Analisa Sentimen Opini Publik Tentang Vaksinasi Covid-19 di Indonesia Menggunakan Naïve bayes dan Decission Tree

Harun, Ahmad; Ananda, Dea Putri

doi:10.57152/malcom.v1i1.63

Cited by 9 publications

(8 citation statements)

References 10 publications

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“…Klasifikasi biner untuk kelas positif dan negatif saja, lebih mudah untuk mendapatkan akurasi yang tinggi menggunakan machine learning, seperti pada [9], yang mendeteksi sentimen menggunakan Naïve Bayes dan Decission Tree, atau di dalam [10] untuk mendeteksi apakah terjadi serangan atau tidak pada suatu jaringan komputer.…”

Section: Pendahuluanunclassified

Klasifikasi Sentimen Vaksin Covid-19 Menggunakan K-Nearest Neighbor Berdasarkan Word Embeddings Fasttext Pada Twitter

Naldi,

Agustian

2023

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Pada akhir 2019 muncul penyakit semacam flu yang menginfeksi paru-paru di kota Wuhan. Diduga penyakit tersebut diduga berasal dari kelelawar. WHO memberi nama penyakit ini dengan nama Covid-19 dan virus ini tersebar ke seluruh dunia sehingga menyebabkan pandemi. Pemerintah mengambil indakan vaksinasi untuk mengatasi virus ini, namun mendapat respon pro dan kontra dari masyarakat. Ada banyak penelitian yang membahas sentimen masyarakat terhadap vaksinasi salah satunya adalah klasifikasi sentimen. Penelitian ini membahas klasifikasi sentimen terhadap vaksin covid-19 menggunakan algoritma K-Nearest Neighbor dan Fasttext pada twitter. Data diperoleh dengan cara crawling menggunakan bahasa pemograman pyton dan Twitter API. Pelabelan data dilakukan dengan teknik crowdsourcing dan majority voting. Data yang digunakan setelah proses penyeimbangan adalah 6000 data training, 778 data development dan 400 data test. Hasil pengujian setelah berbagai eksperimen dan feature engineering mendapatkan hasil terbaik dengan nilai akurasi 69% dan f1-score 60%. Hasil ini merupakan hasil terbaik dibanding penelitian sebelumnya dengan dataset yang sama.

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Section: Pendahuluanunclassified

Klasifikasi Sentimen Vaksin Covid-19 Menggunakan K-Nearest Neighbor Berdasarkan Word Embeddings Fasttext Pada Twitter

Naldi,

Agustian

2023

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“…Algorithms that use the concept of chance or what is usually called the probability used in classification for sentiment analysis it is called as a Naive Bayes Classifier. Naive Bayes Classifiers are also included in the algorithm easy to use and simple and can predicting an event based on the results from classification well [13]. Following is a formula for the calculation equation of value probability of Naive Bayes Classifier method:…”

Section: E Classificationmentioning

confidence: 99%

“…The method used in this research is the confusion matrix method. Where the method used to calculate accuracy is by comparing the actual classification results with the classification results from the method [13]. For actual data classification, this research carried out labeling manually to determine the polarity.…”

Section: G Testingmentioning

confidence: 99%

Implementation of the Naive Bayes Classifier for Sentiment Analysis of Shopee E-Commerce Application Review Data on the Google Play Store

Rizkya,

Rianto,

Gufroni

2023

JAISI

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E-commerce in Indonesia is growing very quickly every year. The Ministry of Communication and Information (KEMKOMINFO) stated that Indonesia is the 10th largest e-commerce growth country with score 78%. One of the effects from increasing number of internet users in Indonesia is the mushrooming of shopping activities through internet media. This causes internet users want everything that instant and easy. Knowing this, most business people use it to market their products, especially in the field of goods and services. As it grows, e-commerce becomes easier to use and download. One example of an e-commerce application that is in great demand is Shopee and can be downloaded via the Google Play Store. Google Play Store has a review feature which contains user comments about the downloaded apps. Sentiment analysis is carried out to extract information related to Shopee E-commerce. The Naïve Bayes Classifier algorithm is suitable for use in sentiment analysis because this algorithm is purposeful as a classification method into positive and negative categories. The data was used from November 2022 to January 2023. From a total of 4902 review data obtained, after going through preprocessing, translation and then classification, the total data is obtained that is 4849 review data. From the data obtained it is classified 2348 positive reviews, 1259 neutral reviews, and 1242 negative reviews. Based on the results of the naive Bayes classifier method and testing with the confusion matrix, an accuracy value of 79% has been obtainednprecision 77%, recall 86%, and f1-score 81% on positive sentiment with support 2127. For neutral sentiment with an accuracy value of 83%, precision 87%, and recall 85% with support 1209, while for negative sentiment is with an accuracy value of 78%, precision 64%, and recall 70% with support 1513. From this data it is obtained micro AVG values for precision 80%, recall 79%, f1-score 79%, and support 4849, then for weighted average for precision 79%, recall 79%, f1- score 79%, and support 4849.

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“…Penelitian ini mengacu pada penelitian yang dilakukan oleh A.Harun, D.P Ananda, 2021 penelitian membahas tentang Analisa Sentimen Opini Publik Tentang Vaksinasi Covid-19 di Indonesia Menggunakan Naïve bayes dan Decission Tree, hasil penelitian membahas cenderung ke tanggapan negatif dengan nilai akurasi 100.00% menggunakan algoritma NBC dan 50.39% menggunakan algoritma Decision Tree. [11] Berdasarkan model yang digunakan pada penelitian ini, penulis akan melakukan hal yang sama dengan menggunakan ketiga model tersebut pada penelitian ini, yaitu dengan membandingkan model Naive Bayes dengan Decision Tree dan SVM.…”

Section: Pendahuluanunclassified

Analisis Sentimen Opini Masyarakat Terhadap Vaksinasi Booster COVID-19 Dengan Perbandingan Metode Naive Bayes, Decision Tree dan SVM

Aldisa¹,

Maulana²

2022

bits

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The impact of the COVID-19 pandemic is very broad, especially in Indonesia. In early 2022, Indonesia entered the early stages of recovering conditions caused by the pandemic. The government has an option for the community to carry out a third dose of vaccination (booster). However, there are a number of pros and cons in the community regarding the booster vaccine. This study aims to conduct sentiment analysis related to public opinion on the COVID-19 booster vaccination in Indonesia with Naive Bayes model. The data source used comes from Twitter. The workflow of this research includes data crawling, labeling, preprocessing, dataset sharing, and model testing and comparison with other models, namely Decision Tree and SVM. The results of this study indicate that the largest AUC score falls to the SVM model (75.40%), but for more accurate precision falls to the Naive Bayes model (83.81%). In addition, there is a confusion matrix which shows that the Naive Bayes model trial is running well.

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Analisa Sentimen Opini Publik Tentang Vaksinasi Covid-19 di Indonesia Menggunakan Naïve bayes dan Decission Tree

Cited by 9 publications

References 10 publications

Klasifikasi Sentimen Vaksin Covid-19 Menggunakan K-Nearest Neighbor Berdasarkan Word Embeddings Fasttext Pada Twitter

Klasifikasi Sentimen Vaksin Covid-19 Menggunakan K-Nearest Neighbor Berdasarkan Word Embeddings Fasttext Pada Twitter

Implementation of the Naive Bayes Classifier for Sentiment Analysis of Shopee E-Commerce Application Review Data on the Google Play Store

Analisis Sentimen Opini Masyarakat Terhadap Vaksinasi Booster COVID-19 Dengan Perbandingan Metode Naive Bayes, Decision Tree dan SVM

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