scholarly journals Analisis Sentimen Sistem Ganjil Genap di Tol Bekasi Menggunakan Algoritma Support Vector Machine

2019 ◽  
Vol 3 (2) ◽  
pp. 243-250
Author(s):  
Heru Sukma Utama ◽  
Didi Rosiyadi ◽  
Bobby Suryo Prakoso ◽  
Dedi Ariadarma
Keyword(s):  
Social Media ◽  
Support Vector Machine ◽  
Opinion Mining ◽  
Confusion Matrix ◽  
Support Vector ◽  
Support Vector Machine Algorithm ◽  
Toll Road ◽  
Svm Algorithm ◽  
Svm Model ◽  
Textual Data

Analysis of the odd even-numbered sentiment systems in Bekasi toll using the Support Vector Machine Algorithm, is a process of understanding, extracting, and processing textual data automatically from social media. The purpose of this study was to determine the level of accuracy, recall and precision of opinion mining generated using the Support Vector Machine algorithm to provide information community sentiment towards the effectiveness of the odd system of Bekasi tiolls on social media. The research method used in this study was to do text mining in comments-comments regarding posts regarding even odd oddities on Bekasi toll on Twitter, Instagram, Youtube and Facebook. The steps taken are starting from preprocessing, transformation, datamining and evaluation, followed by information gaon feature selection, select by weight and applying SVM Algorithm model. The results obtained from the study using the SVM model are obtained Confusion Matrix result, namely accuracyof 78.18%, Precision of 74.03%, and Sensitivity or Recall of 86.82%. Thus this study concludes that the use of Support Vector Machine Algorithms can analyze even odd sentiments on the Bekasi toll road.  

scholarly journals SENTIMEN ANALISIS KEBIJAKAN GANJIL GENAP DI TOL BEKASI MENGGUNAKAN ALGORITMA NAIVE BAYES DENGAN OPTIMALISASI INFORMATION GAIN

2019 ◽  
Vol 15 (2) ◽  
pp. 247-254
Author(s):  
Heru Sukma Utama ◽  
Didi Rosiyadi ◽  
Dedi Aridarma ◽  
Bobby Suryo Prakoso
Keyword(s):  
Social Media ◽  
Opinion Mining ◽  
Naive Bayes ◽  
Information Gain ◽  
Confusion Matrix ◽  
Naïve Bayes ◽  
Support Vector ◽  
Toll Road ◽  
Textual Data ◽  
Bayes Algorithm

Analysis of the odd even-numbered sentiment systems in Bekasi toll using the Naïve Bayes Algorithm, is a process of understanding, extracting, and processing textual data automatically from social media. The purpose of this study was to determine the level of accuracy, recall and precision of opinion mining generated using the Naïve Bayes algorithm to provide information community sentiment towards the effectiveness of the odd system of Bekasi tiolls on social media. The research method used in this study was to do text mining in comments-comments regarding posts regarding even odd oddities on Bekasi toll on Twitter, Instagram, Youtube and Facebook. The steps taken are starting from preprocessing, transformation, datamining and evaluation, followed by information gaon feature selection, select by weight and applying NB Algorithm model. The results obtained from the study using the NB model are obtained Confusion Matrix result, namely accuracy of 79,55%, Precision of 80,51%, and Sensitivity or Recall of 80,91%. Thus this study concludes that the use of Support Vector Machine Algorithms can analyze even odd sentiments on the Bekasi toll road.

scholarly journals Opinion mining on newspaper headlines using SVM and NLP

2019 ◽  
Vol 9 (3) ◽  
pp. 2152 ◽  
Author(s):  
Chaudhary Jashubhai Rameshbhai ◽  
Joy Paulose
Keyword(s):  
Support Vector Machine ◽  
Natural Language Processing ◽  
Language Processing ◽  
Opinion Mining ◽  
Confusion Matrix ◽  
Support Vector ◽  
Text Data ◽  
Mining Technique ◽  
Svm Model ◽  
Linear Svm

<p>Opinion Mining also known as Sentiment Analysis, is a technique or procedure which uses Natural Language processing (NLP) to classify the outcome from text. There are various NLP tools available which are used for processing text data. Multiple research have been done in opinion mining for online blogs, Twitter, Facebook etc. This paper proposes a new opinion mining technique using Support Vector Machine (SVM) and NLP tools on newspaper headlines. Relative words are generated using Stanford CoreNLP, which is passed to SVM using count vectorizer. On comparing three models using confusion matrix, results indicate that Tf-idf and Linear SVM provides better accuracy for smaller dataset. While for larger dataset, SGD and linear SVM model outperform other models.</p>

scholarly journals Analisis Sentimen Data Twitter Tentang Pasangan Capres-Cawapres Pemilu 2019 Dengan Metode Lexicon Based Dan Support Vector Machine

2019 ◽  
Vol 11 (2) ◽  
pp. 144
Author(s):  
Danar Wido Seno ◽  
Arief Wibowo
Keyword(s):  
Social Media ◽  
Support Vector Machine ◽  
Sentiment Analysis ◽  
Vice President ◽  
Training Data ◽  
Support Vector ◽  
New Words ◽  
Textual Data ◽  
Data Content ◽  
Combination Of Methods

Social media writing content growing make a lot of new words that appear on Twitter in the form of words and abbreviations that appear so that sentiment analysis is increasingly difficult to get high accuracy of textual data on Twitter social media. In this study, the authors conducted research on sentiment analysis of the pairs of candidates for President and Vice President of Indonesia in the 2019 Elections. To obtain higher accuracy results and accommodate the problem of textual data development on Twitter, the authors conducted a combination of methods to conduct the sentiment analysis with unsupervised and supervised methods. namely Lexicon Based. This study used Twitter data in October 2018 using the search keywords with the names of each pair of candidates for President and Vice President of the 2019 Elections totaling 800 datasets. From the study with 800 datasets the best accuracy was obtained with a value of 92.5% with 80% training data composition and 20% testing data with a Precision value in each class between 85.7% - 97.2% and Recall value for each class among 78, 2% - 93.5%. With the Lexicon Based method as a labeling dataset, the process of labeling the Support Vector Machine dataset is no longer done manually but is processed by the Lexicon Based method and the dictionary on the lexicon can be added along with the development of data content on Twitter social media.

scholarly journals KLASIFIKASI TEKS SOSIAL MEDIA TWITTER MENGGUNAKAN SUPPORT VECTOR MACHINE (Studi Kasus Penusukan Wiranto)

2019 ◽  
Vol 2 (2) ◽  
pp. 43
Author(s):  
Lalu Mutawalli ◽  
Mohammad Taufan Asri Zaen ◽  
Wire Bagye
Keyword(s):  
Social Media ◽  
Support Vector Machine ◽  
Big Data ◽  
Mass Communication ◽  
Confusion Matrix ◽  
Classification Model ◽  
Support Vector ◽  
Large Set ◽  
Appearance Time ◽  
Data Production

In the era of technological disruption of mass communication, social media became a reference in absorbing public opinion. The digitalization of data is very rapidly produced by social media users because it is an attempt to represent the feelings of the audience. Data production in question is the user posts the status and comments on social media. Data production by the public in social media raises a very large set of data or can be referred to as big data. Big data is a collection of data sets in very large numbers, complex, has a relatively fast appearance time, so that makes it difficult to handle. Analysis of big data with data mining methods to get knowledge patterns in it. This study analyzes the sentiments of netizens on Twitter social media on Mr. Wiranto stabbing case. The results of the sentiment analysis showed 41% gave positive comments, 29% commented neutrally, and 29% commented negatively on events. Besides, modeling of the data is carried out using a support vector machine algorithm to create a system capable of classifying positive, neutral, and negative connotations. The classification model that has been made is then tested using the confusion matrix technique with each result is a precision value of 83%, a recall value of 80%, and finally, as much as 80% obtained in testing the accuracy.

Modeling of Flexible Structures by Means of Least Square Support Vector Machine

2015 ◽  
Author(s):  
Hammam Tamimi ◽  
Dirk Söffker
Keyword(s):  
Support Vector Machine ◽  
Least Squares ◽  
Flexible Structures ◽  
Operating Conditions ◽  
Least Square ◽  
Support Vector ◽  
Suitable Model ◽  
Support Vector Machine Algorithm ◽  
Training Time ◽  
Svm Algorithm

This paper investigates modeling of flexible structures by means of the least squares support vector machine (LS-SVM) algorithm. Modeling is the first step to obtain a suitable model-based controller for any given system. Accurate modeling of a flexible structure based on experimental data using LS-SVM algorithm requires less knowledge about the physical system. Least squares support vector machine algorithm can achieve global and unique solution when compared with other soft computing algorithms. Also, LS-SVM algorithm requires less training time. In this paper, the successful use of support vector machine algorithm to model the flexible cantilever is demonstrated. The acquired model is able to provide accurate prediction of the system output under different operating conditions. Experimental results demonstrate the efficiency and high precision of the proposed approach.

scholarly journals Comparison of Support Vector Machine-Based Techniques for Detection of Bearing Faults

2018 ◽  
Vol 2018 ◽  
pp. 1-13
Author(s):  
Lijun Wang ◽  
Shengfei Ji ◽  
Nanyang Ji
Keyword(s):  
Support Vector Machine ◽  
Global Optimization ◽  
Fault Diagnosis ◽  
Model Simulation ◽  
Rolling Bearing ◽  
Support Vector ◽  
Denoising Method ◽  
Svm Algorithm ◽  
Svm Model ◽  
Shuffled Frog Leaping

This paper presents a method that combines Shuffled Frog Leaping Algorithm (SFLA) with Support Vector Machine (SVM) method in order to identify the fault types of rolling bearing in the gearbox. The proposed method improves the accuracy of fault diagnosis identification after processing the collected vibration signals through wavelet threshold denoising. The global optimization and high computational efficiency of SFLA are applied to the SVM model. Simulation results show that the SFLA-SVM algorithm is effective in fault diagnosis. Compared with SVM and Particle Swarm Optimization SVM (PSO-SVM) algorithms, it is demonstrated that the SFLA-SVM algorithm has the advantages of better global optimization, higher accuracy, and better reliability of diagnosis. Its accuracy is further improved through the integration of the wavelet threshold denoising method.

Ooredoo Rayek

2020 ◽  
Vol 11 (2) ◽  
pp. 66-81
Author(s):  
Badia Klouche ◽  
Sidi Mohamed Benslimane ◽  
Sakina Rim Bennabi
Keyword(s):  
Social Media ◽  
Support Vector Machine ◽  
Text Mining ◽  
Sentiment Analysis ◽  
Experimental Results ◽  
Support Vector ◽  
Textual Data ◽  
New Strategy ◽  
Set Up

Sentiment analysis is one of the recent areas of emerging research in the classification of sentiment polarity and text mining, particularly with the considerable number of opinions available on social media. The Algerian Operator Telephone Ooredoo, as other operators, deploys in its new strategy to conquer new customers, by exploiting their opinions through a sentiments analysis. The purpose of this work is to set up a system called “Ooredoo Rayek”, whose objective is to collect, transliterate, translate and classify the textual data expressed by the Ooredoo operator's customers. This article developed a set of rules allowing the transliteration from Algerian Arabizi to Algerian dialect. Furthermore, the authors used Naïve Bayes (NB) and (Support Vector Machine) SVM classifiers to assign polarity tags to Facebook comments from the official pages of Ooredoo written in multilingual and multi-dialect context. Experimental results show that the system obtains good performance with 83% of accuracy.

scholarly journals Wavelet and cuckoo search-support vector machine conjugation for drought forecasting using Standardized Precipitation Index (case study: Urmia Lake, Iran)

2018 ◽  
Vol 20 (4) ◽  
pp. 975-988 ◽  
Author(s):  
Mehdi Komasi ◽  
Soroush Sharghi ◽  
Hamid R. Safavi
Keyword(s):  
Time Series ◽  
Support Vector Machine ◽  
Standardized Precipitation Index ◽  
Cuckoo Search ◽  
Precipitation Index ◽  
Urmia Lake ◽  
Support Vector ◽  
Drought Forecasting ◽  
Svm Algorithm ◽  
Svm Model

Abstract In this study, wavelet-support vector machine (WSVM) is proposed for drought forecasting using the Standardized Precipitation Index (SPI). In this way, the SPI time series of Urmia Lake watershed is decomposed to multiple frequency time series by wavelet transform. Then, these time sub-series are applied as input data to the support vector machine (SVM) model to forecast drought. Also, a cuckoo search (CS)-based approach is proposed for parameter optimization of SVM, finding the best initial constant parameters of the SVM algorithm. The obtained results indicate that the radial basis function (RBF)-kernel function of the SVM algorithm has high efficiency in the SPI modeling, resulting in a determination coefficient (DC) of 0.865 in verification step. In the WSVM model, the Coif1, which is considered as a mother wavelet function with decomposition level of five, shows a better performance with DC of 0.954 in verification step, revealing that the proposed hybrid WSVM model outperforms the single SVM model in forecasting SPI time series. Also, DC of cuckoo search-support vector machine (CS-SVM) is calculated to be 0.912 in verification step, indicating the fact that the proposed CS-SVM model shows better efficiency than single SVM model.

scholarly journals Alumni Info-Com with Distinct Classification of Data using Support Vector Machine Algorithm

2020 ◽  
Vol 8 (6) ◽  
pp. 3363-3367
Keyword(s):  
Support Vector Machine ◽  
Support Vector ◽  
Support Vector Machine Algorithm ◽  
Single System ◽  
Web Based ◽  
Svm Algorithm ◽  
Alumni Associations

This paper presents to create a centralized alumni network for betterment of institutions and upcoming student’s community. The System is able to collect and store alumni information for future communication. Former students of institution can communicate with their immediate friends as well as forthcoming students and various members involved in the institution community. Apart from the alumni, the institutions/organization also benefitted when sustains this network. This single system can satisfy almost every requirement of the alumni. Usually, alumni associations are organized in colleges, but may also be organized in a place where the alumni can meet each other. Despite the fact that there are many existing systems in colleges to maintain the alumni information, they are manual and more time consuming to current students to reach out their alumni and maintaining the privacy of the alumni. To overcome these issues, we proposed a web based application which allows alumni to update their information and students can connect with them and can view the filtered events posted by alumni and admin through Support Vector Machine algorithm (SVM). Proposed method, SVM algorithm used to classify the alumni members and their posting message from others in this community.

scholarly journals A Comparative Study of Different Classification Techniques for Sentiment Analysis

2020 ◽  
Vol 11 (1) ◽  
pp. 49-57
Author(s):  
Soumadip Ghosh ◽  
Arnab Hazra ◽  
Abhishek Raj
Keyword(s):  
Social Media ◽  
Support Vector Machine ◽  
Logistic Regression ◽  
Decision Tree ◽  
Comparative Study ◽  
Sentiment Analysis ◽  
Opinion Mining ◽  
Support Vector ◽  
Product Reviews ◽  
Accurate Model

Sentiment analysis denotes the analysis of emotions and opinions from text. The authors also refer to sentiment analysis as opinion mining. It finds and justifies the sentiment of the person with respect to a given source of content. Social media contain vast amounts of the sentiment data in the form of product reviews, tweets, blogs, and updates on the statuses, posts, etc. Sentiment analysis of this largely generated data is very useful to express the opinion of the mass in terms of product reviews. This work is proposing a highly accurate model of sentiment analysis for reviews of products, movies, and restaurants from Amazon, IMDB, and Yelp, respectively. With the help of classifiers such as logistic regression, support vector machine, and decision tree, the authors can classify these reviews as positive or negative with higher accuracy values.

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