PERBANDINGAN KINERJA ALGORITMA RANDOM FOREST DAN SUPPORT VECTOR REGRESSION DALAM PREDIKSI HARGA SAHAM BERDASARKAN DATA HISTORIS (Studi Kasus PT. Temas TBK)

RAMADHANI, DAFA PUTRA (2025) PERBANDINGAN KINERJA ALGORITMA RANDOM FOREST DAN SUPPORT VECTOR REGRESSION DALAM PREDIKSI HARGA SAHAM BERDASARKAN DATA HISTORIS (Studi Kasus PT. Temas TBK). S1 thesis, Universitas Mercu Buana Jakarta.

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Abstract

This research aims to apply the Random Forest algorithm and Support Vector Regression in building a stock price prediction model of PT Temas Tbk (TMAS) by utilising historical data. Stock price fluctuations, which are influenced by various factors, are the main focus of this research. By using historical data of TMAS stock price, including opening, closing, highest, lowest, trading volume, as well as technical and fundamental indicators, the Random Forest model and Support Vector Machine is expected to produce accurate predictions. Model performance will be evaluated using metrics such as Mean Squared Error (MSE), Mean Absolute Error (MAE), and R-squared. This research is expected to contribute to the development of a stock price prediction model that can help investors make better investment decisions. Keywords: Predictions, Stocks, Random Forest, Support Vector Regression, Machine learning, Historical Data. Penelitian ini bertujuan untuk menerapkan algoritma Random Forest dan Support Vector Regression dalam membangun model prediksi harga saham PT. Temas Tbk (TMAS) dengan memanfaatkan data historis. Fluktuasi harga saham, yang dipengaruhi oleh berbagai faktor, menjadi fokus utama dalam penelitian ini. Dengan menggunakan data historis harga saham TMAS, termasuk harga pembukaan, penutupan, tertinggi, terendah, volume perdagangan, serta indikator teknikal dan fundamental, model Random Forest dan Support Vector Machine diharapkan dapat menghasilkan prediksi yang akurat. Kinerja model akan dievaluasi menggunakan metrik seperti Mean Squared Error (MSE), Mean Absolute Error (MAE), dan R-squared. Penelitian ini diharapkan dapat memberikan kontribusi dalam pengembangan model prediksi harga saham yang dapat membantu para investor dalam pengambilan keputusan investasi yang lebih baik. Kata kunci: Prediksi, Saham, Random Forest, Support Vector Regression, Machine learning, Data Historis.

Item Type:	Thesis (S1)
Call Number CD:	FIK/INFO. 25 009
NIM/NIDN Creators:	41520010141
Uncontrolled Keywords:	Prediksi, Saham, Random Forest, Support Vector Regression, Machine learning, Data Historis
Subjects:	000 Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 000. Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 004 Data Processing, Computer Science/Pemrosesan Data, Ilmu Komputer, Teknik Informatika 000 Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 000. Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 006 Special Computer Methods/Metode Komputer Tertentu > 006.3 Artificial Intelligence/Kecerdasan Buatan > 006.31 Machine Learning/Pembelajaran Mesin 500 Natural Science and Mathematics/Ilmu-ilmu Alam dan Matematika > 510 Mathematics/Matematika > 518 Numerical Analysis/Analisis Numerik, Analisa Numerik > 518.1 Algorithms/Algoritma
Divisions:	Fakultas Ilmu Komputer > Informatika
Depositing User:	khalimah
Date Deposited:	01 Feb 2025 07:43
Last Modified:	01 Feb 2025 07:43
URI:	http://repository.mercubuana.ac.id/id/eprint/93818

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