Sentiment Analysis of the New Indonesian Government Policy (Omnibus Law) on Social Media Twitter

Sukma, Eki Aidio; Hidayanto, Achmad Nizar; Pandesenda, Adam Imansyah; Yahya, Arif Nur; Widharto, Punto; Rahardja, Untung

doi:10.1109/icimcis51567.2020.9354287

Cited by 26 publications

(11 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The emergence of digital marketing is a manifestation of the industrial revolution 4.0, and a form of technological development progress along with mobile technology where all the information needed can be easily obtained. Making the company easily recognizable and accessible to customers is the goal of digital marketing [18].…”

Section: Literature Reviewmentioning

confidence: 99%

Utilizing Digital Marketing As A Business Strategy

Bintaro

Sokibi²,

Amsyar

et al. 2022

SABDA

View full text Add to dashboard Cite

With the advancement of information technology has grown so rapidly. One form of development that can be seen in the existence of a platform that connects one another or what can be said is social media. Instagram, Facebook, Twitter and Tiktok are examples of social media that are the most popular platforms. Starting from children to adults all use social media[1]. With this, social media has become a concern for business people. Now, almost all business people do not hesitate to market their products or services through social media. On that basis, this article was written. The goal is to see how digital marketing is useful in today's business strategy.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Utilizing Digital Marketing As A Business Strategy

Bintaro

Sokibi²,

Amsyar

et al. 2022

SABDA

View full text Add to dashboard Cite

show abstract

“…In the education sector, prediction models have developed to predict student performance [1], graduation success [2], student retention [3], online discussion performance [4], critical course [5], and interactive volume [6]. In the health sector, there are models predicting diabetes [7], disease diagnosis [8], risk of infectious disease [9], and hypoglycemia [10]. There are also prediction models of performance areas [11] [12][13],predicting targets [14], and predicting progress [15].…”

Section: Literature Reviewmentioning

confidence: 99%

Using Case Based Reasoning, in the Student Graduation Prediction System (CBR)

Rahardja¹

2022

AJRI

Self Cite

View full text Add to dashboard Cite

The performance of an educational institution is considered to have a negative impact due to the high percentage of students who do not graduate on time. Therefore, the number of students must be calculated and anticipated, so this paper needs to propose a model for predicting student graduation rates and predicting the time of graduation for each student. The K-Nearest Neighbor (KNN) method and algorithm were developed using Case-Based Reasoning (CBR). The research was conducted with training data taken from data from students who had graduated, and as a trial, the sixth-semester student data was used. The research attributes required to consist of the grade 1 to semester 5 of the achievement index (IPS) and the students' age. Model testing is carried out with four scenarios where the distribution of training data and testing data is also needed as support. The experimental results show that the stability of the data accuracy value on CBS using the KNN algorithm is found in the 90% training data scenario and 10% test data, namely 90.24% with the closest neighbors K = 5, K = 7, and K = 9. It can be concluded that the model. The resulting prediction system can predict whether students will graduate on time or not. This research will be helpful if implemented in higher education performance to increase student achievement to graduate on time.

show abstract

“…Setelah tahapan crawling data, didapatkan sekumpulan data (dataset) yang banyak yang masih dalam berbentuk unstructured data yaitu data yang tidak terstruktur dan belum siap dilakukannya analisis lebih lanjut maka dataset harus terlebih dahulu melalui tahapan proses text pre-processing yang bertujuan untuk menghilangkan atau mengatasi noisy pada data agar hasil dari perhitungan menjadi lebih baik. Alur pada text pre-processing yaitu, case folding, filtering, tokenizing, dan stopword removal [17].…”

Section: Pre-processingunclassified

Analisis Sentimen Vaksinasi Booster Berdasarkan Twitter Menggunakan Algoritma Naïve Bayes dan K-NN

Rozaqi

Triayudi

Aldisa

2022

json

View full text Add to dashboard Cite

Covid-19 or the corona virus has spread throughout the world, one of which is Indonesia. There have been many problems due to this virus for 2 years in Indonesia, and various efforts and policies have been made by the government to control the impact that does not become worse by this corona virus, these efforts are vaccination actions against the Indonesian people, and in early 2022 the government started a new program, namely booster vaccination. Many people are pro and contra to the program on social media Twitter. This study was conducted with the aim of knowing the sentiment of Indonesians towards booster vaccination in Indonesia.The data obtained as many as 2000 tweets obtained from the keyword "booster vaccine" on Twitter. Then the data is divided into training data and test data (training) then made into three different portions, namely 60/40, 70/30, and 80/20. The test results are that the best performance is found in testing a portion of 80% of the training data 20% of the test data using the K-NN algorithm, the test produced the highest value results, namely 78.62% accuracy and AUC 0.845 and categorized as good classification. The results show that the K-NN algorithm model with an 80% portion of training data is the best in the classification of booster vaccination sentiment analysis. The sentiment results in the test data were positive with 303 tweet data and negative sentiment totaled 93 tweet data. The results of more positive sentiments show that booster vaccinations in Indonesia are acceptable and get a lot of support from the Indonesian people on social media Twitter.

show abstract

Sentiment Analysis of the New Indonesian Government Policy (Omnibus Law) on Social Media Twitter

Cited by 26 publications

References 14 publications

Utilizing Digital Marketing As A Business Strategy

Utilizing Digital Marketing As A Business Strategy

Using Case Based Reasoning, in the Student Graduation Prediction System (CBR)

Analisis Sentimen Vaksinasi Booster Berdasarkan Twitter Menggunakan Algoritma Naïve Bayes dan K-NN

Contact Info

Product

Resources

About