Analisis Sentimen Pada Kurikulum Merdeka Menggunakan Word2Vec Dan Algoritma Long Short-Term Memory

Octhavia Alin
Ema Utami


DOI: https://doi.org/10.29100/jipi.v10i3.6516

Abstract


Kurikulum Merdeka, sebagai penyempurnaan dari Kurikulum 2013, telah memicu berbagai opini publik sejak diterapkan pada Tahun Ajaran 2022/2023. Opini-opini ini, yang dibagikan melalui platform media sosial seperti Twitter dan YouTube, mencerminkan pandangan beragam dari masyarakat. Penelitian ini menganalisis sentimen terhadap Kurikulum Merdeka menggunakan algoritma LSTM yang dipadukan dengan Word2Vec. Dataset yang digunakan terdiri dari 23.577 teks dari Twitter dan YouTube, yang dikategorikan menjadi sentimen negatif, positif, dan netral. Penelitian ini membandingkan dua arsitektur Word2Vec. Skip-gram dan Continuous Bag of Words (CBOW), serta mengeksplorasi pengaruh optimizer (Adam dan RMSprop) dan learning rate terhadap kinerja model. Hasil penelitian menunjukkan bahwa arsitektur CBOW, yang dikombinasikan dengan optimizer Adam dan learning rate 0.001, memberikan hasil yang paling akurat dan stabil, dengan akurasi mencapai 97% dan tingkat loss yang rendah. Skip-Gram lebih sensitif terhadap perubahan parameter, yang menyebabkan ketidakstabilan kinerja. Penelitian ini menegaskan efektivitas kombinasi Word2Vec dan LSTM untuk analisis sentimen, dengan CBOW dan optimizer Adam sebagai konfigurasi yang paling optimal

Keywords


Kurikulum Merdeka; LSTM; Sentimen Analisis; Word2Vec;

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