Analisis Sentimen Penggunaan Hastag Motor Bensin dan Motor Listrik Pada Media Sosial Twitter dengan Metode Support Vector Machine (SVM)

Pradhana, Rhizki Dwi (2023) Analisis Sentimen Penggunaan Hastag Motor Bensin dan Motor Listrik Pada Media Sosial Twitter dengan Metode Support Vector Machine (SVM). Undergraduate thesis, Politeknik Negeri Jember.

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Abstract

According to the Ministry of Home Affairs through the Directorate General of the Population and Civil Registration Service, Zudan Arif Fakrullah stated that the current population of Indonesia has reached 273,879,750 people. (Ministry of Home Affairs data releases the latest population, there are 273.8 million people in Indonesia, n.d.). Fossil fuels have a huge impact on sustaining human life, such as the depletion of petroleum reserves, rising prices for fuel oil (BBM) due to increased demand that is not proportional to existing oil production and the effects of air pollution. On the other hand, the government also encourages people to switch from fuel-based vehicles (BBM) to electric vehicles, this regulation also already exists, namely Minister of Transportation Regulation (Permenhub) No. 56 of 2020 concerning the conversion of electric motorcycles to motorbikes This can be followed up to find out public opinion about oil-fueled vehicles with electric vehicles. Sentiment analysis is a process needed to filter public opinion through various social media, for example on Twitter, which then classifies the data into positive and negative classes. Sentiment analysis on Twitter itself can provide benefits to the community, government, or service or product provider companies through the opinions written on Twitter. Manufacturers of electric motorbikes can use this opinion as an evaluation to improve service or quality of the product itself and for the community it can be taken into consideration in choosing electric motorbikes. Keywords : Classification, Sentiment Analysis, Sentiment Analysis, Twitter, Support Vector Machine (SVM), Scrapping.

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