Matrik: jurnal manajemen, teknik informatika, dan rekayasa komputer最新文献

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Comparison of Machine Learning Methods for Classifying User Satisfaction Opinions of the PeduliLindungi Application PeduliLindungi应用用户满意度意见分类的机器学习方法比较

Matrik: jurnal manajemen, teknik informatika, dan rekayasa komputer

Pub Date : 2023-06-16 DOI: 10.30812/matrik.v22i3.2860

Putu Tisna Putra, Anthony Anggrawan, Hairani Hairani

Since the emergence of the Covid-19 virus, the Indonesian government urged people to study, work, and worship or work from home. The social restriction policy has changed people's behavior which requires physical distance in social interaction. The government developed an application to minimize the spread of Covid-19, namely the PeduliLindungi application. The PeduliLindungi application is a tracking application to prevent the spread of Covid-19. The government's policy of implementing the PeduliLindungi application during Covid-19 aroused pros and cons from the public. The volume of PeduliLindungi application review data on Google Play was increasing, so manual analysis could not be done. New analytical approaches needed to be carried out, such as sentiment analysis. This research aimed to analyze user reviews of the PeduilLindungi application using classification methods, namely Support Vector Machine (SVM), Random Forest, and Naïve Bayes. The methods used were Synthetic Minority Oversampling Technique (SMOTE), Random Forest, SVM, and Naïve Bayes. SMOTE was used to balance user review data on the PeduliLindungi application. After the data had been balanced, classification was carried out. The results of this study showed that the Random Forest method with SMOTE got better accuracy than the SVM and Naive Bayes methods, which was 96.3% based on the division of training and testing data using 10-fold cross-validation. Thus, using the SMOTE method could improve the accuracy of classification methods in classifying opinions of user satisfaction with the PeduliLindungi application.

自新冠病毒出现以来，印尼政府敦促人们学习、工作、做礼拜或在家工作。社会约束政策改变了人们的行为，使得人们在社会交往中需要身体距离。政府开发了“PeduliLindungi”应用程序，以最大限度地减少新冠病毒的传播。PeduliLindungi应用程序是防止新冠病毒传播的跟踪应用程序。政府在新冠肺炎疫情期间实施PeduliLindungi申请的政策引起了公众的赞成和反对。由于Google Play上PeduliLindungi应用审查数据的数量在不断增加，因此无法进行人工分析。需要采用新的分析方法，例如情绪分析。本研究旨在使用支持向量机(SVM)、随机森林(Random Forest)和Naïve贝叶斯(Bayes)分类方法对PeduilLindungi应用程序的用户评论进行分析。使用的方法有合成少数过采样技术(SMOTE)、随机森林、支持向量机和Naïve贝叶斯。SMOTE用于平衡PeduliLindungi应用程序上的用户评论数据。数据平衡后，进行分类。本研究结果表明，基于10倍交叉验证的训练和测试数据分割，SMOTE随机森林方法的准确率达到96.3%，优于SVM和朴素贝叶斯方法。因此，使用SMOTE方法可以提高分类方法对PeduliLindungi应用用户满意度意见进行分类的准确性。

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