Analisis Sentimen Pemilu Amerika Serikat Menggunakan Metode Combine Convolational Neural Network (CNN) Dan Recurrent Neural Network (RNN)

Hendra Sukianto; Anthony Anggrawan; Galih Hendro Martono

doi:10.69693/ijmst.v4i3.13067

Authors

Hendra Sukianto Universitas Bumigora
Anthony Anggrawan Universitas Bumigora
Galih Hendro Martono Universitas Bumigora

DOI:

https://doi.org/10.69693/ijmst.v4i3.13067

Keywords:

Analisis Sentimen, Pemilu Amerika Serikat, Convolutional Neural Network, Recurrent Neural Network, Deep Learning, Media Sosial

Abstract

Media sosial seperti X (sebelumnya Twitter) telah menjadi platform utama masyarakat dalam menyampaikan pandangan politik secara real-time, namun analisis sentimen pada data media sosial menghadapi tantangan berupa kompleksitas bahasa dan volume data yang besar. Penelitian ini bertujuan untuk menganalisis sentimen opini publik terhadap Pemilu Amerika Serikat 2024 menggunakan metode hybrid Convolutional Neural Network (CNN) dan Recurrent Neural Network (RNN). Penelitian ini mengusulkan pendekatan hybrid CNN-RNN yang menggabungkan keunggulan CNN dalam ekstraksi fitur lokal dan RNN dalam pemahaman konteks sekuensial untuk meningkatkan akurasi klasifikasi sentiment Hasil penelitian menunjukkan bahwa model Hybrid CNN-RNN memberikan performa terbaik dengan akurasi 69,53%, precision 70,12%, recall 69,53%, dan F1-Score 66,33%, mengungguli model CNN (akurasi 66,95%, F1-Score 67,30%) dan model RNN (akurasi 51,93%, F1-Score 41,83%). Temuan penelitian mengungkapkan bahwa kombinasi CNN dan RNN dalam model hybrid efektif menggabungkan keunggulan kedua metode, menghasilkan representasi data yang lebih lengkap sehingga meningkatkan kemampuan klasifikasi sentimen. Penelitian ini berkontribusi bagi pengamat politik, analis data, dan lembaga riset untuk memantau dan menganalisis sentimen publik, membantu dalam memprediksi hasil pemilu, dan merencanakan strategi kampanye yang lebih efektif.

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