• Aretha Safira
  • L. M. Sarudi As.
  • Afifa Puspitasari
  • Nur Mayke Eka Normasari
  • Achmad Pratama Rifai Universitas Gadjah Mada



klasifikasi, kualitas air, jaringan saraf tiruan, akurasi prediksi, jumlah neuron


Research on artificial intelligence to determine water quality has been widely developed as a human endeavor to
improve the quality of life. This study employs an artificial neural network (ANN) to determine the optimal
classification model for determining the safety of water. This study uses existing Kaggle generic datasets. Numerous
preprocesses were performed on the dataset starting from cleaning the data from missing values and outliers to
equalizing the weights of each parameter with the min-max scaler. This study compares the accuracy of ANN model
in various scenarios constructed with 10, 15, 20, and 30 neurons. Scaled Conjugate Gradient is implemented as the
learning algorithm for developing the prediction model. The obtained results of the experiments vary between
scenarios. Overall accuracy increases when the number of neurons is between 10 and 20, and decreases when the
number of neurons is between 20 and 30.


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