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Rusdah Rusdah
Fakultas Teknologi Informasi Universitas Budi Luhur
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Journal : Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI)

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XgBoost Hyper-Parameter Tuning Using Particle Swarm Optimization for Stock Price Forecasting Pebrianti, Dwi; Kurniawan, Haris; Bayuaji, Luhur; Rusdah, Rusdah
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 9 No. 4 (2023): December
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i4.27712

Abstract

Investment in the capital market has become a lifestyle for millennials in Indonesia as seen from the increasing number of SID (Single Investor Identification) from 2.4 million in 2019 to 10.3 million in December 2022. The increase is due to various reasons, starting from the Covid-19 pandemic, which limited the space for social interaction and the easy way to invest in the capital market through various e-commerce platforms. These investors generally use fundamental and technical analysis to maximize profits and minimize the risk of loss in stock investment. These methods may lead to problem where subjectivity and different interpretation may appear in the process. Additionally, these methods are time consuming due to the need in the deep research on the financial statements, economic conditions and company reports. Machine learning by utilizing historical stock price data which is time-series data is one of the methods that can be used for the stock price forecasting. This paper proposed XGBoost optimized by Particle Swarm Optimization (PSO) for stock price forecasting. XGBoost is known for its ability to make predictions accurately and efficiently. PSO is used to optimize the hyper-parameter values of XGBoost. The results of optimizing the hyper-parameter of the XGBoost algorithm using the Particle Swarm Optimization (PSO) method achieved the best performance when compared with standard XGBoost, Long Short-Term Memory (LSTM), Support Vector Regression (SVR) and Random Forest. The results in RSME, MAE and MAPE shows the lowest values in the proposed method, which are, 0.0011, 0.0008, and 0.0772%, respectively. Meanwhile, the  reaches the highest value. It is seen that the PSO-optimized XGBoost is able to predict the stock price with a low error rate, and can be a promising model to be implemented for the stock price forecasting. This result shows the contribution of the proposed method.
Co-Authors Abdulhakim Madiyoh Achmad Saleh Achmad Solichin Afrianto, Whisnu Febry Ahadti Puspa Sari Alfad Zebua, Vivid Kristiani Andi Andara Andi Rukmana Anidnya Putri Pradiptha Anita Diana Anubhakti, Dian Ary Maulana Pratama Aryabima, Muhammad Iqbal Bregastantyo, Brian Agni Brury Trya Sartana Budiyoko, Budiyoko Deasy Aprilla Wulandari Deni Mahdiana Devit Setiono Dewi Kusumaningsih Diwi Apriana Dwi Achadiani Dwi Kristanto Eka Dewi Satriana Elfy Susanti Ernita Rahayu Fauzan, Muhammad Rafi Hari Soetanto Haris Kurniawan, Haris Hin, Law Li Humisar Hasugian Ilham Akbar Muharrom Ilyas, Aldrin Nur Imam Halim Mursyidin Indah Puspasari Handayani Indra Nugraha Irawati, Riri Izzati, Fildza Joko Christian Chandra Joko Sutrisno Juliasari, Noni Kardena, Sucinda Kirana, Anindya Sasi Kusumaningsih, Dewi Lauw Li Hin Linda Ratna Sari Lis Suryadi, Lis Luhur Bayuaji, Luhur Mahesworo Langgeng Wicaksono Marimin , Mawarni, Ajeng Citra Mehmet Sıtkı ā°lkay Mohammad Syafrullah Muhamad Sobirin Jamil Muhammad Fauzan Hadi Saputra Muhammad Rifqi Mukhtar, Ridha Painem, Painem Painem, Painem Patlisan, Patlisan Pebrianti, Dwi Prayoga, Adistiar Pudoli, Ahmad Purwanto Purwanto Putri, Ine Widyaningrum Mustama Raden Rahmad Rafi Naufal AlBasri Rahmat Fajar Rahmawati Alvira Rahmawati, Fadilla Salsabila Raissa, Benita Hasna Ratna Ujiandari Renaldi Setiawan Putra Rizky Pradana, Rizky Roeswidiah, Ririt Rohmad Atkha Rosyadi, Ibnu Fallah Ruwirohi, Jan Everhard Setyawan Widyarto Shintya Yulianti Sri Hanafi Sri Wahyuningsih Subandi, Nurul Arifin Supardi Supardi Susi Widyawati Tri Annisa Hidayati Triana Anggraini Yulianawati Yulianawati Yulianawati Yulianawati Yuliazmi, Yuliazmi Zaqi Kurniawan