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All Journal KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer)
Pralabaika, Andini
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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Analisa Sentimen Transisi Kendaraan Konvensional Ke Listrik Dengan Menerapkan Algoritma Text Mining dan Term Frequency Inverse Document Frequency (TF-IDF) Pralabaika, Andini
KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer) Vol 7, No 1 (2024): Transformasi Komputasi Kuantum Untuk Percepatan Teknologi Baru
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/komik.v7i1.7918

Abstract

The development of transportation technology can have an impact on better human life in order to facilitate movement and meet all the needs of life. But sometimes there are problems that do not support the change in the transition of conventional vehicles to electricity, due to the lack of adequate infrastructure for charging electric energy, inconclusive weather conditions, and the value of prices that are still high, so that the community itself supports and does not support the change in the transition. Therefore, there is a solution that is used in the Text Mining Algorithm: the discovery of new information that was previously unknown and the extraction of valuable information from text automatically from different sources, while the TF-IDF Algorithm is used to determine the frequency value of words in documents. In this research, sentiment refers to people's views on the transition of conventional vehicles to electricity, whether they are positive or negative. The final result of this sentiment analysis is a positive sentiment value of 71.821%, while the negative sentiment value is 28.179%. So it is expected to provide information about the extent to which the transition from conventional to electric vehicles can be accepted by the public by understanding public sentiment. In addition, this research also conceptualizes the Text Mining Algorithm and the TF-IDF Algorithm as powerful tools for analyzing text data in the context of sentiment analysis.
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