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All Journal CESS (Journal of Computer Engineering, System and Science) JURNAL REKAYASA TEKNOLOGI INFORMASI IJISTECH (International Journal Of Information System & Technology) JASIEK (Jurnal Aplikasi Sains, Informasi, Elektronika dan Komputer) IJAIT (International Journal of Applied Information Technology) Journal of Appropriate Technology for Community Services Jurnal Teknik Informatika (JUTIF) IJISTECH Jurnal Pendidikan dan Pengabdian Masyarakat
Euis Nur Fitriani Dewi
Universitas Siliwangi
Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mathematics Mechanical Engineering Social Sciences Other

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Journal : IJISTECH

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COVID-19 Vaccination Sentiment Analysis on Twitter Using Random Forest and Information Gain Andi Nur Rachman; Husni Mubarok; Euis Nur Fitriani Dewi; Mitha Maharani
IJISTECH (International Journal of Information System and Technology) Vol 6, No 3 (2022): October
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/ijistech.v6i3.246

Abstract

Covid-19 in Indonesia has increased from January 2021 unti February 2021 there were 1,217,468 people who were confirmed positive for the corona virus. As a result the increase in the number, the government has taken preventive measures, one of which is the distribution of vaccines or vaccinating the Indonesian people, which has been started since January 13,2021. The government’s covid-19 vaccination efforts had a broad influence on the community through social media (especially Twitter) which then led to pros and cons. Therefore, sentiment analysis is needed to predict the tendency of public opinion regarding the Covid-19 vaccination policy which is classified into positive opinions, neutral opinions, and negative opinions. Random Forest Classifier has high performance compared to other machine learning methods. But the Random Forest Classifier is weak in the level of accuracy and stability of data, so it requires a selection feature to increase its accuracy by applying Information Gain which can increase accuracy by optimizing data features. Measurement of accuracy and sentiment prediction is measured by confusion matrix and classification report. The results show that the application of Information Gain can improve accuracy with the highest accuracy obtained in experiment 1 of 0.00747, that is 0.94776 from 0.94029 with a precision value of 0.65, recall 0.43 and f1-score 0.47 and have a tendency to have a neutral opinion on public tweets about the Covid-19 vaccination on Twitter
Comparative Sentiment Analysis of Delivery Service PT.POS Indonesia and J&T Express on Twitter Social Media Using The Support Verctor Machine Algorithm Euis Nur Fitriani Dewi; Aldy Putra Aldya; Andi Nur Rachman; Ara Ramdani
IJISTECH (International Journal of Information System and Technology) Vol 6, No 5 (2023): February
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/ijistech.v6i5.284

Abstract

Based on a survey conducted by the Top Brand Award in the courier service category, the J&T Express company is in the highest position from 2018 to 2021 beating Pos Indonesia. Social media Twitter is a place often used by customers to submit complaints and opinions regarding the services of a company. The method used to determine the tendency of the views to contain positive or negative sentiments is sentiment analysis. Sentiment analysis will classify the polarity of the text in sentences or documents to determine whether the opinions expressed are positive or negative. This study uses the Support Vector Machine (SVM) algorithm. The results of the user tweet data used are as many as 1000 data with details of data 206 (20.6%) have positive sentiments and 794 (79.4%) have negative sentiments. In the Pos Indonesia tweet data, 110 positive sentiment data were obtained, while the positive sentiment data in the J&T Express tweet data was 96 data. This shows that the Pos Indonesia delivery service has better customer service than J&T Express. The highest level of accuracy using the SVM algorithm in classifying sentiment is 80.14% with a comparison of 70% training data and 30% test data with an average precision of 90%, an average recall of 51.74%, and an average f-measure of 47.80%.
Co-Authors Aldy Putra Aldya Andi Nur Rachman Andi Nur Rachman Andi Nur Rachman Andi Nur Rachman Andi Nur Rachman Andi Nur Rahman Ara Ramdani Arif Muhamad Nurdin Arif Muhamad Nurdin Astri Srigustini Cecep Muhamad Sidik Ramdani Fera Sulastri Gilang Saeful Anwar Heni Sulastri Husni Mubarok Insan Saleh Ramadhan Mitha Maharani Muhammad Adi Khairul Anshary Purwayoga, Vega Rahmi Nur Shofa Reynaldi Akbar Maulana Rezi Syahriszani Supratman Supratman Visi Tinta Manik Zakwan Gusnadi