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All Journal Brahmana : Jurnal Penerapan Kecerdasan Buatan
Armin, Edmund Uncok
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Computer Science & IT Decision Sciences, Operations Research & Management Education Engineering

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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
Publisher : LPPM STIKOM Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/brahmana.v5i1.280

Abstract

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.
Co-Authors Fakhri Ikhwanul Alifin Gina Lova Sari Ikhwanussafa Sadidan, Ikhwanussafa Venny Ulya Bunga