Pemanfaatan Machine Learning untuk Memprediksi Kandungan Dissolved Oxygen (DO) pada Air Sungai Menggunakan Metode Decision Tree Regressor (DTR) dan Support Vector Regressor (SVR)

Sadidan, Ikhwanussafa
Sari, Gina Lova
Armin, Edmund Uncok
Alifin, Fakhri Ikhwanul
Bunga, Venny Ulya

Brahmana : Jurnal Penerapan Kecerdasan Buatan
Vol 5, No 1 (2023): Edisi Desember

Water quality is a key factor in maintaining a healthy river ecosystem and supporting the life of aquatic organisms. The measurement of Dissolved Oxygen (DO) content in water is one of the crucial parameters that assess the level of dissolved oxygen, directly influencing the life of aquatic organisms. This study aims to predict the Dissolved Oxygen (DO) content in the Citarum River Irrigation Area by employing Support Vector Regression (SVR) analysis and Decision Tree Regressor. The predictive model was developed by analyzing the relationship between other water quality parameters such as Chemical Oxygen Demand (COD), Biological Oxygen Demand (BOD), and temperature. The analysis results indicate that the accuracy score of the Decision Tree Regressor analysis is superior to that of the Support Vector Regression (SVR) analysis.