Perbandingan SVM dan Logistic Regression Berbasis SMOTE pada Analisis Sentimen Menteri Keuangan di YouTube

Sirozul Huda; Afril Efan Pajri; Sahri

doi:10.30865/json.v7i4.9632

Authors

Sirozul Huda Univesitas Nahdlatul Ulama Sunan Giri
Afril Efan Pajri
Sahri

DOI:

https://doi.org/10.30865/json.v7i4.9632

Keywords:

Analisis Sentimen, Menteri Keuangan, Support Vector Machine, Logistic Regression, SMOTE

Abstract

YouTube telah berkembang menjadi salah satu ruang utama bagi masyarakat untuk menyampaikan opini terhadap isu publik, termasuk respons terhadap figur pejabat pemerintah. Penelitian ini bertujuan untuk mengevaluasi dan memetakan persepsi publik terhadap Menteri Keuangan Republik Indonesia melalui analisis sentimen berbasis machine learning, dengan mengkomparasikan kinerja algoritma Support Vector Machine dan Logistic Regression. Data berjumlah 4.003 data komentar dikumpulkan dari komentar berbagai kanal YouTube berdasarkan pencarian topik terkait, kemudian melalui tahapan pre-processing dan pelabelan otomatis menggunakan Indonesian Sentiment Lexicon (InSet). Meskipun pelabelan otomatis berbasis leksikon ini efisien, pendekatan ini memiliki keterbatasan untuk mendeteksi sarkasme dan menangkap konteks kalimat yang kompleks. Setelah fitur diekstraksi menggunakan pembobotan TF-IDF, teknik Synthetic Minority Over-sampling Technique (SMOTE) diterapkan untuk mengatasi masalah ketidakseimbangan label pada data latih. Pengujian model membuktikan bahwa pendekatan yang diusulkan berhasil melakukan klasifikasi dengan sangat baik. Hasil evaluasi menunjukkan bahwa Support Vector Machine memberikan tingkat akurasi tertinggi sebesar 90%, sedikit mengungguli Logistic Regression yang mencatatkan akurasi sebesar 89%. Kontribusi ilmiah dalam studi ini menegaskan bahwa algoritma klasifikasi berbasis SMOTE efektif dalam menangani masalah ketimpangan data, dengan menunjukkan peningkatan performa model dibandingkan penelitian terdahulu yang serupa.

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