Pendekatan Machine Learning: Analisis Sentimen Masyarakat Terhadap Kendaraan Listrik Pada Sosial Media X
Main Article Content
Abstract
Article Details
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.
How to Cite
References
Agustian, A., Tukino, T., & Nurapriani, F. (2022). Sentiment analysis, text mining application of sentiment analysis and naive bayes to electric vehicle usage opinions on twitter. Jurnal Tika, 7(3), 243–249. https://doi.org/10.51179/TIKA.V7I3.1550
Alfarizi, S., & Fitriani, E. (2023). Analisis sentimen kendaraan listrik menggunakan algoritma naive bayes dengan seleksi fitur information gain dan particle swarm optimization. Indonesian Journal on Software Engineering (IJSE), 9(1), 19–27. https://ejournal.bsi.ac.id/ejurnal/index.php/ijse/article/view/15671
Aryanti, R., Misriati, T., & Hidayat, R. (2023). Klasifikasi risiko kesehatan ibu hamil menggunakan random oversampling untuk mengatasi ketidakseimbangan data. Klik: Kajian Ilmiah Informatika Dan Komputer, 3(5), 409–416. https://djournals.com/klik/article/view/728
Badan Pusat Statistik. (2022). Perkembangan jumlah kendaraan bermotor di indonesia. Bps.Go.Id. https://www.bps.go.id/id/statistics-table/2/NTcjMg==/perkembangan-jumlah-kendaraan-bermotor-menurut-jenis--unit-.html
Christidis, P., & Focas, C. (2019). Factors affecting the uptake of hybrid and electric vehicles in the european union. Energies, 12(18), 3414. https://doi.org/10.3390/EN12183414
Efendi, A. (2020). Rancang bangun mobil listrik sula politeknik negeri subang. Jurnal Pendidikan Teknologi Dan Kejuruan, 17(1), 75–84. https://doi.org/10.23887/JPTK-UNDIKSHA.V17I1.23057
Erfina, A., & Lestari, R. A. (2023). Sentiment analysis of electric vehicles using the naïve bayes algorithm. Sistemasi: Jurnal Sistem Informasi, 12(1), 178–185. https://doi.org/10.32520/STMSI.V12I1.2417
Fitriani, R. D., Yasin, H., & Tarno, T. (2021). Penanganan klasifikasi kelas data tidak seimbang dengan random oversampling pada naive bayes (studi kasus: status peserta kb iud di kabupaten kendal). Jurnal Gaussian, 10(1), 11–20. https://ejournal3.undip.ac.id/index.php/gaussian/article/view/30243
Fitrianto, H. (2023). Analisis penggunaan kendaraan listrik sebagai upaya penurunan emisi lingkungan case study kendaraan listrik di provinsi sumatera utara. Cakrawala Repositori IMWI, 6(2), 1056–1067. https://doi.org/10.52851/CAKRAWALA.V6I2.302
Ghaniyyu, F. F., & Husnita, N. (2021). Upaya pengendalian perubahan iklim melalui pembatasan kendaraan berbahan bakar minyak di indonesia berdasarkan paris agreement. Morality: Jurnal Ilmu Hukum, 7(1), 110–129. https://doi.org/10.52947/MORALITY.V7I1.196
Handayani, A., & Zufria, I. (2023). Analisis sentimen terhadap bakal capres ri 2024 di twitter menggunakan algoritma svm. Journal of Information System Research (JOSH), 5(1), 53–63. https://doi.org/10.47065/JOSH.V5I1.4379
Liu, B. (2012). Sentiment analysis and opinion mining. Morgan & Claypool Publishers. https://www.cs.uic.edu/~liub/FBS/SentimentAnalysis-and-OpinionMining.pdf
Muchtari, F. A., Putra, A. M. N., & Bandri, S. (2023). Analisis pengaruh perubahan arus terhadap waktu dan temperatur pengisian baterai kendaraan listrik. Ensiklopedia of Journal, 5(3), 115–119. https://jurnal.ensiklopediaku.org/ojs-2.4.8-3/index.php/ensiklopedia/article/view/1629
Mustofa, A., & Novita, R. (2022). Klasifikasi sentimen masyarakat terhadap pemberlakuan pembatasan kegiatan masyarakat menggunakan text mining pada twitter. Building of Informatics, Technology and Science (BITS), 4(1), 200−208-200−208. https://doi.org/10.47065/BITS.V4I1.1628
Noviana, R., & Rasal, I. (2023). Penerapan algoritma naive bayes dan svm untuk analisis sentimen boy band bts pada media sosial twitter. Jurnal Teknik Dan Science, 2(2), 51–60. https://doi.org/10.56127/JTS.V2I2.791
Nurmalasari, D., Hermanto, T. I., & Nugroho, I. M. (2023). Perbandingan algoritma svm, knn dan nbc terhadap analisis sentimen aplikasi loan service. Jurnal Media Informatika Budidarma, 7(3), 1521–1530. https://ejurnal.stmik-budidarma.ac.id/index.php/mib/article/view/6427
Pambudi, D. I., & Sulastri, S. (2023). Perbandingan naïve bayes dan knn dalam klasifikasi tweet bbm subsidi. Elkom : Jurnal Elektronika Dan Komputer, 16(1), 35–44. https://journal.stekom.ac.id/index.php/elkom/article/view/961
Pambudi, I., & Juwono, V. (2023). Electric vehicles in indonesia: public policy, impact, and challenges. Asian Journal of Social and Humanities, 2(2), 347–360. https://doi.org/10.59888/AJOSH.V2I2.173
Pratama, Y., Murdiansyah, D. T., & Lhaksmana, K. M. (2023). Analisis sentimen kendaraan listrik pada media sosial twitter menggunakan algoritma logistic regression dan principal component analysis. Jurnal Media Informatika Budidarma, 7(1), 529–535. https://ejurnal.stmik-budidarma.ac.id/index.php/mib/article/view/5575
Purnajaya, A. R., & Hanggara, F. D. (2021). Perbandingan performa teknik sampling data untuk klasifikasi pasien terinfeksi covid-19 menggunakan rontgen dada. Journal of Applied Informatics and Computing (JAIC), 5(1), 37–42. https://pdfs.semanticscholar.org/6667/688f78652f9f0e9ff993d07f03cc195bfa16.pdf
Rahayu, A. S., Fauzi, A., & Rahmat, R. (2022). Komparasi algoritma naïve bayes dan support vector machine (svm) pada analisis sentimen spotify. Jurnal Sistem Komputer Dan Informatika (JSON), 4(2), 349–354. https://doi.org/10.30865/JSON.V4I2.5398
Saddam, M. A., Dewantara, E. K., & Solichin, A. (2023). Sentiment analysis of flood disaster management in jakarta on twitter using support vector machines. Sinkron: Jurnal Dan Penelitian Teknik Informatika, 7(1), 470–479. https://doi.org/10.33395/SINKRON.V8I1.12063
Sidik, A. D., & Ansawarman, A. (2022). Prediksi jumlah kendaraan bermotor menggunakan machine learning. Formosa Journal of Multidisciplinary Research, 1(3), 559–568. https://doi.org/10.55927/FJMR.V1I3.745
Subiantoro, H., Elok, A., & Maharani, P. (2023). Analysis of presidential regulation no. 55/2019 related to electric vehicle program in the context of realizing transportation environmentally friendly. International Journal of Business, Economics and Law, 30(2). http://sdgs.bappenas.go.id/tujuan-7/
Wang, H., & Wang, Y. (2020). A review of online product reviews. Journal of Service Science and Management, 13(1), 88–96. https://doi.org/10.4236/JSSM.2020.131006
Wayan Ernawati, N., Nyoman Satya Kumara, I., Setiawan, W., Raya Kampus Unud Jimbaran, J., Kuta Sel, K., & Badung, K. (2023). Perbandingan metode klasifikasi support vector machine dan naïve bayes pada analisis sentimen kendaraan listrik. Jurnal SPEKTRUM, 10(3), 106–114. https://doi.org/10.24843/SPEKTRUM.2023.V10.I03.P12