MULTI-ASPECT SENTIMENT ANALYSIS TERHADAP NVIDIA RTX DENGAN SUPPORT VECTOR MACHINE DAN WORD EMBEDDINGS
Abstract
Beberapa bulan belakangan ini, tren meningkatnya pembelian GPU terutama seri RTX kian meningkat, banyak orang berlomba lomba untuk membuat PC untuk meningkatkan kinerja dengan kondisi WFH(Work From Home) selama terjadinya pandemi. Peningkatan terhadap penggunaan GPU seri RTX ini tentunya mengundang pendapat dari berbagai macam kalangan. Hal ini menciptakan pro dan kontra di kalangan PC enthusiast, terutama di beberapa platform Media Sosial. Dari munculnya pro dan kontra ini, dapat dilakukan ekstraksi emosi terhadap komentar yang mengandung pro dan kontra tersebut. Metode ini disebut juga dengan Analisis Sentimen, tahapan yang perlu dilakukan untuk melakukan Analisis Sentimen ini yang pertama adalah preprocessing yang terdiri dari Noise Removal, Tokenizing, Stopword Removal. Kemudian dilanjukan dengan proses yang kedua, yaitu Ekstraksi Fitur dengan Word Embeddings dan Support Vector Machine. Berdasarkan Analisis Performansi pada model yang telah dibuat, menunjukkan bahwa pada penelitian ini SVM kernel Linear menunjukkan hasil yang terbaik pada seluruh dataset, sedangkan kernel rbf pada penelitian ini menunjukkan hasil yang kurang memuaskan sekalipun telah melakukan perubahan parameter pada Gamma dan C. Penelitian ini dibuat bertujuan untuk menganalisa performansi model dalam mengklasifikasikan sentimen sebuah teks dengan model SVM.
Kata kunci: support vector machine, preprocessing, word embeddings
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