Chandra, Aldo (2025) Analisis minat warga negara Indonesia terhadap mobil listrik menggunakan metode bidirectional encoder representations from transformers (bert) dan metode named entity recognition (ner). Bachelor thesis, Universitas Pelita Harapan.
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Abstract
Peralihan ke kendaraan listrik menjadi solusi yang menjanjikan untuk
mengurangi emisi gas rumah kaca dan meningkatkan kualitas udara di kawasan
perkotaan. Peningkatan signifikan penggunaan kendaraan listrik di Indonesia pada
periode 2021-2023 menunjukkan minat masyarakat yang terus bertumbuh terhadap
teknologi ini. Namun, adopsi kendaraan listrik di Indonesia masih menghadapi
berbagai tantangan, termasuk persepsi masyarakat terhadap teknologi ini serta
faktor ekonomi dan teknis. Oleh sebab itu, penelitian ini bertujuan untuk
menganalisis minat masyarakat Indonesia terhadap mobil listrik dengan
menggunakan metode Bidirectional Encoder Representations from Transformers
(BERT) dan Named Entity Recognition (NER). BERT, sebagai metode pemrosesan
bahasa alami berbasis deep learning, digunakan untuk menganalisis sentimen dari
teks yang terkait dengan mobil listrik, sementara NER diterapkan untuk
mengidentifikasi entitas penting dalam teks, seperti merek mobil, lokasi, dan faktorfaktor yang memengaruhi minat terhadap mobil listrik. Hasil penelitian
menunjukkan bahwa kinerja metode BERT dalam analisis sentimen memiliki
akurasi sebesar 71,71%, presisi 83,56%, dan recall 71,71%, dengan nilai
missclassification error sebesar 28,29%. Analisis minat mengungkapkan bahwa
mayoritas data menunjukkan sentimen negatif (48,45%), diikuti oleh sentimen
netral (30,60%), dan sentimen positif (20,95%). Selanjutnya, metode NER
menunjukkan bahwa faktor-faktor utama yang memengaruhi minat masyarakat
terhadap mobil listrik meliputi pertimbangan lokasi penggunaan, harga, spesifikasi
teknis, serta reputasi perusahaan dan kualitas produk. / The transition to electric vehicles offers a promising solution to reducing
greenhouse gas emissions and improving air quality in urban areas. The significant
increase in the use of electric vehicles in Indonesia during the 2021–2023 period
indicates a growing public interest in this technology. However, the adoption of
electric vehicles in Indonesia still faces various challenges, including public
perceptions of this technology as well as economic and technical factors. Therefore,
this study aims to analyze the interest of Indonesian society in electric cars using
the Bidirectional Encoder Representations from Transformers (BERT) method and
Named Entity Recognition (NER). BERT, as a deep learning-based natural
language processing method, is used to analyze sentiment from texts related to
electric cars, while NER is applied to identify key entities in the texts, such as car
brands, locations, and factors influencing interest in electric cars. The study's
results show that the performance of the BERT method in sentiment analysis
achieved an accuracy of 71.71%, precision of 83.56%, and recall of 71.71%, with
a misclassification error rate of 28.29%. The interest analysis revealed that the
majority of the data indicated negative sentiment (48.45%), followed by neutral
sentiment (30.60%), and positive sentiment (20.95%). Furthermore, the NER
method showed that the main factors influencing public interest in electric cars
include considerations of usage location, price, technical specifications, as well as
company reputation and product quality.
Item Type: | Thesis (Bachelor) |
---|---|
Creators: | Creators NIM Email ORCID Chandra, Aldo NIM03082210022 aldchndra@gmail.com UNSPECIFIED |
Contributors: | Contribution Contributors NIDN/NIDK Email Thesis advisor Belferik, Ronald NIDN0105048702 ronald.belferik@uph.edu |
Uncontrolled Keywords: | Mobil listrik; analisis minat; metode bidirectional encoder representations from transformers (bert); metode named entity recognition (ner) |
Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
Divisions: | University Subject > Current > Faculty/School - UPH Medan > School of Information Science and Technology > Informatics Current > Faculty/School - UPH Medan > School of Information Science and Technology > Informatics |
Depositing User: | Aldo Chandra |
Date Deposited: | 25 Apr 2025 01:34 |
Last Modified: | 25 Apr 2025 01:34 |
URI: | http://repository.uph.edu/id/eprint/68201 |