Prediksi Harga Saham PT Bank Rakyat Indonesia Tbk Menggunakan AUTOML H2O
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https://doi.org/10.32409/jikstik.23.3.3624Abstract
Bank BRI is a government-owned company with share prices recorded in the Initial Public Offering (IPO) which has the status of a public company. BRI Bank's share price experienced fluctuations caused by some factors. Predicting BRI Bank share prices is important to make it easier for investors to enter make investment decisions. Auto Machine Learning (AutoML) refers to the concept of machine learning and training automatic parameter setting. H2OAutoML can be used to predict stock prices with deliver program code and accelerate the development of accurate algorithms. H2OAutoML provides various algorithms, but the one used in this research is the Generalized Linear Model (GLM), Distributed Random Forest (DRF), Gradient Boosting Machine (GBM), and stacked ensemble. The aim of this research is to find out the optimal algorithm and prediction results produced by H2OAutoML on close stock prices. Algorithm The best basis according to H2OAutoML is GBM with the smallest MAPE value and the largest R Square. However, when this basic algorithm combined with stacking techniques produces better predictions. The basic algorithm used to build stacked ensembles are DRF, XRT, GLM, and GBM. This stacked ensemble is constructed sequentially automatically by H2OAutoML with the GLM metalearning algorithm. Thus, stacked ensembles are capable predicts with fairly good accuracy and can explain data variability.
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References
Ni Luh Dwik Suryacahyani Gunadi dan Jose Widyatama, “Perhitungan sebagai Seorang Investor Saham Atas Besaran Pajak yang Harus Dibayarkan kepada Negara”, Jurnal Locus Delicti, vol. 2., nomor 1, pp 13 sd 23, 2021.
Andik Setiawan, Irma Mbae dan Ratno, “Analisis Volume Foreign Net Inflow terhadap Return Saham Bank Rakyat Indonesia dengan Volume Transaksi sebagai Variabel Moderasi Pasca Satu Tahun Pandemi Covid-19”, Jurnal Ilmiah Ekomen.6 Vol. 22., nomor 1, pp 16 sd 27, 2022.
Geadalfa Giyanda dan Siti Saidah, “Auto Machine Learning dengan Menggunakan H2O AutoML untuk Prediksi Harga Bitcoin”, Jurnal Ilmiah Komputasi, vol. 20., nomor 2, pp 189 sd 198, 2021.
Kenji Ikemura, Goldsteain, James Szymanski, Eran Bellin, Lindsay, S., Yukako, Y., Mahmoud, S., Katelyn Stahl, Yukako Yagi, Mahmoud Saada, Katelyn Simone dan Morayma Gil Reyes, “ Using Automated-Machine Learning to Predict COVID- 19 Patient Survival: Indentify Influential Biomarkers”, Journal Med Internet Res, vol. 23, nomor 2, pp 1 sd 15, 2020.
Adi Misykatul Anwar, “Pengaruh Current Ratio (CR), Debt To Equity Ratio (DER), Return on Asset (ROA) terhadap Harga Saham (Studi Kasus pada Perusahaan Sektor Makanan dan Minuman yang Terdaftar di BEI Tahun 2017-2019)”, Jurnal Ilmiah Mahasiswa Akuntansi, vol. 1, nomor 2, pp 146 sd 157, 2021.
Alan Prahutama, Agus Rusgiyono dan Tiani Wahyu Utami, “Pemodelan Vector Autoregresive Exogenous (VARX) pada Nilai Inflasi Terhadap PDRB di Jawa Tengah”, Jurnal Statistika, vol. 7, nomor 2, pp 133 sd 137, 2019.
Nykodym Tomas, Tom Kraljevic, Amy Wang, Wendy Wong, dan Thomas Fryda, “Generalized Linear Modeling with H2O (Issue November)”, H2O.ai, Amerika, 2016.
Dani Al Mahkya, Khairil Anwar Notodiputro dan Bagus Sartono, “Extra Trees Method for Stock Price Forecasting With Rolling Origin Accuracy Evaluation”. Jurnal Media Statistika, vol. 15, nomor 1, pp 36 sd 47, 2022.