Perbandingan Metode Decision Tree Dan K-Nearest Neighbor Terhadap Ulasan Pengguna Aplikasi Mypertamina Menggunakan Confusion Matrix
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Ade Syahril
*
Universitas Buana Perjuangan Karawang, Karawang,
Indonesia
- Yana Cahyana Universitas Buana Perjuangan Karawang, Karawang, Indonesia
- Dwi Sulistya Kusumaningrum Universitas Buana Perjuangan Karawang, Karawang, Indonesia
- Tatang Rohana Universitas Buana Perjuangan Karawang, Karawang, Indonesia (*) Corresponding Author
Abstract
The large number of vehicles in Indonesia makes fuel oil (BBM) very important, especially for cars and motorbikes. The Indonesian government works closely with PT Pertamina Persero and requires transactions using the MyPertamina application to ensure that fuel subsidies are properly targeted. However, the MyPertamina app has received mixed feedback and criticism from users, such as complaints about frequent bugs, instability of the app during use and difficulties in the registration or login process. User feedback on the app has been both positive and negative. Users also provided their ratings and reviews on the Google Play Store. The purpose of this research is to analyse the opinions of MyPertamina application user comments and compare the accuracy of the Decision Tree and K-Nearest Neighbor algorithms. This research includes scraping, text preprocessing, weighting, algorithm implementation and evaluation. The data used was obtained from Google Play Store as much as 10,000 data based on the latest reviews, after data cleaning such as removing duplicate data and missing values obtained 8,072 reviews. The data is then grouped into positive classes (2,506 reviews) and negative classes (5,566 reviews), with more negative data. The classification results using the Decision Tree and K-NN methods, it is known that the Decision Tree method has a higher accuracy of 83%, while K-NN method is 58%. This finding indicates that the Decision Tree method is more effective in analysing user reviews of the MyPertamina application compared to the K-NN method.
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