Attentive Convolutional Neural Network Untuk Analisis Sentimen Terhadap ChatGPT

• DEDI IRAWAN
• 14207097

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ABSTRAK

ABSTRAK

Nama                 : Dedi Irawan
NIM                    : 14207097
Program Studi    : Ilmu Komputer
Fakultas              : Teknologi Informasi
Jenjang               : Strata Dua (S2)
Konsentrasi         : Data Mining
Judul            : Attentive Convolutional Neural Network Untuk Analisis Sentimen Terhadap ChatGPT

Analisis sentimen adalah salah satu teknik yang mampu untuk menganalisis sebuah ulasan, sentimen, prilaku dan emosi orang terhadap entitas seperti layanan, produk, organisasi, peristiwa dan media sosial. Perkembangan Artificial Intelligence (AI) yang saat ini banyak membantu menusia dalam menyelesaikan suatu masalah. Dan salah satu terobosan yang banyak mengejutkan masyarakat adalah hadirnya sebuah inovasi teknologi pada bulan november tahun 2022 yang bernama Chat Generative Pre Trained Transformer (ChatGPT). Maka dari itu, analisis sentimen pengguna ChatGPT menjadi berharga, agar mengetahui seberapa pengaruhnya teknologi ChatGPT dalam kehidupan bermasyarakat. Penelitian ini menerapkan model Attention yang digunakan untuk menangkap kata-kata penting sehingga model tersebut dapat memahami konteks yang relevan. Penelitian ini juga mengimplementasikan model Attentive menggunakan 3 jenis attention yaitu Attention network, Multi-head Attention dan Convolutional Block Attention Module (CBAM). Perpaduan model Attentive dan CNN mampu meningkatkan nilai akurasi, salah satu caranya adalah dengan hyperparameter tuning setting nya. Dari hasil pemilihan hyperparameter terbaik tersebut membuktikan bahwa model Attentive mechnism mampu menaikan performansi dari model baseline nya, masing-masing nilai akurasinya yang naik adalah Attention network (AN) 0.37%, Multi-head Attention (MA) 0.43%, dan CBAM 0.21%. nilai peningkatan yang tertinggi didapatkan oleh Multi-head Attention.

KATA KUNCI

Analisis Sentimen,Deep Learning,CNN,Attentive Deep Convolutional Neural Network

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DAFTAR PUSTAKA

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