PERBANDINGAN ALGORITMA NAÏVE BAYES DAN SUPPORT VECTOR MACHINE  DALAM ANALISIS SENTIMEN APLIKASI TEMAN BUSE

COMPARISON OF THE NAÏVE BAYES ALGORITHM AND SUPPORT VECTOR MACHINE IN SENTIMENT ANALYSIS of "TEMAN BUS" APPLICATION

Penelitian ini membandingkan kinerja dua algoritma klasifikasi, yaitu Naïve Bayes dan Support Vector Machine, dalam menganalisis sentimen ulasan pengguna terhadap aplikasi Teman Bus di Google Play Store. Dalam konteks analisis sentimen, penelitian sebelumnya telah menggunakan kedua algoritma ini, namun perbandingan langsung dalam konteks aplikasi Teman Bus belum dilakukan. Proses analisis sentimen melibatkan pemrosesan teks dan klasifikasi sentimen untuk mengevaluasi tanggapan pengguna terhadap layanan bus tersebut.

Hasil perbandingan menunjukkan variasi dalam performa klasifikasi sentimen antara Naïve Bayes dan Support Vector Machine. Support Vector Machine, khususnya dengan kernel RBF, menunjukkan akurasi sebesar 85%, lebih unggul dalam menangani pola sentimen kompleks dan tidak linier dibandingkan dengan Naïve Bayes yang memiliki akurasi 82%, terutama pada dataset dengan rasio pembagian data 30:70.

Penelitian ini memberikan pemahaman lebih dalam tentang evaluasi pengguna terhadap layanan Teman Bus melalui ulasan di Google Play Store serta memberikan wawasan berguna dalam pemilihan algoritma yang tepat untuk tugas serupa di masa depan. Support Vector Machine dengan kernel RBF cenderung menjadi pilihan yang lebih unggul dalam menganalisis sentimen aplikasi Teman Bus, namun pemilihan algoritma terbaik bergantung pada konteks dan karakteristik data yang ada.

Kata Kunci : Analisis Sentimen, Naïve Bayes, Support Vector Machine, Support Vector Machine, Aplikasi Teman Bus, Google Play Store

This research compares the performance of two classification algorithms, Naïve Bayes and Support Vector Machine, in analyzing sentiment from user reviews of the Teman Bus application on Google Play Store. While both algorithms have been used in sentiment analysis before, direct comparison within the context of the Teman Bus application has not been done. The sentiment analysis process involves text processing and sentiment classification to evaluate user responses to the bus service.

The comparison results show variation in sentiment classification performance between Naïve Bayes and Support Vector Machine. Support Vector Machine, particularly with the RBF kernel, demonstrates an accuracy of 85%, excelling in handling complex and non-linear sentiment patterns compared to Naïve Bayes, which has an accuracy of 82%, especially on datasets with a 30:70 data split ratio.

This research provides deeper insights into user evaluations of the Teman Bus service through reviews on Google Play Store and offers valuable insights into selecting the appropriate algorithm for similar tasks in the future. Support Vector Machine with the RBF kernel tends to be a preferred choice for analyzing sentiment in the Teman Bus application; however, the choice of the best algorithm depends on the context and characteristics of the data.

Keywords: Sentiment Analysis, Naïve Bayes, Support Vector Machine, Support Vector Machine, Teman Bus Application, Google Play Store.