Analisis Sentimen Multi-Aspek terhadap Ulasan Pengguna Aplikasi M-Pajak Menggunakan Model IndoBERT

Authors

  • Hansen Utomo Gunawan Universitas Bunda Mulia, Jakarta, Indonesia
  • Fergie Joanda Kaunang Universitas Bunda Mulia, Jakarta, Indonesia

DOI:

https://doi.org/10.62411/tc.v25i1.15761

Abstract

Aplikasi M-Pajak menghadapi tantangan dalam evaluasi layanan akibat banyaknya ulasan tidak terstruktur. Penelitian ini bertujuan untuk menganalisis sentimen multi-aspek menggunakan model Deep Learning berbasis arsitektur IndoBERT yang dioptimalkan dengan teknik Random Oversampling untuk menangani ketidakseimbangan data (class imbalance). Lima aspek dominan diekstraksi menggunakan metode Latent Dirichlet Allocation (LDA) yang divalidasi dengan nilai Topic Coherence. Hasil pengujian menunjukkan bahwa model mampu mencapai rata-rata Macro-F1-Score sebesar 93.86% pada kelima aspek yang diuji. Validitas kinerja model diperkuat oleh analisis kurva Receiver Operating Characteristic (ROC) yang mencatatkan nilai Area Under Curve (AUC) rata-rata di atas 0.99, serta pengujian 10-Fold Cross-Validation yang menghasilkan standar deviasi rendah (±0,0032), mengindikasikan bahwa model memiliki daya diskriminasi yang sangat tinggi dan tegas dalam membedakan sentimen negatif, netral, dan positif. Penelitian ini menyimpulkan bahwa integrasi IndoBERT dengan penanganan data tidak seimbang sangat efektif untuk klasifikasi teks ulasan layanan publik yang kompleks.   Kata kunci - Analisis sentimen, IndoBERT, M-Pajak, multi-aspek, Latent Dirichlet Allocation.

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Published

2026-02-26

Issue

Vol. 25 No. 1 (2026): February 2026

Section

Articles

License

Copyright (c) 2026 Hansen Utomo Gunawan, Fergie Joanda Kaunang

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