Viscosity Modeling and Prediction of Amorphophallus oncophyllus and Sapindus rarak Using Machine Learning Methods

Fathaddin, Muhammad Taufiq (Universitas Trisakti)
Mardiana, Dwi Atty (Universitas Trisakti)
Sutiadi, Andrian (Universitas Trisakti)
Maulida, Fajri (Universitas Trisakti)

JFA (Jurnal Fisika dan Aplikasinya)
Vol 21, No 1 (2025)

Viscosity plays an important role in regulating the mobility of fluids injected into the reservoir to increase the efficiency of oil sweeping. This study discusses the application of Machine Learning methods, namely ANN and ANFIS, to model the correlation of physical properties of Amorphophallus oncophyllus and Sapindus rarak solutions. The purpose of this study is to obtain a correlation to determine the viscosity of the polymer solutions. The data used include viscosity measurements for 21 samples of Amorphophallus oncophyllus and Sapindus rarak solutions with variations in concentration and salinity. The data is augmented by digitization for modeling. The results show that both Machine Learning methods can estimate viscosity values well. Very accurate results are achieved by applying ANN and ANFIS with average correlation coefficients of 0.997240 and 0.995124, respectively.