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All Journal Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer
Oceandra Audrey
Fakultas Ilmu Komputer, Universitas Brawijaya
Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering

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Analisis Sentimen Pengguna Twitter Terhadap Opini Non Fungible Token di Indonesia Menggunakan Algoritma Random Forest Classifier Oceandra Audrey; Dian Eka Ratnawati; Issa Arwani
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 6 No 12 (2022): Desember 2022
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Non-fungible tokens are a way to record, verify, and track the ownership of an asset, both physical and digital, in the form of artwork, music, in-game items, domain names for websites, sports highlight videos to digital products. NFT's popularity in Indonesia started to rise when an account owner on the OpenSea NFT marketplace platform named Ghozali Everyday sold a collection of selfies of himself with sales of more than 1.5 billion rupiah. The popularity of the NFT trend has also triggered people in Indonesia to participate in selling resident identity cards in the form of KTPs which are then used as NFTs. The misuse of this trend has led to many positive, negative and neutral opinions from the public regarding the development of NFTs in Indonesia, one of which is through social media Twitter. A company engaged in the software house sector, Technobit Indonesia, has a similar product in the form of an NFT marketplace application. Technobit Indonesia is re-development taking into account the public's view of the current NFT, thereby delaying the release of their marketplace application to the public. Based on these problems, a classification method is needed that can classify tweet data that discusses NFT in Indonesia. This study uses the Random Forest Classifier method as a classification method for sentiment analysis. This research has several stages, including data collection, preprocessing, weighting of words with TF-IDF, Random Forest classification, testing and analysis. The test results using the best result parameters get an average of 93% for precision, recall, f-1 score, and accuracy with a total of 500 trees and a tree depth of 100 trees.
Co-Authors Dian Eka Ratnawati Issa Arwani