Analisis Media Sosial Twitter Terhadap Topik Vaksinasi Covid-19

Yanuarti, Rosita

doi:10.32528/justindo.v6i2.5503

Cited by 7 publications

(7 citation statements)

References 1 publication

(1 reference statement)

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“…Twitter merupakan salah satu platform yang sering digunakan selama periode PPKM. Dilansir dari Global Digital Statistic "Digital, Social & Mobile in 2019", pada tahun 2019 pengguna media sosial di Indonesia berjumlah 150 juta pengguna, twitter menyumbang angka terbanyak yang mencakup lebih dari 52% [2]. Opini publik bersifat tidak dibatasi dan bebas di twitter [3].…”

Section: Pendahuluanunclassified

Analisis Sentimen Masyarakat Terhadap Kebijakan Penerapan PPKM Di Media Sosial Twitter Dengan Menggunakan Metode Xgboost

Widiarta,

Dwiyansaputra,

Aranta

2023

JTIKA

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Corona Virus Disease (Covid-19) is a virus that causes respiratory infections in humans. Indonesia is a country that has been infected with this virus, the implementation of restrictions on community activities (PPKM) is implemented by the government as a policy to reduce the spread of Covid-19. Pros and cons arise due to the impact of the policy. Therefore, assessing how public opinion or sentiment is towards this policy is important to do. This study aims to implement the XGBoost algorithm in the sentiment classification process. Sentiment analysis targets public opinion on Twitter, the dataset used is 1958 positive tweets and 3980 negative tweets. At the preprocessing stage, case-folding, stopwords removal, tokenizing, and stemming are carried out. Giving weights to terms uses the Term Frequency-Relevance Frequency method to turn each term into a number. In the final stage, classification is carried out by implementing the XGBoost method with optimal hyperparameter scores. K-fold cross validation is used to evaluate model performance. Based on the evaluation results, the best performance was obtained by a model with a hyperparameter value with an n_estimator of 1000, a learning_rate of 0.1, a max_depth of 6, a subsample of 1, a gamma of 0 and utilizing the stem-ming process in preprocessing with an accuracy value of 85.27%. precision of 86.07%, and recall of 85.23%.

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Section: Pendahuluanunclassified

Analisis Sentimen Masyarakat Terhadap Kebijakan Penerapan PPKM Di Media Sosial Twitter Dengan Menggunakan Metode Xgboost

Widiarta,

Dwiyansaputra,

Aranta

2023

JTIKA

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“…Setiap tahunnya data mengalami peningkatan mencapai 80% terdiri dari data yang sifatnya tidak terstruktur khususnya seperti data dalam bentuk teks. Data yang tidak terstruktur tersebut banyak dihasilkan dari media sosial seperti twitter [5].…”

Section: Pendahuluanunclassified

Impementasi Algoritma Naive Bayes Terhadap Analisis Sentimen Opini Masyarakat Indonesia Mengenai Drama Korea Pada Twitter

Amelia

Darmansah²,

Prastiwi³

et al. 2022

Jur. Ris. Kom.

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Korean culture in the last two decades has shaken the whole world, including Indonesia, almost all millennial children talk about Korean culture, whether from dramas, films, songs, fashion, lifestyle, industrial products have begun to penetrate the lives of Indonesian people. One of the trends of public interest is Korean film drama. However, in the expansion of Korean film dramas in Indonesia, there must still be a negative perspective, so an analysis of the sentiments of Indonesian people's opinions regarding Korean culture is currently on the rise. This sentiment analysis data is taken from comments about Korean dramas that are widely written on Twitter. From the many opinions about Korean dramas, a classification is needed according to the existing sentiments so that it will be easy to get the tendency of opinions written on Twitter towards Korean dramas whether they tend to have positive, neutral or negative opinions. In the analysis of opinion sentiment using a Naive Bayes approach taken from Twitter Social Media. The application of the Nave Bayes algorithm in grouping positive, neutral and negative sentiments based on Korean drama commentary review data collected. The purpose of this study is to analyze public opinion and sentiment on Korean dramas on Twitter based on 100 data taken, using Orange tools for the sentiment analysis process. The results given show a percentage value of 69% by calculating the Naive Bayes algorithm

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“…Meanwhile, similar research was also conducted by (Yanuarti, 2021) entitled "Twitter Social Media Analysis of the Covid-19 Vaccination Topic", this study looked at social media (Twitter) with the aim of extracting and analyzing public comments or tweets about topics related to the implementation of the Covid-19 vaccination in Indonesia. The results of this study indicate that from the analysis visualization in the form of graph tweets, 185 nodes and 101 edges are generated, the value of modularity: is 0.815, the number of communities is 20 groups, and Influential Users are 'tv One News', ryokan daft", renatarecreio', detik.com".…”

Section: Introductionmentioning

confidence: 99%

Strengthening Agile Government in Implementation of the Covid-19 Vaccination Policy in Kalimantan Island

Kamaludin

Mutiarin

2023

JGPP

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This study aims to determine the Strengthening of Agile Government in implementing Covid-19 Vaccination Policies: Studies in 5 Provinces on the Island of Kalimantan. This research is a qualitative case study designed to analyze the findings with four characteristics of agile government: responsiveness, strategic adaptation, focus on results, and management anticipates future problems. This study shows that the best responsiveness in East Kalimantan Province, the responsiveness of handling the Covid-19 vaccine is considered very agile, the responsiveness level is 25.00%. In contrast, the province that is still under its responsiveness is West Kalimantan Province with its responsiveness. 14,00% rate. Furthermore, the highest strategy adaptation is the Province of South Kalimantan and Central Kalimantan, 22.00%, both of which have the same percentage value. At the same time, the lowest percentage is in North Kalimantan Province, which is 16.00%. In addition to Focus on Results, the highest percentage of achievement results in West Kalimantan Province, which is 31.00%. At the same time, Central Kalimantan, East Kalimantan, and North Kalimantan Provinces have a low achievement rate of 15.00%. Then in the Management of Overcoming Future Problems, the highest percentage is the Province of West Kalimantan, which is 26.00%, and the Provinces of East Kalimantan and Central Kalimantan, which have a low yield of 15.00%. The five provinces have different strategies for dealing with vaccination.

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Analisis Media Sosial Twitter Terhadap Topik Vaksinasi Covid-19

Cited by 7 publications

References 1 publication

Analisis Sentimen Masyarakat Terhadap Kebijakan Penerapan PPKM Di Media Sosial Twitter Dengan Menggunakan Metode Xgboost

Analisis Sentimen Masyarakat Terhadap Kebijakan Penerapan PPKM Di Media Sosial Twitter Dengan Menggunakan Metode Xgboost

Impementasi Algoritma Naive Bayes Terhadap Analisis Sentimen Opini Masyarakat Indonesia Mengenai Drama Korea Pada Twitter

Strengthening Agile Government in Implementation of the Covid-19 Vaccination Policy in Kalimantan Island

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