Pub Date : 2018-05-01DOI: 10.1109/ICOICT.2018.8528762
S. Putra, M. Gunawan, Agung Suryatno
Tokenization is an important process used to break the text into parts of a word. N-gram model now is widely used in computational linguistics for predicting the next item in such a contiguous sequence of $mathbf{n}$ items from a particular sample of text. This paper focuses on the implementation of tokenization and n-gram model using RapidMiner to produce unigram and bigram word for indexing Indonesian Translation of the Quran (ITQ). This study uses ITQ data sets consisting of 114 documents. The methods are data extracting and preprocessing text including tokenization, stemming, stopword removal, transformation cases, and n-grams. The results of this study showed the model produces the 6794 and 60323 tokens combination unigram and bigram use for index ITQ. Significant the contribution of this study is to enhance the digital index of ITQ.
{"title":"Tokenization and N-Gram for Indexing Indonesian Translation of the Quran","authors":"S. Putra, M. Gunawan, Agung Suryatno","doi":"10.1109/ICOICT.2018.8528762","DOIUrl":"https://doi.org/10.1109/ICOICT.2018.8528762","url":null,"abstract":"Tokenization is an important process used to break the text into parts of a word. N-gram model now is widely used in computational linguistics for predicting the next item in such a contiguous sequence of $mathbf{n}$ items from a particular sample of text. This paper focuses on the implementation of tokenization and n-gram model using RapidMiner to produce unigram and bigram word for indexing Indonesian Translation of the Quran (ITQ). This study uses ITQ data sets consisting of 114 documents. The methods are data extracting and preprocessing text including tokenization, stemming, stopword removal, transformation cases, and n-grams. The results of this study showed the model produces the 6794 and 60323 tokens combination unigram and bigram use for index ITQ. Significant the contribution of this study is to enhance the digital index of ITQ.","PeriodicalId":266335,"journal":{"name":"2018 6th International Conference on Information and Communication Technology (ICoICT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115218616","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 : 2018-05-01DOI: 10.1109/ICOICT.2018.8528788
Jaka E. Sembodo, E. B. Setiawan, M. Bijaksana
Tweet is being informative as well as news articles, so that the automatic tweet classifier based on news category could be useful to make ease in searching tweet based on certain interesting category. We identified those are 11 categories: religion, business, entertainment, law and crime, health, motivation, sport, government, education, politics and technology. In the learning process, we use ZeroR, Naive Bayes Multinomial (NBM), Support Vector Machine (SVM), Random Forest (RF) and Sequential Minimal Optimization (SMO) algorithm based on previous work that has similar topic with this paper. In experiments, we experiment classifier using all tweet and various maximum number of tweets and terms in each category. In evaluating performance system, we used 10-fold cross validation and use accuracy (correctly classified instances) as performance paramater. In the experiments result, NBM performs the highest performance with 77,47% accuracy with maximum number of tweets and terms in every category is 500 tweets and 1000 terms. At the last, we built automatic tweet classifier with NBM due to this classifier and experiment result perform the best performances using web-based programming.
{"title":"Automatic Tweet Classification Based on News Category in Indonesian Language","authors":"Jaka E. Sembodo, E. B. Setiawan, M. Bijaksana","doi":"10.1109/ICOICT.2018.8528788","DOIUrl":"https://doi.org/10.1109/ICOICT.2018.8528788","url":null,"abstract":"Tweet is being informative as well as news articles, so that the automatic tweet classifier based on news category could be useful to make ease in searching tweet based on certain interesting category. We identified those are 11 categories: religion, business, entertainment, law and crime, health, motivation, sport, government, education, politics and technology. In the learning process, we use ZeroR, Naive Bayes Multinomial (NBM), Support Vector Machine (SVM), Random Forest (RF) and Sequential Minimal Optimization (SMO) algorithm based on previous work that has similar topic with this paper. In experiments, we experiment classifier using all tweet and various maximum number of tweets and terms in each category. In evaluating performance system, we used 10-fold cross validation and use accuracy (correctly classified instances) as performance paramater. In the experiments result, NBM performs the highest performance with 77,47% accuracy with maximum number of tweets and terms in every category is 500 tweets and 1000 terms. At the last, we built automatic tweet classifier with NBM due to this classifier and experiment result perform the best performances using web-based programming.","PeriodicalId":266335,"journal":{"name":"2018 6th International Conference on Information and Communication Technology (ICoICT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130157899","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 : 2018-05-01DOI: 10.1109/ICOICT.2018.8528792
Gregorius Aldo Radityatama, Charles Lim, Heru Purnomo Ipung
Next Generation Firewall (NGFW) adds new capabilities of a standard firewall with an ability to inspect packets' contents, thus increasing precision. Three main usages of NGFW are to improve the Quality of Service (QoS) of a business, as an application-based filtering firewall, and to protect the network from known security threats. A complete NGFW system has three main components: Deep Packet Inspection (DPI), Intrusion Prevention System (IPS), and an extra-firewall intelligence mechanism. One example of open-source DPI implementations is called nDPI. As the number of enterprise applications (used in the commercial organizations) continues to rise, nDPI is also lagging in terms of coverage for enterprise software support. The aim of this research is to design and implement better enterprise-grade software support protocols on nDPI. Five common enterprise applications were chosen and implemented. The experiment results were then compared with the commercial implementation of NGFW in terms of overall precision and performance of nDPI. The results show that the accuracy of nDPI the new protocols implemented reaches more than 90% with a small (less than 3,5%) increase of CPU execution time and very small (less than 1%) increase of peak heap memory usage.
{"title":"Toward Full Enterprise Software Support on nDPI","authors":"Gregorius Aldo Radityatama, Charles Lim, Heru Purnomo Ipung","doi":"10.1109/ICOICT.2018.8528792","DOIUrl":"https://doi.org/10.1109/ICOICT.2018.8528792","url":null,"abstract":"Next Generation Firewall (NGFW) adds new capabilities of a standard firewall with an ability to inspect packets' contents, thus increasing precision. Three main usages of NGFW are to improve the Quality of Service (QoS) of a business, as an application-based filtering firewall, and to protect the network from known security threats. A complete NGFW system has three main components: Deep Packet Inspection (DPI), Intrusion Prevention System (IPS), and an extra-firewall intelligence mechanism. One example of open-source DPI implementations is called nDPI. As the number of enterprise applications (used in the commercial organizations) continues to rise, nDPI is also lagging in terms of coverage for enterprise software support. The aim of this research is to design and implement better enterprise-grade software support protocols on nDPI. Five common enterprise applications were chosen and implemented. The experiment results were then compared with the commercial implementation of NGFW in terms of overall precision and performance of nDPI. The results show that the accuracy of nDPI the new protocols implemented reaches more than 90% with a small (less than 3,5%) increase of CPU execution time and very small (less than 1%) increase of peak heap memory usage.","PeriodicalId":266335,"journal":{"name":"2018 6th International Conference on Information and Communication Technology (ICoICT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132742964","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 : 2018-03-28DOI: 10.1109/ICOICT.2018.8528805
Ismail Rusli, B. Trilaksono, W. Adiprawita
Inferring walls configuration of indoor environment could help robot “understand” the environment better. This allows the robot to execute a task that involves inter-room navigation, such as picking an object in the kitchen. In this paper, we present a method to inferring walls configuration from a moving RGB-D sensor. Our goal is to combine a simple wall configuration model and fast wall detection method in order to get a system that works online, is real-time, and does not need a Manhattan World assumption. We tested our preliminary work, i.e. wall detection and measurement from moving RGB-D sensor, with MIT Stata Center Dataset. The performance of our method is reported in terms of accuracy and speed of execution.
推断室内环境的墙体配置可以帮助机器人更好地“理解”环境。这使得机器人可以执行包括房间间导航的任务，比如在厨房里挑选一个物体。在本文中，我们提出了一种从移动的RGB-D传感器推断壁面结构的方法。我们的目标是结合一个简单的墙配置模型和快速的墙检测方法，以获得一个在线工作的系统，是实时的，不需要曼哈顿世界的假设。我们使用MIT Stata Center数据集测试了我们的初步工作，即移动RGB-D传感器的墙壁检测和测量。我们的方法在准确性和执行速度方面的性能得到了报道。
{"title":"Mapping Walls of Indoor Environment Using Moving RGB-D Sensor","authors":"Ismail Rusli, B. Trilaksono, W. Adiprawita","doi":"10.1109/ICOICT.2018.8528805","DOIUrl":"https://doi.org/10.1109/ICOICT.2018.8528805","url":null,"abstract":"Inferring walls configuration of indoor environment could help robot “understand” the environment better. This allows the robot to execute a task that involves inter-room navigation, such as picking an object in the kitchen. In this paper, we present a method to inferring walls configuration from a moving RGB-D sensor. Our goal is to combine a simple wall configuration model and fast wall detection method in order to get a system that works online, is real-time, and does not need a Manhattan World assumption. We tested our preliminary work, i.e. wall detection and measurement from moving RGB-D sensor, with MIT Stata Center Dataset. The performance of our method is reported in terms of accuracy and speed of execution.","PeriodicalId":266335,"journal":{"name":"2018 6th International Conference on Information and Communication Technology (ICoICT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122862868","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：