Perbandingan Algoritma SVM dan RF pada Analisis Sentimen menggunakan Pendekatan Machine Learning

Tristanto Ariaji; Sri Ngudi Wahyuni; Muhammad Ikhsan

doi:10.47134/jacis.v5i1.107

Authors

Tristanto Ariaji Universitas Amikom Yogyakarta
Sri Ngudi Wahyuni Universitas Amikom Yogyakarta
Muhammad Ikhsan Universitas Amikom Yogyakarta

DOI:

https://doi.org/10.47134/jacis.v5i1.107

Keywords:

SVM, Random Forest, Analisis Sentimen, machine learning, twitter

Abstract

Analisis sentimen tentang kelangkaan Bahan Bakar Minyak (BBM) di Indonesia merupakan salah satu cara untuk mengetahui opini masyarakat tentang kelangkaan BBM. Analisis sentimen digunakan sebagai dasar pengambilan keputusan oleh pihak berwenang sebagai upaya penyelesaian masalah, sehingga prediksi sentimen perlu dilakukan. Tujuan penelitian ini adalah melakukan perbandingan akurasi algoritma Support Vector Machine (SVM), dan Random Forest (RF) untuk analisis sentimen. Kontribusi pada penelitian ini adalah penentuan algoritma yang efektif dalam analisis sentimen Bahan Bakar Minyak di Indonesia. Adapun Tools olah data menggunakan Google Colab, dengan bahasa pemrograman Python dan pendekatan Machine Learning. Data eksperimen menggunakan data Twitter, diambil pada tanggal 1 -30 Juli 2022 dan terkumpul 6602 data dalam bahasa inggris. Hasil eksperimen menunjukkan bahwa hasil uji SVM untuk nilai Pressision, F1-Score dan support sebesar 0.98 lalu 0,97, kemudian 0.98 dan 67, sehingga nilai akurasi secara keseluruhan SVM adalah 0.98. Sedangkan RF memiliki hasil uji nilai Pressision, Recall. F1-Score dan support sebesar 0,86 kemudian 0,99 lalu 0,92 dan 67. Sedangkan nilai akurasi secara keseluruhan RF adalah 0.90. sehingga secara keseluruhan model SVM lebih direkomendasikan untuk pemodelan prediksi khususnya analisis sentimen pada kasus kelangkaan BBM melalui data Twitter.

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