2018 International Conference on Information and Communications Technology (ICOIACT)最新文献_第4页

A text classification on the downstreaming potential of biomedicine publications in Indonesia 关于印度尼西亚生物医学出版物下游潜力的文本分类

2018 International Conference on Information and Communications Technology (ICOIACT) Pub Date : 2018-03-06 DOI: 10.1109/ICOIACT.2018.8350778

Mesnan Silalahi, R. Hardiyati, I. M. Nadhiroh, T. Handayani, M. Amelia, R. Rahmaida

引用次数: 3

Indonesian Twitter Cyberbullying Detection using Text Classification and User Credibility 使用文本分类和用户可信度的印尼Twitter网络欺凌检测

2018 International Conference on Information and Communications Technology (ICOIACT) Pub Date : 2018-03-06 DOI: 10.1109/ICOIACT.2018.8350758

Hani Nurrahmi, Dade Nurjanah

{"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}

引用次数: 35

Simple and secure image steganography using LSB and triple XOR operation on MSB 使用LSB和MSB上的三重异或操作进行简单安全的图像隐写

2018 International Conference on Information and Communications Technology (ICOIACT) Pub Date : 2018-03-06 DOI: 10.1109/ICOIACT.2018.8350661

Yani Parti Astuti, D. Setiadi, E. H. Rachmawanto, C. A. Sari

引用次数: 33

Indonesian traffic sign detection and recognition using color and texture feature extraction and SVM classifier 印尼交通标志检测与识别采用颜色和纹理特征提取和SVM分类器

2018 International Conference on Information and Communications Technology (ICOIACT) Pub Date : 2018-03-06 DOI: 10.1109/ICOIACT.2018.8350804

C. Rahmad, I. F. Rahmah, R. A. Asmara, S. Adhisuwignjo

{"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}

引用次数: 16

Deep reinforcement learning for recommender systems 推荐系统的深度强化学习

2018 International Conference on Information and Communications Technology (ICOIACT) Pub Date : 2018-03-06 DOI: 10.1109/ICOIACT.2018.8350761

Isshu Munemasa, Yuta Tomomatsu, K. Hayashi, T. Takagi

引用次数: 34

Sliding window method for eye movement detection based on electrooculogram signal 基于眼电图信号的滑动窗眼动检测方法

2018 International Conference on Information and Communications Technology (ICOIACT) Pub Date : 2018-03-06 DOI: 10.1109/ICOIACT.2018.8350779

Catur Atmaji, A. E. Putra, A. Hanif

{"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}

引用次数: 3

Predicting student's psychomotor domain on the vocational senior high school using linear regression 运用线性回归预测职业高中学生心理运动域

2018 International Conference on Information and Communications Technology (ICOIACT) Pub Date : 2018-03-06 DOI: 10.1109/ICOIACT.2018.8350768

R. Harimurti, Y. Yamasari, Ekohariadi, Munoto, B. I. Asto

引用次数: 10

Robustness of classical fuzzy C-means (FCM) 经典模糊c均值(FCM)的鲁棒性

2018 International Conference on Information and Communications Technology (ICOIACT) Pub Date : 2018-03-06 DOI: 10.1109/ICOIACT.2018.8350729

B. I. Nasution, R. Kurniawan

引用次数: 5

Design of fractional-order proportional-integral-derivative controller: Hardware realization 分数阶比例-积分-导数控制器的设计:硬件实现

2018 International Conference on Information and Communications Technology (ICOIACT) Pub Date : 2018-03-06 DOI: 10.1109/ICOIACT.2018.8350813

Ibnu Masngut, G. Pratama, A. Cahyadi, S. Herdjunanto, J. Pakpahan

引用次数: 3

Web-based geographic information system for school mapping and disaster mitigation 基于网络的地理信息系统，用于学校制图和减灾

2018 International Conference on Information and Communications Technology (ICOIACT) Pub Date : 2018-03-06 DOI: 10.1109/ICOIACT.2018.8350764

Yuliana Ariyanti, R. Yuana, Aris Budianto

{"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}

引用次数: 5