Sentiment Analysis of Honda Esaf Frame Quality Based on Reviews on Platform X using Support Vector Machine Algorithm

Jefri Setyawan

doi:10.59934/jaiea.v5i2.1901

Authors

Jefri Setyawan Universitas Bina Sarana Informatika

DOI:

https://doi.org/10.59934/jaiea.v5i2.1901

Keywords:

Analisis Sentimen, Rangka eSAF Honda, Support Vector Machine, TF-IDF, Lexicon-Based

Abstract

This study analyzes public sentiment towards Honda's eSAF frame through 1513 reviews on Platform X during the period of January 2023-October 2025, which was triggered by crucial issues related to the potential for rust, corrosion, and fracture in motorcycle frames. Using a quantitative method with a computational approach, this study applies the Support Vector Machine (SVM) Algorithm with data preprocessing (Case Folding, Cleaning, Tokenizing, Stopword Removal, Stemming), TF-IDF weighting, and Lexicon-based sentiment labeling to classify positive and negative perceptions. The evaluation results show that the SVM-TF-IDF model achieved 98% accuracy on the test data, with negative sentiment dominated by the keywords "rust" and "damaged", while positive sentiment centered on "strong" and "safe", providing an objective picture of public perception as a basis for evaluating product quality and improving corporate communication strategies.

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References

A. Aldila Safitri, A. Rahmadhany, and I. Irwansyah, “Penerapan Teori Penetrasi Sosial pada Media Sosial: Pengaruh Pengungkapan Jati Diri melalui TikTok terhadap Penilaian Sosial,” Jurnal Teknologi Dan Sistem Informasi Bisnis, vol. 3, no. 1, pp. 1–9, Jan. 2021, doi: 10.47233/jteksis.v3i1.180.

A. Cleary Syafi’i and A. Davy Wiranata, “KLIK: Kajian Ilmiah Informatika dan Komputer Analisis Sentimen Terhadap Rangka E-SAF Honda Pada Media Sosial X Dengan Algoritma Naïve Bayes,” Media Online), vol. 5, no. 1, pp. 57–66, 2024, doi: 10.30865/klik.v5i1.1993.

M. Ma’rufudin and A. Yudhistira, “Analisis Sentimen Petani Milenial Pada Media Sosial X Menggunakan Algortitma Support Vector Machine (SVM),” Jurnal Pendidikan dan Teknologi Indonesia, vol. 5, no. 3, pp. 845–857, Mar. 2025, doi: 10.52436/1.jpti.717.

A. N. Ihsan and S. Tresnawati, “ANALISIS SENTIMEN PADA PLATFORM X TERHADAP LAYANAN PROVIDER TRI MENGGUNAKAN NAÏVE BAYES DAN SUPPORT VECTOR MACHINE,” Jurnal Informatika dan Teknik Elektro Terapan, vol. 12, no. 3S1, Oct. 2024, doi: 10.23960/jitet.v12i3s1.5264.

F. A. Ryandi, D. Pratiwi, and S. Sari, “Analisis Sentimen Masyarakat Di Media Sosial X Terhadap Kemenkes Dengan Naive Bayes dan SVM,” Jurnal Sains dan Teknologi, vol. 7, no. 1, pp. 1–6, 2025, doi: 10.55338/saintek.v7i1.4615.

J. Junifer Pangaribuan and O. Putra Barus, “IMPLEMENTASI ALGORITMA TF-IDF DAN SUPPORT VECTOR MACHINE TERHADAP ANALISIS PENDETEKSI KOMENTAR CYBERBULLYING DI MEDIA SOSIAL TIKTOK,” JURNAL DEVICE, vol. 13, no. 1, pp. 124–134.

N. Hendrastuty, A. Rahman Isnain, and A. Yanti Rahmadhani, “Analisis Sentimen Masyarakat Terhadap Program Kartu Prakerja Pada Twitter Dengan Metode Support Vector Machine,” vol. 6, no. 3, 2021, [Online]. Available: http://situs.com

Ahmad Hilman Dani, Eva Yulia Puspaningrum, and Retno Mumpuni, “Studi Performa TF-IDF dan Word2Vec Pada Analisis Sentimen Cyberbullying,” no. 2, pp. 94–106, 2024, doi: 10.62951/router.v2i2.76.

M. Nouval, D. Ramadhan, and A. Gunaryati, “KLASIFIKASI SENTIMEN PUBLIK TERHADAP KEBIJAKAN KENDARAAN LISTRIK MENGGUNAKAN ALGORITMA NAÏVE BAYES DAN SUPPORT VECTOR MACHINE,” 2025.

R. Rahman Salam, M. Fajri Jamil, and Y. Ibrahim, “MALCOM: Indonesian Journal of Machine Learning and Computer Science Sentiment Analysis of Cash Direct Assistance Distribution for Fuel Oil Using Support Vector Machine Analisis Sentimen Terhadap Bantuan Langsung Tunai (BLT) Bahan Bakar Minyak (BBM) Menggunakan Support Vector Machine,” vol. 3, pp. 27–35, 2023.

G. Lifiano Jamot Munthe and M. Yasir Alghifari, “ANALISIS SENTIMEN PUBLIK TERHADAP KEBIJAKAN PEMERINTAH TERBARU TENTANG PENDISTRIBUSIAN GAS ELPIGI SUBSIDI PADA MASYARAKAT MENGGUNAKAN METODE ALGORITMA SVM,” 2025.

Irma Surya Kumala Idris, Yasin Aril Mustofa, and Irvan Abraham Salihi, “Analisis Sentimen Terhadap Penggunaan Aplikasi Shopee Mengunakan Algoritma Support Vector Machine (SVM),” Jambura Journal of Electrical and Electronics Engineering, vol. 5, pp. 823–848, Jan. 2023, doi: 10.1177/0165551510388123.

A. A. Nurrahman, M. Mauladi, and A. Rahman, “Analisis Sentimen Masyarakat terhadap Kenaikan Harga Bahan Bakar Minyak Menggunakan Support Vector Machine dan SMOTE,” sudo Jurnal Teknik Informatika, vol. 4, no. 2, pp. 50–56, Jun. 2025, doi: 10.56211/sudo.v4i2.908.

A. Fauziah Nur and Y. Salim, “Analisis Sentimen Pengguna X Terhadap Perkembangan Artificial Intelligence (AI) Menggunakan Algoritma Machine Learning,” Literatur Informatika & Komputer, vol. 1, no. 4, pp. 347–357, 2024, doi: 10.33096/linier.v1i4.2534.

Okta Ihza Gifari, Muh. Adha, Ivan Rifky Hendrawan, and Fernandito Freddy Setlight Durrand, “406540-film-review-sentiment-analysis-using-tf-2f07f120,” JIFOTECH (JOURNAL OF INFORMATION TECHNOLOGY), vol. 2 No.1, no. Analisis Sentimen Review Film Menggunakan TF-IDF dan Support Vector Machine, p. 347, Mar. 2022.