Penerapan Aspect Based Sentiment Analysis pada Produk GPU

Joshua Aprivaldis Toelle; Imanuel Jeremiah Garis Ramba; Fajar Akhbarudin Rosnah Wangge; Sebastianus Adi Santoso Mola

doi:10.30591/jpit.v11i2.9039

Penerapan Aspect Based Sentiment Analysis pada Produk GPU

Joshua Aprivaldis Toelle, Imanuel Jeremiah Garis Ramba, Fajar Akhbarudin Rosnah Wangge, Sebastianus Adi Santoso Mola

Abstract

Meningkatnya permintaan Graphics Processing Unit (GPU) yang didorong sektor gaming, komputasi awan, dan artificial intelligence menciptakan tantangan bagi konsumen dalam memilih produk yang sesuai kebutuhan, di mana ulasan produk konvensional cenderung terbatas pada aspek performa dan kurang memberikan analisis komprehensif terhadap berbagai dimensi produk. Penelitian ini bertujuan menganalisis sentimen pengguna terhadap aspek-aspek spesifik produk GPU menggunakan pendekatan Aspect Based Sentiment Analysis (ABSA) untuk memberikan wawasan yang lebih mendalam kepada calon pembeli. Metodologi penelitian menggunakan pendekatan kuantitatif berbasis Natural Language Processing dengan implementasi model RoBERTa pre-trained untuk menganalisis data dari platform Reddit, khususnya subreddit r/GPU, r/buildapc, dan r/hardware, dengan fokus perbandingan Nvidia GeForce RTX 5070 Ti dan AMD Radeon RX 9070 XT pada lima kategori aspek: performance, power consumption, compatibility, price, dan future-proof. Hasil penelitian menunjukkan bahwa model RoBERTa mampu mengklasifikasikan sentimen dengan akurasi tinggi pada setiap aspek yang dianalisis, dengan RTX 5070 Ti menunjukkan sentimen positif dominan pada aspek performance dan future-proof, sedangkan RX 9070 XT unggul pada aspek price dan power consumption. Penelitian ini membuktikan efektivitas ABSA dalam memberikan analisis sentimen yang lebih granular dan dapat membantu konsumen membuat keputusan pembelian yang lebih informatif.

Keywords

Aspect-Based Sentiment Analysis, GPU, RoBERTa, Reddit, Natural Language Processing, Analisis Sentimen, Graphics Processing Unit, Machine Learning

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DOI: https://doi.org/10.30591/jpit.v11i2.9039