Implementasi Support Vector Machine untuk Analisis Sentimen Ulasan Google Maps pada Layanan Perbaikan Ponsel
DOI:
https://doi.org/10.29407/ft2x7k27Keywords:
Analisis Sentimen, Google Maps, SVM, TF-IDF, Ulasan PelangganAbstract
Layanan perbaikan ponsel merupakan salah satu sektor jasa yang banyak menerima ulasan pelanggan melalui platform Google Maps. Ulasan tersebut mengandung opini yang dapat dimanfaatkan untuk mengevaluasi kualitas pelayanan, namun bersifat tidak terstruktur sehingga sulit dianalisis secara manual. Penelitian ini bertujuan untuk melakukan analisis sentimen terhadap ulasan Google Maps pada layanan perbaikan ponsel dengan menerapkan algoritma Support Vector Machine (SVM). Data ulasan dikumpulkan melalui teknik web scraping, diproses melalui tahapan text preprocessing yang meliputi cleaning, case folding, tokenizing, stopword removal, normalization, dan stemming, serta diberi label sentimen secara otomatis. Selanjutnya, data direpresentasikan menggunakan metode Term Frequency–Inverse Document Frequency (TF-IDF) dan dibagi menjadi data latih serta data uji dengan rasio 80:20. Proses analisis sentimen dilakukan dengan mengklasifikasikan ulasan ke dalam kelas sentimen positif, netral, dan negatif menggunakan algoritma SVM. Evaluasi kinerja model dilakukan menggunakan confusion matrix dengan parameter accuracy, precision, recall, dan F1-score. Hasil penelitian menunjukkan bahwa model yang dibangun menghasilkan nilai akurasi sebesar 96%, dengan nilai precision, recall, dan F1-score masing-masing sebesar 96%. Hasil tersebut menunjukkan bahwa algoritma SVM efektif digunakan untuk analisis sentimen ulasan Google Maps dan mampu memberikan gambaran objektif mengenai persepsi pelanggan terhadap kualitas layanan perbaikan ponsel.
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