Pub Date : 2022-11-03DOI: 10.1109/COMNETSAT56033.2022.9994376
Minh-Tri Huynh, Hoang-Trung Le, Xuan-Ha Nguyen, Kim-Hung Le
The typical intrusion detection system (IDS) based on machine learning classifies normal and attack network traffic by extracting and analyzing network features. However, several extracted features are irrelevant and may degrade the classification accuracy. In addition, they also increase the training time and model size. Therefore, feature selection is an essential process in building an IDS system. In this paper, we propose a feature selection method for IDS by employing a Deep Neural Network model to search for and select the most crucial features. The proposal is evaluated with two datasets: UNSW-NB15 and CIC-IDS2017, and archives superior results compared with other feature selection algorithms with accuracy up to 99.96% for UNSW-NB15, 99.88% for CIC-IDS2017 while combining with LSTM-based IDS. It also reduces significant data size and time for training.
{"title":"Deep Feature Selection for Machine Learning based Attack Detection Systems","authors":"Minh-Tri Huynh, Hoang-Trung Le, Xuan-Ha Nguyen, Kim-Hung Le","doi":"10.1109/COMNETSAT56033.2022.9994376","DOIUrl":"https://doi.org/10.1109/COMNETSAT56033.2022.9994376","url":null,"abstract":"The typical intrusion detection system (IDS) based on machine learning classifies normal and attack network traffic by extracting and analyzing network features. However, several extracted features are irrelevant and may degrade the classification accuracy. In addition, they also increase the training time and model size. Therefore, feature selection is an essential process in building an IDS system. In this paper, we propose a feature selection method for IDS by employing a Deep Neural Network model to search for and select the most crucial features. The proposal is evaluated with two datasets: UNSW-NB15 and CIC-IDS2017, and archives superior results compared with other feature selection algorithms with accuracy up to 99.96% for UNSW-NB15, 99.88% for CIC-IDS2017 while combining with LSTM-based IDS. It also reduces significant data size and time for training.","PeriodicalId":221444,"journal":{"name":"2022 IEEE International Conference on Communication, Networks and Satellite (COMNETSAT)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133766969","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 : 2022-11-03DOI: 10.1109/COMNETSAT56033.2022.9994570
A. Wijayanto, A. D. Ramadhani, Alon Jala Tirta Segara, Muhamad Azrino Gustalika
Indonesia is one of the largest countries in Asia with a very dense population. According to data from The World Bank, human population indicators in Indonesia in 2019 increased by 270 milion people. This shows that population density in Indonesia is related to world problems related to waste generated from households. The household sector contributes as the top waste producer in Indonesia. Landfilling that occurs without any waste sorting, results in waste being more difficult to decompose and difficult to recycle. Therefore, to overcome this problem, it is necessary to increase public awareness about waste sorting and processing. We propose to create a device that can help sort organic and non-organic waste with Computer Vision-based Artificial Intelligence technology using the Eigenface method and the Internet of Things. Eigenface is a method that has a working principle by using XML files in performing face recognition. The result of testing in this system can run well, where the system detects organic objects the door of the chopping machine can open and if it detects nonorganic, the machine door is closed. The accuracy result for organics is 70% and for inorganic 75%. This is due to the lack of variation in the dataset and changes in the physical condition of the object.
{"title":"Performance Analysis of Eigenface Method for Detecting Organic and Non-Organic Waste Type","authors":"A. Wijayanto, A. D. Ramadhani, Alon Jala Tirta Segara, Muhamad Azrino Gustalika","doi":"10.1109/COMNETSAT56033.2022.9994570","DOIUrl":"https://doi.org/10.1109/COMNETSAT56033.2022.9994570","url":null,"abstract":"Indonesia is one of the largest countries in Asia with a very dense population. According to data from The World Bank, human population indicators in Indonesia in 2019 increased by 270 milion people. This shows that population density in Indonesia is related to world problems related to waste generated from households. The household sector contributes as the top waste producer in Indonesia. Landfilling that occurs without any waste sorting, results in waste being more difficult to decompose and difficult to recycle. Therefore, to overcome this problem, it is necessary to increase public awareness about waste sorting and processing. We propose to create a device that can help sort organic and non-organic waste with Computer Vision-based Artificial Intelligence technology using the Eigenface method and the Internet of Things. Eigenface is a method that has a working principle by using XML files in performing face recognition. The result of testing in this system can run well, where the system detects organic objects the door of the chopping machine can open and if it detects nonorganic, the machine door is closed. The accuracy result for organics is 70% and for inorganic 75%. This is due to the lack of variation in the dataset and changes in the physical condition of the object.","PeriodicalId":221444,"journal":{"name":"2022 IEEE International Conference on Communication, Networks and Satellite (COMNETSAT)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122065028","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 : 2022-11-03DOI: 10.1109/COMNETSAT56033.2022.9994367
Tufail Akhmad Satrio, Wahyu Adi Prabowo, Trihastuti Yuniati
Video Steganography is one type that can use to hide secret messages. Video Steganography is a technique to hide messages in video media by inserting messages into one of the video frames. Cryptography can be combined with the Steganography technique to secure hidden messages in video files. This research was conducted to analyze the LSB (Least Significant Bit) steganography test combined with the Fernet cryptographic process. This study investigates the file insertion process, the test extraction process, the speed of system implementation, the visual attack, the Peak Signal Noise Ratio (PSNR) value, and the audio comparison between original video and video with embedded files. The results of this test indicate that the embedding process in the video is directly proportional to the results received. The larger the original video size, the larger the embedded video size will be.
{"title":"Hiding Document Format Files Using Video Steganography Techniques With Least Significant Bit Method","authors":"Tufail Akhmad Satrio, Wahyu Adi Prabowo, Trihastuti Yuniati","doi":"10.1109/COMNETSAT56033.2022.9994367","DOIUrl":"https://doi.org/10.1109/COMNETSAT56033.2022.9994367","url":null,"abstract":"Video Steganography is one type that can use to hide secret messages. Video Steganography is a technique to hide messages in video media by inserting messages into one of the video frames. Cryptography can be combined with the Steganography technique to secure hidden messages in video files. This research was conducted to analyze the LSB (Least Significant Bit) steganography test combined with the Fernet cryptographic process. This study investigates the file insertion process, the test extraction process, the speed of system implementation, the visual attack, the Peak Signal Noise Ratio (PSNR) value, and the audio comparison between original video and video with embedded files. The results of this test indicate that the embedding process in the video is directly proportional to the results received. The larger the original video size, the larger the embedded video size will be.","PeriodicalId":221444,"journal":{"name":"2022 IEEE International Conference on Communication, Networks and Satellite (COMNETSAT)","volume":"100 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124604692","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 : 2022-11-03DOI: 10.1109/COMNETSAT56033.2022.9994416
Cindy Ulan Purwanti, H. Mahmudah, Rahardita Widyatra Sudibyo, Ilham Dwi Pratama, Nur Menik Rohmawati
The transportation and industrial sectors are growing rapidly, with positive and negative consequences in the form of air pollution. According to the Global Alliance on Health and Pollution (GAHP), 3.4 million people died from air pollution-related causes worldwide in 2017, with 123,700 of them dying as a result of air pollution. As a result, a portable system was built in this study to monitor air quality and categorize it using the Artificial Neural Network (ANN), with the classification results displayed on an Android application. Air quality classification is accomplished by varying the hyperparameters of the Artificial Neural Network (ANN), such as the number of hidden layer neurons, dropout, and batch size, while utilizing the gas parameters PM10, PM2.5, NO2, SO2, CO, and 03. The classification results will also be classified into five categories: good, moderate, satisfactory, poor, and very poor air quality. The system is intended to give accurate results.
{"title":"Portable Air Quality Monitoring System in ANN Using Combination Hidden Layer Hyperparameters","authors":"Cindy Ulan Purwanti, H. Mahmudah, Rahardita Widyatra Sudibyo, Ilham Dwi Pratama, Nur Menik Rohmawati","doi":"10.1109/COMNETSAT56033.2022.9994416","DOIUrl":"https://doi.org/10.1109/COMNETSAT56033.2022.9994416","url":null,"abstract":"The transportation and industrial sectors are growing rapidly, with positive and negative consequences in the form of air pollution. According to the Global Alliance on Health and Pollution (GAHP), 3.4 million people died from air pollution-related causes worldwide in 2017, with 123,700 of them dying as a result of air pollution. As a result, a portable system was built in this study to monitor air quality and categorize it using the Artificial Neural Network (ANN), with the classification results displayed on an Android application. Air quality classification is accomplished by varying the hyperparameters of the Artificial Neural Network (ANN), such as the number of hidden layer neurons, dropout, and batch size, while utilizing the gas parameters PM10, PM2.5, NO2, SO2, CO, and 03. The classification results will also be classified into five categories: good, moderate, satisfactory, poor, and very poor air quality. The system is intended to give accurate results.","PeriodicalId":221444,"journal":{"name":"2022 IEEE International Conference on Communication, Networks and Satellite (COMNETSAT)","volume":"141 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122662626","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 : 2022-11-03DOI: 10.1109/COMNETSAT56033.2022.9994553
Alya Melati Putri, Kevin Wijaya, Owen Albert Salomo, Alexander Agung Santoso Gunawan, Anderies
There are so many health problems that affect humans. One of them is depression. Depression is a mental health disorder that would trigger suicidal tendencies if not treated carefully. People who are depressed tend to have less concentration and productivity. However, detecting depression is not easy due to the self-denial of some patients, and they keep depression untreated and undiagnosed. Some factors of untreated or undiagnosed depression are poor knowledge and recognition in many places the patient is shy to talk to a psychologist, and the stereotypes in public that say people who come to a psychologist are “insane.” Depression symptoms of a user can be shown in social media posts, and these symptoms can be detected using a machine learning algorithm. These Machine learning algorithms can be an alternative for detecting depression or as a supporting document for psychologist diagnoses. The algorithm obtains accurate that varies depending on the dataset. For this reason, we conducted a systematic literature review to find out which machine learning has the best accuracy in detecting depression. We also provide information about stable algorithms to detect a given dataset and the popular dataset used in previous studies based on the most frequent text that is easy to test. In conclusion, the greatest accuracy is obtained from Logistic Regression with an accuracy value of 99.80%. Stable algorithms are obtained by LR and SVM because the machine learning method obtains values above 70%. The most popular dataset used in previous studies is the Twitter dataset.
{"title":"A Review Paper: Accuracy of Machine Learning for Depression Detection in Social Media","authors":"Alya Melati Putri, Kevin Wijaya, Owen Albert Salomo, Alexander Agung Santoso Gunawan, Anderies","doi":"10.1109/COMNETSAT56033.2022.9994553","DOIUrl":"https://doi.org/10.1109/COMNETSAT56033.2022.9994553","url":null,"abstract":"There are so many health problems that affect humans. One of them is depression. Depression is a mental health disorder that would trigger suicidal tendencies if not treated carefully. People who are depressed tend to have less concentration and productivity. However, detecting depression is not easy due to the self-denial of some patients, and they keep depression untreated and undiagnosed. Some factors of untreated or undiagnosed depression are poor knowledge and recognition in many places the patient is shy to talk to a psychologist, and the stereotypes in public that say people who come to a psychologist are “insane.” Depression symptoms of a user can be shown in social media posts, and these symptoms can be detected using a machine learning algorithm. These Machine learning algorithms can be an alternative for detecting depression or as a supporting document for psychologist diagnoses. The algorithm obtains accurate that varies depending on the dataset. For this reason, we conducted a systematic literature review to find out which machine learning has the best accuracy in detecting depression. We also provide information about stable algorithms to detect a given dataset and the popular dataset used in previous studies based on the most frequent text that is easy to test. In conclusion, the greatest accuracy is obtained from Logistic Regression with an accuracy value of 99.80%. Stable algorithms are obtained by LR and SVM because the machine learning method obtains values above 70%. The most popular dataset used in previous studies is the Twitter dataset.","PeriodicalId":221444,"journal":{"name":"2022 IEEE International Conference on Communication, Networks and Satellite (COMNETSAT)","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131431582","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}
This research aims to produce spectrum analyzer instructional media that will be implemented in Radio Transmission Operations and Maintenance subjects. Research development adopts the ADDIE model according to William W. Lee & Diana L. Owens, which includes: analysis, design, development and implementation, and evaluation. In the development and implementation steps, there are activities in the form of expert validation of instructional media based on Android applications. Theory experts, media experts, and users were necessary for the evaluation. Furthermore, the spectrum analyzer instructional media was tested on learning activity in Telecommunication Transmission Engineering Skill Competency at SMK Telkom Jakarta. The gain from the range of questionnaire values is converted to determine the eligibility category. The results showed that teaching and learning activities in operating and maintaining radio transmission subjects require instructional media for practical activity learning in the form of software on an Android-based smartphone consisting of theory, quiz, and job sheets; and a manual guide of media. The research results show that the value of the spectrum analyzer instructional media quality, in general, is 3.46, which is interpreted in the excellent category. Because the Android-based spectrum analyzer instructional media obtained an excellent feasibility value, this media reliable to be implemented in classroom learning.
本研究旨在制作频谱分析仪教学媒体，以供无线电传输操作与维修科目使用。研究开发采用William W. Lee和Diana L. Owens的ADDIE模型，包括:分析、设计、开发和实施、评估。在开发和实施步骤中，有基于Android应用的教学媒体专家验证形式的活动。评价需要理论专家、媒体专家和用户。此外，本研究亦以频谱分析仪教学媒体为测试对象，对SMK电信公司的电信传输工程技能胜任力学习活动进行测试。从问卷值的范围中获得的收益被转换为确定资格类别。研究结果表明，无线电传输学科的操作与维护教学活动需要教学媒介进行实践活动学习，教学媒介的形式为基于android的智能手机上的软件，包括理论、测验和作业单;以及媒体手册指南。研究结果表明，频谱分析仪教学媒体质量的总体值为3.46，处于优秀的范畴。由于基于android的频谱分析仪教学媒体获得了极好的可行性价值，该媒体可可靠地在课堂学习中实施。
{"title":"Design of Spectrum Analyzer Android-based Instructional Media for Vocational High School Student","authors":"Assa Kesthy Rohana, Rohani Cristyn, Adythia Esha Nugraha, Kukuh Harsanto, Garrison Lee","doi":"10.1109/COMNETSAT56033.2022.9994494","DOIUrl":"https://doi.org/10.1109/COMNETSAT56033.2022.9994494","url":null,"abstract":"This research aims to produce spectrum analyzer instructional media that will be implemented in Radio Transmission Operations and Maintenance subjects. Research development adopts the ADDIE model according to William W. Lee & Diana L. Owens, which includes: analysis, design, development and implementation, and evaluation. In the development and implementation steps, there are activities in the form of expert validation of instructional media based on Android applications. Theory experts, media experts, and users were necessary for the evaluation. Furthermore, the spectrum analyzer instructional media was tested on learning activity in Telecommunication Transmission Engineering Skill Competency at SMK Telkom Jakarta. The gain from the range of questionnaire values is converted to determine the eligibility category. The results showed that teaching and learning activities in operating and maintaining radio transmission subjects require instructional media for practical activity learning in the form of software on an Android-based smartphone consisting of theory, quiz, and job sheets; and a manual guide of media. The research results show that the value of the spectrum analyzer instructional media quality, in general, is 3.46, which is interpreted in the excellent category. Because the Android-based spectrum analyzer instructional media obtained an excellent feasibility value, this media reliable to be implemented in classroom learning.","PeriodicalId":221444,"journal":{"name":"2022 IEEE International Conference on Communication, Networks and Satellite (COMNETSAT)","volume":"100 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127504417","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 : 2022-11-03DOI: 10.1109/COMNETSAT56033.2022.9994492
Muhamad Azrino Gustalika, Sudianto Sudianto, D. C. Fransisca, Fahrudin Mukti Wibowo, M. Afandi, Reni Dyah Wahyuningrum
One of the components that support the smart city program is the existence of a smart environment. A smart environment is a form of environmental management by paying attention to the environment in future city development. The current problem is that smart cities in Indonesia are not optimal, especially regarding flood mitigation handling sources from rivers and river water quality. Floods in urban areas often cause material losses and cause fatalities, especially with the increasing significance of the impact of climate change, which is difficult to predict. Thus, there is a need for sustainable Internet of Things (IoT)-based river monitoring to monitor river water levels and quality. This research aims to apply the Internet of Things for flood mitigation and environmental sustainability. The architecture used is using Antares as a cloud media. The results obtained by monitoring the river showed that the influence of the provider in sending sensor data is influenced by the availability of network service facilities in locations. In addition, the fastest data transmission lasts five seconds. At the same time, the suitability of data transmission occurs in under two minutes.
{"title":"Implementation of the Internet of Things for Flood Mitigation and Environmental Sustainability","authors":"Muhamad Azrino Gustalika, Sudianto Sudianto, D. C. Fransisca, Fahrudin Mukti Wibowo, M. Afandi, Reni Dyah Wahyuningrum","doi":"10.1109/COMNETSAT56033.2022.9994492","DOIUrl":"https://doi.org/10.1109/COMNETSAT56033.2022.9994492","url":null,"abstract":"One of the components that support the smart city program is the existence of a smart environment. A smart environment is a form of environmental management by paying attention to the environment in future city development. The current problem is that smart cities in Indonesia are not optimal, especially regarding flood mitigation handling sources from rivers and river water quality. Floods in urban areas often cause material losses and cause fatalities, especially with the increasing significance of the impact of climate change, which is difficult to predict. Thus, there is a need for sustainable Internet of Things (IoT)-based river monitoring to monitor river water levels and quality. This research aims to apply the Internet of Things for flood mitigation and environmental sustainability. The architecture used is using Antares as a cloud media. The results obtained by monitoring the river showed that the influence of the provider in sending sensor data is influenced by the availability of network service facilities in locations. In addition, the fastest data transmission lasts five seconds. At the same time, the suitability of data transmission occurs in under two minutes.","PeriodicalId":221444,"journal":{"name":"2022 IEEE International Conference on Communication, Networks and Satellite (COMNETSAT)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123581068","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 : 2022-11-03DOI: 10.1109/COMNETSAT56033.2022.9994527
H. H. Triharminto, Lenny Iryani, Ridwan
This paper focuses on developing DSS (Decision Support System) that is implemented in command control for military operations. The DSS decides on the military operation field primarily to obtain air sovereignty. The system employs two algorithms, i.e., the decision tree algorithm and the fuzzy algorithm. The decision tree algorithm solves the whole branch of the decision-making process, and fuzzy algorithm is to cope with the partial decision-making in a particular attribute for continued input. The input of the decision tree is aircraft, divided into four attributes, i.e., altitude, velocity, and position of the enemy's aircraft. The altitude and velocity attributes determine a decision using fuzzy logic with a specific membership function that reflects the possible condition. The membership function consists of two or three states. The proposed mathematical modeling calculates the total friendly aircraft fighter to chase the enemy's aircraft. The outputs of the decision-making process are air force base, squadron, type, and total aircraft. The experimental setup is conducted in different scenarios to examine the proposed method. The results show that the application of decision tree and fuzzy algorithm can be used in military operations as DSS.
{"title":"Integration of Decision Tree-Fuzzy Algorithm for Decision Support System in Air Force Operation","authors":"H. H. Triharminto, Lenny Iryani, Ridwan","doi":"10.1109/COMNETSAT56033.2022.9994527","DOIUrl":"https://doi.org/10.1109/COMNETSAT56033.2022.9994527","url":null,"abstract":"This paper focuses on developing DSS (Decision Support System) that is implemented in command control for military operations. The DSS decides on the military operation field primarily to obtain air sovereignty. The system employs two algorithms, i.e., the decision tree algorithm and the fuzzy algorithm. The decision tree algorithm solves the whole branch of the decision-making process, and fuzzy algorithm is to cope with the partial decision-making in a particular attribute for continued input. The input of the decision tree is aircraft, divided into four attributes, i.e., altitude, velocity, and position of the enemy's aircraft. The altitude and velocity attributes determine a decision using fuzzy logic with a specific membership function that reflects the possible condition. The membership function consists of two or three states. The proposed mathematical modeling calculates the total friendly aircraft fighter to chase the enemy's aircraft. The outputs of the decision-making process are air force base, squadron, type, and total aircraft. The experimental setup is conducted in different scenarios to examine the proposed method. The results show that the application of decision tree and fuzzy algorithm can be used in military operations as DSS.","PeriodicalId":221444,"journal":{"name":"2022 IEEE International Conference on Communication, Networks and Satellite (COMNETSAT)","volume":"106 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124220124","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 : 2022-11-03DOI: 10.1109/COMNETSAT56033.2022.9994419
Elsa Lolita Anggraini, G. Hendrantoro, T. Suryani
The use of High Frequency (HF) channels equipped with data-link protocols for the process of sending data has proven to be useful for communication systems in remote areas. However, the HF channel has propagation conditions that tend to be unstable, therefore a Multiple Input Multiple Output (MIMO) system is needed to be able to increase the capacity of the HF channel by utilizing the wave bending in the ionosphere layer that forms two wave modes, namely Ordinary (O) waves and Extraordinary (X) waves. The two waves have orthogonal polarizations, making them suitable to be applied to MIMO systems. In this paper, we utilize a MIMO 2×2 system to maximize the ability of the HF channel and use the third generation of Automatic Link Establishment (ALE) data-link protocol which is equipped with double layer error coding in the form of Cyclic Redundancy Check (CRC) and Convolutional Code (CC). The MIMO 2×2 system will be compared with the Single Input Single Output system to see the increase in channel capacity. From these results, the addition of a MIMO 2×2 system and the third generation of ALE proved to increase the channel capacity and protect the data from errors due to disturbances during the transmission process through the HF channel.
{"title":"Error Rate Performance of Equatorial HF Skywave MIMO Packet Radio","authors":"Elsa Lolita Anggraini, G. Hendrantoro, T. Suryani","doi":"10.1109/COMNETSAT56033.2022.9994419","DOIUrl":"https://doi.org/10.1109/COMNETSAT56033.2022.9994419","url":null,"abstract":"The use of High Frequency (HF) channels equipped with data-link protocols for the process of sending data has proven to be useful for communication systems in remote areas. However, the HF channel has propagation conditions that tend to be unstable, therefore a Multiple Input Multiple Output (MIMO) system is needed to be able to increase the capacity of the HF channel by utilizing the wave bending in the ionosphere layer that forms two wave modes, namely Ordinary (O) waves and Extraordinary (X) waves. The two waves have orthogonal polarizations, making them suitable to be applied to MIMO systems. In this paper, we utilize a MIMO 2×2 system to maximize the ability of the HF channel and use the third generation of Automatic Link Establishment (ALE) data-link protocol which is equipped with double layer error coding in the form of Cyclic Redundancy Check (CRC) and Convolutional Code (CC). The MIMO 2×2 system will be compared with the Single Input Single Output system to see the increase in channel capacity. From these results, the addition of a MIMO 2×2 system and the third generation of ALE proved to increase the channel capacity and protect the data from errors due to disturbances during the transmission process through the HF channel.","PeriodicalId":221444,"journal":{"name":"2022 IEEE International Conference on Communication, Networks and Satellite (COMNETSAT)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123433915","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 : 2022-11-03DOI: 10.1109/COMNETSAT56033.2022.9994427
Minh-Tri Vo, Trieu-Vu Tran, Duc-The Pham, Trong-Hop Do
Flight delay is an unexpected incident in the field of aviation in particular and transportation in general. Predicting the possibility or delay of flights plays a vital role in proactively arranging a time for the airline as well as increasing the reputation of the airline among users. This study presents an implementation of a real-time flight delay prediction system. To ensure the practicality, the entire system is built using big data technology. Apache Kafka is used to stream the flight data to trained machine learning models integrated inside Apache Spark to output real-time prediction results, which will be displayed through a dash-board and stored in Cassandra database simultaneously. Consequently, the system can process a huge amount of input data and produce prediction results in real-time. Index Terms— Flight Delay Prediction, Machine Learning, Spark, Kafka, Streaming, Cassandra.
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