This repository contain my all detail about the final report my study for majoring Information System
Predicting-Pulp-Export-Values-in-Indonesia In this project, I utilized Long Short Term Memory (LSTM) models to predict future pulp production outcomes, making use of historical data and various influencing factors. The study involved a comprehensive review of the LSTM method, data collection, data preparation, and data visualization to gain deeper insights into the field.
LSTM is a specialized RNN architecture that enables the model to capture long-term dependencies in sequential data. It is widely used in natural language processing, speech recognition, and time-series analysis. LSTMs consist of memory cells that can store, read, and write information, making them capable of learning and remembering patterns over extended sequences.
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