Investment in the capital market can help boost a country’s economic growth. Without doubt, in investing, a technical analysis of the condition of the stock is needed at that time. One of the technical analyses that can be done is to look at the historical data of a stock. Candlestick charts can summarize historical data that contain price data for Open, High, Low, Close (OHLC) in a chart. A group of candlesticks will form a pattern that can help investors to see whether the stock is trending up or down. The number of candlestick patterns and the manual determination of candlestick patterns may take time and effort. Feedforward Neural Network (FNN) is one of the algorithms that can help mapping input and output. This study aims to implement FNN to classify candlestick patterns found in a stock’s historical data. The test
results show that the accuracy for each model does not guarantee whether all patterns can be recognized properly. This is mainly because the dataset is not balanced and the classification process cannot be done properly. Tests with the original data have an accuracy of above 85% on each stock, but the average F1-score is below 45%. Further experiments by using random under-sampling and Synthetic Minority Oversampling Technique, result in decreased accuracy value, where the lowest is 59% in PT Bukit Asam Tbk shares and the average F1-score increased, but less than 15%.