Analisa Sentimen Tweet Berbahasa Indonesia Dengan Menggunakan Metode Lexicon Pada Topik Perpindahan Ibu Kota Indonesia

Abstract

This study proposes a classification of public response to the government's decision to move the Indonesian capital using the lexicon method. The results of testing accuracy are measured using a confusion matrix. The data in this study use data from Twitter in the form of tweets. The data contains tweets of community responses to the decision to move the Indonesian capital. Data passes through 5 preprocessing processes, namely case folding, punctuation removal, stopword removal, stemming, and tokenizing. Lexicon is used because it produces good accuracy values. In this study also will look for a dictionary that has the best classification results. The results of this study show the results of a good classification by approaching the results by experts.

Analysis of Public Sentiment Towards Goverment Efforts to Break the Chain of Covid-19 Transmission in Indonesia Using CNN and Bidirectional LSTM

“…The second stage is case folding. Case folding is the process of converting all letters in text or word tokens into lowercase or uppercase, but lowercase is more commonly used in this process [12]. Results from the previous stages are processed for case folding, as shown in Figure 3.…”

Sentiment Analysis Terkait Pemindahan Ibu Kota Indonesia Menggunakan Metode Random Forest Berdasarkan Tweet Warga Negara Indonesia

“…Sentiment analysis merupakan suatu proses memahami, mengekstrak dan mengolah data tekstual dengan otomatis guna mendapatkan informasi sentimen yang berada pada suatu kalimat dalam opini seseorang [5]. Terdapat beberapa metode yang digunakan untuk sentiment analysis diantaranya Naïve Bayes, Support Vector Machine, Lexicon, Neighbor Weighted K-Nearest Neighbor, K-Nearest Neighbor, Decision Tree, Random Forest [6,7,8,9,10]. Random Forest merupakan metode pengklasifikasian terdiri dari beberapa pohon keputusan yang dibangun dengan menggunakan vektor acak [11].…”