Sentiment Analysis about Large-Scale Social Restrictions in Social Media Twitter Using Algoritm K-Nearest Neighbor

Sentiment analysis is a data processing to recognize topics that people talk about and their sentiments toward the topics, one of which in this study is about large-scale social restrictions (PSBB). This study aims to classify negative and positive sentiments by applying the K-Nearest Neighbor algorithm to see the accuracy value of 3 types of distance calculation which are cosine similarity, euclidean, and manhattan distance for Indonesian language tweets about large-scale social restrictions (PSBB) from social media twitter. With the results obtained, the K-Nearest Neighbor accuracy by the Cosine Similarity distance 82% at k = 3, K-Nearest Neighbor by the Euclidean Distance with an accuracy of 81% at k = 11 and K-Nearest Neighbor by Manhattan Distance with an accuracy 80% at k = 5, 7, 9, 11, and 13. So, in this study the K-Nearest Neighbor algorithm with the Cosine Similarity Distance calculation gets the highest point

PERBANDINGAN ALGORITMA LINEAR REGRESSION, LSTM, DAN GRU DALAM MEMPREDIKSI HARGA SAHAM DENGAN MODEL TIME SERIES

Penelitian ini bertujuan untuk memprediksi harga saham dengan membandingkan algoritma Linear Regression, Long Short-Term Memory (LSTM), dan Gated Recurrent Unit (GRU) dengan dataset publik kemudian menentukan performa terbaik dari ketiga algoritma tersebut. Dataset yang diuji bersumber dari Indonesia Stock Exchange (IDX), yaitu dataset harga saham KEJU berbentuk time series dari tanggal 15 November 2019 sampai dengan 08 Juni 2021. Parameter yang digunakan untuk pengukuran perbandingan adalah RMSE (Root Mean Square Error), MSE (Mean Square Error), dan MAE (Mean Absolute Error). Setelah dilakukan proses training dan testing, dihasilkan sebuah analisis bahwa dari hasil perbandingan algoritma yang digunakan, algoritma Gated Recurrent Unit (GRU) memiliki performance paling baik dibandingkan Linear Regression dan Long-Short Term Memory (LSTM) dalam hal memprediksi harga saham, dibuktikan dengan nilai RMSE, MSE, dan MAE dari uji coba GRU paling rendah, yaitu nilai RMSE 0.034, MSE 0.001, dan nilai MAE 0.024