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All Journal Jurnal Penelitian Pendidikan IPA (JPPIPA) Journal of Electronics, Electromedical Engineering, and Medical Informatics
Shahmirul Hafizullah Imanuddin
Universitas Diponegoro
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering Materials Science & Nanotechnology Physics

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Sentiment Analysis Naive Bayes Method on SatuSehat Application Shahmirul Hafizullah Imanuddin; Kusworo Adi; Rahmat Gernowo
Jurnal Penelitian Pendidikan IPA Vol 9 No 7 (2023): July
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v9i7.4054

Abstract

The SatuSehat application is an application that provides health services to users. This application is a development of the PeduliLindungi application which is used to handle vaccination history in the new normal era. Therefore, it is important to classify user reviews into positive and negative sentiments using the Naïve Bayes method. The use of this method because it can produce a model that is quite accurate and effective. The results of data collection in this study were 25,000 of which 18,359 were negative and 6,641 were positive. The results of the Naïve Bayes accuracy test are 97% with negative sentiment results, namely precision has a value of 98%, recall has a value of 98% and f1-score has a value of 98%, while positive sentiment results, namely precision has a value of 94%, recall has a value of 94 % and f1-score has a value of 94%. This study aims to classify user reviews of the SatuSehat application into positive and negative sentiments and assess the performance of the Naïve Bayes method regarding public opinion on the use of the SatuSehat application based on reviews from the Google Playstore application.
Sentiment Analysis on Satusehat Application Using Support Vector Machine Method Shahmirul Hafizullah Imanuddin; Kusworo Adi; Rahmat Gernowo
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 5 No 3 (2023): July
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeemi.v5i3.304

Abstract

Sentiment analysis is important in language processing and machine learning. SVM is proven to classify positive and negative sentiments with high accuracy effectively. SatuSehat application provides users with various health services and medical information, previously known as the PeduliLindungi Application. Once, this application was used to handle vaccination history used in the new normal era. Along the way, many problems arose due to the immaturity of the application after it was launched, which resulted in many user reviews being given through the Google Play Store application. Therefore, this study aims to determine SVM's performance in classifying user reviews of the SatuSehat application into positive and negative sentiments and to show visualization to find out the most frequent words from user reviews. Based on the research results, 25,000 data were divided into 18,359 negative class data and 6,641 positive class data. At the SVM classification stage, it produces a negative sentiment of 73.4% and a positive sentiment of 26.6%. In addition, the results of the SVM accuracy test obtained a result of 91% with a positive sentiment, namely having a precision test of 92%, a recall of 71%, and an f1-score of 80%, while for negative sentiment, namely having a precision test of 90%, a recall of 98% and f1-score of 94%. The visualization results found that the topics often appearing in positive reviews are good and sometimes great. In contrast, the negative reviews are update, difficult, strange, login, and bug.
Co-Authors Kusworo Adi Rahmat Gernowo