Aspect-Based Sentiment Analysis Ulasan VGA RTX 5060 Berbasis LLM dan Naive Bayes

Ikhsan Fahruliazmi; Dedi Saputra; Siti Nurdiani; Juniato Sidauruk

doi:10.59395/jitp.v6i1.198

Authors

Ikhsan Fahruliazmi Program Studi Informatika, Universitas Bina Sarana Informatika, Pontianak, Kalimantan Barat, Indonesia
Dedi Saputra Program Studi Informatika, Universitas Bina Sarana Informatika, Pontianak, Kalimantan Barat, Indonesia
Siti Nurdiani Program Studi Informatika, Universitas Bina Sarana Informatika, Pontianak, Kalimantan Barat, Indonesia
Juniato Sidauruk Program Studi Informatika, Universitas Bina Sarana Informatika, Pontianak, Kalimantan Barat, Indonesia

DOI:

https://doi.org/10.59395/jitp.v6i1.198

Keywords:

Aspect-Based Sentiment Analysis, Complement Naive Bayes, Large Language Model, TF-IDF, Easy Data Augmentation, VGA RTX 5060

Abstract

Ulasan konsumen di platform e-commerce mengandung informasi berharga mengenai persepsi pasar terhadap produk teknologi. Penelitian ini mengusulkan sistem Aspect-Based Sentiment Analysis (ABSA) pada ulasan VGA NVIDIA GeForce RTX 5060 dari platform Tokopedia, Shopee, dan YouTube menggunakan metode Complement Naive Bayes (CNB) dengan representasi fitur TF-IDF bigram. Kontribusi utama meliputi: (1) pipeline pengumpulan data multi-platform yang menghasilkan 4.675 ulasan asli, diperkaya melalui augmentasi data menjadi 4.982 ulasan; (2) peningkatan kualitas label menggunakan Large Language Model Llama 3.1 melalui Groq API yang mencapai agreement rate 79,9% (Cohen's Kappa = 0,699), dengan 20,1% label berbeda dari pendekatan keyword-based; (3) augmentasi data menggunakan Easy Data Augmentation (EDA) untuk menyeimbangkan distribusi kelas menjadi hampir merata (Positif 33,5%; Netral 32,7%; Negatif 33,8%); serta (4) identifikasi lima aspek ulasan: Harga, Kualitas Produk, Performa, Pengiriman, dan Pelayanan Toko. Model CNB global mencapai akurasi 77,4% dengan F1-Score weighted 77,3%. Rata-rata akurasi ABSA per aspek mencapai 91,9% dengan rata-rata F1-Score 92,5%, dengan performa tertinggi pada aspek Harga (95,4%) dan terendah pada aspek Performa (86,7%). Hasil ini menunjukkan efektivitas integrasi LLM dalam proses pelabelan data untuk meningkatkan kualitas sistem ABSA berbasis Naive Bayes.

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