Tua, Yosua Firhot Bangkit (2025) Analisis Sentimen Terhadap Aplikasi m-Tix Pada Google Play Store Menggunakan Naive Bayes dan SVM. S1 thesis, Universitas Mercu Buana Jakarta - Menteng.
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Abstract
Penelitian Penelitian ini bertujuan untuk menganalisis sentimen pengguna terhadap aplikasi m-Tix di Google Play Store dengan menggunakan algoritma Naive Bayes dan Support Vector Machine (SVM). Aplikasi m-Tix, yang digunakan untuk pemesanan tiket bioskop secara daring, memiliki banyak ulasan yang mencerminkan pengalaman pengguna. Ulasan tersebut menjadi data penting untuk memahami persepsi serta kebutuhan pengguna. Dalam penelitian ini, data dikumpulkan melalui metode web scraping dengan mengambil sebanyak 4714 ulasan pengguna. Data tersebut kemudian diproses melalui tahapan preprocessing, seperti pembersihan teks, tokenisasi, dan pembobotan kata. Algoritma Naive Bayes digunakan karena memiliki keunggulan dalam kecepatan dan kesederhanaan klasifikasi teks, sedangkan SVM dipilih karena kemampuannya dalam menangani data yang lebih kompleks. Hasil analisis menunjukkan bahwa kedua algoritma memiliki tingkat akurasi yang signifikan, dengan SVM menunjukkan kinerja yang lebih unggul dalam metrik akurasi dan F1-score dibandingkan dengan Naive Bayes. Temuan ini diharapkan dapat memberikan wawasan bagi pengembang aplikasi m-Tix dalam meningkatkan kualitas layanan berdasarkan pola sentimen pengguna yang telah teridentifikasi. Selain itu, penelitian ini juga berkontribusi pada pengembangan metode analisis sentimen berbasis machine learning dalam industri aplikasi hiburan serta memperluas penerapan Natural Language Processing (NLP) dalam memahami opini publik. This study aims to analyze user sentiment toward the m-Tix application on the Google Play Store using the Naive Bayes and Support Vector Machine (SVM) algorithms. m-Tix, an online cinema ticket booking application, has received numerous user reviews reflecting their experiences. These reviews serve as valuable data for understanding user perceptions and needs. In this research, data was collected through web scraping, gathering a total of 4714 user reviews. The data was then processed through preprocessing stages such as text cleaning, tokenization, and word weighting. The Naive Bayes algorithm was selected for its efficiency and simplicity in text classification, while SVM was chosen for its ability to handle more complex data. The results indicate that both algorithms achieve significant accuracy levels, with SVM outperforming Naive Bayes in terms of accuracy and F1-score. These findings are expected to provide insights for m-Tix developers in enhancing service quality based on identified user sentiment patterns. Additionally, this study contributes to the development of machine learning-based sentiment analysis methods in the entertainment application industry and expands the application of Natural Language Processing (NLP) for understanding public opinion.
| Item Type: | Thesis (S1) |
|---|---|
| NIM/NIDN Creators: | 41521010151 |
| Uncontrolled Keywords: | Analisis Sentimen, Naive Bayes, Support Vector Machine. Sentiment Analysis, Naive Bayes, 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 > 004 Data Processing, Computer Science/Pemrosesan Data, Ilmu Komputer, Teknik Informatika |
| Divisions: | Fakultas Ilmu Komputer > Informatika |
| Depositing User: | NAIMAH NUR ISLAMIDIYANAH |
| Date Deposited: | 25 Aug 2025 08:11 |
| Last Modified: | 25 Aug 2025 08:11 |
| URI: | http://repository.mercubuana.ac.id/id/eprint/97089 |
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