An Early Warning Detection System of Terrorism in Indonesia from Twitter Contents using Naïve Bayes Algorithm

2020 ◽  
Author(s):  
Mediana Aryuni ◽  
Eka Miranda ◽  
Yudi Fernando ◽  
Tia Mariatul Kibtiah
Keyword(s):  
Early Warning ◽  
Naive Bayes ◽  
Detection System ◽  
Naïve Bayes ◽  
Bayes Algorithm

scholarly journals An Indonesian Hoax News Detection System Using Reader Feedback and Naïve Bayes Algorithm

2020 ◽  
Vol 20 (1) ◽  
pp. 82-94
Author(s):  
Badrus Zaman ◽  
Army Justitia ◽  
Kretawiweka Nuraga Sani ◽  
Endah Purwanti
Keyword(s):  
Performance Evaluation ◽  
System Performance ◽  
Naive Bayes ◽  
Detection System ◽  
Naïve Bayes ◽  
Bayes Algorithm ◽  
F Measure ◽  
System Performance Evaluation

AbstractHoax news in Indonesia spread at an alarming rate. To reduce this, hoax news detection system needs to be created and put into practice. Such a system may use readers’ feedback and Naïve Bayes algorithm, which is used to verify news. Overtime, by using readers’ feedback, database corpus will continue to grow and could improve system performance. The current research aims to reach this. System performance evaluation is carried out under two conditions ‒ with and without sources (URL). The system is able to detect hoax news very well under both conditions. The highest precision, recall and f-measure values when including URL are 0.91, 1, and 0.95 respectively. Meanwhile, the highest value of precision, recall and f-measure without URL are 0.88, 1 and 0.94, respectively.

scholarly journals Intelligent Intrusion Detection System using Supervised Learning

2021 ◽  
Author(s):  
Sandipan Roy ◽  
Apurbo Mandal ◽  
Debraj Dey
Keyword(s):  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Naive Bayes ◽  
Detection System ◽  
Classification Problem ◽  
Naïve Bayes ◽  
Support Vector ◽  
Detection Systems ◽  
Malicious Activity ◽  
Bayes Algorithm

Going digital involves networking with so many connected devices, so network security becomes a critical task for everyone. But an intrusion detection system can help us to detect malicious activity in a system or network. But generally, intrusion detection systems (IDS) are not reliable and sustainable also they require more resources. In recent years so many machine learning methods are proposed to give higher accuracy with minimal false alerts. But analyzing those huge traffic data is still challenging. So, in this article, we proposed a technique using the Support Vector Machine & Naive Bayes algorithm, by using this we can solve the classification problem of the intrusion detection system. For evaluating our proposed method, we use NSL-KDD and UNSW-NB15 dataset. And after getting the result we see that the SVM works better than the Naive Bayes algorithm on that dataset.

scholarly journals Klasifikasi Anomali Intrusion Detection System (IDS) Menggunakan Algoritma Naïve Bayes Classifier dan Correlation-Based Feature Selection

2019 ◽  
Vol 2 (4) ◽  
pp. 135
Author(s):  
Saipul Anwar ◽  
Fajar Septian ◽  
Ristasari Dwi Septiana
Keyword(s):  
Feature Selection ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Naive Bayes ◽  
Detection System ◽  
Naïve Bayes ◽  
Attribute Selection ◽  
Correlation Technique ◽  
Correlation Based Feature Selection ◽  
Bayes Algorithm

Intrusion Detection System (IDS) is useful for detecting an attack or disturbance on a network or information system. Anomaly detection is a type of IDS that can detect a deviate attack on the network based on statistical probability. The increasing use of the internet also increases interference or attacks from intruders or crackers that exploit weak internet protocols and application software. When many data packets arrive, a problem arises that needs to be analyzed. The right technique to analyze the data package is data mining. This study aims to classify IDS anomalies using the Naïve Bayes classification algorithm from the results of attribute selection with correlation-based feature selection. This study uses a UNSW-NB15 intrusion detection system data collection consisting of 49 attributes and 321,283 data records. Performance measurements are based on accuracy, precision, F-Measure and ROC Area. The results of attribute selection with correlation-based feature selection leave 4 attributes. The results of the evaluation of IDS anomaly classification using the naïve Bayes algorithm without the precedence of the attributes selected by the correlation technique obtained an accuracy rate of 71.2%. While the classification results if preceded by the attributes selected by the correlation technique obtained an accuracy of 74.8%. Classification with the naïve Bayes algorithm can be improved its accuracy which is preceded by the selection of attributes with correlation techniques.

Sea Wave Detection System Using Web-Based Naive Bayes Algorithm

2021 ◽  
Author(s):  
Bimo Falaka ◽  
Randy Erfa Saputra ◽  
Casi Setianingsih ◽  
Muhammad Ary Murti
Keyword(s):  
Naive Bayes ◽  
Detection System ◽  
Naïve Bayes ◽  
Web Based ◽  
Wave Detection ◽  
Bayes Algorithm ◽  
Sea Wave

scholarly journals Cyberbullying Detection System Using Machine Learning

2021 ◽  
Vol 9 (9) ◽  
pp. 2059-2063
Author(s):  
Ms. Shama Kabeer
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Naive Bayes ◽  
Detection System ◽  
Naïve Bayes ◽  
The Internet ◽  
Bullying Behavior ◽  
Bayes Algorithm ◽  
Cyberbullying Detection ◽  
Online Form

Abstract: Cyberbullying is an online form of harassment. By posting, commenting, sending, or distributing personal, derogatory, false, or nasty stuff about others that can shame or humiliate them, this conduct is done with the goal of harming others. Once such content is published on the internet, it remains accessible indefinitely. This activity is considered unlawful, and it is more widespread among children and teenagers. Cyberbullying is an online epidemic that has the potential to result in devastating outcomes such as violence and suicide, and so must be dealt with swiftly and properly. To detect bullying behavior in textual messages, a real-time cyberbullying detection system based on machine learning—Naïve Bayes Algorithm is presented. The model was created to determine whether a tweet was bullying or non-bullying in nature. Also, to assist victims in dealing with bullying difficulties without their identities being revealed. Keywords: Machine Learning, Cyberbullying, Naïve Bayes, Cybercrimes, Cyberbullying Detection

scholarly journals Naive Bayes modification for intrusion detection system classification with zero probability

2021 ◽  
Vol 10 (5) ◽  
pp. 2751-2758
Author(s):  
Yogiek Indra Kurniawan ◽  
Fakhrur Razi ◽  
Nofiyati Nofiyati ◽  
Bangun Wijayanto ◽  
Muhammad Luthfi Hidayat
Keyword(s):  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Naive Bayes ◽  
Detection System ◽  
Naïve Bayes ◽  
Multiplication Operation ◽  
Bayes Algorithm ◽  
Research Show ◽  
Better Than

One of the methods used in detecting the intrusion detection system is by implementing Naïve Bayes algorithm. However, Naïve Bayes has a problem when one of the probabilities is 0, it will cause inaccurate prediction, or even no prediction was found. This paper proposed two modifications for Naïve Bayes algorithm. The first modification eliminated the variable that has 0 probability and the second modification changed the multiplication operations to addition operations. This modification is only applied when the Naïve Bayes algorithm does not find any prediction results caused by zero probabilities. The results of this research show that the value of precision, recall, and accuracy in the modification made tends to increase and better than the original Naïve Bayes algorithm. The highest precision, recall, and accuracy are obtained from modification by changing the multiplication operation to the addition. Increasing precision can reach 4%, increasing recall reaches 2% and increasing accuracy reaches 2%.

Algorithm Comparation of Naive Bayes and Support Vector Machine based on Particle Swarm Optimization in Sentiment Analysis of Freight Forwarding Services

2020 ◽  
Vol 4 (2) ◽  
pp. 362-369
Author(s):  
Sharazita Dyah Anggita ◽  
Ikmah
Keyword(s):  
Support Vector Machine ◽  
Sentiment Analysis ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Support Vector ◽  
The Public ◽  
Svm Algorithm ◽  
Bayes Algorithm ◽  
Freight Forwarding ◽  
Improved Accuracy

The needs of the community for freight forwarding are now starting to increase with the marketplace. User opinion about freight forwarding services is currently carried out by the public through many things one of them is social media Twitter. By sentiment analysis, the tendency of an opinion will be able to be seen whether it has a positive or negative tendency. The methods that can be applied to sentiment analysis are the Naive Bayes Algorithm and Support Vector Machine (SVM). This research will implement the two algorithms that are optimized using the PSO algorithms in sentiment analysis. Testing will be done by setting parameters on the PSO in each classifier algorithm. The results of the research that have been done can produce an increase in the accreditation of 15.11% on the optimization of the PSO-based Naive Bayes algorithm. Improved accuracy on the PSO-based SVM algorithm worth 1.74% in the sigmoid kernel.

Analysis of Sentiment of Moving a National Capital with Feature Selection Naive Bayes Algorithm and Support Vector Machine

2020 ◽  
Vol 4 (3) ◽  
pp. 504-512
Author(s):  
Faried Zamachsari ◽  
Gabriel Vangeran Saragih ◽  
Susafa'ati ◽  
Windu Gata
Keyword(s):  
Social Media ◽  
Support Vector Machine ◽  
Feature Selection ◽  
Public Opinion ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Capital City ◽  
Support Vector ◽  
National Capital ◽  
Bayes Algorithm

The decision to move Indonesia's capital city to East Kalimantan received mixed responses on social media. When the poverty rate is still high and the country's finances are difficult to be a factor in disapproval of the relocation of the national capital. Twitter as one of the popular social media, is used by the public to express these opinions. How is the tendency of community responses related to the move of the National Capital and how to do public opinion sentiment analysis related to the move of the National Capital with Feature Selection Naive Bayes Algorithm and Support Vector Machine to get the highest accuracy value is the goal in this study. Sentiment analysis data will take from public opinion using Indonesian from Twitter social media tweets in a crawling manner. Search words used are #IbuKotaBaru and #PindahIbuKota. The stages of the research consisted of collecting data through social media Twitter, polarity, preprocessing consisting of the process of transform case, cleansing, tokenizing, filtering and stemming. The use of feature selection to increase the accuracy value will then enter the ratio that has been determined to be used by data testing and training. The next step is the comparison between the Support Vector Machine and Naive Bayes methods to determine which method is more accurate. In the data period above it was found 24.26% positive sentiment 75.74% negative sentiment related to the move of a new capital city. Accuracy results using Rapid Miner software, the best accuracy value of Naive Bayes with Feature Selection is at a ratio of 9:1 with an accuracy of 88.24% while the best accuracy results Support Vector Machine with Feature Selection is at a ratio of 5:5 with an accuracy of 78.77%.

Sign in / Sign up

Export Citation Format

Share Document