Meningkatkan Akurasi Long-Short Term Memory (LSTM) pada Analisis Sentimen Vaksin Covid-19 di Twitter dengan Glove
DOI:
https://doi.org/10.61769/telematika.v16i2.400Keywords:
Long Short Term Memory (LSTM), Bi-LSTM, sentiment analysis, Glove method, Covid-19, analisis sentimen.Abstract
Covid-19 began to appear in early 2020. The spread of this outbreak is often discussed on Twitter, especially about vaccine procurement. For this reason, it is necessary to have a sentiment analysis on the opinion on vaccine procurement. Sentiment analysis will use the Long Short Term Memory (LSTM) method. However, the level of accuracy of LSTM itself is not accurate enough compared to another method, such as Bi-LSTM. Therefore, it is necessary to optimize so that the LSTM model can predict accurately and compete with the accuracy of Bi-LSTM. Optimization is done by using the Glove method. The Glove method works by counting the occurrences of one word with another and then converting it to a vector. Words that often appear together will have vector values that are close to each other. This vector value is then used as a reference and inserted into the embedding layer of the LSTM model. The application of LSTM coupled with the Glove method resulted in an accuracy of 89% (87% for LSTM and 88% for Bi-LSTM). In this study, the Glove method could increase the accuracy of the used model by 2%.
Covid-19 mulai muncul di awal tahun 2020. Penyebaran wabah ini sering dibicarakan di Twitter, terutama tentang pengadaan vaksin. Untuk itu, perlu adanya analisis sentimen terhadap opini pengadaan vaksin. Analisis sentimen akan menggunakan metode Long Short Term Memory (LSTM). Namun, tingkat akurasi LSTM sendiri belum cukup akurat dibandingkan dengan metode lainnya, seperti Bi-LSTM. Oleh karena itu, perlu dilakukan optimalisasi agar model LSTM dapat memprediksi secara akurat dan dapat menyaingi akurasi Bi-LSTM. Optimalisasi dilakukan dengan menggunakan metode Glove. Metode Glove bekerja dengan menghitung kemunculan satu kata dengan kata lainnya lalu mengonversinya menjadi vektor. Kata yang sering muncul secara bersamaan akan memiliki nilai vektor yang saling mendekati. Nilai vektor ini kemudian dijadikan referensi dan dimasukkan ke lapisan embedding pada model LSTM. Penerapan LSTM yang ditambah dengan metode Glove menghasilkan akurasi sebesar 89% (87% untuk LSTM dan 88% untuk Bi-LSTM). Dalam penelitian ini penerapan metode Glove dapat meningkatkan akurasi model sebesar 2%.
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