Sentiment Analysis Of Public Enthusiasm Towards Electric Motorcycles Using The Naïve Bayes Algorithm
DOI:
https://doi.org/10.51601/ijse.v5i3.217Keywords:
Sentiment Analysis; Naïve Bayes; Electric Motorcycles and Public Perception.Abstract
Electric motorcycles have emerged as an alternative to reduce dependence on fossil fuels and support environmentally friendly transportation. In Indonesia, the local brand Polytron has introduced several electric motorcycle products at affordable prices. However, public responses remain varied, influenced by price, infrastructure, and awareness. This study aims to analyze public enthusiasm for Polytron electric motorcycles using the Naïve Bayes Classifier (NBC), which has been proven effective in text classification [1]. A dataset of 1000 comments was collected from social media platform X through web crawling. The preprocessing included case folding, cleaning, tokenizing, normalization, stopword removal, and stemming[2]. Sentiment labeling was conducted using the InSetLexicon, and TF-IDF weighting was applied before classification in Python using Google Colab [3]. The results indicated that most public opinions expressed positive sentiment, highlighting benefits such as cost savings and environmental friendliness [4]. Negative sentiments focused on limited charging infrastructure and higher purchase prices. The Naïve Bayes model achieved reliable performance, confirming its suitability for Indonesian sentiment analysis tasks [5]. This study contributes to understanding public perception of local electric vehicles and provides useful insights for policymakers and manufacturers in promoting sustainable transportation.
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S. Bahri and H. Herlina, “Pengaruh citra merek dan kualitas produk terhadap keputusan pembelian telepon selular merek Samsung pada mahasiswa Fakultas Ekonomi dan Bisnis Universitas Malikussaleh,” J. Visioner Strateg., vol. 6, no. 2, 2017.
P. P. O. Mahawardana, G. A. Sasmita, and I. P. A. E. Pratama, “Analisis Sentimen Berdasarkan Opini dari Media Sosial Twitter terhadap ‘Figure Pemimpin’ Menggunakan Python,” J. Ilm. Teknol. dan Komput., vol. 3, no. 1, pp. 810–820, 2022.
M. A. Djamaludin, A. Triayudi, and E. Mardiani, “Analisis sentimen tweet kri nanggala 402 di twitter menggunakan metode naïve bayes classifier,” J. JTIK (Jurnal Teknol. Inf. dan Komunikasi), vol. 6, no. 2, pp. 161–166, 2022.
A. Karimah, G. Dwilestari, and M. Mulyawan, “Analisis Sentimen Komentar Video Mobil Listrik Di Platform Youtube Dengan Metode Naive Bayes,” JATI (Jurnal Mhs. Tek. Inform., vol. 8, no. 1, pp. 737–767, 2024.
L. K. Bayu and T. Wurijanto, “Analisis sentimen mobil listrik menggunakan metode Naïve Bayes Classifier,” INFOTECH J. Inform. Teknol., vol. 5, no. 2, pp. 328–335, 2024.
E. Nurraharjo, “Analisis Sentimen Dan Klasifikasi Tweet Terkait Naiknya Kasus Omicron Menggunakan Naive Bayes Classifier,” J. Inform. dan Rekayasa Elektron., vol. 6, no. 1, pp. 1–8, 2023.
A. Farhani, “Analisis Sentimen Terhadap Kendaraan Listrik di Indonesia Menggunakan Metode Klasifikasi Naïve Bayes,” J. Indones. Manaj. Inform. Dan Komun., vol. 5, no. 3, pp. 2680–2690, 2024.
D. A. Prawinata, “Analisis Sentimen Kendaraan Listrik Pada Twitter Menggunakan Metode Long Short Term Memory.” Universitas Pembangunan Nasional Veteran Jawa Timur, 2024.
P. Cahyani and L. Abdillah, “Perbandingan Performa Algoritma Naïve Bayes, SVM dan Random Forest Studi Kasus Analisis Sentimen Pengguna Sosial Media X,” KALBIScientia J. Sains dan Teknol., vol. 11, no. 02, pp. 12–21, 2024.
S.Alfarizi andE.Fitriani,“Analisis sentimen kendaraan listrik menggunakan algoritma naive bayes dengan seleksi fitur information gain dan particle swarm optimization,”Indones.J.Softw.Eng.,vol.9,no.1,pp.19–27, 2023.
Y. Akbar and T. Sugiharto, “Analisis Sentimen Pengguna Twitter di Indonesia Terhadap ChatGPT Menggunakan Algoritma C4. 5 dan Naïve Bayes,” J. Sains dan Teknol., vol. 5, no. 1, pp. 115–122, 2023.
G. Ginabila, A. Fauzi, R. L. Pratiwi, S. Fauziah, and Z. I. Alfianti, “Analisis Sentimen Perkembangan Motor Listrik Menggunakan Support Vector Machine Dan Optimasi Particle Swarm Optimization,” INTI Nusa Mandiri, vol. 19, no. 1, pp. 77–83, 2024.
S. Sriani and S. Suhardi, “Analisis Sentimen Pengguna Aplikasi Mobile Jkn Menggunakan Algoritma Naïve Bayes Classifier Dan C4. 5,” J. Sci. Soc. Res., vol. 7, no. 2, pp. 555–563, 2024.
S. Sriani, A. Armansyah, and M. A. Rambe, “Analisis Sentimen Pengguna Aplikasi Dana Menggunakan Metode Naïve Bayes,” J. Inf. Technol., vol. 4, no. 2, pp. 174–183, 2024.
K. S. Putri, I. R. Setiawan, and A. Pambudi, “Analisis Sentimen Terhadap Brand Skincare Lokal Menggunakan Naïve Bayes Classifier,” Technol. J. Ilm., vol. 14, no. 3, pp. 227–232, 2023.
D.Normawati andS.A.Prayogi,“Implementasi Naïve Bayes classifier dan confusion matrix pada analisis sentimen berbasis teks pada Twitter,”J-SAKTI(Jurnal Sains Komput.dan Inform.,vol.5,no.2,pp.697–711, 2021.
M. Kholilullah, M. Martanto, and U. Hayati, “Analisis Sentimen Pengguna Twitter (X) Tentang Piala Dunia Usia 17 Menggunakan Metode Naive Bayes,” JATI (Jurnal Mhs. Tek. Inform.,vol. 8, no. 1, pp. 392–398, 2024.
S. I. Nurhafida and F. Sembiring, “Analisis Text Clustering Masyarakat Di Twiter Mengenai Mcdonald’Sxbts Menggunakan Orange Data Mining,” in Prosiding Seminar Nasional Sistem Informasi Dan Manajemen Informatika Universitas Nusa Putra, 2021, pp. 28–35.
D. Li et al., “Roman Urdu sentiment analysis using transfer learning,” Appl. Sci., vol. 12, no. 20, p. 10344, 2022.
A. Nugroho, “Analisis sentimen pada media sosial twitter menggunakan naive bayes classifier dengan ekstrasi fitur N-gram,” J-SAKTI (Jurnal Sains Komput. dan Inform., vol. 2, no. 2, pp. 200–209, 2018.
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