PENERAPAN ALGORITMA HIDDEN MARKOV MODEL PADA PREDIKSI EKSPOR KOMODITI BIJI KOPI
##plugins.themes.academic_pro.article.main##
Abstract
Fluktuasi volume dan nilai ekspor komoditas biji kopi Indonesia dipengaruhi oleh faktor dinamis yang tidak dapat diamati secara langsung, seperti pergeseran kondisi pasar internasional dan dinamika iklim. Penelitian ini bertujuan untuk menerapkan algoritma Hidden Markov Model (HMM) dalam memodelkan tren tersembunyi (hidden states) serta memprediksi tren ekspor biji kopi. Dataset yang digunakan merupakan data historis bulanan ekspor biji kopi periode 2020–2024. Estimasi parameter dilakukan menggunakan algoritma Baum-Welch, sedangkan performa model dievaluasi menggunakan metrik Mean Absolute Error (MAE) dan Root Mean Square Error (RMSE) serta dibandingkan dengan metode Seasonal Naïve (S-Naïve). Hasil penelitian menunjukkan bahwa HMM mampu mengidentifikasi tiga state utama (Naik, Stabil, dan Turun) dengan nilai MAE sebesar 20,81 dan RMSE sebesar 28,45. Performa HMM melampaui metode S-Naïve yang memiliki MAE sebesar 35,12. Dengan demikian, pendekatan HMM terbukti adaptif dalam menangkap volatilitas dan transisi kondisi pasar pada komoditas biji kopi.
##plugins.themes.academic_pro.article.details##
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
[2] S. Tayefeh Hashemi, O. M. Ebadati, and H. Kaur, “Cost estimation and prediction in construction projects: a systematic review on machine learning techniques,” SN Applied Sciences, vol. 2, no. 10, 2020.
[3] A. E. Elfaki, S. Alatawi, and E. Abushandi, “Using Intelligent Techniques in Construction Project Cost Estimation: 10-Year Review,” Advances in Civil Engineering, vol. 2014, 2014.
[4] F. Antoniou and K. Konstantinidis, “Client-Oriented Highway Construction Cost Estimation Models Using Machine Learning,” Applied Sciences, vol. 15, no. 18, 2025.
[5] T. Chen and C. Guestrin, “XGBoost: A Scalable Tree Boosting System,” in Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016.
[6] L. Prokhorenkova, G. Gusev, A. Vorobev, A. V. Dorogush, and A. Gulin, “CatBoost: unbiased boosting with categorical features,” Advances in Neural Information Processing Systems, vol. 31, 2018.
[7] J. T. Hancock and T. M. Khoshgoftaar, “CatBoost for big data: an interdisciplinary review,” Journal of Big Data, vol. 7, no. 1, 2020.
[8] B. So, “Enhanced gradient boosting for zero-inflated insurance claims and comparative analysis of CatBoost, XGBoost, and LightGBM,” Scandinavian Actuarial Journal, no. 10, pp. 1013–1035, 2024.
[9] F. Yusfida, “A Hybrid Approach for Recommender Systems Based on Alternating Least Squares and CatBoost,” Jurnal Teknik Informatika (Jutif), vol. 6, no. 4, 2025.
[10] S. M. Robu, “A Review on Artificial Intelligence Technologies in Cost Estimation of Construction Projects,” Discover Civil Engineering, vol. 1, no. 16, 2024.
[11] K. K. K. Al-Bayati and A. M. J. Al-Khafaji, “Review of Artificial Intelligence Techniques for Cost Estimation in Construction Projects,” International Journal of Construction Management, 2023.
[12] Y. Wang, “Price Prediction of Second-Hand Housing in Chengdu Based on CatBoost Algorithm,” in 2022 4th International Conference on Artificial Intelligence and Advanced Manufacture, 2022.
[13] J. Hancock and T. M. Khoshgoftaar, “Performance of CatBoost and XGBoost in Medicare Fraud Detection,” in 2020 19th IEEE International Conference on Machine Learning and Applications (ICMLA), 2020.
[14] A. R. Putri, N. A. Santoso, and B. A. Santoso, “Implementasi Algoritma Regresi Linier dan ARIMA untuk Prediksi Harga Emas,” Jurnal Sistem Komputer dan Kecerdasan Buatan, vol. 6, no. 2, 2023.
[15] V. K. Gupta and A. Kumar, "Comparative Analysis of Machine Learning Models for Sales Prediction," International Journal of Computer Applications, vol. 175, no. 2, 2020.