Analisis sentimen pengguna kendaraan listrik pada media sosial x (twitter) menggunakan metode support vector machine (svm)
Penerbit : FTI - Usakti
Kota Terbit : Jakarta
Tahun Terbit : 2025
Pembimbing 1 : Dian Pratiwi
Kata Kunci : Sentiment Analysis, Support Vector Machine, Social Media, Electric Vehicles.
Status Posting : Published
Status : Lengkap
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1. | 2025_SK_STF_064002000013_Halaman-Judul.pdf | ||
2. | 2025_SK_STF_064002000013_Surat-Pernyataan-Revisi-Terakhir.pdf | 1 | |
3. | 2025_SK_STF_064002000013_Surat-Hasil-Similaritas.pdf | 1 | |
4. | 2025_SK_STF_064002000013_Halaman-Pernyataan-Persetujuan-Publikasi-Tugas-Akhir-untuk-Kepentingan-Akademis.pdf | 1 | |
5. | 2025_SK_STF_064002000013_Lembar-Pengesahan.pdf | 1 | |
6. | 2025_SK_STF_064002000013_Pernyataan-Orisinalitas.pdf | 1 | |
7. | 2025_SK_STF_064002000013_Formulir-Persetujuan-Publikasi-Karya-Ilmiah.pdf | 1 | |
8. | 2025_SK_STF_064002000013_Bab-1-Pendahuluan.pdf | ||
9. | 2025_SK_STF_064002000013_Bab-2-Landasan-Teori.pdf |
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10. | 2025_SK_STF_064002000013_Bab-3-Metodologi-Penelitian.pdf |
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11. | 2025_SK_STF_064002000013_Bab-4-Analisis-dan-Pembahasan.pdf |
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12. | 2025_SK_STF_064002000013_Bab-5-Kesimpulan-dan-Saran.pdf | ||
13. | 2025_SK_STF_064002000013_Daftar-Pustaka.pdf | ||
14. | 2025_SK_STF_064002000013_Lampiran.pdf |
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D Di era globalisasi ini kemajuan teknologi sangat berkembang salah satu nya pada transportasi yang di mana terciptanya kendaraan listrik untuk mengurangi permasalahan polusi. Penelitian ini bertujuan untuk menganalisis sentimen pengguna kendaraan listrik di media sosial X (Twitter) menggunakan metode Support Vector Machine (SVM). Data dikumpulkan melalui crawling data pada rentang waktu Maret hingga Juni 2024 dengan kata kunci seperti “kendaraan listrikâ€, dan “motor listrikâ€. Setelah melalui proses preprocessing, data dilabeli secara VADER Lexicon untuk menghasilkan sentimen positif, netral, negatif. Hasil penelitian menunjukkan bahwa mayoritas tweet memiliki sentimen positif (55,23%), diikuti netral (28,93%) dan negatif (15,84%). Model SVM yang digunakan mencapai akurasi 79% dengan precission, recall dan F1-Score yang bervariasi di antara kategori. Penelitian ini menunjukkan bahwa pengguna X (Twitter) secara umum mendukung kendaraan listrik, meskipun terdapat kekhawatiran terhadap harga, infrastruktur dan kinerja baterai.
I In this era of globalization, technological advances are very advanced, one of which is in transportation where electric vehicles are created to reduce pollution problems. This study aims to analyze the sentiment of electric vehicle users on social media X (Twitter) using the Support Vector Machine (SVM) method. Data was collected through data crawling in the period March to June 2024 with keywords such as \\\"electric vehicles\\\" and \\\"electric motors\\\". After going through the preprocessing process, the data was labeled using VADER Lexicon to produce positive, neutral, negative sentiments. The results showed that most tweets had positive sentiment (55,23%), followed by neutral (28,93%) and negative (15,84%). The SVM model used achieved an accuracy of 79% with varying precision, recall and F1-Score between categories. This study shows that X (Twitter) users generally support electric vehicles, despite concerns about price, infrastructure and battery performance.