scholarly journals Line Bot Chat Filtering using Naïve Bayes Algorithm

2019 ◽  
Vol 8 (12) ◽  
pp. 4877-4882
Keyword(s):  
Good Accuracy ◽  
Naive Bayes ◽  
Instant Messaging ◽  
Naïve Bayes ◽  
Spam Filtering ◽  
Training Process ◽  
Huge Amount ◽  
Instant Messenger ◽  
Bayes Algorithm ◽  
Incoming Message

Instant messaging has changed and simplified the way people communicate, whether in professional or personal life. Most communication is done through instant messaging, and it is common for people to miss important information. This is due to the huge amount of incoming message notifications, so users tend to accidentally ignore them. This is also experienced by Universitas Multimedia Nusantara (UMN) student committees who communicate via LINE instant messenger. This research showed LINE bot was made by using the Naive Bayes algorithm to classify between important messages and unimportant messages on the committee group. The Naive Bayes algorithm is a classification algorithm based on probability and statistical methods. The Naive Bayes algorithm is chosen because it is widely implemented in spam filtering; the method is simple and has good accuracy. The classification process is done by calculating the probability of chat in each class based on the value of the word likelihood which generated in the training process. This research produces spam precision and spam recall as 94.2% and 95.6% respectively

scholarly journals Analysis of Naïve Bayes Algorithm for Email Spam Filtering across Multiple Datasets

2017 ◽  
Vol 226 ◽  
pp. 012091 ◽  
Author(s):  
Nurul Fitriah Rusland ◽  
Norfaradilla Wahid ◽  
Shahreen Kasim ◽  
Hanayanti Hafit
Keyword(s):  
Naive Bayes ◽  
Naïve Bayes ◽  
Spam Filtering ◽  
Multiple Datasets ◽  
Bayes Algorithm ◽  
Email Spam

scholarly journals KLASIFIKASI OPINI GREEN AND CLEAN KABUPATEN LAMONGAN MENGGUNAKAN ALGORITMA MULTINOMIAL NAIVE BAYES

JOUTICA ◽  
2018 ◽  
Vol 3 (1) ◽  
pp. 125
Author(s):  
Agus Setia Budi
Keyword(s):  
Opinion Mining ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Training Process ◽  
Subjective Test ◽  
Public Sentiment ◽  
Negative Comments ◽  
Bayes Algorithm ◽  
Two Phases ◽  
Manual Classification

This study aims to process and classify an opinion (Opinion mining), opinion is a subjective statement that reflects public sentiment or perception of the entity or activity. Most opinions has not been managed well, if The Opinions properly managed will provide important information can be used to make improvements toward better at an activity or program. This study focuses on the processing of opinions that come from public opinion In Lamongan against LGC program which includes cleanliness, green and financial. The study was divided into two phases, namely the training process to produce data (dataset) to perform the classification process and the subjective (datates). Both processes are aimed to extract attributes and object components that have been commented upon in any document and to determine whether positive or negative comments. The results of the subjective test classification using Multinomial Naive Bayes algorithm has a success rate above 80% classification accuracy when it is matched with the manual classification

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%.

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