Penggunaan metode optimized random forest dalam menganalisis prediksi tren saham Indonesia = The use of optimized random forest method in analyzing the prediction of stock trends in Indonesia

Darmawan, Anak Agung Ayu Diva Shanty (2024) Penggunaan metode optimized random forest dalam menganalisis prediksi tren saham Indonesia = The use of optimized random forest method in analyzing the prediction of stock trends in Indonesia. Bachelor thesis, Universitas Pelita Harapan.

[thumbnail of Title]
Preview
Text (Title)
Title.pdf
Available under License Creative Commons Attribution Non-commercial Share Alike.

Download (124kB) | Preview
[thumbnail of Abstract]
Preview
Text (Abstract)
Abstract.pdf
Available under License Creative Commons Attribution Non-commercial Share Alike.

Download (337kB) | Preview
[thumbnail of ToC]
Preview
Text (ToC)
ToC.pdf
Available under License Creative Commons Attribution Non-commercial Share Alike.

Download (321kB) | Preview
[thumbnail of Chapter1]
Preview
Text (Chapter1)
Chapter1.pdf
Available under License Creative Commons Attribution Non-commercial Share Alike.

Download (324kB) | Preview
[thumbnail of Chapter2] Text (Chapter2)
Chapter2.pdf
Restricted to Registered users only
Available under License Creative Commons Attribution Non-commercial Share Alike.

Download (460kB)
[thumbnail of Chapter3] Text (Chapter3)
Chapter3.pdf
Restricted to Registered users only
Available under License Creative Commons Attribution Non-commercial Share Alike.

Download (428kB)
[thumbnail of Chapter4] Text (Chapter4)
Chapter4.pdf
Restricted to Registered users only
Available under License Creative Commons Attribution Non-commercial Share Alike.

Download (583kB)
[thumbnail of Chapter5] Text (Chapter5)
Chapter5.pdf
Restricted to Registered users only
Available under License Creative Commons Attribution Non-commercial Share Alike.

Download (320kB)
[thumbnail of Bibliography]
Preview
Text (Bibliography)
Bibliography.pdf
Available under License Creative Commons Attribution Non-commercial Share Alike.

Download (323kB) | Preview
[thumbnail of Appendices] Text (Appendices)
Appendices.pdf
Restricted to Repository staff only
Available under License Creative Commons Attribution Non-commercial Share Alike.

Download (1MB)

Abstract

Saham adalah tanda kepemilikan dari suatu individu atau suatu lembaga yang merupakan instrumen keuangan berjangka panjang dan dapat diperjualbelikan. Penelitian ini menggunakan optimized random forest sebagai metode utama untuk membantu dalam memprediksi tren saham sektor energi. Saham yang digunakan adalah ADRO.JK, INDY.JK, PTBA.JK, TOBA.JK, dan UNTR.JK. Langkah pertama adalah mencari technical indicator akan digunakan sebagai variabel untuk membantu dalam memprediksi tren saham dengan menggunakan harga penutup yang sudah disesuaikan dari saham. Model akan dibangun dari dua data yaitu data berdasarkan saham dan data berdasarkan tahun. Terdapat empat tahapan dalam pembangunan model: model random forest, model optimized random forest, model random forest dengan feature importance, serta model optimized random forest dengan feature importance. Model akan dievaluasi berdasasrkan nilai akurasi, F1, dan nilai AUC. Hasil yang diperoleh dari metode optimized random forest akan dibandingkan dengan hasil yang didapatkan dari metode random forest. Penelitian menunjukkan bahwa optimized random forest merupakan metode yang lebih akurat untuk digunakan dalam memprediksi tren saham. Selain itu, hasil prediksi model terbaik juga akan digunakan dalam sebuah simulasi perdagangan saham. / Stocks are ownership symbols of an individual or an institution, which are long-term financial instruments and can be traded. This research employs the optimized random forest as the primary method to assist in predicting stock trends in the energy sector. The stocks used are ADRO.JK, INDY.JK, PTBA.JK, TOBA.JK, and UNTR.JK. The first step involves identifying the technical indicators that will be used as variables to aid in predicting stock trends, using adjusted closing prices of the stocks. The model will be constructed using two sets of data: stock-based data and year-based data. There are four stages in the model development: random forest model, optimized random forest model, random forest model with feature importance, and optimized random forest model with feature importance. The model will be evaluated based on accuracy, F1 score, and AUC value. The results obtained from the optimized random forest method will be compared with those from the random forest method. The research demonstrates that the optimized random forest is a more accurate method for predicting stock trends. Furthermore, the predictions from the best-performing model will be used in a stock trading simulation.
Item Type: Thesis (Bachelor)
Creators:
Creators
NIM
Email
ORCID
Darmawan, Anak Agung Ayu Diva Shanty
NIM01112190018
shantydarmawan@gmail.com
UNSPECIFIED
Contributors:
Contribution
Contributors
NIDN/NIDK
Email
Thesis advisor
Saputra, Kie Van Ivanky
NIDN0401038203
kie.saputra@uph.edu
Thesis advisor
Ferdinand, Ferry Vincenttius
NIDN0323059001
ferry.vincenttius@uph.edu
Uncontrolled Keywords: random forest; optimized random forest; klasifikasi; saham; technical indicator; multi-class
Subjects: Q Science > QA Mathematics
Divisions: University Subject > Current > Faculty/School - UPH Karawaci > Faculty of Science and Technology > Mathematics
Current > Faculty/School - UPH Karawaci > Faculty of Science and Technology > Mathematics
Depositing User: Anak Agung Ayu Diva Shanty Darmawan
Date Deposited: 02 Feb 2024 01:42
Last Modified: 02 Feb 2024 01:42
URI: http://repository.uph.edu/id/eprint/61354

Actions (login required)

View Item
View Item