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

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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Comparison of Naive Bayes Algorithm and Support Vector Machine using PSO Feature Selection for Sentiment Analysis on E-Wallet Review

Journal of Physics Conference Series ◽

10.1088/1742-6596/1641/1/012085 ◽

2020 ◽

Vol 1641 ◽

pp. 012085

Author(s):

Dwi Andini Putri ◽

Dinar Ajeng Kristiyanti ◽

Elly Indrayuni ◽

Acmad Nurhadi ◽

Denda Rinaldi Hadinata

Keyword(s):

Support Vector Machine ◽

Feature Selection ◽

Sentiment Analysis ◽

Naive Bayes ◽

Naïve Bayes ◽

Support Vector ◽

Selection For ◽

Bayes Algorithm

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SENTIMENT ANALYSIS ON E-SPORTS FOR EDUCATION CURRICULUM USING NAIVE BAYES AND SUPPORT VECTOR MACHINE

Jurnal Ilmu Komputer dan Informasi ◽

10.21609/jiki.v13i2.885 ◽

2020 ◽

Vol 13 (2) ◽

pp. 109-122

Author(s):

Rian Ardianto ◽

Tri Rivanie ◽

Yuris Alkhalifi ◽

Fitra Septia Nugraha ◽

Windu Gata

Keyword(s):

Social Media ◽

Support Vector Machine ◽

Naive Bayes ◽

Marketing Research ◽

Naïve Bayes ◽

Support Vector ◽

The Public ◽

Accuracy Difference ◽

Auc Value ◽

Bayes Algorithm

The development of e-sports education is not just playing games, but about start making, development, marketing, research and other forms education aimed at training skills and providing knowledge in fostering character. The opinions expressed by the public can take form support, criticism and input. Very large volume of comments need to be analyzed accurately in order separate positive and negative sentiments. This research was conducted to measure opinions or separate positive and negative sentiments towards e-sports education, so that valuable information can be sought from social media. Data used in this study was obtained by crawling on social media Twitter. This study uses a classification algorithm, Naïve Bayes and Support Vector Machine. Comparison two algorithms produces predictions obtained that the Naïve Bayes algorithm with SMOTE gets accuracy value 70.32%, and AUC value 0.954. While Support Vector Machine with SMOTE gets accuracy value 66.92% and AUC value 0.832. From these results can be concluded that Naïve Bayes algorithm has a higher accuracy compared to Support Vector Machine algorithm, it can be seen that the accuracy difference between naïve Bayes and the vector machine support is 3.4%. Naïve Bayes algorithm can thus better predict the achievement of e-sports for students' learning curriculum.

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

Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) ◽

10.29207/resti.v4i2.1840 ◽

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.

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

Jurnal Pilar Nusa Mandiri ◽

10.33480/pilar.v15i2.705 ◽

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.

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Thematic Textual Hadith Classification: An Experiment in Rapidminer using Support Vector Machine (SVM) and Naïve Bayes Algorithm

International Journal of Advanced Trends in Computer Science and Engineering ◽

10.30534/ijatcse/2020/262942020 ◽

2020 ◽

Vol 9 (4) ◽

pp. 5967-5972

Author(s):

Norzihani Yusof

Keyword(s):

Support Vector Machine ◽

Naive Bayes ◽

Naïve Bayes ◽

Support Vector ◽

Bayes Algorithm

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Marketplace Sentiment Analysis Using Naive Bayes And Support Vector Machine

PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic ◽

10.33558/piksel.v8i2.2272 ◽

2020 ◽

Vol 8 (2) ◽

pp. 91-100

Author(s):

Muhamad Azhar ◽

Noor Hafidz ◽

Biktra Rudianto ◽

Windu Gata

Keyword(s):

Support Vector Machine ◽

Feature Selection ◽

Particle Swarm Optimization ◽

Sentiment Analysis ◽

Cross Validation ◽

Naive Bayes ◽

Particle Swarm ◽

Naïve Bayes ◽

Support Vector ◽

Swarm Optimization

Abstract Technology implementation in the marketplace world has attracted the attention of researchers to analyze the reviews from customers. The Klik Indomaret application page on GooglePlay is one application that can be used to get information on review data collection. However, getting information on consumer’s opinion or review is not an easy task and need a specific method in categorizing or grouping these reviews into certain groups, i.e. positive or negative reviews. The sentiment analysis study of a review application in GooglePlay is still rare. Therefore, this paper analysis the customer’s sentiment from klikindomaret app using Naive Bayes Classifier (NB) algorithm that is compared to Support Vector Machine (SVM) as well as optimizing the Feature Selection (FS) using the Particle Swarm Optimization method. The results for NB without using FS optimization were 69.74% for accuracy and 0.518 for Area Under Curve (AUC) and for SVM without using FS optimization were 81.21% for accuracy and 0.896 for AUC. While the results of cross-validation NB with FS are 75.21% for accuracy and 0.598 for AUC and cross-validation of SVM with FS is 81.84% for accuracy and 0.898 for AUC, while there is an increase when using the Feature Selection (FS) Particle Swarm Optimization and also the modeling algorithm SVM has a higher value compared to NB for the dataset used in this study. Keywords: Naive Bayes, Particle Swarm Optimization, Support Vector Machine, Feature Selection, Consumer Review.

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KOMPARASI NAÏVE BAYES, SUPPORT VECTOR MACHINE DAN K-NEAREST NEIGHBOR UNTUK MENGETAHUI AKURASI TERTINGGI PADA PREDIKSI KELANCARAN PEMBAYARAN TV KABEL

ILKOM Jurnal Ilmiah ◽

10.33096/ilkom.v11i1.408.11-16 ◽

2019 ◽

Vol 11 (1) ◽

pp. 11-16

Author(s):

Mohamad Efendi Lasulika

Keyword(s):

Neural Network ◽

Support Vector Machine ◽

Nearest Neighbor ◽

Naive Bayes ◽

Naïve Bayes ◽

Support Vector ◽

K Nearest Neighbor ◽

Data Types ◽

Neural Network Algorithm ◽

Bayes Algorithm

One obstacle of the default payment is the lack of analysis in the new customer acceptance process which is only reviewed from the form provided at registration, as for the purpose of this study to find out the highest accuracy results from the comparison of Naïve Bayes, SVM and K-NN Algorithms. It can be seen that the Naïve Bayes algorithm which has the highest accuracy value is 96%, while the K-Neural Network algorithm has the highest accuracy at K = 3 which is 92%, while Support Vector Machine only gets accuracy of 66%. The ROC Curve results show that Naïve Bayes achieved the best AUC value of 0.99. Comparison between data mining classification algorithms namely Naïve Bayes, K-Neural Network and Support Vector Machine for predicting smooth payment using multivariate data types, Naïve Bayes method is an accurate algorithm and this method is also very dominant towards other methods. Based on Accuracy, AUC and T-tests this method falls into the best classification category.

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ALGORITMA KLASIFIKASI NAIVE BAYES DAN SUPPORT VECTOR MACHINE DALAM LAYANAN KOMPLAIN MAHASISWA

JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) ◽

10.33480/jitk.v5i2.1181 ◽

2020 ◽

Vol 5 (2) ◽

pp. 211-220 ◽

Cited By ~ 2

Author(s):

Hermanto Hermanto ◽

Ali Mustopa ◽

Antonius Yadi Kuntoro

Keyword(s):

Support Vector Machine ◽

Text Mining ◽

Naive Bayes ◽

Naïve Bayes ◽

Support Vector ◽

Machine Method ◽

The Status ◽

Auc Value ◽

Bayes Algorithm

Service in the world of education is an important element for the creation of an academic atmosphere that is conducive to the implementation of a successful teaching and learning process. The process of service to students there is a tendency to be implemented not following the minimum service standards that must be provided to students so that students tend to complain about the services provided. Submission of criticism, complaints, input, or suggestions for dissatisfaction and problems that exist in the university environment is still very limited. Complaints can be constructive if submitted to the right place and party. In this research the data processing of email complaints from students conducted at the academic student body (students.bsi.ac.id). Student complaint data that will be processed is data in the form of * .xls complaint file. Before text data is analyzed using text mining methods, the pre-processing text needs to be done including tokenizing, case folding, stopwords, and stemming. After pre-processing, the classification method is then performed in classifying each complaint category and dividing the status into two parts, namely complaint and not complaint so that the status becomes a normal condition in text mining research. The purpose of this study is to obtain the most accurate algorithm in the classification of student complaints and can find out the results of the classification of the Naïve Bayes algorithm method and Support vector Machine used and compared. In this study, the results of testing by measuring the performance of these two algorithms using Cross-Validation, Confusion Matrix, and ROC Curves. The obtained Support vector Machine algorithm has the highest accuracy value compared to Naïve Bayes. AUC value = 0.922. for the Support vector machine method using the student academic data collection dataset (students.bsi.ac.id) has 84.45%, from the Naïve Bayes algorithm has an accuracy rate of about 69.75% and AUC value = 0.679.

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Analisis Sentimen Opini Terhadap Vaksin Covid - 19 pada Media Sosial Twitter Menggunakan Support Vector Machine dan Naive Bayes

Jurnal Komtika ◽

10.31603/komtika.v5i1.5185 ◽

2021 ◽

Vol 5 (1) ◽

pp. 19-25

Author(s):

Frizka Fitriana ◽

Ema Utami ◽

Hanif Al Fatta

Keyword(s):

Support Vector Machine ◽

Naive Bayes ◽

Naïve Bayes ◽

World Health ◽

Support Vector ◽

Support Vector Machine Algorithm ◽

Bayes Algorithm ◽

Health Organization ◽

The Right ◽

The Impact

The corona virus outbreak, commonly referred to as COVID-19, has been officially designated a global pandemic by the World Health Organization (WHO). To minimize the impact caused by the virus, one of the right steps is to develop a vaccine, however, with the vaccination for the Indonesian people, it is controversial so that it invites many people to give an opinion assessment, but the limited space makes it difficult for the public to express their opinion, because Therefore, people choose social media as a place to channel public opinion. Support vector machine algorithm has better performance in terms of accuracy, precision and recall with values of 90.47%, 90.23%, 90.78% with performance values on the Bayes algorithm, namely 88.64%, 87.32%, 88, 13%, with a difference of 1.83% accuracy, 2.91% precision and 2.65% recall, while for time the Naive Bayes algorithm has a better performance level with a value of 8.1 seconds and the Support vector machine algorithm gets a time speed of 11 seconds with a difference of 2, 9 seconds. With the results of sentiment analysis neutral 8.76%, negative 42.92% and positive 48.32% for Bayes and neutral 10.56%, negative 41.28% and positive 48.16% for SVM.

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