Sentiment Analysis of the Enforcement of PSBB Part II in Jakarta

2020 ◽  
Vol 9 (2) ◽  
pp. 259
Author(s):  
Gede Putra Aditya Brahmantha ◽  
I Wayan Santiyasa
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Nearest Neighbor ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Web Crawling ◽  
K Nearest Neighbor ◽  
Bayes Method ◽  
The Public ◽  
Naive Bayes Method

In addition to communicating, Social Media is a place to issue opinions by the public on many things that are currently taking place, Twitter is one of these social medias that is widely used in conveying opinions regardless of whether these opinions are negative, positive, or even neutral. Tweets data about the Enforcement of PSBB Part II in Jakarta were obtained as many as 200 opinions using web crawling then advanced to the preprocessing stage before being classified using the K-Nearest Neighbor and Multinomial Naive Bayes algorithms. In 3 tests, the highest accuracy was 65.00% for K-Nearest Neighbor and the highest accuracy was 85.00% for Multinomial Naive Bayes method.

scholarly journals Perbandingan Kinerja Metode Naive Bayes dan K-Nearest Neighbor untuk Klasifikasi Artikel Berbahasa indonesia

2018 ◽  
Vol 5 (4) ◽  
pp. 427 ◽  
Author(s):  
Riri Nada Devita ◽  
Heru Wahyu Herwanto ◽  
Aji Prasetya Wibawa
Keyword(s):  
Nearest Neighbor ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Training Data ◽  
Classification Methods ◽  
K Nearest Neighbor ◽  
Bayes Method ◽  
K Nearest Neighbors ◽  
Naive Bayes Method ◽  
Main Factor

<p class="Abstrak">Kecocokan isi artikel dengan sebuah tema jurnal menjadi faktor utama diterima tidaknya sebuah artikel. Tetapi masih banyak mahasiswa yang bingung untuk menentukan jurnal yang sesuai dengan artikel yang dimilikinya. Untuk itu diperlukannya sebuah metode klasifikasi dokumen yang dapat mengelompokkan artikel secara otomatis dan akurat. Terdapat banyak metode klasifikasi yang dapat digunakan. Metode yang digunakan dalam penelitian ini adalah <em>Naive Bayes</em> dan sebagai <em>baseline </em>digunakan metode <em>K-Nearest Neighbor</em>. Metode <em>Naive Bayes </em>dipilih karena dapat menghasilkan akurasi yang maksimal dengan data latih yang sedikit. Sedangkan metode <em>K-Nearest Neighbor</em> dipilih karena metode tersebut tangguh terhadap data <em>noise</em>. Kinerja dari kedua metode tersebut akan dibandingkan, sehingga dapat diketahui metode mana yang lebih baik dalam melakukan klasifikasi dokumen. Hasil yang didapatkan menunjukkan metode <em>Naive Bayes </em>memiliki kinerja yang lebih baik dengan tingkat akurasi 70%, sedangkan metode <em>K-Nearest Neighbor </em>memiliki tingkat akurasi yang cukup rendah yaitu 40%.</p><p class="Abstrak"> </p><p class="Abstrak"><em><strong>Abstract</strong></em></p><p class="Abstrak"><em>One way to be accepted in a journal conference and get the publication is to create an article with perfect suitability content of the journal. Matching the content of the article with a journal theme is the main factor for acceptability an article. But there are still many students who are confused to choose the journal in accordance with the articles it has. So we need a method to classification article documents category automatically and accurately group articles. There are many classification methods that can be used. The method used in this study is Naive Bayes and as a baseline the K-Nearest Neighbor method. Naive Bayes method is chosen because it can produce maximum accuracy with little training data. While K-Nearest Neighbor method was chosen because the method is robust to data noise. The performance of the two methods will be compared, so we can be known which method is better in classifying the document. The results show that the Naive Bayes method performs is more accurate with 70% accuracy and K-Nearest Neighbors method has a fairly low accuracy of 40% on classification test.</em></p>

scholarly journals Estimation System For Late Payment Of School Tuition Fees

2020 ◽  
Vol 1 (1) ◽  
pp. 1-6
Author(s):  
Muqorobin Muqorobin ◽  
Kusrini Kusrini ◽  
Siti Rokhmah ◽  
Isnawati Muslihah
Keyword(s):  
Naive Bayes ◽  
Information Gain ◽  
Educational Institution ◽  
Confusion Matrix ◽  
Naïve Bayes ◽  
K Nearest Neighbor ◽  
Tuition Fees ◽  
Bayes Method ◽  
Estimation System ◽  
Naive Bayes Method

The Surakarta Al-Islam Vocational School is a private educational institution that requires all students to pay school tuition fees. Education is an obligation for all Indonesian citizens. The cost of education is one of the most important input components in implementing education. Because cost is the main requirement in achieving educational goals. SPP School is a routine school fee that is carried out every month. Based on last year's School Admin report, many students were late in paying school tuition fees, around 60%. This is a very big problem because the income of school funds comes from school tuition. The purpose of this research is that the researcher will build a prediction system using the best classification method, which is to compare the accuracy level of the Naïve Bayes method with the K-K-Nearest Neighbor method. Because both methods can make class classifications right or late, in paying school fees. processing using dapodic data for 2017/2018 as many as 236 data. In improving accuracy, the researcher also applies feature selection with Information Gain, which is useful for selecting optimal parameters. System testing is carried out using the Confusion Matrix method. The final results of this study indicate that the Naïve Bayes Method + Information Gain Method produces the highest accuracy, namely 95% compared to the Naïve Bayes method alone, namely 85% and the K-NN method, namely 81%.

scholarly journals Analisis Sentimen dan Pemodelan Topik Pariwisata Lombok Menggunakan Algoritma Naive Bayes dan Latent Dirichlet Allocation

2021 ◽  
Vol 5 (1) ◽  
pp. 123-131
Author(s):  
Ni Luh Putu Merawati Putu ◽  
Ahmad Zuli Amrullah ◽  
Ismarmiaty
Keyword(s):  
Social Media ◽  
Latent Dirichlet Allocation ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Classification Model ◽  
Bayes Method ◽  
A Value ◽  
Negative Class ◽  
Naive Bayes Method ◽  
Dirichlet Allocation

Lombok Island is one of the favorite tourist destinations. Various topics and comments about Lombok tourism experience through social media accounts are difficult to manually identify public sentiments and topics. The opinion expressed by tourists through social media is interesting for further research. This study aims to classify tourists' opinions into two classes, positive and negative, and topics modelling by using the Naive Bayes method and modeling the topic by using Latent Dirichlet Allocation (LDA). The stages of this research include data collection, data cleaning, data transformation, data classification. The results performance testing of the classification model using Naive Bayes method is shown with an accuracy value of 92%, precision of 100%, recall of 84% and specificity of 100%. The results of modeling topics using LDA in each positive and negative class from the coherence value shows the highest value for the positive class was obtained on the 8th topic with a value of 0.613 and for the negative class on the 12th topic with a value of 0.528. The use of the Naive Bayes and LDA algorithms is considered effective for analyzing the sentiment and topic modelling for Lombok tourism.  

scholarly journals Analisis Sentimen Masyarakat Terhadap COVID-19 Pada Media Sosial Twitter

2021 ◽  
Vol 1 (1) ◽  
pp. 42-51
Author(s):  
Ardianne Luthfika Fairuz ◽  
Rima Dias Ramadhani ◽  
Nia Annisa Ferani Tanjung
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Nearest Neighbor ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
K Nearest Neighbor

Akhir tahun 2019 lalu dunia digemparkan oleh munculnya suatu penyakit yang disebabkan oleh virus SARS-CoV-2 yang merupakan jenis virus terbaru dari coronavirus. Penyakit ini dikenal dengan nama COVID-19. Penyebaran penyakit ini terbilang cukup luas dan cepat. Dalam waktu singkat penyakit ini mulai menyebar ke segala penjuru dunia tak terkecuali Indonesia. Dengan tingkat penyebaran yang begitu tinggi dan belum ditemukannya vaksin untuk COVID-19, menyebabkan kekacauan di tengah masyarakat. Hal ini mempengaruhi banyak sektor kehidupan masyarakat. Tak sedikit masyarakat yang aktif bersosial media dan menuliskan pendapat, opini serta pemikirannya di platform media sosial seperti Twitter. Terjadinya pandemi ini mendorong masyarakat untuk menuliskan opini, pemikiran serta pendapatnya terhadap COVID-19 pada media sosial Twitter. Dibutuhkan suatu model sentiment analysis untuk mengklasifikasi tweet masyarakat di Twitter menjadi positif dan negatif. Sentiment analysis merupakan bagian dari Natural Language Processing yang membuat sebuah sistem guna mengenali serta mengekstraksi opini dalam  bentuk teks. Pada penelitian ini digunakan algoritma Naive Bayes dan K-Nearest Neighbor untuk digunakan dalam membangun model sentiment analysis terhadap tweet pengguna Twitter terhadap COVID-19. Didapatkan akurasi sebesar 85% untuk algoritma Naïve Bayes dan 82% untuk algoritma K-Nearest Neighbor pada nilai k=6, 8, dan 14.

scholarly journals TEXT MINING DALAM MEMBANDINGKAN METODE NAÏVE BAYES DENGAN C.45 DALAM MENGIDENTIFIKASI BERITA HOAX PADA MEDIA SOSIAL

2022 ◽  
Vol 5 (1) ◽  
pp. 116-123
Author(s):  
Yola Tri Handika ◽  
Sarjon Defit ◽  
Gunadi Widi Nurcahyo
Keyword(s):  
Social Media ◽  
Naive Bayes ◽  
Detection System ◽  
High Accuracy ◽  
Naïve Bayes ◽  
Major Influence ◽  
Bayes Method ◽  
Training Stage ◽  
Political Conditions ◽  
Naive Bayes Method

Hoax news (hocus to trick) has a very big influence in disseminating information, especially in the world of social media. News has an important impact on social and political conditions, and news can move the economy of a country. For this reason, it is necessary to have an analysis to classify hoax news and not hoaxes, and have high accuracy in classifying the news. In this study, two methods were used as a comparison in achieving high accuracy, namely the Naïve Bayes method which is famous for having high accuracy in classification with little data, and the C.45 method which can minimize noise in the data. The data used are 300 articles with 10 topics which contain hoax and non-hoax news. The data is obtained from the internet through social media, such as Twitter, Instagram and Facebook. Testing using the Naïve Bayes method has a higher accuracy than the C.45 method. The amount of data used has a major influence on the test results, if more data enters the training stage, then this study will have higher accuracy. However, the results of this test can be recommended to increase accuracy in the construction of a hoax news detection system.

Sentiment Analysis of Tweets Using Naïve Bayes, KNN, and Decision Tree

2020 ◽  
Vol 10 (4) ◽  
pp. 35-49
Author(s):  
Kadda Zerrouki ◽  
Reda Mohamed Hamou ◽  
Abdellatif Rahmoun
Keyword(s):  
Decision Tree ◽  
Sentiment Analysis ◽  
Nearest Neighbor ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
K Nearest Neighbor ◽  
Use Of Social Media ◽  
The Masses

Making use of social media for analyzing the perceptions of the masses over a product, event, or a person has gained momentum in recent times. Out of a wide array of social networks, the authors chose Twitter for their analysis as the opinions expressed there are concise and bear a distinctive polarity. Sentiment analysis is an approach to analyze data and retrieve sentiment that it embodies. The paper elaborately discusses three supervised machine learning algorithms—naïve bayes, k-nearest neighbor (KNN), and decision tree—and compares their overall accuracy, precision, as well as recall values, f-measure, number of tweets correctly classified, number of tweets incorrectly classified, and execution time.

Sign in / Sign up

Export Citation Format

Share Document