IMPLEMENTASI METODE GATED RECURRENT UNIT UNTUK KLASIFIKASI ARAH TREN HARGA SAHAM JANGKA MENENGAH (SWING TRADING) BERBASIS WEB

GUNAWAN, MUHAMMAD FIRDAUS (2026) IMPLEMENTASI METODE GATED RECURRENT UNIT UNTUK KLASIFIKASI ARAH TREN HARGA SAHAM JANGKA MENENGAH (SWING TRADING) BERBASIS WEB. S1 thesis, Universitas PGRI Madiun.

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Abstract

Stock investment is an economic activity that carries high risks due to dynamic price fluctuations, requiring investors to use efficient analysis tools to minimize losses. This study aims to implement the Deep Learning method using the Gated Recurrent Unit (GRU) algorithm to predict medium-term stock price trend directions (swing trading) and build a web-based application for easy user access. The case study used is the stock of PT Astra Agro Lestari Tbk (AALI) with daily historical data from August 24, 2022, to January 9, 2026. The system development follows the Agile method using the Python programming language and the Flask framework. To achieve optimal model performance, optimization was conducted using the Hyperparameter Tuning Grid Search technique. The system performance was tested using Black Box Testing, while model accuracy was evaluated using Mean Sequence Error (MSE). The results show that the web application functions correctly according to the design. The GRU model is capable of predicting stock prices with high accuracy, evidenced by a MSE value of 4.99%. Thus, this system can serve as a decision support tool for investors in analyzing stock price trends.

Item Type:	Thesis/Skripsi/Tugas Akhir (S1)
Kata Kunci:	Gated Recurrent Unit (GRU), Stock Prediction, Web Application, Flask, Swing Trading. Gated Recurrent Unit (GRU), Prediksi Saham, Aplikasi Web, Flask, Swing Trading.
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:	Fakultas Teknik > Teknik Informatika
Depositing User:	GUNAWAN FIRDAUS MUHAMMAD
Date Deposited:	18 Sep 2026 07:06
Last Modified:	18 Sep 2026 07:06
URI:	http://eprint.unipma.ac.id/id/eprint/9769

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