SETIAWAN, NANDA RESSQ (2023) SENTIMEN ANALISIS REVIEW APLIKASI DIGITAL KORLANTAS PADA GOOGLE PLAY STORE MENGGUNAKAN METODE SUPPORT VECTOR MACHINE. S1 thesis, Universitas Mercu Buana Jakarta.
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Abstract
The SIM renewal service is familiar with a complicated service process. Addressing complaints in the community, Korlantas Polri facilitates fast and easy online services through the digital application for the SINAR Korlantas Polri service. However, the rating, which is accompanied by various negative and positive reviews, shows that the service provided has not fully met the expectations of the application's users. In order to find out how optimal the Digital Korlantas Polri application can be found by analyzing the sentiments of user reviews. The purpose of this study is to implement the sentiment analysis review of the Korlantas Digital Application on the Google Play Store using the SVM method. User review data for the Digital Korlantas Polri application obtained through the Google Play website with a total sample of 1200 review data. Collecting data in this study by crawling data using the google-play-scrapper library. The testing method uses a confusion matrix. The results of the study show that the SVM algorithm can perform sentiment analysis on the Korlantas digital application review with the results of 598 positive sentiments and 511 negative sentiments. Based on the test results the SVM model has good performance in the 90:10 data ratio scenario with an accuracy value of 0.82 and the SVM model with the worst performance is in the 80:20 and 60:40 data ratio scenario with an accuracy of 0.74. Keywords: Digital Korlantas, Google Play Store, Application Reviews, Sentiment Analysis, Support Vector Machine. Layanan perpanjangan SIM familiar dengan proses pelayanan yang rumit. Mengatasi keluhan di masyarakat, Korlantas Polri memfasilitasi pelayanan online yang cepat dan mudah melalui aplikasi digital layanan SINAR Korlantas Polri. Namun rating yang disertai berbagai ulasan negatif dan positif menunjukkan bahwa pelayanan yang diberikan belum sepenuhnya memenuhi harapan dari pengguna aplikasi tersebut. Agar dapat mengetahui seberapa optimal aplikasi Digital Korlantas Polri dapat diketahui dengan menganalisis sentimen ulasan pengguna. Tujuan penelitian ini adalah mengimplementasikan sentimen analisis review Aplikasi Digital Korlantas pada Google Play Store menggunakan Metode SVM. Data ulasan pengguna aplikasi Digital Korlantas Polri yang didapatkan melalui website Google Play dengan jumlah sampel sebanyak 1200 data ulasan. Pengumpulan data dalam penelitian ini dengan cara crawling data menggunakan library google-play-scrapper. Metode pengujian menggunakan confusion matrix. Hasil penelitian menunjukkan bahwa algoritma SVM dapat melakukan analisis sentimen pada ulasan aplikasi digital korlantas dengan hasil 598 sentimen positif dan 511 sentimen negatif. Berdasarkan hasil pengujian model SVM memiliki kinerja yang baik pada skenario rasio data 90:10 dengan nilai akurasi sebesar 0.82 dan model SVM dengan kinerja terburuk ada pada skenario rasio data 80:20 dan 60:40 yakni dengan akurasi sebesar 0.74. Kata Kunci : Digital Korlantas, Google Play Store, Review Aplikasi, Sentimen Analisis, Support Vector Machine.
| Item Type: | Thesis (S1) |
|---|---|
| Call Number CD: | FIK/INFO. 23 166 |
| Call Number: | SIK/15/23/053 |
| NIM/NIDN Creators: | 41518110159 |
| Uncontrolled Keywords: | Digital Korlantas, Google Play Store, Review Aplikasi, Sentimen Analisis, Support Vector Machine. |
| Subjects: | 000 Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 000. Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum 000 Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 000. Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 004 Data Processing, Computer Science/Pemrosesan Data, Ilmu Komputer, Teknik Informatika |
| Divisions: | Fakultas Ilmu Komputer > Informatika |
| Depositing User: | Annas Tsabatulloh |
| Date Deposited: | 21 Oct 2023 06:41 |
| Last Modified: | 21 Oct 2023 06:41 |
| URI: | http://repository.mercubuana.ac.id/id/eprint/80823 |
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