Perbandingan Akurasi Analisis Sentimen Tweet terhadap Pemerintah Provinsi DKI Jakarta di Masa Pandemi

Himawan, Ragil Dimas; Eliyani, Eliyani

doi:10.26418/jp.v7i1.41728

Cited by 14 publications

(15 citation statements)

References 9 publications

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“…Algoritma machine learning dapat digunakan untuk melakukan proses klasifikasi analisis sentimen. Ada banyak algoritma yang dapat digunakan seperti algoritma Multinomial Naive Bayes, Linear SVM, dan Random Forest Classification [2]. Selain ketiga jenis algoritma tersebut, algoritma K-Nearest Neighbors (KNN) juga dapat digunakan karena algoritma ini dapat mengolah data dalam jumlah yang besar serta dapat diterapkan dalam perhitungan yang sederhana [3].…”

Section: Pendahuluanunclassified

Untitled

Fatiya¹,

Yusliani²,

Marieska³

et al. 2022

JLK

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Section: Pendahuluanunclassified

Untitled

Fatiya¹,

Yusliani²,

Marieska³

et al. 2022

JLK

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“…Penelitiannya untuk mengetahui sentimen data tweet terhadap akun Twitter resmi Pemerintah Provinsi DKI Jakarta di masa pandemi Covid-19 dengan query pada tweet yang mengandung kata atau menyebut username @dkijakarta, berdasarkan kelas sentimen negatif, netral, dan positif dengan menggunakan TF-IDF Vectorizer diperoleh hasil hasil akurasi sebesar 75,81%, algoritma naive bayes dengan hasil akurasi 75,22%, dan algoritma support vector machine 77,58%. Dilakukan proses analisis sentimen pada tweet dengan presentase hasil negatif, netral, dan positif masing-masing yaitu 8,8%, 83,6%, 7,6% (Himawan & Eliyani, 2021).…”

Section: Jurnal Jsrcsunclassified

Analisis Sentimen Masyarakat Terhadap Kebijakan Pemerintah Selama Pandemi Covid-19 Menggunakan Algoritma Naïve Bayes

Emeraldi¹,

Ekawati²,

Sumadyo³

2022

JSRCS

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The increase in data is very large, one of the sources comes from social media, especially Twitter which talks a lot about Covid-19 . The news through Twitter media regarding the impact of the Covid-19 virus is widely discussed because it causes unrest for the public which has led to the issuance of various government policies with the aim of preventing the spread of Covid-19 . Related to this, it is necessary to conduct a sentiment analysis of the text contained in the Twitter media. In this study, a sentiment analysis process was carried out related to public sentiment towards government policies during the Covid-19 pandemic in Indonesia on Twitter social media using the Naive Bayes Classifier method where the data used was classified into 2 sentiment values, namely positive and negative sentiment. The data used are 300 positive tweets data and 300 negative tweets data, where 80% of the total data is used as training data and 20% data is used as test data. Based on the test results, the data with a total of 120 tweets obtained the results of measuring the recall value of 93.33%, precision 93.33%, F-Score 93.33% and an average accuracy of 93.33%. Keywords: Covid-19 , naive bayes classification, sentiment analysis. Abstrak Pertambahan data yang sangat banyak, salah satu sumbernya berasal dari media sosial khususnya Twitter yang banyak membahas Covid-19 . Pemberitaan melalui media Twitter mengenai dampak dari virus Covid-19 marak dibicarakan karena menimbulkan keresahan bagi masyarakat yang menyebabkan dikeluarkannya berbagai kebijakan pemerintah dengan tujuan untuk mencegah penyebaran Covid-19 . Terkait hal tersebut perlu dilakukan sentimen analisis terhadap teks yang terdapat pada media Twitter. Pada penelitian ini, dilakukan proses sentimen analisis terkait sentimen masyarakat terhadap kebijakan pemerintah selama pandemi Covid-19 di Indonesia pada sosial media Twitter dengan menggunakan metode Naive Bayes Classifier dimana data yang digunakan digolongkan menjadi 2 nilai sentimen yaitu sentimen positif dan negatif. Data yang digunakan sebanyak 300 data tweets positif dan 300 data tweets negatif, dimana 80% dari keseluruhan data digunakan sebagai data latih dan 20% data digunakan sebagai data uji. Berdasarkan hasil pengujian, data dengan jumlah sebanyak 120 tweet diperoleh hasil pengukuran nilai recall 93.33%, precission 93.33%, F-Score 93.33% serta rata-rata akurasi 93.33%. Kata kunci: analisis sentimen, Covid-19 , klasifikasi naive bayes.

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“…In its use, people can ask directly about government policies, public services or even just comment to socialize with the community. Opinions can be used as an assessment for a company or agency [5] [6].…”

Section: Introductionmentioning

confidence: 99%

Comparison of the Accuracy of Sentiment Analysis on the Twitter of the DKI Jakarta Provincial Government during the COVID-19 Vaccine Time

Winanto¹,

Budihartanti²

2022

jcse. journal. of. computer. science. and. engineering.

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Currently, the Government is intensively utilizing social media, one of which is Twitter as a place of interaction with the community. The results of these interactions can be used as feedback to determine whether public opinion on public policies is positive or negative. Tweets from users can be a supporting parameter for the government in evaluating future policies and decision making by applying the sentiment analysis method. This study aims to determine positive or negative sentiments on user tweets against the official twitter account of the DKI Jakarta Provincial Government during the COVID19 vaccine period. The data obtained are 1658 lines from March 30 to April 5, 2021 with queries on tweets containing words or mentioning the username @dkijakarta, which will be grouped by sentiment class, namely negative and positive using the TF-IDF Vectorizer for word weighting and classification using several methods, namely, nave Bayes with accuracy values. 82.50% with class recall on positive sentiment 88% and negative 77% and in class precision showing positive at 79.28% and negative at 86.52% in the rapid miner application then k-NN with an accuracy value of 81.50% with class recall on positive sentiment 85% and negative 78% and class precision shows positive at 79.44% and negative at 83.87% in the rapid miner application. And the accuracy value of the best method in this training data classification comparison is nave Bayes, the results the end of testing the sample dataset using the nave Bayes method with 84.80% accuracy with class recall at 85.01% positive sentiment and 84.59% negative sentiment and at c lass precision shows positive at 85.21% and negative at 84.38% in rapid mining applications.

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Perbandingan Akurasi Analisis Sentimen Tweet terhadap Pemerintah Provinsi DKI Jakarta di Masa Pandemi

Cited by 14 publications

References 9 publications

Untitled

Untitled

Analisis Sentimen Masyarakat Terhadap Kebijakan Pemerintah Selama Pandemi Covid-19 Menggunakan Algoritma Naïve Bayes

Comparison of the Accuracy of Sentiment Analysis on the Twitter of the DKI Jakarta Provincial Government during the COVID-19 Vaccine Time

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