Pub Date : 2021-11-03DOI: 10.1109/ICIC54025.2021.9632943
Muhammad Haris Al Farisi, Arini, Luh Kesuma Wardhani, Iik Muhamad Malik Matin, Yusuf Durachman, R. Adelina, Faisal Nurdin
Equity and quality of education must be guaranteed in the national education system. To that end, the government issued a new student admission policy with a zoning system. To ensure the implementation of new student admissions (PPDB), the zoning system needs to be evaluated for community responses. However, evaluation using conventional techniques still has limitations. Sentiment analysis is a new approach to explore computing-based opinion. In this paper, we conduct a sentiment analysis of the new student admissions system (PPDB) zoning policy. We identify two types of sentiment namely positive and negative. We used the Levenshtein Distance algorithm for word normalization and clustered using the K-Means algorithm. The results of clustering are classified based on the confusion matrix. The data sources that we use are taken from 200 comments on Facebook and Youtube channels. The results obtained from public sentiment towards this policy are more negative sentiments than positive sentiments. The results obtained from the accuracy of the K-Means algorithm are 84%, while the combination of the k-means algorithm with Levenshtein distance reaches 90% accuracy.
{"title":"K-Means Algorithm and Levenshtein Distance Algorithm for Sentiment Analysis of School Zonation System Policy","authors":"Muhammad Haris Al Farisi, Arini, Luh Kesuma Wardhani, Iik Muhamad Malik Matin, Yusuf Durachman, R. Adelina, Faisal Nurdin","doi":"10.1109/ICIC54025.2021.9632943","DOIUrl":"https://doi.org/10.1109/ICIC54025.2021.9632943","url":null,"abstract":"Equity and quality of education must be guaranteed in the national education system. To that end, the government issued a new student admission policy with a zoning system. To ensure the implementation of new student admissions (PPDB), the zoning system needs to be evaluated for community responses. However, evaluation using conventional techniques still has limitations. Sentiment analysis is a new approach to explore computing-based opinion. In this paper, we conduct a sentiment analysis of the new student admissions system (PPDB) zoning policy. We identify two types of sentiment namely positive and negative. We used the Levenshtein Distance algorithm for word normalization and clustered using the K-Means algorithm. The results of clustering are classified based on the confusion matrix. The data sources that we use are taken from 200 comments on Facebook and Youtube channels. The results obtained from public sentiment towards this policy are more negative sentiments than positive sentiments. The results obtained from the accuracy of the K-Means algorithm are 84%, while the combination of the k-means algorithm with Levenshtein distance reaches 90% accuracy.","PeriodicalId":189541,"journal":{"name":"2021 Sixth International Conference on Informatics and Computing (ICIC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122876340","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 objective is to present assessment and management to ensure the confidentiality of documents that are permanent, transparent, and sustainable for students by utilizing Blockchain technology and can be accessed directly by students. Maintained confidentiality of documents allows students to personalize the value of education and produce permanent documentation in formal and informal learning and determine the lifelong learning path of each individual. The method in this research using the Literature Review Study to learn problems from research with the theme of blockchain-based student achievement credentials in the world of education. The implication of this research is to provide an analysis of the use of blockchain micro-credentials on student achievement records in universities using the Literature Review Study Method. The results show that blockchain guarantees a foundation for student achievement record credentials that are durable, secure, and offer solid administration in managing student credentials. They can control them on an ongoing basis. Universities are also given the advantage of increasing student file security and reducing administrative and bureaucratic costs. Further research can be carried out by combining blockchain with AI innovation and private key and decentralized cloud to address issues concerning productivity, flexibility, capacity, and tighter security. With future updates, it is hoped that the use of blockchain in student achievement credentials can become more mature and can be applied to 1000 universities in Indonesia.
{"title":"Sustainable Learning Micro-Credential using Blockchain for Student Achievement Records","authors":"Bambang Mardisentosa, U. Rahardja, Kenita Zelina, Fitra Putri Oganda, Marviola Hardini","doi":"10.1109/ICIC54025.2021.9632913","DOIUrl":"https://doi.org/10.1109/ICIC54025.2021.9632913","url":null,"abstract":"This research objective is to present assessment and management to ensure the confidentiality of documents that are permanent, transparent, and sustainable for students by utilizing Blockchain technology and can be accessed directly by students. Maintained confidentiality of documents allows students to personalize the value of education and produce permanent documentation in formal and informal learning and determine the lifelong learning path of each individual. The method in this research using the Literature Review Study to learn problems from research with the theme of blockchain-based student achievement credentials in the world of education. The implication of this research is to provide an analysis of the use of blockchain micro-credentials on student achievement records in universities using the Literature Review Study Method. The results show that blockchain guarantees a foundation for student achievement record credentials that are durable, secure, and offer solid administration in managing student credentials. They can control them on an ongoing basis. Universities are also given the advantage of increasing student file security and reducing administrative and bureaucratic costs. Further research can be carried out by combining blockchain with AI innovation and private key and decentralized cloud to address issues concerning productivity, flexibility, capacity, and tighter security. With future updates, it is hoped that the use of blockchain in student achievement credentials can become more mature and can be applied to 1000 universities in Indonesia.","PeriodicalId":189541,"journal":{"name":"2021 Sixth International Conference on Informatics and Computing (ICIC)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115431567","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 : 2021-11-03DOI: 10.1109/ICIC54025.2021.9632894
Hanny Haryanto, Aripin, Acun Kardianawati, Umi Rosyidah, E. Z. Astuti, Erlin Dolphina
Interactivity and experience as the main characteristics of serious game has made it considered one of the most promising learning tools. Those characteristics supported mainly by game activity. Therefore, activity design is one of the most important element in developing serious game. One of the activity design concepts is to use Appreciative Learning, which consists of the stages of Discovery, Dream, Design and Destiny. The activity of exploration in Discovery stage is the main activity which is dominated by search and exploration. Because it is a search and exploration activity, it takes a long time and contains uncertainty in achievement. Dynamic rewards are needed to support the continuity of this Discovery activity. A good reward keeps the player’s focus on searching and exploration by providing indicators of achievement. This study uses fuzzy logic to form dynamic reward behavior in Discovery activities. Fuzzy logic considered one of the artificial intelligence methods that is suitable for games because of lightweight computation and could produce expressive AI behavior. The criteria used as input are the percentage of exploration and time, which will generate dynamic rewards for Discovery activities. The results of this study, fuzzy logic can produce three levels of variance of reward.
{"title":"Fuzzy-based Dynamic Reward for Discovery Activity in Appreciative Serious Game","authors":"Hanny Haryanto, Aripin, Acun Kardianawati, Umi Rosyidah, E. Z. Astuti, Erlin Dolphina","doi":"10.1109/ICIC54025.2021.9632894","DOIUrl":"https://doi.org/10.1109/ICIC54025.2021.9632894","url":null,"abstract":"Interactivity and experience as the main characteristics of serious game has made it considered one of the most promising learning tools. Those characteristics supported mainly by game activity. Therefore, activity design is one of the most important element in developing serious game. One of the activity design concepts is to use Appreciative Learning, which consists of the stages of Discovery, Dream, Design and Destiny. The activity of exploration in Discovery stage is the main activity which is dominated by search and exploration. Because it is a search and exploration activity, it takes a long time and contains uncertainty in achievement. Dynamic rewards are needed to support the continuity of this Discovery activity. A good reward keeps the player’s focus on searching and exploration by providing indicators of achievement. This study uses fuzzy logic to form dynamic reward behavior in Discovery activities. Fuzzy logic considered one of the artificial intelligence methods that is suitable for games because of lightweight computation and could produce expressive AI behavior. The criteria used as input are the percentage of exploration and time, which will generate dynamic rewards for Discovery activities. The results of this study, fuzzy logic can produce three levels of variance of reward.","PeriodicalId":189541,"journal":{"name":"2021 Sixth International Conference on Informatics and Computing (ICIC)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115154757","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 : 2021-11-03DOI: 10.1109/ICIC54025.2021.9632948
I. M. Agus Wirawan, Retantyo Wardoyo, D. Lelono, Sri Kusrohmaniah, Saifudin Asrori
Emotions play an essential role in human social interactions. Its importance has sparked research on emotion recognition mainly based on electroencephalogram signals. However, differences in individual characteristics significantly affect the electroencephalogram signal pattern and impact the emotion recognition process. Several studies have used the baseline reduction approach with the Difference method to represent the differences in individual characteristics on electroencephalogram signals. On the other hand, the baseline reduction process on signal data, in general, can also use the Relative Difference and Fractional Difference methods. Therefore, the contribution of this research is to compare the performance of the three baseline reduction methods on emotion recognition based on electroencephalogram signals. In this study, feature extraction and representation were also carried out using Differential Entropy and 3D Cube. Furthermore, Convolutional Neural Network and Decision Tree methods are used to classify emotions. The experimental results using the DEAP dataset show that the Relative Difference and Fractional Difference methods are superior in reducing the baseline electroencephalogram signal compared to the Difference method. In addition, the Relative Difference and Fractional Difference methods produce a smoother electroencephalogram signal pattern in the baseline reduction process.
{"title":"Comparison of Baseline Reduction Methods for Emotion Recognition Based On Electroencephalogram Signals","authors":"I. M. Agus Wirawan, Retantyo Wardoyo, D. Lelono, Sri Kusrohmaniah, Saifudin Asrori","doi":"10.1109/ICIC54025.2021.9632948","DOIUrl":"https://doi.org/10.1109/ICIC54025.2021.9632948","url":null,"abstract":"Emotions play an essential role in human social interactions. Its importance has sparked research on emotion recognition mainly based on electroencephalogram signals. However, differences in individual characteristics significantly affect the electroencephalogram signal pattern and impact the emotion recognition process. Several studies have used the baseline reduction approach with the Difference method to represent the differences in individual characteristics on electroencephalogram signals. On the other hand, the baseline reduction process on signal data, in general, can also use the Relative Difference and Fractional Difference methods. Therefore, the contribution of this research is to compare the performance of the three baseline reduction methods on emotion recognition based on electroencephalogram signals. In this study, feature extraction and representation were also carried out using Differential Entropy and 3D Cube. Furthermore, Convolutional Neural Network and Decision Tree methods are used to classify emotions. The experimental results using the DEAP dataset show that the Relative Difference and Fractional Difference methods are superior in reducing the baseline electroencephalogram signal compared to the Difference method. In addition, the Relative Difference and Fractional Difference methods produce a smoother electroencephalogram signal pattern in the baseline reduction process.","PeriodicalId":189541,"journal":{"name":"2021 Sixth International Conference on Informatics and Computing (ICIC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125899586","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 : 2021-11-03DOI: 10.1109/ICIC54025.2021.9632981
El Miana Assni Ernamia, Asti Herliana, D. Alamsyah, A. Ihsan, Yusron Razak
Strategy against the spread of the Covid-19 virus in Indonesia by enacting Large-Scale Social Restrictions. The implementation of the Scale Social Restrictions forced all universities in Indonesia to close their institutes and conduct lectures online. Online lectures are considered as a solution to continue the teaching process during a pandemic. However, the lack of adaptation and sudden changes caused various responses and public opinions on social media. For this reason, this study aims to conduct text mining on Twitter in order to analyze public sentiment on the topic of "online lectures" the data obtained are classified into 2 classes (positive and negative). The results of the accuracy of the nave Bayes test with the cross validation technique obtained a result of 81.57%. For class precision, positive predictions are 100%, while for negative predictions the results are 73.06% and recall from true positive is 63.13% for true negative is 100%. And for the accuracy of K-Nearest Neighbor 62.10%, for class precision positive prediction is 62.06% while for negative prediction results are 62.13% and recall from true positive is 62.24% for true negative is 61.95%
{"title":"Implementation of Text Mining for Sentiment Analysis of Online Lectures During the Covid-19 Pandemic","authors":"El Miana Assni Ernamia, Asti Herliana, D. Alamsyah, A. Ihsan, Yusron Razak","doi":"10.1109/ICIC54025.2021.9632981","DOIUrl":"https://doi.org/10.1109/ICIC54025.2021.9632981","url":null,"abstract":"Strategy against the spread of the Covid-19 virus in Indonesia by enacting Large-Scale Social Restrictions. The implementation of the Scale Social Restrictions forced all universities in Indonesia to close their institutes and conduct lectures online. Online lectures are considered as a solution to continue the teaching process during a pandemic. However, the lack of adaptation and sudden changes caused various responses and public opinions on social media. For this reason, this study aims to conduct text mining on Twitter in order to analyze public sentiment on the topic of \"online lectures\" the data obtained are classified into 2 classes (positive and negative). The results of the accuracy of the nave Bayes test with the cross validation technique obtained a result of 81.57%. For class precision, positive predictions are 100%, while for negative predictions the results are 73.06% and recall from true positive is 63.13% for true negative is 100%. And for the accuracy of K-Nearest Neighbor 62.10%, for class precision positive prediction is 62.06% while for negative prediction results are 62.13% and recall from true positive is 62.24% for true negative is 61.95%","PeriodicalId":189541,"journal":{"name":"2021 Sixth International Conference on Informatics and Computing (ICIC)","volume":"34 8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123148320","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 : 2021-11-03DOI: 10.1109/ICIC54025.2021.9632928
Muhammad Arif Azhari Halim, M. Othman, Aa Zezen Zaenal Abidin, E. Hamid, N. Harum, W. Shah
This project develops a face recognition-based door locking system with two-factor authentication using OpenCV. It uses Raspberry Pi 4 as the microcontroller. Face recognition-based door locking has been around for many years, but most of them only provide face recognition without any added security features, and they are costly. The design of this project is based on human face recognition and the sending of a One-Time Password (OTP) using the Twilio service. It will recognize the person at the front door. Only people who match the faces stored in its dataset and then inputs the correct OTP will have access to unlock the door. The Twilio service and image processing algorithm Local Binary Pattern Histogram (LBPH) has been adopted for this system. Servo motor operates as a mechanism to access the door. Results show that LBPH takes a short time to recognize a face. Additionally, if an unknown face is detected, it will log this instance into a "Fail" file and an accompanying CSV sheet.
{"title":"Face Recognition-based Door Locking System with Two-Factor Authentication Using OpenCV","authors":"Muhammad Arif Azhari Halim, M. Othman, Aa Zezen Zaenal Abidin, E. Hamid, N. Harum, W. Shah","doi":"10.1109/ICIC54025.2021.9632928","DOIUrl":"https://doi.org/10.1109/ICIC54025.2021.9632928","url":null,"abstract":"This project develops a face recognition-based door locking system with two-factor authentication using OpenCV. It uses Raspberry Pi 4 as the microcontroller. Face recognition-based door locking has been around for many years, but most of them only provide face recognition without any added security features, and they are costly. The design of this project is based on human face recognition and the sending of a One-Time Password (OTP) using the Twilio service. It will recognize the person at the front door. Only people who match the faces stored in its dataset and then inputs the correct OTP will have access to unlock the door. The Twilio service and image processing algorithm Local Binary Pattern Histogram (LBPH) has been adopted for this system. Servo motor operates as a mechanism to access the door. Results show that LBPH takes a short time to recognize a face. Additionally, if an unknown face is detected, it will log this instance into a \"Fail\" file and an accompanying CSV sheet.","PeriodicalId":189541,"journal":{"name":"2021 Sixth International Conference on Informatics and Computing (ICIC)","volume":"121 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128414414","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 : 2021-11-03DOI: 10.1109/ICIC54025.2021.9633008
Aang Kisnu Darmawan, Moh. Bhanu Setyawan, Adi Fajaryanto Cobantoro, Fauzan Masykur, Agus Komarudin, Mohammad Waail al Wajieh
The user experience (UX) of an item must be evaluated by assessing its user experience as a key feature of product growth. There are several frameworks for user experience assessment questionnaires, one of which is very popular: meCUE. However, the meCUE framework was originally developed in German, then in English, and no research has yet been conducted to develop the Indonesian version of the meCUE framework. This study aims to adapt the meCUE 2.0 framework into the Indonesian version using cross-cultural adaptation and reliability testing. The meCUE 2.0 framework is a user experience questionnaire consisting of 33 questions detailed in Modules I and II, on Perception of instrumental and non-instrumental product qualities, Module III on Emotions, and Module IV on Consequences. This adaptation version is then tested against the Smart Regency Service Application, namely Pamekasan Smart Mobile Application (PSMA), involving 15 respondents from technical and non-technical backgrounds who will be given facial validity and 60 respondents to verify the validity of the Indonesian version of meCUE 2.0 for the various populations. The test results of Cronbach's Alpha from the adaptation version in Indonesian for the meCUE 2.0 framework are 0.868 for module I, 0.870 for module II, 0.894 for module III, and 0.841 for module IV, which concludes that this version can be relied on for use by user experience practitioners. This adaptation version is expected to help researchers and user experience practitioners in Indonesia evaluate product user experiences.
{"title":"Adaptation of the meCUE 2.0 Version for User Experience(UX) Measurement Approach into Indonesian Context","authors":"Aang Kisnu Darmawan, Moh. Bhanu Setyawan, Adi Fajaryanto Cobantoro, Fauzan Masykur, Agus Komarudin, Mohammad Waail al Wajieh","doi":"10.1109/ICIC54025.2021.9633008","DOIUrl":"https://doi.org/10.1109/ICIC54025.2021.9633008","url":null,"abstract":"The user experience (UX) of an item must be evaluated by assessing its user experience as a key feature of product growth. There are several frameworks for user experience assessment questionnaires, one of which is very popular: meCUE. However, the meCUE framework was originally developed in German, then in English, and no research has yet been conducted to develop the Indonesian version of the meCUE framework. This study aims to adapt the meCUE 2.0 framework into the Indonesian version using cross-cultural adaptation and reliability testing. The meCUE 2.0 framework is a user experience questionnaire consisting of 33 questions detailed in Modules I and II, on Perception of instrumental and non-instrumental product qualities, Module III on Emotions, and Module IV on Consequences. This adaptation version is then tested against the Smart Regency Service Application, namely Pamekasan Smart Mobile Application (PSMA), involving 15 respondents from technical and non-technical backgrounds who will be given facial validity and 60 respondents to verify the validity of the Indonesian version of meCUE 2.0 for the various populations. The test results of Cronbach's Alpha from the adaptation version in Indonesian for the meCUE 2.0 framework are 0.868 for module I, 0.870 for module II, 0.894 for module III, and 0.841 for module IV, which concludes that this version can be relied on for use by user experience practitioners. This adaptation version is expected to help researchers and user experience practitioners in Indonesia evaluate product user experiences.","PeriodicalId":189541,"journal":{"name":"2021 Sixth International Conference on Informatics and Computing (ICIC)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131927596","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 : 2021-11-03DOI: 10.1109/ICIC54025.2021.9632897
Ragiel Hadi Prayitno, S. Sudiro, S. Madenda
This article describes the AES encryption and decryption process without using lookup tables in the MixColumns transformation. The encryption process consists of transforming subbytes, shiftrows, mixcolumns and addroundkey. The process was carried out for 10 rounds, but in round 10 the mixcolumns transformation was not carried out. The decryption process consists of inverse mixcolumns, inverse shiftrows, inverse subbytes and addroundkey. In this study, the AES encryption and decryption process was carried out using two methods, namely based on the lookup table and without using the lookup table on the MC/IMC transformation. The method in this article is applied to Matlab software. The experimental results show that the encryption and decryption process using a lookup table is slower than the method without a lookup table. The encryption process without a lookup table on the MC transformation takes 0.091 seconds while using a lookup table takes 0.399 seconds. The decryption process without a lookup table on the IMC transformation takes 0.149 seconds while using a lookup table takes 0.206 seconds.
{"title":"Avoiding Lookup Table in AES Algorithm","authors":"Ragiel Hadi Prayitno, S. Sudiro, S. Madenda","doi":"10.1109/ICIC54025.2021.9632897","DOIUrl":"https://doi.org/10.1109/ICIC54025.2021.9632897","url":null,"abstract":"This article describes the AES encryption and decryption process without using lookup tables in the MixColumns transformation. The encryption process consists of transforming subbytes, shiftrows, mixcolumns and addroundkey. The process was carried out for 10 rounds, but in round 10 the mixcolumns transformation was not carried out. The decryption process consists of inverse mixcolumns, inverse shiftrows, inverse subbytes and addroundkey. In this study, the AES encryption and decryption process was carried out using two methods, namely based on the lookup table and without using the lookup table on the MC/IMC transformation. The method in this article is applied to Matlab software. The experimental results show that the encryption and decryption process using a lookup table is slower than the method without a lookup table. The encryption process without a lookup table on the MC transformation takes 0.091 seconds while using a lookup table takes 0.399 seconds. The decryption process without a lookup table on the IMC transformation takes 0.149 seconds while using a lookup table takes 0.206 seconds.","PeriodicalId":189541,"journal":{"name":"2021 Sixth International Conference on Informatics and Computing (ICIC)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127523379","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 : 2021-11-03DOI: 10.1109/ICIC54025.2021.9632980
R. Delima, Retantyo Wardoyo, K. Mustofa
Requirements engineering (RE) is an essential initial stage in the software engineering process. RE activities include elicitation, analysis, specification, and validation. The efficiency of the RE process relies heavily on a systems analyst to perform software specifications. Automation of activities in RE can increase time efficiency. In this study, the Automatic Requirements Engineering Model (AREM) was developed to automate the analysis, specification, and validation processes of the RE. The model was developed by integrating a goal-oriented model and text pre-processing technique. At the elicitation stage, is developed a standard input document that refers to the GORE elements. The requirements analysis was developed by applying the breadth-first search method, forward chaining, and the agent approach. Text pre-processing techniques were used for document extraction and preparation of requirements specifications. The model produces requirements specifications in the form of goal trees, class diagrams, use case diagrams, and sequence diagrams
{"title":"Automatic Requirements Engineering Model using Goal-Oriented Modelling with Text Pre-Processing Technique","authors":"R. Delima, Retantyo Wardoyo, K. Mustofa","doi":"10.1109/ICIC54025.2021.9632980","DOIUrl":"https://doi.org/10.1109/ICIC54025.2021.9632980","url":null,"abstract":"Requirements engineering (RE) is an essential initial stage in the software engineering process. RE activities include elicitation, analysis, specification, and validation. The efficiency of the RE process relies heavily on a systems analyst to perform software specifications. Automation of activities in RE can increase time efficiency. In this study, the Automatic Requirements Engineering Model (AREM) was developed to automate the analysis, specification, and validation processes of the RE. The model was developed by integrating a goal-oriented model and text pre-processing technique. At the elicitation stage, is developed a standard input document that refers to the GORE elements. The requirements analysis was developed by applying the breadth-first search method, forward chaining, and the agent approach. Text pre-processing techniques were used for document extraction and preparation of requirements specifications. The model produces requirements specifications in the form of goal trees, class diagrams, use case diagrams, and sequence diagrams","PeriodicalId":189541,"journal":{"name":"2021 Sixth International Conference on Informatics and Computing (ICIC)","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134380440","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 : 2021-11-03DOI: 10.1109/ICIC54025.2021.9632946
R. E. Indrajit, Marsetio Marsetio, R. Gultom, P. Widodo, R. W. Putro, Pantja Djati, Siswo Hadi, B. Pramono, L. Simbolon
Developing effective policies to build cyber sovereignty is not easy. Such a complex policy ecosystem requires holistic and comprehensive thinking. Indonesia as a developing country feels the need to conduct a thorough study of the issues behind the high complexity. The purpose of this study is to describe and at the same time unravel the complexities of managing cyber ecosystems in Indonesia. The research methodology used is qualitative, where data is obtained through interviews with a number of experts, literature review, and direct observation in the field. The first three steps of Soft System Methodology are being used as the baseline of this study. The result is a rich picture segmented into eleven main domains, which form the ecosystem of cyber defense.
{"title":"Unraveling the Complexity of Developing a National Cyber Defense Sovereignty Policy: A Case Study of Indonesia","authors":"R. E. Indrajit, Marsetio Marsetio, R. Gultom, P. Widodo, R. W. Putro, Pantja Djati, Siswo Hadi, B. Pramono, L. Simbolon","doi":"10.1109/ICIC54025.2021.9632946","DOIUrl":"https://doi.org/10.1109/ICIC54025.2021.9632946","url":null,"abstract":"Developing effective policies to build cyber sovereignty is not easy. Such a complex policy ecosystem requires holistic and comprehensive thinking. Indonesia as a developing country feels the need to conduct a thorough study of the issues behind the high complexity. The purpose of this study is to describe and at the same time unravel the complexities of managing cyber ecosystems in Indonesia. The research methodology used is qualitative, where data is obtained through interviews with a number of experts, literature review, and direct observation in the field. The first three steps of Soft System Methodology are being used as the baseline of this study. The result is a rich picture segmented into eleven main domains, which form the ecosystem of cyber defense.","PeriodicalId":189541,"journal":{"name":"2021 Sixth International Conference on Informatics and Computing (ICIC)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124559561","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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