IMPLEMENTASI LONG SHORT-TERM MEMORY (LSTM) TERHADAP PREDIKSI HARGA SAHAM PERUSAHAAN SEKTOR TELEKOMUNIKASI DI INDONESIA

Salsabila Fauziah, . (2025) IMPLEMENTASI LONG SHORT-TERM MEMORY (LSTM) TERHADAP PREDIKSI HARGA SAHAM PERUSAHAAN SEKTOR TELEKOMUNIKASI DI INDONESIA. Skripsi thesis, Universitas Pembangunan Nasional Veteran Jakarta.

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Abstract

Stocks are one of the investment instruments in the form of capital participation in a company by investors, aimed at gaining profits from the difference between the selling and buying prices, known as capital gains. The elecommunications sector is among the actively traded sectors in the capital market. However, the high volatility in historical stock prices and the complex temporal relationships make it difficult for investors to analyze stocks and predict their future prices. Therefore, this study aims to implement stock price prediction using a machine learning approach, specifically the Long Short-Term Memory (LSTM) method, in the telecommunications sector. The results of the study indicate strong predictive performance based on evaluation metrics, with a Mean Absolute Percentage Error (MAPE) of less than 10% and a low Root Mean Square Error (RMSE) within the range of a few hundred rupiahs. The addition of technical analysis indicators did not improve accuracy but did enhance computational learning efficiency.

Item Type: Thesis (Skripsi)
Additional Information: [No.Panggil: 2110511016] [Pembimbing 1: Neny Rosmawarni] [Pembimbing 2: I Wayan Rangga Pinastawa] [Penguji 1: I Wayan Widi Pradnyana] [Penguji 2: Kharisma Wiati Gusti]
Uncontrolled Keywords: LSTM, Prediction, Stock, Tellecomunication
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Fakultas Ilmu Komputer > Program Studi Informatika (S1)
Depositing User: SALSABILA FAUZIAH
Date Deposited: 13 Jul 2025 04:33
Last Modified: 18 Jul 2025 03:02
URI: http://repository.upnvj.ac.id/id/eprint/37082

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