Perbandingan Algoritma LSTM, BI-LSTM, dan CNN untuk Klasifikasi Komentar Masyarakat: Pembangkitan Serigala Direwolf pada Media X

Agung Novriyandi; Nirwana Hendrasuty

doi:10.47065/bits.v7i1.7398

Agung Novriyandi Universitas Teknokrat Indonesia, Bandar Lampung, Indonesia
Nirwana Hendrasuty * Universitas Teknokrat Indonesia, Bandar Lampung, Indonesia

(*) Corresponding Author

DOI: https://doi.org/10.47065/bits.v7i1.7398

Keywords: Direwolf Resurrection; Sentiment Analysis; LSTM; BI-LSTM; CNN; Cloning and Gene Editing; Comment Classification

Abstract

The resurrection of the Direwolf by Colossal Biosciences, a biotechnology company based in Dallas, Texas, USA, through cloning and gene editing technology has sparked widespread debate and discussion in society. Cloning is the process of creating a genetically identical copy of an organism, and in this context, it is used to bring back the Direwolf, a species that has been extinct for around 12,500 years. This study aims to compare the performance of Long Short-Term Memory (LSTM), Bidirectional LSTM (BI-LSTM), and Convolutional Neural Network (CNN) algorithms in classifying public comments related to the resurrection of the Direwolf on Media X. Using a dataset of 3400 comments, after undergoing cleaning and preprocessing to eliminate noise and improve data quality, 1424 valid comments were obtained, consisting of 869 negative, 270 positive, and 285 neutral comments. This study will evaluate the performance of the three algorithms based on metrics such as accuracy, precision, and recall. The evaluation results show that the LSTM model has the highest accuracy at 73%, followed by BI-LSTM at 70%, and CNN at 66%. Based on these results, the LSTM approach can be considered a better approach in classifying public comments related to the topic of Direwolf resurrection. The results of this study are expected to provide useful information for the development of sentiment analysis systems and understanding public opinion related to cloning and gene editing technology.

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