Viriya, Aurelius Alexander and Intan Sartika Eris Maghfiroh, S.E., M.B.A. and Nanang Yudi Setiawan, S.T., M.Kom (2024) Analisis Sentimen Ulasan Pengguna Aplikasi Mobile Gapura UB pada Google Play Store Menggunakan Algoritma Support Vector Machine. Sarjana thesis, Universitas Brawijaya.
Abstract
Perkembangan teknologi dan peran aplikasi mobile dalam kehidupan siswa sangat memengaruhi bagaimana mereka berinteraksi dan belajar. Informasi akademik, jadwal kuliah, dan layanan universitas lainnya dapat diakses dengan mudah dan cepat melalui aplikasi mobile, seperti Gapura UB. Namun, ulasan pengguna di Google Play Store menunjukkan ketidakpuasan dan keluhan terkait penggunaan aplikasi mobile Gapura UB, dengan skor rata-rata 3.3 dari skala 5. Penelitian ini bertujuan untuk menganalisis sentimen pengguna terhadap aplikasi mobile Gapura UB. Penelitian dilakukan dengan pengumpulan data ulasan pengguna aplikasi mobile Gapura UB menggunakan web scraping. Selanjutnya, pra-pemrosesan data dilakukan agar data ulasan layak digunakan untuk diberi label sentimen dengan metode Lexicon. Kemudian, dilakukan implementasi SVM untuk memprediksi sentimen pada data yang telah dilabeli. Hasil analisis sentimen dievaluasi menggunakan Confusion Matrix serta Root Cause Analysis: 5 Why’s. Hasil penelitian menunjukkan bahwa dari 494 ulasan yang diklasifikasi, terdapat 345 ulasan negatif dan 149 ulasan positif. Penggunaan metode SVM dengan Lexicon Based Approach memberikan akurasi 98%. Evaluasi menggunakan confusion matrix menunjukkan precision 99%, recall 97%, dan F1-score 98%. Visualisasi word cloud mengungkap kata-kata positif seperti "mantap", "login", dan "keren", di mana ulasan positif mencerminkan kepuasan terhadap aplikasi dan pujian kepada pegawai yang terlibat. Di sisi lain, kata-kata negatif seperti "login", "aplikasi", dan "absen" pada ulasan negatif menyoroti kendala dalam kesulitan login, masalah fungsionalitas aplikasi, dan isu terkait absensi.
English Abstract
The rapid development of technology and the significant role of mobile applications among students have greatly influenced their interactions and learning experiences. Academic information, class schedules, and various university services can be easily accessed through mobile applications, such as Gapura UB. However, user reviews on the Google Play Store indicate dissatisfaction and complaints regarding the use of the Gapura UB mobile application, with an average score of 3.3 out of 5. This research aims to analyze user sentiments towards the Gapura UB mobile application. The study involved the collection of user review data for the Gapura UB mobile application through web scraping. Subsequently, data preprocessing was conducted to ensure that the review data was suitable for sentiment labeling using the Lexicon method. The Support Vector Machine (SVM) algorithm was then implemented to predict sentiments in the labeled data. The results of the sentiment analysis were evaluated using the Confusion Matrix and Root Cause Analysis: 5 Why’s. The research findings indicate that out of 494 classified reviews, there were 345 negative reviews and 149 positive reviews. The SVM method with the Lexicon- Based Approach achieved an accuracy of 98%. The evaluation using the confusion matrix revealed a precision of 99%, recall of 97%, and an F1-score of 98%. Visualization of the word cloud unveiled positive words such as "excellent," "login," and "cool," where positive reviews reflected satisfaction with the application and praised the involved staff. On the other hand, negative words like "login," "application," and "attendance" in negative reviews highlighted challenges related to login difficulties, application functionality issues, and attendance-related issues.
| Item Type: | Thesis (Sarjana) |
|---|---|
| Identification Number: | 0524150042 |
| Uncontrolled Keywords: | analisis sentimen, Gapura UB, ulasan, Support Vector Machine (SVM)-sentiment analysis, Gapura UB, review, Support Vector Machine (SVM). |
| Divisions: | Fakultas Ilmu Komputer > Sistem Informasi |
| Depositing User: | soegeng sugeng |
| Date Deposited: | 19 Feb 2024 02:22 |
| Last Modified: | 19 Feb 2024 02:22 |
| URI: | http://repository.ub.ac.id/id/eprint/215350 |
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