Muhammad Iqbal, - (2021) PENERAPAN LEARNING VECTOR QUANTIZATION 2 UNTUK MEMPREDIKSI PENYAKIT SKIZOFRENIA. Skripsi thesis, Universitas Islam Negeri Sultan Syarif Kasim Riau.

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Abstract

Schizophrenia is a mental disorder characterized by chaos in thinking patterns, perceptual processes, affections and social behavior. Patients diagnosed with schizophrenia usually also show positive symptoms, such as hallucinations and delusions and negative symptoms, such as withdrawal from the social environment, self-neglect, loss of motivation and initiative and blunt emotions. Learning Vector Quantization 2 with 15 variables. While the output results are 2 classes, namely Paranoid and Undifferentiated. The parameters used in this study are epoch 1000, the variations in Learning Rate are 0.0001, 0.001, 0.1 and 0.2, windows 0, 0.1 and 0.3, m 0.2 and 0.4, a minimum Learning Rate of 0.0001, and a reduction in Learning Rate of 0.1. Data comparison 70:30, 80:20, 90:10. The best accuracy is found at a learning rate of 0.01, 0.1 and 0.2. The best parameters are ε 0.1 and 0.3 with an accuracy of 94%. Therefore, the Learning Vector Quantization 2 method can be applied to predict schizophrenia. Keywords: Schizophrenia, Learning Vector Quantization, Prediction, Learning Vector Quantization 2.

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