Perbandingan Metode Klasifikasi Analisis Sentimen Tokoh Politik Pada Komentar Media Berita Online

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.

Analisis Sentimen Pembelajaran Daring Menggunakan Metode Naïve Bayes, KNN, dan Decision Tree

“…Tujuan penelitian adalah untuk mendapatkan metode yang terbaik dalam klasifikasi analisis sentimen. Dalam penelitian yang dilakukan Sigit (2019) ternyata metode Support vector machines (SVMs) yang berbasis Particle Swarm Optimization mendapat nilai yang baik sebesar 78.40% akurasi dan AUC 0.850 [5]. Hasil dari penelitian ini akan memberikan gambaran apakah Pemberlakuan Perkuliahan Secara Online/Daring mendapat opini positif atau negatif, berdasarkan tingkat akurasi dari ketiga metode tersebut dan membandingkannya.…”

“…Merupakan proses pemecahan kata menjadi beberapa bagian kata [6,5]. Proses ini juga digunakan untuk menghilangkan tanda baca yang tidak diperlukan.…”

Analisis Sentimen Zoom Cloud Meetings di Play Store Menggunakan Naïve Bayes dan Support Vector Machine

“…Pada penelitian sebelumnya mengenai analisis sentimen Korps Brigade Mobil Kepolisian Indonesia berdasarkan posting twitter menggunakan metode SVM Dan NB, menghasilkan nilai akurasi dengan pendekatan SVM 86,96% nilai presisi 86,96%, dan nilai recall 86,96% [7]. Penelitian mengenai perbandingan metode klasifikasi analisis sentimen tokoh politik pada komentar media berita online, menggunakan metode klasifikasi NB dan SVM mendapatkan hasil bahwa, SVM adalah metode terbaik dengan akurasi 78,40% dan AUC 0,850 [8]. Hasil penelitian algoritma Naïve Bayes, Decision Tree, dan SVM untuk klasifikasi persetujuan pembiayaan nasabah Koperasi Syariah menunjukan akurasi algoritma Naïve Bayes 77,29%, Decision Tree 89,02% dan yang tertinggi Support Vector Machine 89,86% [9].…”

N-Gram Feature for Comparison of Machine Learning Methods on Sentiment in Financial News Headlines

“…Sentiment analysis is the process of categorizing opinions or topics in the text. This process is to determine whether opinions or topics are positive, negative, or neutral [1]. Sentiment analysis research has been carried out in various sectors, such as finance [2], politics [3], companies [4], and so on.…”