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Richi Andrianto
Institut Teknologi Dan Sains Padang Lawas Utara
Religion Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Mechanical Engineering Physics Public Health Social Sciences Transportation Other

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Journal : Journal of Applied Data Sciences

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Implementation of Stacking Technique Combining Machine Learning and Deep Learning Algorithms Using SMOTE to Improve Stock Market Prediction Accuracy Munthe, Ibnu Rasyid; Rambe, Bhakti Helvi; Hanum, Fauziah; Amanda, Ade Trya; Hutagaol, Anita Sri Rejeki; andrianto, Richi
Journal of Applied Data Sciences Vol 5, No 4: DECEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i4.421

Abstract

This study introduces a stacking technique that integrates machine learning (ML) and deep learning (DL) algorithms to enhance the accuracy of stock market trend predictions. The stacking model utilizes XGBoost and Random Forest as base models from the ML domain, while Logistic Regression and LSTM (Long Short-Term Memory) function as meta models to optimize predictive accuracy. A significant challenge in stock market data is class imbalance, where certain trends, such as stock price drops, are underrepresented. To mitigate this, we applied the Synthetic Minority Over-sampling Technique (SMOTE) to generate synthetic data for the minority class. This approach helps the model better capture patterns from the underrepresented data while preserving essential information from the majority class. The implementation of SMOTE, coupled with the stacking technique, yielded a substantial improvement in prediction accuracy. The results showed that the Random Forest algorithm achieved an accuracy of 85% with precision, recall, and F1-score all at 85%, while XGBoost and Logistic Regression achieved accuracies of 82% and 81% respectively. For the deep learning models, LSTM reached an accuracy of 83%, while the Stacking Meta Model with LSTM achieved an accuracy of 83% with slightly better precision and recall at 84%. The stacking model, with Logistic Regression as the meta model, ultimately achieved the highest accuracy of 86%, outperforming individual models such as SVM (Support Vector Machine), LSTM, Random Forest, and Logistic Regression (LR). These findings demonstrate the efficacy of combining SMOTE with stacking to address data imbalance and improve stock market predictions. The novelty of this study lies in the integration of advanced ML and DL models within a stacking framework to handle class imbalance in financial datasets. Future research will explore the deployment of this model in a real-time web-based application to support investor decision-making in stock market trend analysis.
Co-Authors Abdul Azis Akhyar Nasution Alfian Hasonangan Hasibuan Amanda, Ade Trya Amanda, Putri Ida Anita Febriani Anita Sri Rejeki Hutagaol Anita Sri Rejeki Hutagaol, Anita Sri Rejeki Annisa Hannum Harahap Ari Rahmad Rahmad Siregar Babang Andika Citra Khoiriah Dandi Rusadi Dermilan Dermilan Dewi Elok Dewi Liana Dini Mawarni F. Irawan Fajri Rahmansyah Farhan Fikriyan Fauziah Hanum Febri Listiana Harahap Feri Irawan Fransiska Harahap Hanif Ahmad Hasibuan Hannum, Sopia Hanum, Fauziah Harahap, Aminah Harahap, Arman Harahap, Aziddin Harahap, Yuskana Harianja, Noviyarnita Hartati, Juni Hasibuan, Hotmaini Hasibuan, Maya Gusrina Hendry Kurniawan, Hendry ibnu Rasyid munthe Ibra Dianaran Siregar Intan Maimunah Irawan, Rina Khoirinnysa Harahap Lubis, Mustopa Husein Muhammad Gusti Fhaturrahman Daulay Muhammad Husein Muhammad Sampurna Mulia Garang Munawir Sajali Harahap Munthe, Ibnu Rasyid Nasution, Rina Irawan Nidar, Putri Novali, Mhd Noviyanti, Risma Nurhadi Nurhanna Harahap Panusunan Panusunan Patimah Harahap Pebriana Panggabean Pohan, Eviana Purnomo, Nopi Putri Hanafia Putri, Perra Budiarti Rahayu Putri, Perra Budiarti Rahyu Qodri Mahendar Siregar Rahayu Putri, Perra Budiarti Rahmat Maulana Rambe, Bhakti Helvi Ranizah Mungkur Refni Wahyuni Rina Irawan Sahroki Fikri Harahap Salimah, Sukrina Saputra, Haris Tri Sariona Sariona Siregar, Agus Trinanda Siregar, Novita Siregar, Putri Irawan Sundari, Ayu Supriyanto, Asep Syahputra Harahap, Hasmi Tanjung, Akhir Abadi Tiara Siregar Utami, Urfi Wati, Emilia Yona, Sri Nelvi Yuda Irawan