Pengaruh Jenis Stemmer Terhadap Algoritma Svm Pada Analisis Sentimen Berbasis Lexicon Dengan Afinn Lexicon Resource
AbstractAnalisis sentimen merupakan bidang ilmu yang memiliki potensi besar dalam penelitian dan aplikasi praktis. Ini merupakan sebuah tugas dari NLP yang dieksploitasi untuk mengekstraksi dan mengklasifikasi konten berdasarkan sentimen emosi baik positive, negative dan netral. Analisis sentimen sendiri dibagi menjadi tiga teknik: teknik berbasis leksikon (lexicon-based), teknik berbasis machine learning (machine learning-based), dan teknik hybrid-based. Penelitian ini mengangkat teknik hybrid-based. Penelitian ini befokus untuk menemukan jenis stemmer yang dapat meningkatkan performa dari algoritma SVM pada analisis sentimen berbasis lexicon. Penelitian ini menerapkan tiga jenis stemmer yang berbeda yakni porter stemmer, snowball stemmer, dan Lancaster stemmer. Kemudian menggunakan AFINN lexicon dictionary. Terakhir algoritma SVM akan dievaluasi menggunakan confusion matrix. Penelitian ini melakukan tiga skenario, yakni gabungan antara jenis stemmer yang digunakan dengan algoritma SVM. Dari ketiga skenario yang dilakukan, gabungan SVM dan Snowball stemmer mendapatkan nilai Accuracy, Precision, Recall dan F1-Score paling tinggi dari dua skenario lainnya. Yakni dengan nilai Accuracy sebesar 95,67 %, Precision sebesar 95,68 %, Recall sebesar 95,67 % dan F1-Score sebesar 95,67 %.
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