Comparison of Naïve Bayes, CART, dan CART Adaboost Methods in Predicting Tire Product Sales

moch anjas  aprihartha; Fitri  Astutik; Nani  Sulistianingsih

doi:10.20956/j.v20i3.33187

Authors

moch anjas aprihartha Program Studi PJJ Informatika, Fakultas Ilmu Komputer, Dian Nuswantoro University
Fitri Astutik Program Studi Sistem dan Teknologi Informasi, Fakultas Teknik, Universitas Muhammadiyah Mataram
Nani Sulistianingsih Program Studi Sistem dan Teknologi Informasi, Fakultas Teknik, Universitas Muhammadiyah Mataram

DOI:

https://doi.org/10.20956/j.v20i3.33187

Keywords:

CART, discrete adaboost, naive bayes

Abstract

Data mining is a term to describe the process of moving through large databases in search of certain previously unknown patterns. In finding certain patterns, you need a supporting technique, called machine learning. Machine learning involves learning hidden patterns in data and further using patterns to classify or predict an event related to a problem. One of the problems can be solved with machine learning such as predicting the sales rate of tire products. This can help companies predict tire products that are selling well in the market. In producing an accurate prediction model, it will be compared with decision tree classification methods of CART, CART + Discrete Adaboost, and Naive Bayes applied to tire sales data by PT. Mitra Mekar Mandiri. The results of the study based on successive model performance evaluations are model Naive Bayes < model CART < model CART+Discrete Adaboost. The Discrete Adaboost model with a data proportion of 90:10 is the best model for predicting tire sales. The accuracy, sensitivity and specificity values for the model were 79.17%; 89.47%; and 68.84%. The AUC value is 0.8 which indicates the model is good

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References

. Abdullah, H. A., Putra, D. R. D., & Azhar, Y, 2022. Analisa Penjualan Video Game Menggunakan Metode Ensemble. Just IT: Jurnal Sistem Informasi, Teknologi Informasi dan Komputer, Vol. 12, No. 3, 8-16.

. Alhajeri, M. S., Alnajdi, A., Abdullah, F., & Christofides, P. D, 2023. On Generalization Error of Neural Network Models and Its Application to Predictive Control of Nonlinear Processes. Chemical Engineering Research and Design, Vol. 189, 664-679.

. Bouke, M. A., Abdullah, A., ALshatebi, S. H., Abdullah, M. T., & El Atigh, H, 2023. An Intelligent Ddos Attack Detection Tree-Based Model Using Gini Index Feature Selection Method. Microprocessors and Microsystems, Vol. 98, 104823.

. Erliani, N., Suryowati, K., & Jatipaningrum, M. T. 2023, Klasifikasi Tingkat Penjualan Laptop Di E-Commerce Menggunakan Algoritma Classification and Regression Tree (CART). Jurnal Statistika Industri dan Komputasi, Vol. 8, No. 2, 40-47.

. Freund, Y., Schapire, R.E. 1997, A Decision Theoretic Generalization of Online Learning and an Application to Boosting. Journal of Computer and System Sciences, Vol. 55, 119 - 139.

. Friedman, J., Hastie, T., & Tibshirani, R., 2000. Additive Logistic Regression a Statistical View of Boosting. Annals of Statistics, Vol. 28, No. 2, pp. 337-374.

. Ghiasi, M. M., & Mohammadi, A. H., 2017. Application of decision tree learning in modelling CO2 equilibrium absorption in ionic liquids. Journal of Molecular Liquids, Vol. 242, pp. 594-605.

. Gorunescu, F, 2011. Data Mining: Concepts, Models and Techniques. Springer Science & Business Media, Berlin.

. Hafiz, M. I, 2019. Pemanfaatan Metode Cart Untuk Memprediksi Omset Sepatu Pria. Pelita Informatika: Informasi dan Informatika, Vol. 8, No. 2, 227-235.

. Han, J., Pei, J., & Tong, H, 2022. Data Mining: Concepts and Techniques. Morgan Kaufmann, United States.

. Kantardzic, M., 2011. Data mining: concepts, models, methods, and algorithms. John Wiley & Sons.

. Kuhn, M., & Johnson, K, 2013. Applied predictive modeling. Springer, New York.

. López, V., Fernández, A., García, S., Palade, V., & Herrera, F, 2013. An Insight into Classification with Imbalanced Data: Empirical Results and Current Trends on Using Data Intrinsic Characteristics. Information Sciences, Vol. 250, 113–141.

. Maimon, O. Z., & Rokach, L, 2014. Data Mining with Decision Trees: Theory and Applications. World Scientific, Singapore.

. Munshi, T. A., Jahan, L. N., Howladar, M. F., & Hashan, M, 2024. Prediction Of Gross Calorific Value from Coal Analysis Using Decision Tree-Based Bagging and Boosting Techniques. Heliyon, Vol. 10, No. 1.

. Nurlaela, D. 2020. Penerapan Adaboost Untuk Meningkatkan Akurasi Naive Bayes Pada Prediksi Pendapatan Penjualan Film. Inti Nusa Mandiri, Vol. 14, No. 2, 181-188.

. Saputra, M. J., & Herdiansyah, M. I, 2022. Penerapan Naive bayes Dalam Memprediksi Penjualan Dan Persediaan Kain Jumputan Pada Toko Batiq Colet Tuan Kentang Palembang. Jurnal Mantik, Vol. 6, No. 2, 2502-2507.

. Wijaya, F, 2018. Implementasi Algoritma Naive Bayes dalam Klasifikasi Produk Ban Terlaris pada PT. Mitra Mekar Mandiri, Skripsi. Fakultas Ilmu Komputer, Institut Informatika dan Bisnis Darmajaya, Bandar Lampung.