scholarly journals Sentiment Analysis Of Government Policy On Corona Case Using Naive Bayes Algorithm

2021 ◽  
Vol 15 (1) ◽  
pp. 55
Author(s):  
Auliya Rahman Isnain ◽  
Nurman Satya Marga ◽  
Debby Alita
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Naive Bayes ◽  
Text Processing ◽  
Naïve Bayes ◽  
New Normal ◽  
Public Sentiment ◽  
N Gram ◽  
Bayes Algorithm ◽  
Economic Stabilization

 The Indonesian government has enforced the New Normal rule in maintaining economic stabilization and also restraining the spread of the virus during the Covid 19 pandemic. This has become a hot topic of conversation on social media Twitter, many people think positive and negative.The research conducted is a representation of text mining and text processing using machine learning using the Naive Bayes Classifier classification method, the objective of the analysis is to determine whether public sentiment towards the New Normal policy is positive or negative, and also as a basis for measuring the performance of the TF-IDF feature extraction and N-gram in machine learning uses the Naive Bayes method.The results of this study resulted in the accuracy rate of the Naive Bayes method with the TF-IDF feature selection. The total accuracy was 81% with a Precision value of 78%, Recall 91%, and f1-Score 84%. The highest results were obtained from the use of the Naive Bayes and Trigram algorithm parameters, namely 84%, namely 84% Precision, 86% Recall, and 85% f1-Score. The Naive Bayes algorithm with the use of the trigram type N-Gram feature extraction shows a fairly good performance in the process of classifying public tweet data.

scholarly journals Klasifikasi Rating Otomatis pada Dokumen Teks Ulasan Produk Elektronik Menggunakan Metode N-gram dan Naïve Bayes

2020 ◽  
Vol 5 (3) ◽  
pp. 295
Author(s):  
Rahmawan Bagus Trianto ◽  
Andri Triyono ◽  
Dhika Malita Puspita Arum
Keyword(s):  
Feature Extraction ◽  
Naive Bayes ◽  
Automatic Classification ◽  
Naïve Bayes ◽  
Lack Of Information ◽  
N Gram ◽  
Bayes Algorithm ◽  
Online Product Ratings ◽  
Product Description

Online product ratings usually provide descriptive reviews and also reviews in the form of ratings. Likewise, what was done at the Lazada online store. Descriptive review can provide a clear view compared to a rating review to other potential buyers. However, in reality there is a mismatch between the description review and the rating given. This creates a lack of information for sellers as well as potential buyers. Automatic classification of buyer descriptive reviews is proposed in this study so that there is a match between descriptive reviews and rating reviews. This automatic classification descriptive review uses the Naive Bayes algorithm with n-gram feature extraction and TF-IDF word weighting. The results of this study obtained the best accuracy of 94.06%, a recall of 91.73% and precision of 90.71% in Bigram feature extraction. With this accuracy value it can be used as a reference or model for classifying product description reviews, so that the feedback process between sellers and buyers can run well.

scholarly journals SMS CLASSIFICATION: CONJOINT ANALYSIS OF MULTINOMIAL NAIVE BAYES APPLICATION

2021 ◽  
Vol 9 (08) ◽  
pp. 1165-1173
Author(s):  
Vedad Burgic ◽  
◽  
Dino Keco ◽  
Keyword(s):  
Machine Learning ◽  
Naive Bayes ◽  
Text Processing ◽  
Training Model ◽  
Naïve Bayes ◽  
Processing Technologies ◽  
Collection Data ◽  
Bayes Algorithm ◽  
And Training

Nowadays there are ham and spam messages that are sent to the users via SMS. The aim of this article is to show how machine learning and text processing technologies can be used in order to predict the trustworthiness of SMS messages. The data we are going to use is collected from Kaggle. This study is very important because it helps us to understand how machine learning and text processing can be used in order to predict message trustworthiness. At the time of writing this article, there was not an article explaining how this can be done using the Multinomial Naive Bayes algorithm. The methodology we used in this article consists of dataset collection, data cleaning, data analysis, text preparation, and training model. This will be seen in the methodology section in great detail. At the end of this article, we will show to u the accuracy that we have got when implementing a Multinomial Naive Bayes algorithm for the classification of SMS messages. This study was quite beneficial because anyone can see how Multinomial Naive Bayes algorithm usage can be beneficial in order to predict the trustworthiness of SMS messages.

The Sentiment Analysis Reviewing Indosat Services from Twitter Using the Naive Bayes Classifier

2020 ◽  
Vol 1 (2) ◽  
pp. 61-66
Author(s):  
Febri Astiko ◽  
Achmad Khodar
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Sentiment Analysis ◽  
Naive Bayes ◽  
Learning Model ◽  
Naïve Bayes ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier ◽  
Machine Learning Model ◽  
Bayes Algorithm

This study aims to design a machine learning model of sentiment analysis on Indosat Ooredoo service reviews on social media twitter using the Naive Bayes algorithm as a classifier of positive and negative labels. This sentiment analysis uses machine learning to get patterns an model that can be used again to predict new data.

scholarly journals Effectiveness of Classification Methods on the Diabetes System

2021 ◽  
pp. 33-43
Author(s):  
Ahmed T. Shawky ◽  
Ismail M. Hagag
Keyword(s):  
Machine Learning ◽  
Naive Bayes ◽  
Early Stage ◽  
Research Paper ◽  
Naïve Bayes ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Machine Learning Classification ◽  
Bayes Algorithm

In today’s world using data mining and classification is considered to be one of the most important techniques, as today’s world is full of data that is generated by various sources. However, extracting useful knowledge out of this data is the real challenge, and this paper conquers this challenge by using machine learning algorithms to use data for classifiers to draw meaningful results. The aim of this research paper is to design a model to detect diabetes in patients with high accuracy. Therefore, this research paper using five different algorithms for different machine learning classification includes, Decision Tree, Support Vector Machine (SVM), Random Forest, Naive Bayes, and K- Nearest Neighbor (K-NN), the purpose of this approach is to predict diabetes at an early stage. Finally, we have compared the performance of these algorithms, concluding that K-NN algorithm is a better accuracy (81.16%), followed by the Naive Bayes algorithm (76.06%).

scholarly journals ANALISIS SENTIMEN PADA PEMERINTAHAN TERPILIH PADA PILPRES 2019 DITWITTER MENGGUNAKAN ALGORITME NAÏVEBAYES

JURTEKSI ◽  
2020 ◽  
Vol 7 (1) ◽  
pp. 101-106
Author(s):  
Febby Apri Wenando ◽  
Regiolina Hayami ◽  
Agung Jefrianto Anggrawan
Keyword(s):  
Presidential Election ◽  
Naive Bayes ◽  
Vice President ◽  
Naïve Bayes ◽  
Weighting Method ◽  
The Social ◽  
Twitter Account ◽  
N Gram ◽  
Bayes Algorithm ◽  
Modeling Data

Abstract: The Presidential general election on 2019 became one of the most popular topics on twitter nowdays.  The society give their opinion about the  pair of  candidates that they are support through the social media. This research was predicts about the society sentimens toward the candidates of President and Vice President of Republic of Indonesia. The data was used  based on the tweet on the @jokowi twitter account. The retrieval of data by using the Tweepy library with the Python 2.7 programming language. This research was classified became of two of society sentiments classes, namely positive and negative. The modeling was used of the weighting method Unigram, Bigram, Trigram, N-Gram (1-2) and N-Gram (1-3)  that used the Naïve Bayes Algorithm on the Weka Application. The modeling data was used by the dataset of 646 sentences. The highest results  of this reseach were obtained  by Unigram Weighting, namely: 81.4% accuracy, 81.5% precision, 81.3% recall with a time of 0.3 s.Keywords: classification, naïve bayes, 2019 presidential election, twitter, unigram Abstrak: Pemilihan Umum tentang Pilpres 2019 menjadi salah satu topik yang ramai diperbincangkan di Twitter. Adu pendapat di sosial media oleh masyarakat mengandung opini terhadap pasangan calon yang didukungnya. Penelitian ini memprediksi sentimen masyarakat kepada pasangan calon Presiden dan Wakil Presiden Republik Indonesia. Data yang digunakan adalah tweet yang ada pada akun Twitter @jokowi. Pengambilan data menggunakan library Tweepy dengan bahasa pemrograman Python 2.7. Penelitian ini mengklasifikasi sentimen masyarakat menjadi 2 kelas, yaitu positif dan negatif. Kemudian dilakukan pemodelan dengan metode pembobotan Unigram, Bigram, Trigram, N-Gram (1-2) Dan N-Gram (1-3) menggunakan Algoritme Naïve Bayes pada Aplikasi Weka. Pembuatan model menggunakan dataset yang berjumlah 646 kalimat. Hasil tertinggi yang diperoleh pada penelitian ini adalah dengan menggunakan Pembobotan Unigram, yaitu : akurasi 81,4%, presisi 81,5 % , recall 81,3 % dengan catatan waktu 0,3s.Kata kunci: klasifikasi, naïve bayes, pilpres 2019, twitter, unigram.

scholarly journals SENTIMENT ANALYSIS OF TWITTER DATA ON REMOTE LEARNING USING NAÏVE BAYES ALGORITHM

2021 ◽  
Vol 3 (3) ◽  
pp. 203-210
Author(s):  
Putri Rana Khairina ◽  
Desti Fitriati
Keyword(s):  
Sentiment Analysis ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Learning System ◽  
Public Sentiment ◽  
Global Pandemic ◽  
Distance Learning System ◽  
Twitter Users ◽  
The Government ◽  
Bayes Algorithm

Covid-19 is widespread, resulting in a global pandemic. Distance Learning System (DLS) is considered as a solution but, the reality of the implementation of DLS is not in accordance with the expectations of the community. Many Twitter users wrote their opinions on DLS. The tendency of public sentiment can be used as a way to improve the existing education system in Indonesia and can be an input for the government in improving the DLS method that is being implemented. Thus, this study produced a system that can analyze tweet sentiment towards DLS. The tweet was obtained using the Twitter API. The method used is Naïve Bayes for the process of classification of positive, negative, and neutral sentiments using 600 data. Then, data sharing is done 80% data training and 20% data testing that will be in the text preprocessing first. The accuracy of sentiment analysis of DLS using the Naïve Bayes method using 3-fold Cross-Validation produces an average of 93%.

scholarly journals Pengaruh N-Gram terhadap Klasifikasi Buku menggunakan Ekstraksi dan Seleksi Fitur pada Multinomial Naïve Bayes

2021 ◽  
Vol 5 (1) ◽  
pp. 264
Author(s):  
Esti Mulyani ◽  
Fachrul Pralienka Bani Muhamad ◽  
Kurnia Adi Cahyanto
Keyword(s):  
Naive Bayes ◽  
Automatic Classification ◽  
Naïve Bayes ◽  
Main Task ◽  
Test Results ◽  
Book Title ◽  
Feature Extraction And Selection ◽  
N Gram ◽  
Bayes Algorithm

Libraries have the main task in the processing of library materials by classifying books according to certain ways. Dewey Decimal Classification (DDC) is the method most commonly used in the world to determine book classification (labeling) in libraries. The advantages of this DDC method are universal and more systematic. However, this method is less efficient considering the large number of books that must be classified in a library, as well as labeling that must follow label updates on the DDC. An automatic classification system will be the perfect solution to this problem. Automatic classification can be done by applying the text mining method. In this study, searching for words in the book title was carried out with N-Gram (Unigram, Bigram, Trigram) as a feature generation. The features that have been raised are then selected for features. The process of book title classification is carried out using the Naïve Bayes Multinomial algorithm. This study examines the effect of Unigram, Bigram, Trigram on the classification of book titles using the feature extraction and selection feature on Multinomial Naïve Bayes algorithm. The test results show Unigram has the highest accuracy value of 74.4%.

scholarly journals THE IMPLEMENTATION OF THE MACHINE LEARNING ALGORITHM FOR THE SENTIMENT ANALYSIS OF INDONESIA’S 2019 PRESIDENTIAL ELECTION

2021 ◽  
Vol 22 (1) ◽  
pp. 78-92
Author(s):  
GA Buntoro ◽  
R Arifin ◽  
GN Syaifuddiin ◽  
A Selamat ◽  
O Krejcar ◽  
...  
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Sentiment Analysis ◽  
Presidential Election ◽  
Naive Bayes ◽  
Learning Algorithm ◽  
Naïve Bayes ◽  
Machine Learning Algorithm ◽  
Presidential Candidates ◽  
N Gram

In 2019, citizens of Indonesia participated in the democratic process of electing a new president, vice president, and various legislative candidates for the country. The 2019 Indonesian presidential election was very tense in terms of the candidates' campaigns in cyberspace, especially on social media sites such as Facebook, Twitter, Instagram, Google+, Tumblr, LinkedIn, etc. The Indonesian people used social media platforms to express their positive, neutral, and also negative opinions on the respective presidential candidates. The campaigning of respective social media users on their choice of candidates for regents, governors, and legislative positions up to presidential candidates was conducted via the Internet and online media. Therefore, the aim of this paper is to conduct sentiment analysis on the candidates in the 2019 Indonesia presidential election based on Twitter datasets. The study used datasets on the opinions expressed by the Indonesian people available on Twitter with the hashtags (#) containing "Jokowi and Prabowo." We conducted data pre-processing using a selection of comments, data cleansing, text parsing, sentence normalization and tokenization based on the given text in the Indonesian language, determination of class attributes, and, finally, we classified the Twitter posts with the hashtags (#) using Naïve Bayes Classifier (NBC) and a Support Vector Machine (SVM) to achieve an optimal and maximum optimization accuracy. The study provides benefits in terms of helping the community to research opinions on Twitter that contain positive, neutral, or negative sentiments. Sentiment Analysis on the candidates in the 2019 Indonesian presidential election on Twitter using non-conventional processes resulted in cost, time, and effort savings. This research proved that the combination of the SVM machine learning algorithm and alphabetic tokenization produced the highest accuracy value of 79.02%. While the lowest accuracy value in this study was obtained with a combination of the NBC machine learning algorithm and N-gram tokenization with an accuracy value of 44.94%. ABSTRAK: Pada tahun 2019 rakyat Indonesia telah terlibat dalam proses demokrasi memilih presiden baru, wakil presiden, dan berbagai calon legislatif negara. Pemilihan presiden Indonesia 2019 sangat tegang dalam kempen calon di ruang siber, terutama di laman media sosial seperti Facebook, Twitter, Instagram, Google+, Tumblr, LinkedIn, dll. Rakyat Indonesia menggunakan platfom media sosial bagi menyatakan pendapat positif, berkecuali, dan juga negatif terhadap calon presiden masing-masing. Kampen pencalonan menteri, gabenor, dan perundangan hingga pencalonan presiden dilakukan melalui media internet dan atas talian. Oleh itu, kajian ini dilakukan bagi menilai sentimen terhadap calon pemilihan presiden Indonesia 2019 berdasarkan kumpulan data Twitter. Kajian ini menggunakan kumpulan data yang diungkapkan oleh rakyat Indonesia yang terdapat di Twitter dengan hashtag (#) yang mengandungi "Jokowi dan Prabowo." Proses data dibuat menggunakan pilihan komentar, pembersihan data, penguraian teks, normalisasi kalimat, dan tokenisasi teks dalam bahasa Indonesia, penentuan atribut kelas, dan akhirnya, pengklasifikasian catatan Twitter dengan hashtag (#) menggunakan Klasifikasi Naïve Bayes (NBC) dan Mesin Vektor Sokongan (SVM) bagi mencapai ketepatan optimum dan maksimum. Kajian ini memberikan faedah dari segi membantu masyarakat meneliti pendapat di Twitter yang mengandungi sentimen positif, neutral, atau negatif. Analisis Sentimen terhadap calon dalam pemilihan presiden Indonesia 2019 di Twitter menggunakan proses bukan konvensional menghasilkan penjimatan kos, waktu, dan usaha. Penyelidikan ini membuktikan bahawa gabungan algoritma pembelajaran mesin SVM dan tokenisasi abjad menghasilkan nilai ketepatan tertinggi iaitu 79.02%. Manakala nilai ketepatan terendah dalam kajian ini diperoleh dengan kombinasi algoritma pembelajaran mesin NBC dan tokenisasi N-gram dengan nilai ketepatan 44.94%.

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