Pub Date : 2022-07-06DOI: 10.1109/ICoDSA55874.2022.9862938
R. V. Priyanka Hafidz, Amelia Setiawan
With digital developments that continue to occur, it can affect several parts of the institution or company by changing the system from manual to online. With this progress, government institutions have also begun to implement a computerized system and payroll is one of the sections that is affected by it. For example, payroll which is usually given in person, can now be sent via bank transfer. This study was conducted to analyze the quality of information, system quality, and information system security on user satisfaction in the payroll section of a government institution, namely, the Center for Financial Transaction Reports and Analysis. This study will use data that has been processed in two ways. The first is one of the functions of Microsoft Excel, namely data analysis and the second uses SEM PLS analysis to test the three pre-determined hypotheses. The results of hypothesis testing indicate that the quality of information and information system security affect user satisfaction significantly, while system quality does not substantially affect user satisfaction. The limitation of this research is the limited number of employees in the payroll section of the Financial Transaction Reports and Analysis Center. Suggestions for further research are to use a more general section that has more employees.
{"title":"What Affects User Satisfaction of Payroll Information Systems?","authors":"R. V. Priyanka Hafidz, Amelia Setiawan","doi":"10.1109/ICoDSA55874.2022.9862938","DOIUrl":"https://doi.org/10.1109/ICoDSA55874.2022.9862938","url":null,"abstract":"With digital developments that continue to occur, it can affect several parts of the institution or company by changing the system from manual to online. With this progress, government institutions have also begun to implement a computerized system and payroll is one of the sections that is affected by it. For example, payroll which is usually given in person, can now be sent via bank transfer. This study was conducted to analyze the quality of information, system quality, and information system security on user satisfaction in the payroll section of a government institution, namely, the Center for Financial Transaction Reports and Analysis. This study will use data that has been processed in two ways. The first is one of the functions of Microsoft Excel, namely data analysis and the second uses SEM PLS analysis to test the three pre-determined hypotheses. The results of hypothesis testing indicate that the quality of information and information system security affect user satisfaction significantly, while system quality does not substantially affect user satisfaction. The limitation of this research is the limited number of employees in the payroll section of the Financial Transaction Reports and Analysis Center. Suggestions for further research are to use a more general section that has more employees.","PeriodicalId":339135,"journal":{"name":"2022 International Conference on Data Science and Its Applications (ICoDSA)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115539285","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 : 2022-07-06DOI: 10.1109/ICoDSA55874.2022.9862849
Nabila Ammara Diliana, Indrawati
Indonesia reaches more than 200 million internet users in 2022. Considering a large number of internet users in Indonesia, there are many digital-based businesses and one of them is e-commerce platforms. Understanding user in social media plays an important role in determining a suitable digital marketing strategy, one of which is by knowing the user interaction or User Generated Content in social media. The research related to User Generated Content is commonly used as one the data to analyze marketing strategy in social media. One of the previous research about User Generated Content as data to analyze marketing strategy for an educational platform and tourism platform. Both the previous study used User Generated Content and Social Network Analysis to analyze the data. Therefore, this study aims to form a network using data sources based on tweets on social media that models Lazada network interaction in Indonesia. We use User Generated Content as a source to find the influencer's and communities' representations of these are the two most important in deciding on a digital marketing strategy, especially for their social media. The results of this study identify the influencers and communities of the social network. The research will be worthwhile for business opportunities, policymakers, and Lazada.
{"title":"Identification of Influencers and Community of Lazada Using Social Network Analysis","authors":"Nabila Ammara Diliana, Indrawati","doi":"10.1109/ICoDSA55874.2022.9862849","DOIUrl":"https://doi.org/10.1109/ICoDSA55874.2022.9862849","url":null,"abstract":"Indonesia reaches more than 200 million internet users in 2022. Considering a large number of internet users in Indonesia, there are many digital-based businesses and one of them is e-commerce platforms. Understanding user in social media plays an important role in determining a suitable digital marketing strategy, one of which is by knowing the user interaction or User Generated Content in social media. The research related to User Generated Content is commonly used as one the data to analyze marketing strategy in social media. One of the previous research about User Generated Content as data to analyze marketing strategy for an educational platform and tourism platform. Both the previous study used User Generated Content and Social Network Analysis to analyze the data. Therefore, this study aims to form a network using data sources based on tweets on social media that models Lazada network interaction in Indonesia. We use User Generated Content as a source to find the influencer's and communities' representations of these are the two most important in deciding on a digital marketing strategy, especially for their social media. The results of this study identify the influencers and communities of the social network. The research will be worthwhile for business opportunities, policymakers, and Lazada.","PeriodicalId":339135,"journal":{"name":"2022 International Conference on Data Science and Its Applications (ICoDSA)","volume":"abs/1402.4986 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128089975","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 : 2022-07-06DOI: 10.1109/ICoDSA55874.2022.9862848
Lulus Wahyu Prasetya Adi, Satria Mandala, Y. Nugraha
Distributed Denial-of-Service (DDoS) is an attack launched over a computer network to make the server unable to provide services to users. DDoS is also effectively used to stop services on Internet of Things systems based on the message Queuing Telemetry Transport (MQTT) protocol. In the system, attackers usually attack brokers who are used to manage data traffic between the issuer and the customer. Several research projects have been undertaken to detect DDoS in the Internet of Things (IoT) using machine learning. However, existing research projects still generally have low detection accuracy in predicting DDoS. This study provides a solution to the above problems by proposing the development of a machine learning model based on Neural Network (NN) to detect DDoS. Furthermore, this study also compared the results of NN predictions with K-Nearest Neighbor (KNN). The methods used in this study are as follows: 1. Conducting literature studies. 2. Develop both machine learning models. 3. Conduct analysis. Rigorous experiments have been carried out using dataset derived from other research and dataset generated through DDOS simulations in IoT environments. By using the dataset generated through simulation, the results obtained showed that the accuracy of NN is better than KNN, which is 99.99% and 99.82%, respectively.
{"title":"DDoS Attack Detection System using Neural Network on Internet of Things","authors":"Lulus Wahyu Prasetya Adi, Satria Mandala, Y. Nugraha","doi":"10.1109/ICoDSA55874.2022.9862848","DOIUrl":"https://doi.org/10.1109/ICoDSA55874.2022.9862848","url":null,"abstract":"Distributed Denial-of-Service (DDoS) is an attack launched over a computer network to make the server unable to provide services to users. DDoS is also effectively used to stop services on Internet of Things systems based on the message Queuing Telemetry Transport (MQTT) protocol. In the system, attackers usually attack brokers who are used to manage data traffic between the issuer and the customer. Several research projects have been undertaken to detect DDoS in the Internet of Things (IoT) using machine learning. However, existing research projects still generally have low detection accuracy in predicting DDoS. This study provides a solution to the above problems by proposing the development of a machine learning model based on Neural Network (NN) to detect DDoS. Furthermore, this study also compared the results of NN predictions with K-Nearest Neighbor (KNN). The methods used in this study are as follows: 1. Conducting literature studies. 2. Develop both machine learning models. 3. Conduct analysis. Rigorous experiments have been carried out using dataset derived from other research and dataset generated through DDOS simulations in IoT environments. By using the dataset generated through simulation, the results obtained showed that the accuracy of NN is better than KNN, which is 99.99% and 99.82%, respectively.","PeriodicalId":339135,"journal":{"name":"2022 International Conference on Data Science and Its Applications (ICoDSA)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127862285","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 : 2022-07-06DOI: 10.1109/ICoDSA55874.2022.9862899
Imaduddin Muhammad Fadhil, Y. Sibaroni
Twitter is a popular social media platform that gives users the ability to send text messages with a maximum length of 280 characters which causes a lot of use of word variations that cause vocabulary writing errors and nowadays more and more tweets are spread and because of the very rapid spread it causes information overload. From the problems raised, it is necessary to be able to recognize words that have errors in writing and categorize tweets into certain categories. Therefore, this study aims to build a topic classification system on tweets that can study writing errors in a word and feature expansion using pretrained from FastText can be used to recognize writing errors in a word because the process of building word vectors from FastText can learn the internal structure of a word that will be used in the Support Vector Machine. The best results from this study get an accuracy of 76.88% with the application of feature expansion on top-1 but the application of feature expansion using pretrained classification Support Vector Machine.
{"title":"Topic Classification in Indonesian-language Tweets using Fast-Text Feature Expansion with Support Vector Machine (SVM)","authors":"Imaduddin Muhammad Fadhil, Y. Sibaroni","doi":"10.1109/ICoDSA55874.2022.9862899","DOIUrl":"https://doi.org/10.1109/ICoDSA55874.2022.9862899","url":null,"abstract":"Twitter is a popular social media platform that gives users the ability to send text messages with a maximum length of 280 characters which causes a lot of use of word variations that cause vocabulary writing errors and nowadays more and more tweets are spread and because of the very rapid spread it causes information overload. From the problems raised, it is necessary to be able to recognize words that have errors in writing and categorize tweets into certain categories. Therefore, this study aims to build a topic classification system on tweets that can study writing errors in a word and feature expansion using pretrained from FastText can be used to recognize writing errors in a word because the process of building word vectors from FastText can learn the internal structure of a word that will be used in the Support Vector Machine. The best results from this study get an accuracy of 76.88% with the application of feature expansion on top-1 but the application of feature expansion using pretrained classification Support Vector Machine.","PeriodicalId":339135,"journal":{"name":"2022 International Conference on Data Science and Its Applications (ICoDSA)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131663076","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 : 2022-07-06DOI: 10.1109/icodsa55874.2022.9862860
{"title":"ICoDSA 2022 Homepage","authors":"","doi":"10.1109/icodsa55874.2022.9862860","DOIUrl":"https://doi.org/10.1109/icodsa55874.2022.9862860","url":null,"abstract":"","PeriodicalId":339135,"journal":{"name":"2022 International Conference on Data Science and Its Applications (ICoDSA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128514653","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 : 2022-07-06DOI: 10.1109/ICoDSA55874.2022.9862932
H. Nuha, Tafta Zani, Muhammad Fadhly Ridha, Adiwijaya
Access to digital communications in remote areas requires a mechanism to increase the robustness of the transmitted data. Many areas in Indonesia still have difficulty accessing the Internet. This is because the location of the settlement is remote from the signal transmitter Convolutional codes are a technique to improve the reliability of data transmission. This article contains a simulation process of Convolutional Code from an application that we developed using Java. The basic difference between block codes and convolution codes in designing and evaluating is that Block codes are based on algebraic techniques or a combination whereas ConvCode based on construction techniques. Some of the excellent features of this application are demo encoding, modulation, noise generation on white Gaussian noise channels, and decoding using the Viterbi algorithm. The error correcting code process begins by checking the bit similarity (hamming distance) in the code word with the trellis diagram which will produce a path with weights depending on the hamming distance. With the Viterbi Algorithm, we will decode the codeword into the initial code by finding the highest probability (Maximum Likelihood) based on the Hamming distance from each state. Experiments show that the application successfully demonstrates the system's reliability to recover information signals damaged by noise.
{"title":"Binary Data Correction Simulation Using Convolutional Code on Additive White Gaussian Noise Channel","authors":"H. Nuha, Tafta Zani, Muhammad Fadhly Ridha, Adiwijaya","doi":"10.1109/ICoDSA55874.2022.9862932","DOIUrl":"https://doi.org/10.1109/ICoDSA55874.2022.9862932","url":null,"abstract":"Access to digital communications in remote areas requires a mechanism to increase the robustness of the transmitted data. Many areas in Indonesia still have difficulty accessing the Internet. This is because the location of the settlement is remote from the signal transmitter Convolutional codes are a technique to improve the reliability of data transmission. This article contains a simulation process of Convolutional Code from an application that we developed using Java. The basic difference between block codes and convolution codes in designing and evaluating is that Block codes are based on algebraic techniques or a combination whereas ConvCode based on construction techniques. Some of the excellent features of this application are demo encoding, modulation, noise generation on white Gaussian noise channels, and decoding using the Viterbi algorithm. The error correcting code process begins by checking the bit similarity (hamming distance) in the code word with the trellis diagram which will produce a path with weights depending on the hamming distance. With the Viterbi Algorithm, we will decode the codeword into the initial code by finding the highest probability (Maximum Likelihood) based on the Hamming distance from each state. Experiments show that the application successfully demonstrates the system's reliability to recover information signals damaged by noise.","PeriodicalId":339135,"journal":{"name":"2022 International Conference on Data Science and Its Applications (ICoDSA)","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116459894","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 : 2022-07-06DOI: 10.1109/ICoDSA55874.2022.9862850
Muhammad Amien Ibrahim, Noviyanti Tri Maretta Sagala, S. Arifin, R. Nariswari, N. Murnaka, P. W. Prasetyo
Social media is an effective tool for connecting with people and distributing information. However, many people often use social media to spread hate speech and abusive languages. In contrast to hate speech, abusive languages are frequently used as jokes with no purpose of offending individuals or groups, even though they may contain profanities. As a result, the distinction between hate speech and abusive language is often blurred. In many cases, individuals who spread hate speech may be prosecuted as it has legal implications. Previous research has focused on binary classification of hate speech and normal tweets. This study aims to classify hate speech, abusive language, and normal messages on Indonesian Twitter. Several machine learning models, such as logistic regression and BERT models, are utilized to accomplish text classification tasks. The model's performance is assessed using the F1-Score evaluation metric. The results show that BERT models outperform other models in terms of F1-Score, with the BERT-indobenchmark model, which was pretrained on social media text data, achieving the highest F1-Score of 85.59. This also demonstrates that pretraining the BERT model using social media data improves the classification model significantly. Developing such classification model that can distinguish between hate speech and abusive language would help individuals in preventing the spread of hate speech that has legal implications.
{"title":"Separating Hate Speech from Abusive Language on Indonesian Twitter","authors":"Muhammad Amien Ibrahim, Noviyanti Tri Maretta Sagala, S. Arifin, R. Nariswari, N. Murnaka, P. W. Prasetyo","doi":"10.1109/ICoDSA55874.2022.9862850","DOIUrl":"https://doi.org/10.1109/ICoDSA55874.2022.9862850","url":null,"abstract":"Social media is an effective tool for connecting with people and distributing information. However, many people often use social media to spread hate speech and abusive languages. In contrast to hate speech, abusive languages are frequently used as jokes with no purpose of offending individuals or groups, even though they may contain profanities. As a result, the distinction between hate speech and abusive language is often blurred. In many cases, individuals who spread hate speech may be prosecuted as it has legal implications. Previous research has focused on binary classification of hate speech and normal tweets. This study aims to classify hate speech, abusive language, and normal messages on Indonesian Twitter. Several machine learning models, such as logistic regression and BERT models, are utilized to accomplish text classification tasks. The model's performance is assessed using the F1-Score evaluation metric. The results show that BERT models outperform other models in terms of F1-Score, with the BERT-indobenchmark model, which was pretrained on social media text data, achieving the highest F1-Score of 85.59. This also demonstrates that pretraining the BERT model using social media data improves the classification model significantly. Developing such classification model that can distinguish between hate speech and abusive language would help individuals in preventing the spread of hate speech that has legal implications.","PeriodicalId":339135,"journal":{"name":"2022 International Conference on Data Science and Its Applications (ICoDSA)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124111137","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 : 2022-07-06DOI: 10.1109/ICoDSA55874.2022.9862886
Komang Uning Sari Devi, Lya Hulliyyatus Suadaa
Search engine results usually show a list of retrieved document titles with document summaries to give a better preview of the retrieved documents, called snippet. This research proposes extractive text summarization models to generate a snippet. A new dataset is constructed for extractive text summarization tasks using Indonesian thesis documents, in which the targeted summaries were created manually by selecting important sentences. In generating snippets, we use Lead-3 and Textrank as baselines and propose fine-tuning Sentence Transformers (SBERT). Based on the evaluation results, SBERT generated a better summary than other baselines with 0.545 Rouge-1, 0.433 Rouge-2, and 0.474 Rouge-L.
{"title":"Extractive Text Summarization for Snippet Generation on Indonesian Search Engine using Sentence Transformers","authors":"Komang Uning Sari Devi, Lya Hulliyyatus Suadaa","doi":"10.1109/ICoDSA55874.2022.9862886","DOIUrl":"https://doi.org/10.1109/ICoDSA55874.2022.9862886","url":null,"abstract":"Search engine results usually show a list of retrieved document titles with document summaries to give a better preview of the retrieved documents, called snippet. This research proposes extractive text summarization models to generate a snippet. A new dataset is constructed for extractive text summarization tasks using Indonesian thesis documents, in which the targeted summaries were created manually by selecting important sentences. In generating snippets, we use Lead-3 and Textrank as baselines and propose fine-tuning Sentence Transformers (SBERT). Based on the evaluation results, SBERT generated a better summary than other baselines with 0.545 Rouge-1, 0.433 Rouge-2, and 0.474 Rouge-L.","PeriodicalId":339135,"journal":{"name":"2022 International Conference on Data Science and Its Applications (ICoDSA)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124860561","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 : 2022-07-06DOI: 10.1109/icodsa55874.2022.9862893
{"title":"ICoDSA 2022 Author Index","authors":"","doi":"10.1109/icodsa55874.2022.9862893","DOIUrl":"https://doi.org/10.1109/icodsa55874.2022.9862893","url":null,"abstract":"","PeriodicalId":339135,"journal":{"name":"2022 International Conference on Data Science and Its Applications (ICoDSA)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125053051","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 : 2022-07-06DOI: 10.1109/ICoDSA55874.2022.9862889
Yoga Samudra, T. Ahmad
To solve daily problems, sometimes information needs to be accessible digitally or online by authorized parties. However, accessing some information may need secure data transmission. This reliable process can be achieved by using data security techniques to protect the transmitted information, such as cryptography or steganography. As part of data hiding methods, steganography works by hiding private information inside other public data, mainly multimedia data, in plain sight. Nevertheless, there are some main concerns regarding previous research, such as the similarity level and hiding capacity of the respective audio file. In this research, private information is embedded into an audio file as cover data so it can be retrieved later by the receiver without any suspicion of other parties. Therefore, this research focuses on providing flexibility in the embedding capacity of cover data by using an interpolation-based approach as the core sampling technique and then enhancing the similarity level by adding bit threshold values to evenly distribute hiding capacity on each sample as even as possible. Experimental results show that this proposed method can achieve an average similarity level of 103.56 dB on 100 Kb private data to an average of 93.95 dB on 300 Kb private data. It is better than some existing studies.
{"title":"Quality Control on Interpolation-based Reversible Audio Data Hiding using Bit Threshold","authors":"Yoga Samudra, T. Ahmad","doi":"10.1109/ICoDSA55874.2022.9862889","DOIUrl":"https://doi.org/10.1109/ICoDSA55874.2022.9862889","url":null,"abstract":"To solve daily problems, sometimes information needs to be accessible digitally or online by authorized parties. However, accessing some information may need secure data transmission. This reliable process can be achieved by using data security techniques to protect the transmitted information, such as cryptography or steganography. As part of data hiding methods, steganography works by hiding private information inside other public data, mainly multimedia data, in plain sight. Nevertheless, there are some main concerns regarding previous research, such as the similarity level and hiding capacity of the respective audio file. In this research, private information is embedded into an audio file as cover data so it can be retrieved later by the receiver without any suspicion of other parties. Therefore, this research focuses on providing flexibility in the embedding capacity of cover data by using an interpolation-based approach as the core sampling technique and then enhancing the similarity level by adding bit threshold values to evenly distribute hiding capacity on each sample as even as possible. Experimental results show that this proposed method can achieve an average similarity level of 103.56 dB on 100 Kb private data to an average of 93.95 dB on 300 Kb private data. It is better than some existing studies.","PeriodicalId":339135,"journal":{"name":"2022 International Conference on Data Science and Its Applications (ICoDSA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129554267","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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