Jurnal EurekaMatika
Implementasi CART-Real Adaboost dalam Memprediksi Minat Pelanggan Membeli Sepatu
Abstract
Machine learning is a field of science related to the development of computer algorithms to transform data into intelligent actions. In machine learning does not escape understanding machine learning algorithms. One popular machine learning algorithm is supervised learning. Supervised learning algorithms are commonly used in solving prediction problems. This study aims to implement supervided learning algorithms using CART and CART-Real Adaboost methods in predicting customer interest in buying shoes. The results of the study obtained the performance of the CART model resulted in an accuracy of 77.5% and an AUC of 0.711 which indicates that the model is quite good. While the performance of the CART-Real Adaboost model obtained the best model at tree depth level 6 or level 8. The model obtained an accuracy of 85.71% and an AUC of 0.8225 which indicates a good model. This makes CART-Real Adaboost the best model compared to the CART model.
Keywords: CART, Prediction, Real Adaboost, Shoes, Supervised Learning.
Abstrak
Pembelajaran mesin merupakan bidang ilmu yang berkaitan pengembangan algoritma komputer untuk mengubah data menjadi suatu tindakan cerdas. Dalam pembelajaran mesin tidak luput dari memahami algoritma pembelajaran mesin. Salah satu algoritma pembelajaran mesin yang populer adalah supervised learning. Algoritma supervised learning umumnya digunakan dalam memecahkan masalah prediksi. Penelitian ini bertujuan untuk menerapkan algoritma supervided learning menggunakan metode CART dan CART-Real Adaboost dalam memprediksi minat pelanggan membeli sepatu. Hasil penelitian diperoleh performa model CART menghasilkan akurasi sebesar 77,5% dan AUC sebesar 0,711 yang menandakan model cukup baik. Sedangkan performa model CART-Real Adaboost diperoleh model terbaik pada kedalaman pohon berada di level 6 atau level 8. Model menghasilkan akurasi sebesar 85,71% dan AUC sebesar 0,8225 yang menandakan model baik. Ini menjadikan CART-Real Adaboost menjadi model terbaik dibandingkan model CART.
Keywords
Full Text:
PDFReferences
Alfarizi, M. R. S., Al-farish, M. Z., Taufiqurrahman, M., Ardiansah, G., & Elgar, M. (2023). Penggunaan Python sebagai bahasa pemrograman untuk machine learning dan deep learning. Karimah Tauhid, 2(1), 1-6.
Bouke, M. A., Abdullah, A., Frnda, J., Cengiz, K., & Salah, B. (2023). BukaGini: A stability-aware gini index feature selection algorithm for robust model performance. IEEE Access, 11, 59386 – 59396.
Chuan, Y., Zhao, C., He, Z., & Wu, L. (2021). The success of AdaBoost and its application in portfolio management. International Journal of Financial Engineering, 8(02), 2142001.
Friedman, J., Hastie, T., & Tibshirani, R. (2000). Additive logistic regression: a statistical view of boosting (with discussion and a rejoinder by the authors). The Annals of Statistics, 28(2), 337-407.
Ghiasi, M. M., & Mohammadi, A. H. (2017). Application of decision tree learning in modeling CO2 equilibrium absorption in ionic liquids. Journal of Molecular Liquids, 242, 594-605.
Ghiasi, M. M., Zendehboudi, S., & Mohsenipour, A. A. (2020). Decision tree-based diagnosis of coronary artery disease: CART model. Computer Methods and Programs in Biomedicine, 192, 105400.
Hu, G., Yin, C., Wan, M., Zhang, Y., & Fang, Y. (2020). Recognition of diseased pinus trees in UAV images using deep learning and AdaBoost classifier. Biosystems Engineering, 194, 138-151.
Jefi, J., Puspita, A., & Fahmi, M. (2019). Prediksi peminatan pelanggan dalam penjualan produk sepatu menggunakan metode decision tree berbasis particle swarm optimization pada PT. Baskara Cipta Pratama. Jurnal Teknik Informatika, 5(1), 10-17.
Li, X., Chen, X., & Yuan, Z. (2021, June). Applicable model of liver disease detection based on the improved CART-AdaBoost algorithm. In 2021 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA) (pp. 1177-1181). IEEE.
Liu, Q., Wang, X., Huang, X., & Yin, X. (2020). Prediction model of rock mass class using classification and regression tree integrated AdaBoost algorithm based on TBM driving data. Tunnelling and Underground Space Technology, 106, 103595.
Nindrea, R. D., Aryandono, T., Lazuardi, L., & Dwiprahasto, I. (2018). Diagnostic accuracy of different machine learning algorithms for breast cancer risk calculation: a meta-analysis. Asian Pacific Journal of Cancer Prevention, 19(7), 1747.
Patro, V. M., & Patra, M. R. (2014). Augmenting weighted average with confusion matrix to enhance classification accuracy. Transactions on Machine Learning and Artificial Intelligence, 2(4), 77-91.
Putri, A. I., Syarif, Y., Jayadi, P., Arrazak, F., & Salisah, F. N. (2023). Implementation of decision tree and Support Vector Machine (SVM) algorithm for stunting risk prediction. MALCOM: Indonesian Journal of Machine Learning and Computer Science, 3(2), 349-357.
Xing, H. J., Liu, W. T., & Wang, X. Z. (2024). Bounded exponential loss function based AdaBoost ensemble of OCSVMs. Pattern Recognition, 148, 110191.
DOI: https://doi.org/10.17509/jem.v12i1.67808
Refbacks
- There are currently no refbacks.
Copyright (c) 2024 Mathematics Program Study, Universitas Pendidikan Indonesia
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
