The Classification of Children Gadget Addiction:  The Employment of Learning Vector Quantization 3

Okfalisa Okfalisa; Elvia Budianita; Musa Irfan; Hidayati  Rusnedy; Saktioto Saktioto

doi:10.25299/itjrd.2021.vol5(2).5681

Authors

Okfalisa Okfalisa Department of Informatics, Universitas Islam Negeri Sultan Syarif Kasim Riau
Elvia Budianita Department of Informatics, Universitas Islam Negeri Sultan Syarif Kasim Riau
Musa Irfan Department of Informatics, Universitas Islam Negeri Sultan Syarif Kasim Riau
Hidayati Rusnedy Department of Informatics, Universitas Islam Negeri Sultan Syarif Kasim Riau
Saktioto Saktioto Department of Physics, Universitas Riau

DOI:

https://doi.org/10.25299/itjrd.2021.vol5(2).5681

Keywords:

Classification, Neural Networks, Gadget Addiction, Learning Vector Quantization 3, Machine Learning

Abstract

The addiction of children to gadgets has a massive influence on their social growth. Thus, it is essential to note earlier on the addiction of children to such technologies. This study employed the learning vector quantization series 3 to classify the severity of gadget addiction due to the nature of this algorithm as one of the supervised artificial neural network methods. By analyzing the literature and interviewing child psychologists, this study highlighted 34 signs of schizophrenia with 2 level classifications. In order to obtain a sample of training and test data, 135 questionnaires were administered to parents as the target respondents. The learning rate parameter (α) used for classification is 0.1, 0.2, 0.3 with window (Ɛ) is 0.2, 0.3, 0.4, and the epsilon values (m) are 0.1, 0.2, 0.3. The confusion matrix revealed that the highest performance of this classification was found in the value of 0.2 learning rate, 0.01 learning rate reduction, window 0.3, and 80:20 of ratio data simulation. This outcome demonstrated the beneficial consequences of Learning Vector Quantization (LVQ) series 3 in the detection of children's gadget addiction.

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