Abstract
General elections are an important part of the political process so that many political figures participate in the process. Electability is one of the concerns, various things are done to be able to increase the electability of political figures who participate in general elections. Media has become one of the important tools used to increase electability, one of which is online news media. Reader comments can be used as an assessment of political figures in the form of sentiment analysis. However, it is not easy to analyze sentiments from comments on online news media, because comments contain unstructured text, especially in Indonesian text. Text pre-processing in text mining is an important part of getting the basic information contained in the comments. This research uses Indonesian text pre-processing using the Gata Framework Tetmining. Then proceed with extracting information using the Naïve Bayes classification algorithm and Support Vector Machine which are optimized using Particle Swarm Optimization. Tests carried out with both methods get the results that, Particle Swarm Optimization based on Support Vector Machine is the best method with an accuracy of 78.40% and AUC 0.850. This study found an algorithm that was effective in classifying positive and negative comments related to political figures from online news media.
Highlights
Pemilihan umum adalah bagian penting dari proses politik sehingga banyak tokoh politik yang berpartisipasi dalam proses tersebut
This study found an algorithm that was effective in classifying positive and negative comments related to political figures from online news media
Comparison on Indonesian social media sentiment analysis
Summary
Pemilihan umum adalah bagian penting dari proses untuk klasifikasi yang digunakan dalam penelitian ini. Penelitian ini menggunakan teknologi pembelajaran mesin untuk menganalisis sentimen elektabilitas tokohtokoh politik yang berasal dari data teks komentar pada media berita online di Indonesia. Mengklasifikasikan dan menganalisis sentimen yang Analisis sentimen ini dilakukan untuk menemukan dikombinasikan dengan tahap pre-processing dengan metode klasifikasi yang dapat membantu menentukan menggunakan alat bantu Gata Framework Textminig. Pada tahap Analisis komentar pembaca di media berita online ini juga dibuat pemahaman untuk menemukan metode tentang pemilihan tokoh politik yang mengikuti proses klasifikasi terbaik sehingga dapat membantu selama pemilihan. Penelitian ini juga diharapkan dapat proses pengolahan data yang akan dilakukan dengan memberikan solusi dalam hal analisis klasifikasi membandingkan hasil algoritma yang digunakan dan sentimen dari tokoh-tokoh politik dan mendukung untuk meningkatkan kinerja metode klasifikasi dapat pembahasan penelitian lebih lanjut tentang objek dilakukan dengan menggunakan pemilihan fitur penelitian lain, seperti analisis sentimen pada tingkat (feature selection).
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