Evaluasi Metode Pelabelan Sentimen Berbasis Leksikon terhadap Ulasan Aplikasi Sekuritas di Google Play Store
DOI:
https://doi.org/10.26418/justin.v13i4.93039Keywords:
analisis sentimen, pelabelan data, leksikon, random forestAbstract
Penelitian ini bertujuan untuk mengevaluasi persepsi pengguna terhadap aplikasi sekuritas di Indonesia melalui pendekatan analisis sentimen berbasis leksikon dan mengevaluasi performa klasifikasi menggunakan algoritma Random Forest. Data penelitian berupa 130.905 ulasan pengguna dari sepuluh aplikasi sekuritas populer di Google Play Store. Dua pendekatan leksikal yang digunakan adalah InSet Lexicon dan SentiWords_ID untuk memberi label sentimen pada ulasan tersebut. Hasil analisis menunjukkan bahwa aplikasi Ajaib memperoleh proporsi sentimen positif tertinggi dan paling representatif dengan jumlah ulasan terbesar dibandingkan aplikasi lain, sedangkan aplikasi MOST menunjukkan proporsi sentimen negatif tertinggi menurut kedua pendekatan. Pemodelan klasifikasi sentimen dilakukan menggunakan ekstraksi fitur TF-IDF dan algoritma Random Forest, yang dievaluasi melalui metrik akurasi, precision, recall dan f1-score. Hasil evaluasi menunjukkan bahwa SentiWords_ID memberikan performa klasifikasi yang lebih unggul dan stabil dibandingkan InSet Lexicon, khususnya dalam mengidentifikasi sentimen positif dan netral.References
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