","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 : 2021-10-20DOI: 10.23919/eecsi53397.2021.9624261
Donny Sabri Ashari, Budhi Irawan, C. Setianingsih
Online transportation services are public transportation that is much in demand by the public. According to the We Are Social 2020 report, as many as 21.7 million people in Indonesia use online transportation services. Customers or consumers often channel their opinions and complaints through various media. One of them is social media Instagram. On Instagram, online transportation services have an official account to provide the latest information about the service and collect comments from the public. When examined further, the collection of comments can be used as a sentiment analysis system. When assembled, we will conclude an online transportation service that has the best sentiment on Instagram. Therefore, the system created can analyze sentiments on online transportation service products using the CNN (Convolutional Neural Network) method. This system is expected to help consumers of online transportation services choose the best service from sentiment analysis. The results of this thesis in classifying sentiments in the Instagram comments column managed to get an accuracy of an average of 94%.
在线交通服务是大众非常需要的公共交通工具。根据《We Are Social 2020》报告，印尼有多达2170万人使用在线交通服务。顾客或消费者经常通过各种媒体表达他们的意见和投诉。其中之一就是社交媒体Instagram。在Instagram上，在线交通服务有一个官方账号，提供有关该服务的最新信息，并收集公众评论。当进一步检查时，评论的收集可以用作情感分析系统。组装完成后，我们将总结出一款Instagram上情绪最好的在线运输服务。因此，该系统可以使用CNN(卷积神经网络)方法分析在线交通服务产品的情绪。预计该系统将帮助在线交通服务消费者通过情感分析选择最佳服务。本文对Instagram评论栏中的情绪进行分类的结果达到了平均94%的准确率。
{"title":"Sentiment Analysis on Online Transportation Services Using Convolutional Neural Network Method","authors":"Donny Sabri Ashari, Budhi Irawan, C. Setianingsih","doi":"10.23919/eecsi53397.2021.9624261","DOIUrl":"https://doi.org/10.23919/eecsi53397.2021.9624261","url":null,"abstract":"Online transportation services are public transportation that is much in demand by the public. According to the We Are Social 2020 report, as many as 21.7 million people in Indonesia use online transportation services. Customers or consumers often channel their opinions and complaints through various media. One of them is social media Instagram. On Instagram, online transportation services have an official account to provide the latest information about the service and collect comments from the public. When examined further, the collection of comments can be used as a sentiment analysis system. When assembled, we will conclude an online transportation service that has the best sentiment on Instagram. Therefore, the system created can analyze sentiments on online transportation service products using the CNN (Convolutional Neural Network) method. This system is expected to help consumers of online transportation services choose the best service from sentiment analysis. The results of this thesis in classifying sentiments in the Instagram comments column managed to get an accuracy of an average of 94%.","PeriodicalId":259450,"journal":{"name":"2021 8th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127919973","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 : 2021-10-20DOI: 10.23919/eecsi53397.2021.9624307
Andi Riansyah, R. Gernowo, Suryono, Dedy Kurniadi
This research was carried out to determine land use with the mapping concept by implementing the fuzzy method in the Kudus district. Land use is one way of utilizing basic resources that has a strategic role and function because in general, the correct land use process will impact the ecosystem. Appropriate land use is necessary to support sustainable development. The purpose of this research is to implement fuzzy into a decision support system. The variables used as input were rainfall, land slope, soil type, and population density which were normalized using reverse score normalization and min-max normalization. The output of the fuzzy process was in the form of a defuzzification value which was then classified according to the type of land use desired and displayed on the mapping of the area. The fuzzy method was used because of the data acquisition and analysis process advantages that contain uncertainty or ambiguity. The results showed that the fuzzy method is the right solution in determining the mapping in determining the function of land into agriculture, plantations, production forests, and protected forests.
{"title":"Fuzzy Implementation for Land Spatial Planning","authors":"Andi Riansyah, R. Gernowo, Suryono, Dedy Kurniadi","doi":"10.23919/eecsi53397.2021.9624307","DOIUrl":"https://doi.org/10.23919/eecsi53397.2021.9624307","url":null,"abstract":"This research was carried out to determine land use with the mapping concept by implementing the fuzzy method in the Kudus district. Land use is one way of utilizing basic resources that has a strategic role and function because in general, the correct land use process will impact the ecosystem. Appropriate land use is necessary to support sustainable development. The purpose of this research is to implement fuzzy into a decision support system. The variables used as input were rainfall, land slope, soil type, and population density which were normalized using reverse score normalization and min-max normalization. The output of the fuzzy process was in the form of a defuzzification value which was then classified according to the type of land use desired and displayed on the mapping of the area. The fuzzy method was used because of the data acquisition and analysis process advantages that contain uncertainty or ambiguity. The results showed that the fuzzy method is the right solution in determining the mapping in determining the function of land into agriculture, plantations, production forests, and protected forests.","PeriodicalId":259450,"journal":{"name":"2021 8th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130447675","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 : 2021-10-20DOI: 10.23919/eecsi53397.2021.9624295
Ahmad Hendra Maulana, P. W. Handayani
The existence of public stigma about the difficulty of obtaining permits in Jakarta is a significant challenge for the Investment and One-Stop Integrated Service Agency of Jakarta (DPMPTSP) in providing public services. Through the Instagram Layananjakarta social media account, DPMPTSP tries to eliminate this stigma. However, with a low level of citizens engagement, which is 0.16%, it becomes a stumbling block in carrying out its primary duties and functions, even though there are 160 million active social media users in Indonesia. This study aims to determine what factors influence citizen's adoption to participate in the Layananjakarta. This study combines the Uses and Gratifications Theory (UGT), Technology Acceptance Model (TAM) theory, and the trust in the platform. This study used quantitative approach by surveying 378 people who had follow Layananjakarta. The data were analyzed using the CB-SEM method and AMOS 26.0 software. The analysis results show that the information seeking, socialization, perceived usefulness, and trust in the platform factors significantly influence the intention to use. Furthermore, the intention to use has a significant effect on the actual adoption of the citizens to participate in the Layananjakarta.
{"title":"Determinants of Citizen Adoption to Engage in Instagram for Public Services","authors":"Ahmad Hendra Maulana, P. W. Handayani","doi":"10.23919/eecsi53397.2021.9624295","DOIUrl":"https://doi.org/10.23919/eecsi53397.2021.9624295","url":null,"abstract":"The existence of public stigma about the difficulty of obtaining permits in Jakarta is a significant challenge for the Investment and One-Stop Integrated Service Agency of Jakarta (DPMPTSP) in providing public services. Through the Instagram Layananjakarta social media account, DPMPTSP tries to eliminate this stigma. However, with a low level of citizens engagement, which is 0.16%, it becomes a stumbling block in carrying out its primary duties and functions, even though there are 160 million active social media users in Indonesia. This study aims to determine what factors influence citizen's adoption to participate in the Layananjakarta. This study combines the Uses and Gratifications Theory (UGT), Technology Acceptance Model (TAM) theory, and the trust in the platform. This study used quantitative approach by surveying 378 people who had follow Layananjakarta. The data were analyzed using the CB-SEM method and AMOS 26.0 software. The analysis results show that the information seeking, socialization, perceived usefulness, and trust in the platform factors significantly influence the intention to use. Furthermore, the intention to use has a significant effect on the actual adoption of the citizens to participate in the Layananjakarta.","PeriodicalId":259450,"journal":{"name":"2021 8th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133806501","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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