SANNY KHAIRANI LUBIS, NPM 2309105195 (2025) ANALISIS PERBANDINGAN ALGORITMA SUPPORT VECTOR MACHINE (SVM) DAN NAÏVE BAYES CLASSIFIER (NBC) DALAM MELAKUKAN ANALISIS SENTIMEN ULASAN PENGGUNA APLIKASI SHOPEE PADA GOOGLE PLAY STORE. Skripsi thesis, Universitas Labuhanbatu.
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Abstract
Perkembangan e-commerce di Indonesia semakin pesat, dengan Shopee sebagai salah satu platform terbesar yang memiliki jutaan ulasan dari pengguna. Namun, jumlah ulasan yang besar membuat analisis manual menjadi tidak efisien. Oleh karena itu, penelitian ini bertujuan untuk menerapkan dan membandingkan kinerja algoritma Support Vector Machine dan Naïve Bayes Classifier dalam melakukan analisis sentimen terhadap ulasan pengguna aplikasi Shopee yang diperoleh dari Google Play Store. Penelitian ini dilakukan dengan beberapa tahapan utama, yaitu pengumpulan data, pelabelan sentimen, preprocessing teks, pembobotan fitur menggunakan TF-IDF, klasifikasi menggunakan Support Vector Machine dan Naïve Bayes Classifier, serta evaluasi kinerja model. Dataset yang digunakan terdiri dari 5000 ulasan yang dikategorikan ke dalam tiga kelas sentimen: Positif, Netral, dan Negatif. Hasil penelitian menunjukkan bahwa Support Vector Machine memiliki akurasi lebih tinggi 72% dibandingkan Naïve Bayes Classifier, dengan nilai 71,45%. Dari hasil ini, dapat disimpulkan bahwa Support Vector Machine lebih cocok untuk analisis sentimen ulasan pengguna Shopee karena kemampuannya dalam mengenali pola kompleks dalam data. Kata Kunci : Analisis Sentimen, Google Play Store, Naïve Bayes Classifier, Shopee, Support Vector Machine. ================================================================================================= The development of e-commerce in Indonesia is increasingly rapid, with Shopee as one of the largest platforms that has millions of reviews from users. However, the large number of reviews makes manual analysis inefficient. Therefore, this study aims to apply and compare the performance of the Support Vector Machine and Naïve Bayes Classifier algorithms in conducting sentiment analysis of Shopee application user reviews obtained from the Google Play Store. This study was conducted with several main stages, namely data collection, sentiment labeling, text preprocessing, feature weighting using TF-IDF, classification using Support Vector Machine and Naïve Bayes Classifier, and model performance evaluation. The dataset used consists of 5000 reviews categorized into three sentiment classes: Positive, Neutral, and Negative. The results of the study showed that the Support Vector Machine has a higher accuracy of 72% compared to the Naïve Bayes Classifier, with a value of 71.45%. From these results, it can be concluded that the Support Vector Machine is more suitable for sentiment analysis of Shopee user reviews because of its ability to recognize complex patterns in data. Keywords : Sentiment Analysis, Google Play Store, Naïve Bayes Classifier, Shopee, Support Vector Machine.
| Item Type: | Thesis (Skripsi) |
|---|---|
| Uncontrolled Keywords: | Analisis Sentimen, Google Play Store, Naïve Bayes Classifier, Shopee, Support Vector Machine. ============================================= Sentiment Analysis, Google Play Store, Naïve Bayes Classifier, Shopee, Support Vector Machine. |
| Subjects: | Z Bibliography. Library Science. Information Resources > ZA Information resources Z Bibliography. Library Science. Information Resources > ZA Information resources > ZA4050 Electronic information resources |
| Divisions: | Fakultas Sains Dan Teknologi > Sistem Informasi |
| Depositing User: | Unnamed user with email repository@ulb.ac.id |
| Date Deposited: | 14 Apr 2025 09:37 |
| Last Modified: | 14 Apr 2025 09:37 |
| URI: | http://repository.ulb.ac.id/id/eprint/1267 |
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