Analisis Sentimen Komentar iPhone 17 pada Platform YouTube Menggunakan IndoBERT dan Support Vector Machine

SITI MARATUS SHOLIHAH; Afril Efan Pajri; Ita Aristia Sa’ida

doi:10.30865/json.v7i3.9507

Authors

SITI MARATUS SHOLIHAH UNIVERSITAS NAHDLATUL ULAMA SUNAN GIRI BOJONEGORO
Afril Efan Pajri Universitas Nahdlatul Ulama Sunan Giri
Ita Aristia Sa’ida Universitas Nahdlatul Ulama Sunan Giri

DOI:

https://doi.org/10.30865/json.v7i3.9507

Keywords:

Analisis Sentimen, YouTube, iPhone 17, IndoBERT, SVM

Abstract

Penelitian ini bertujuan untuk menganalisis sentimen komentar YouTube berbahasa Indonesia terkait iPhone 17 dengan membandingkan metode Support Vector Machine berbasis Term Frequency–Inverse Document Frequency dan model IndoBERT. Data diperoleh melalui proses crawling komentar pada kanal YouTube GadgetIn, kemudian diproses melalui tahapan pre-processing untuk mengurangi noise dan menormalkan teks. Pelabelan sentimen dilakukan secara otomatis menggunakan InSetLexicon dengan dua kelas, yaitu positif dan negatif. Dataset selanjutnya dibagi menggunakan teknik stratified split menjadi data latih, validasi, dan uji. Selain dua model utama, pendekatan ensemble IndoBERT–SVM diuji sebagai metode tambahan untuk menilai stabilitas performa klasifikasi. Evaluasi dilakukan menggunakan confusion matrix serta metrik Accuracy, Precision, Recall, dan F1-score. Hasil pengujian menunjukkan bahwa IndoBERT memperoleh performa terbaik dengan nilai Accuracy sebesar 92, 29%, diikuti oleh model ensemble sebesar 91,63%, dan Support Vector Machine sebesar 88,99%. Temuan ini mengindikasikan bahwa model berbasis transformer lebih efektif dalam memahami konteks bahasa informal pada komentar YouTube dibandingkan metode berbasis fitur tradisional. Dengan demikian, penelitian ini memberikan bukti empiris mengenai efektivitas pendekatan machine learning dan transformer dalam analisis sentimen media sosial berbahasa Indonesia.

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