Article Per Year (5 Year)
p-Index From 2021 - 2026
1.704
P-Index
This Author published in this journals
All Journal Sistemasi: Jurnal Sistem Informasi ILKOM Jurnal Ilmiah KOMPUTIKA - Jurnal Sistem Komputer Buletin Sistem Informasi dan Teknologi Islam Ilmu Komputer untuk Masyarakat Open Community Service Journal Indonesian Journal of Community Services Cel Open Global Scietific Journal International Journal of Artificial Intelligence in Medical Issues
As'ad, Ihwana
Unknown Affiliation
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Dentistry Electrical & Electronics Engineering Environmental Science Health Professions Medicine & Pharmacology Public Health Social Sciences Other

Published : 11 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 11 Documents
Search

 1   2 
K-Nearest Neighbors Analysis for Public Sentiment towards Implementation of Booster Vaccines in Indonesia As'ad, Ihwana; Asis, Muhammad Arfah; Pakka, Hariani Ma'tang; Mursalim, Randi; Noor, Yusnita binti Muhamad
ILKOM Jurnal Ilmiah Vol 15, No 2 (2023)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v15i2.1561.365-372

Abstract

In order to prevent the spread of COVID-19 in Indonesia, the Government of the Republic of Indonesia has been implementing a booster vaccine program since January 12th, 2022, with priority for the elderly and vulnerable groups as well as those who got the second C-19 vaccine longer than 6 months. The implementation of this program raised many pros and cons among public which were expressed either positively or negatively through social media. Therefore, sentiment analysis is needed to examine these phenomenons. This study aims to determine the positive and negative response from public by employing K-Nearest Neighbor method. A total of 2,000 commentary data were collected to be in turn classified based on positive and negative sentiments. There are 500 comments used as training data and divided equally to positive and negative class, each consists of 250 data. Using the value of K = 9, the results show a positive sentiment of 43% while a negative sentiment of 57%. Based on the validity test using 10-fold cross validation, an accuracy of 82.60% was obtained, a recall value was 82.60% with a precision of 83.89%.
Co-Authors Abdullah, Syahrul Mubarak Andiyan Daris, Mega Asfirawati Djamereng, Asdar Dolly Indra Efriyanti, Liza Fahmi Fahmi Fattah, Farnitawati Hariani M. Pakka Hilaluddin, Al Huzain Azis lilis nurhayati Mansyur, St. Hajrah Marni, Rini Alfia Muhammad Arfah Asis Mursalim, Randi Noor, Yusnita binti Muhamad Pakka, Hariani Ma'tang Rahmatullah, Andi Muhammad Ramdan Satra Salawali, Cici Trisnawati Salim, Yulita Sugiarti Tenripada, Andi Ulfa Umar Mansyur