Pendekatan Deep Learning Berbasis LSTM Dalam Analisis Sentimen Untuk Meningkatkan Citra PLN Di Platform Digital

Lisanthoni, Angela Pendekatan Deep Learning Berbasis LSTM Dalam Analisis Sentimen Untuk Meningkatkan Citra PLN Di Platform Digital. Project Report (Praktek Kerja Lapang). UPN Veteran Jawa Timur.

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Abstract

The Field Work Practice Program (PKL) carried out at Ivosights resulted in a Sentiment Analysis project on social media data for the PLN brand during the January 2024 period. This project aims to identify public sentiment towards PLN using Deep Learning techniques based on Long Short-Term Memory (LSTM) which is implemented through the TensorFlow framework. The dataset used was collected from various social media platforms, including Twitter, Instagram, Facebook, YouTube, TikTok, as well as news sources and blogs. This analysis not only provides in-depth insight into public perception of PLN, but also utilizes the implementation of Deep Learning to accurately predict sentiment. The results of this project show that the LSTM model built was able to achieve an accuracy level of 92%, demonstrating the effectiveness of this approach in conducting sentiment analysis on social media data. It is hoped that these findings will help PLN understand and respond better to public opinion and improve their communication and service strategies. Keywords: Field Work Practices, Ivosights, Sentiment Analysis, TensorFlow, LSTM

Item Type:

Monograph (Project Report (Praktek Kerja Lapang))

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