Pub Date : 2018-03-06DOI: 10.1109/ICOIACT.2018.8350778
Mesnan Silalahi, R. Hardiyati, I. M. Nadhiroh, T. Handayani, M. Amelia, R. Rahmaida
This study has the purpose to investigate the potential to downstreaming of biomedicine researches in Indonesia based on scientific publications. It is therefore necessary to extract unstructured information in natural language-based scientific publications. This paper reports result from an investigation on a classification model of the downstreaming potential of biomedical research publications in Indonesia based on text-mining. The predictive computational model was built by testing three classifier algorithms namely KNN, Naive Bayes and SVM, where the results show that the Naive Bayes-based model performs best.
{"title":"A text classification on the downstreaming potential of biomedicine publications in Indonesia","authors":"Mesnan Silalahi, R. Hardiyati, I. M. Nadhiroh, T. Handayani, M. Amelia, R. Rahmaida","doi":"10.1109/ICOIACT.2018.8350778","DOIUrl":"https://doi.org/10.1109/ICOIACT.2018.8350778","url":null,"abstract":"This study has the purpose to investigate the potential to downstreaming of biomedicine researches in Indonesia based on scientific publications. It is therefore necessary to extract unstructured information in natural language-based scientific publications. This paper reports result from an investigation on a classification model of the downstreaming potential of biomedical research publications in Indonesia based on text-mining. The predictive computational model was built by testing three classifier algorithms namely KNN, Naive Bayes and SVM, where the results show that the Naive Bayes-based model performs best.","PeriodicalId":6660,"journal":{"name":"2018 International Conference on Information and Communications Technology (ICOIACT)","volume":"11 1","pages":"515-519"},"PeriodicalIF":0.0,"publicationDate":"2018-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72790176","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 : 2018-03-06DOI: 10.1109/ICOIACT.2018.8350758
Hani Nurrahmi, Dade Nurjanah
Cyberbullying is a repeated act that harasses, humiliates, threatens, or hassles other people through electronic devices and online social networking websites. Cyberbullying through the internet is more dangerous than traditional bullying, because it can potentially amplify the humiliation to an unlimited online audience. According to UNICEF and a survey by the Indonesian Ministry of Communication and Information, 58% of 435 adolescents do not understand about cyberbullying. Some of them might even have been the bullies, but since they did not understand about cyberbullying they could not recognise the negative effects of their bullying. The bullies may not recognise the harm of their actions, because they do not see immediate responses from their victims. Our study aimed to detect cyberbullying actors based on texts and the credibility analysis of users and notify them about the harm of cyberbullying. We collected data from Twitter. Since the data were unlabelled, we built a web-based labelling tool to classify tweets into cyberbullying and non-cyberbullying tweets. We obtained 301 cyberbullying tweets, 399 non-cyberbullying tweets, 2,053 negative words and 129 swear words from the tool. Afterwards, we applied SVM and KNN to learn about and detect cyberbullying texts. The results show that SVM results in the highest f1-score, 67%. We also measured the credibility analysis of users and found 257 Normal Users, 45 Harmful Bullying Actors, 53 Bullying Actors and 6 Prospective Bullying Actors.
{"title":"Indonesian Twitter Cyberbullying Detection using Text Classification and User Credibility","authors":"Hani Nurrahmi, Dade Nurjanah","doi":"10.1109/ICOIACT.2018.8350758","DOIUrl":"https://doi.org/10.1109/ICOIACT.2018.8350758","url":null,"abstract":"Cyberbullying is a repeated act that harasses, humiliates, threatens, or hassles other people through electronic devices and online social networking websites. Cyberbullying through the internet is more dangerous than traditional bullying, because it can potentially amplify the humiliation to an unlimited online audience. According to UNICEF and a survey by the Indonesian Ministry of Communication and Information, 58% of 435 adolescents do not understand about cyberbullying. Some of them might even have been the bullies, but since they did not understand about cyberbullying they could not recognise the negative effects of their bullying. The bullies may not recognise the harm of their actions, because they do not see immediate responses from their victims. Our study aimed to detect cyberbullying actors based on texts and the credibility analysis of users and notify them about the harm of cyberbullying. We collected data from Twitter. Since the data were unlabelled, we built a web-based labelling tool to classify tweets into cyberbullying and non-cyberbullying tweets. We obtained 301 cyberbullying tweets, 399 non-cyberbullying tweets, 2,053 negative words and 129 swear words from the tool. Afterwards, we applied SVM and KNN to learn about and detect cyberbullying texts. The results show that SVM results in the highest f1-score, 67%. We also measured the credibility analysis of users and found 257 Normal Users, 45 Harmful Bullying Actors, 53 Bullying Actors and 6 Prospective Bullying Actors.","PeriodicalId":6660,"journal":{"name":"2018 International Conference on Information and Communications Technology (ICOIACT)","volume":"51 1","pages":"543-548"},"PeriodicalIF":0.0,"publicationDate":"2018-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87464822","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 : 2018-03-06DOI: 10.1109/ICOIACT.2018.8350661
Yani Parti Astuti, D. Setiadi, E. H. Rachmawanto, C. A. Sari
Least Significant Bit (LSB) is a very popular method in the spatial domain of steganographic images. This method is widely used and continues to be developed to date, because of its advantages in steganographic image quality. However, the traditional LSB method is very simple and predictable. It needs a way to improve the security of hidden messages in this way. This research proposes a simple and safe way to hide messages in LSB techniques. Three times the XOR operation is done to encrypt the message before it is embedded on the LSB. To facilitate the process of encryption and decryption of messages, three MSB bits are used as keys in XOR operations. The results of this study prove that this method provides security to messages with very simple operation. The imperceptibility quality of the stego image is also excellent with a PSNR value above 50 dB.
{"title":"Simple and secure image steganography using LSB and triple XOR operation on MSB","authors":"Yani Parti Astuti, D. Setiadi, E. H. Rachmawanto, C. A. Sari","doi":"10.1109/ICOIACT.2018.8350661","DOIUrl":"https://doi.org/10.1109/ICOIACT.2018.8350661","url":null,"abstract":"Least Significant Bit (LSB) is a very popular method in the spatial domain of steganographic images. This method is widely used and continues to be developed to date, because of its advantages in steganographic image quality. However, the traditional LSB method is very simple and predictable. It needs a way to improve the security of hidden messages in this way. This research proposes a simple and safe way to hide messages in LSB techniques. Three times the XOR operation is done to encrypt the message before it is embedded on the LSB. To facilitate the process of encryption and decryption of messages, three MSB bits are used as keys in XOR operations. The results of this study prove that this method provides security to messages with very simple operation. The imperceptibility quality of the stego image is also excellent with a PSNR value above 50 dB.","PeriodicalId":6660,"journal":{"name":"2018 International Conference on Information and Communications Technology (ICOIACT)","volume":"33 1","pages":"191-195"},"PeriodicalIF":0.0,"publicationDate":"2018-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88337513","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 : 2018-03-06DOI: 10.1109/ICOIACT.2018.8350804
C. Rahmad, I. F. Rahmah, R. A. Asmara, S. Adhisuwignjo
This paper presents traffic sign detection and recognition which is necessary to be developed to support several expert systems such as driver assistance and autonomous driving system. This study focused on the detection and recognition process tested on Indonesian traffic signs. There were some major issues on detecting process such as damaged signs, faded color, and natural condition. Therefore, this paper is proposed to address some of these issues and will be done in two main processes. The first one is traffic sign detection which divided into two steps. Start with segmenting image based on RGBN (Normalized RGB), then detects traffic signs by processing blobs that have been extracted by the previous process. The second process is traffic sign recognition process. In this process there are two steps to take. The first one is feature extraction, in this research we propose the combination of some feature extraction that is HOG, Gabor, LBP and use HSV color space. In next recognition stage some classifier are compared such as SVM, KNN, Random Forest, and Naïve Bayes. The propose method has been tasted on Indonesia local traffic sign. The results of the experimental work reveal that the approach of RGBN method showed precision and recall about 98,7% and 95,1% respectively in detecting traffic signs, and 100% for the precision and 86,7% for recall in recognizing process using SVM Classifier.
{"title":"Indonesian traffic sign detection and recognition using color and texture feature extraction and SVM classifier","authors":"C. Rahmad, I. F. Rahmah, R. A. Asmara, S. Adhisuwignjo","doi":"10.1109/ICOIACT.2018.8350804","DOIUrl":"https://doi.org/10.1109/ICOIACT.2018.8350804","url":null,"abstract":"This paper presents traffic sign detection and recognition which is necessary to be developed to support several expert systems such as driver assistance and autonomous driving system. This study focused on the detection and recognition process tested on Indonesian traffic signs. There were some major issues on detecting process such as damaged signs, faded color, and natural condition. Therefore, this paper is proposed to address some of these issues and will be done in two main processes. The first one is traffic sign detection which divided into two steps. Start with segmenting image based on RGBN (Normalized RGB), then detects traffic signs by processing blobs that have been extracted by the previous process. The second process is traffic sign recognition process. In this process there are two steps to take. The first one is feature extraction, in this research we propose the combination of some feature extraction that is HOG, Gabor, LBP and use HSV color space. In next recognition stage some classifier are compared such as SVM, KNN, Random Forest, and Naïve Bayes. The propose method has been tasted on Indonesia local traffic sign. The results of the experimental work reveal that the approach of RGBN method showed precision and recall about 98,7% and 95,1% respectively in detecting traffic signs, and 100% for the precision and 86,7% for recall in recognizing process using SVM Classifier.","PeriodicalId":6660,"journal":{"name":"2018 International Conference on Information and Communications Technology (ICOIACT)","volume":"96 1","pages":"50-55"},"PeriodicalIF":0.0,"publicationDate":"2018-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75601654","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 : 2018-03-06DOI: 10.1109/ICOIACT.2018.8350761
Isshu Munemasa, Yuta Tomomatsu, K. Hayashi, T. Takagi
Services that introduce stores to users on the Internet are increasing in recent years. Each service conducts thorough analyses in order to display stores matching each user's preferences. In the field of recommendation, collaborative filtering performs well when there is sufficient click information from users. Generally, when building a user-item matrix, data sparseness becomes a problem. It is especially difficult to handle new users. When sufficient data cannot be obtained, a multi-armed bandit algorithm is applied. Bandit algorithms advance learning by testing each of a variety of options sufficiently and obtaining rewards (i.e. feedback). It is practically impossible to learn everything when the number of items to be learned periodically increases. The problem of having to collect sufficient data for a new user of a service is the same as the problem that collaborative filtering faces. In order to solve this problem, we propose a recommender system based on deep reinforcement learning. In deep reinforcement learning, a multilayer neural network is used to update the value function.
{"title":"Deep reinforcement learning for recommender systems","authors":"Isshu Munemasa, Yuta Tomomatsu, K. Hayashi, T. Takagi","doi":"10.1109/ICOIACT.2018.8350761","DOIUrl":"https://doi.org/10.1109/ICOIACT.2018.8350761","url":null,"abstract":"Services that introduce stores to users on the Internet are increasing in recent years. Each service conducts thorough analyses in order to display stores matching each user's preferences. In the field of recommendation, collaborative filtering performs well when there is sufficient click information from users. Generally, when building a user-item matrix, data sparseness becomes a problem. It is especially difficult to handle new users. When sufficient data cannot be obtained, a multi-armed bandit algorithm is applied. Bandit algorithms advance learning by testing each of a variety of options sufficiently and obtaining rewards (i.e. feedback). It is practically impossible to learn everything when the number of items to be learned periodically increases. The problem of having to collect sufficient data for a new user of a service is the same as the problem that collaborative filtering faces. In order to solve this problem, we propose a recommender system based on deep reinforcement learning. In deep reinforcement learning, a multilayer neural network is used to update the value function.","PeriodicalId":6660,"journal":{"name":"2018 International Conference on Information and Communications Technology (ICOIACT)","volume":"9 1","pages":"226-233"},"PeriodicalIF":0.0,"publicationDate":"2018-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79135729","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 : 2018-03-06DOI: 10.1109/ICOIACT.2018.8350779
Catur Atmaji, A. E. Putra, A. Hanif
In the past few decades, biomedical signals have played important roles in assisting diagnosis for medical purposes. After the rose of brain-computer interfaces (BCI) and human-machine interaction (HMI) concept, biomedical signals such as electroencephalograph (EEG) and electrooculograph (EOG) begun to be implemented in control and communication systems. EOG, the signal resulted from eye movement, has been used to design various applications from drowsiness detection to virtual keyboard control. The key of the system developed from EOG signal is the detection system for every eye movement. In this study, a sliding window technique is proposed to make eye movement patterns easier be formulated and using overlap window to avoid local extrema when computing the feature. Evaluation of this method shows that combination of 0.5 s-window length and 25% overlap give 17% and 1% false discovery rate (FDR) in vertical and horizontal channel while the true positive rate (TPR) in both channel is 98% The combination of automatic-window and 25% overlap give a better accuracy with 99% and 100% TPR in the two direction while the FDRs are 22% and 1%.
{"title":"Sliding window method for eye movement detection based on electrooculogram signal","authors":"Catur Atmaji, A. E. Putra, A. Hanif","doi":"10.1109/ICOIACT.2018.8350779","DOIUrl":"https://doi.org/10.1109/ICOIACT.2018.8350779","url":null,"abstract":"In the past few decades, biomedical signals have played important roles in assisting diagnosis for medical purposes. After the rose of brain-computer interfaces (BCI) and human-machine interaction (HMI) concept, biomedical signals such as electroencephalograph (EEG) and electrooculograph (EOG) begun to be implemented in control and communication systems. EOG, the signal resulted from eye movement, has been used to design various applications from drowsiness detection to virtual keyboard control. The key of the system developed from EOG signal is the detection system for every eye movement. In this study, a sliding window technique is proposed to make eye movement patterns easier be formulated and using overlap window to avoid local extrema when computing the feature. Evaluation of this method shows that combination of 0.5 s-window length and 25% overlap give 17% and 1% false discovery rate (FDR) in vertical and horizontal channel while the true positive rate (TPR) in both channel is 98% The combination of automatic-window and 25% overlap give a better accuracy with 99% and 100% TPR in the two direction while the FDRs are 22% and 1%.","PeriodicalId":6660,"journal":{"name":"2018 International Conference on Information and Communications Technology (ICOIACT)","volume":"102 1","pages":"628-632"},"PeriodicalIF":0.0,"publicationDate":"2018-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79373136","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 : 2018-03-06DOI: 10.1109/ICOIACT.2018.8350768
R. Harimurti, Y. Yamasari, Ekohariadi, Munoto, B. I. Asto
The educational data can be mined to produce the useful knowledge. This paper focuses on the educational data processing to predict student's psychomotor domain. Here, we apply linear regression method to do it. On process stage, we use 4 regularizations, namely: no regularization, ridge regression, lasso regression and elastic net regression. Furthermore, we exploit 2 sampling methods as the evaluation technique, for examples: cross-validation sampling and random sampling. The experimental result indicates that the best regularization on cross-validation and random sampling are an elastic net regression because this regularization achieves the lowest predicting error. On cross-validation, values of MSE, RMSE, and MAE are 40.079, 6.330 and 5.183, respectively. Additionally, for random sampling, respectively, values of MSE, RMSE, and MAE are 86.910, 8.428 and 6.511.
{"title":"Predicting student's psychomotor domain on the vocational senior high school using linear regression","authors":"R. Harimurti, Y. Yamasari, Ekohariadi, Munoto, B. I. Asto","doi":"10.1109/ICOIACT.2018.8350768","DOIUrl":"https://doi.org/10.1109/ICOIACT.2018.8350768","url":null,"abstract":"The educational data can be mined to produce the useful knowledge. This paper focuses on the educational data processing to predict student's psychomotor domain. Here, we apply linear regression method to do it. On process stage, we use 4 regularizations, namely: no regularization, ridge regression, lasso regression and elastic net regression. Furthermore, we exploit 2 sampling methods as the evaluation technique, for examples: cross-validation sampling and random sampling. The experimental result indicates that the best regularization on cross-validation and random sampling are an elastic net regression because this regularization achieves the lowest predicting error. On cross-validation, values of MSE, RMSE, and MAE are 40.079, 6.330 and 5.183, respectively. Additionally, for random sampling, respectively, values of MSE, RMSE, and MAE are 86.910, 8.428 and 6.511.","PeriodicalId":6660,"journal":{"name":"2018 International Conference on Information and Communications Technology (ICOIACT)","volume":"48 1","pages":"448-453"},"PeriodicalIF":0.0,"publicationDate":"2018-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78765806","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 : 2018-03-06DOI: 10.1109/ICOIACT.2018.8350729
B. I. Nasution, R. Kurniawan
Classical Fuzzy C-Means (FCM) was believed as a robust clustering method when it is optimized and modified. But, at this time many researchers stated that classical FCM is less robust. So this study aims to investigate and prove the robustness of FCM by conducting studies into several data sets and optimization methods and modifications. The results show that FCM is a robust-proven method when viewed from the value of the objective function, the number of iterations, and the time being completed.
{"title":"Robustness of classical fuzzy C-means (FCM)","authors":"B. I. Nasution, R. Kurniawan","doi":"10.1109/ICOIACT.2018.8350729","DOIUrl":"https://doi.org/10.1109/ICOIACT.2018.8350729","url":null,"abstract":"Classical Fuzzy C-Means (FCM) was believed as a robust clustering method when it is optimized and modified. But, at this time many researchers stated that classical FCM is less robust. So this study aims to investigate and prove the robustness of FCM by conducting studies into several data sets and optimization methods and modifications. The results show that FCM is a robust-proven method when viewed from the value of the objective function, the number of iterations, and the time being completed.","PeriodicalId":6660,"journal":{"name":"2018 International Conference on Information and Communications Technology (ICOIACT)","volume":"11 1","pages":"321-325"},"PeriodicalIF":0.0,"publicationDate":"2018-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76635772","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 : 2018-03-06DOI: 10.1109/ICOIACT.2018.8350813
Ibnu Masngut, G. Pratama, A. Cahyadi, S. Herdjunanto, J. Pakpahan
The aim of this paper is to present the implementation of Fractional-Order Proportional-Integral-Derivative (FOPID) to control the step response of the first-order circuit. With FOPID control, we obtain a satisfying result. The FOPID controller outperforms the classical integer PID, wherein both of them are optimized with the Nelder-Mead method. The FOPID controller succeeds to regulate the output of the system to our desired set point with better settling time and rise time than the classical one. In addition, hardware realization is presented with Arduino Uno.
{"title":"Design of fractional-order proportional-integral-derivative controller: Hardware realization","authors":"Ibnu Masngut, G. Pratama, A. Cahyadi, S. Herdjunanto, J. Pakpahan","doi":"10.1109/ICOIACT.2018.8350813","DOIUrl":"https://doi.org/10.1109/ICOIACT.2018.8350813","url":null,"abstract":"The aim of this paper is to present the implementation of Fractional-Order Proportional-Integral-Derivative (FOPID) to control the step response of the first-order circuit. With FOPID control, we obtain a satisfying result. The FOPID controller outperforms the classical integer PID, wherein both of them are optimized with the Nelder-Mead method. The FOPID controller succeeds to regulate the output of the system to our desired set point with better settling time and rise time than the classical one. In addition, hardware realization is presented with Arduino Uno.","PeriodicalId":6660,"journal":{"name":"2018 International Conference on Information and Communications Technology (ICOIACT)","volume":"101 1","pages":"656-660"},"PeriodicalIF":0.0,"publicationDate":"2018-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78094629","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 : 2018-03-06DOI: 10.1109/ICOIACT.2018.8350764
Yuliana Ariyanti, R. Yuana, Aris Budianto
Indonesia is a country with 129 active and 500 inactive volcanoes. Disasters caused by volcanic eruptions have impacted on several sectors including education. Development of geographic information system for mapping disaster-prone areas (KRB) can facilitate the identification of schools that require particular attention when disasters such as volcanic eruptions occur. This research aims to develop a web-based geographic information system for school mapping and disaster mitigation (SIMBAK). SIMBAK can map the schools located in the KRB, present school profile information and display the navigation. After SIMBAK have developed, the test was directed in two stages: limited test and expanded test to confirm the feasibility of a SIMBAK. Limited test completed by information system experts and disaster substance experts. Results from a limited test show a percentage value of 86.3%. The expanded test completed by actors involved in SIMBAK namely administrators, school operators, and users. The results of the expanded test show the percentage of the value of 87.9%. It means that SIMBAK is feasible to apply in areas that are in disaster-affected.
{"title":"Web-based geographic information system for school mapping and disaster mitigation","authors":"Yuliana Ariyanti, R. Yuana, Aris Budianto","doi":"10.1109/ICOIACT.2018.8350764","DOIUrl":"https://doi.org/10.1109/ICOIACT.2018.8350764","url":null,"abstract":"Indonesia is a country with 129 active and 500 inactive volcanoes. Disasters caused by volcanic eruptions have impacted on several sectors including education. Development of geographic information system for mapping disaster-prone areas (KRB) can facilitate the identification of schools that require particular attention when disasters such as volcanic eruptions occur. This research aims to develop a web-based geographic information system for school mapping and disaster mitigation (SIMBAK). SIMBAK can map the schools located in the KRB, present school profile information and display the navigation. After SIMBAK have developed, the test was directed in two stages: limited test and expanded test to confirm the feasibility of a SIMBAK. Limited test completed by information system experts and disaster substance experts. Results from a limited test show a percentage value of 86.3%. The expanded test completed by actors involved in SIMBAK namely administrators, school operators, and users. The results of the expanded test show the percentage of the value of 87.9%. It means that SIMBAK is feasible to apply in areas that are in disaster-affected.","PeriodicalId":6660,"journal":{"name":"2018 International Conference on Information and Communications Technology (ICOIACT)","volume":"41 1","pages":"136-140"},"PeriodicalIF":0.0,"publicationDate":"2018-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88383420","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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