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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal Teknik Komputer AMIK BSI Jurnal Khatulistiwa Informatika Bina Insani ICT Journal Jurnal Pilar Nusa Mandiri Faktor Exacta Jurnal Teknik Informatika STMIK Antar Bangsa Jurnal Mantik Penusa JISICOM (Journal of Information System, Infomatics and Computing) Journal of Information System, Applied, Management, Accounting and Research EVOLUSI : Jurnal Sains dan Manajemen INTI Nusa Mandiri JATI (Jurnal Mahasiswa Teknik Informatika) SPEED - Sentra Penelitian Engineering dan Edukasi J-CEKI
Elly Indrayuni
Universitas Bina Sarana Informatika
Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Library & Information Science Social Sciences

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Journal : Faktor Exacta

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Implementasi Algoritma Naive Bayes, Support Vector Machine, dan K-Nearest Neighbors untuk Analisa Sentimen Aplikasi Halodoc Elly Indrayuni; Acmad Nurhadi; Dinar Ajeng Kristiyanti
Faktor Exacta Vol 14, No 2 (2021)
Publisher : LPPM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/faktorexacta.v14i2.9697

Abstract

During the Covid-19 pandemic, many people access information and even consult health problems online with the best doctors via smartphones. The Halodoc application is considered the most popular with 18 million users in 2020. So that many people have reviewed the application on the Google Play Store application provider. It may take a while to read the full review. However, if only a few comments are read, they are biased. For that, a platform is needed which can automatically identify positive or negative opinions. Sentiment analysis is a solution for the technique of classifying texts or sentiments into positive or negative opinion categories. The method used in this research is an experiment using the Naive Bayes algorithm, Support Vector Machine, and K-Nearest Neighbors. Evaluation is carried out using 10 Fold Cross-Validation. The results showed that K-Nearest Neighbors (KNN) had the best and most accurate performance in the sentiment classification because it produced the highest accuracy value of 95.00% and the largest AUC value of 0.985 compared to the Naive Bayes and Support Vector Machine algorithm.
Co-Authors Achmad Nurhadi Achmad Nurhadi Acmad Nurhadi Belsana Butar Butar Deigo Anugrah Pratama Dinar Ajeng Kristiyanti Ibnu Rizal Mutaqin Muyasar Kevin Rafael Manuela Feltya