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

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 o...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Okfalisa Okfalisa, Elvia Budianita, Musa Irfan, Hidayati Rusnedy, Saktioto Saktioto
Formato:	article
Lenguaje:	EN
Publicado:	UIR Press 2020
Materias:	classification neural networks gadget addiction learning vector quantization 3 machine learning Computer software QA76.75-76.765 Information technology T58.5-58.64 Computer engineering. Computer hardware TK7885-7895
Acceso en línea:	https://doaj.org/article/f1d163a066da4ac2948de66622df5144
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:f1d163a066da4ac2948de66622df5144
record_format	dspace
spelling	oai:doaj.org-article:f1d163a066da4ac2948de66622df51442021-11-04T09:52:53ZThe Classification of Children Gadget Addiction: The Employment of Learning Vector Quantization 32528-40612528-405310.25299/itjrd.2021.vol5(2).5681https://doaj.org/article/f1d163a066da4ac2948de66622df51442020-11-01T00:00:00Zhttps://journal.uir.ac.id/index.php/ITJRD/article/view/5681https://doaj.org/toc/2528-4061https://doaj.org/toc/2528-4053The 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.Okfalisa OkfalisaElvia BudianitaMusa IrfanHidayati RusnedySaktioto SaktiotoUIR Pressarticleclassificationneural networksgadget addictionlearning vector quantization 3machine learningComputer softwareQA76.75-76.765Information technologyT58.5-58.64Computer engineering. Computer hardwareTK7885-7895ENIT Journal Research and Development, Vol 5, Iss 2, Pp 158-170 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	classification neural networks gadget addiction learning vector quantization 3 machine learning Computer software QA76.75-76.765 Information technology T58.5-58.64 Computer engineering. Computer hardware TK7885-7895
spellingShingle	classification neural networks gadget addiction learning vector quantization 3 machine learning Computer software QA76.75-76.765 Information technology T58.5-58.64 Computer engineering. Computer hardware TK7885-7895 Okfalisa Okfalisa Elvia Budianita Musa Irfan Hidayati Rusnedy Saktioto Saktioto The Classification of Children Gadget Addiction: The Employment of Learning Vector Quantization 3
description	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.
format	article
author	Okfalisa Okfalisa Elvia Budianita Musa Irfan Hidayati Rusnedy Saktioto Saktioto
author_facet	Okfalisa Okfalisa Elvia Budianita Musa Irfan Hidayati Rusnedy Saktioto Saktioto
author_sort	Okfalisa Okfalisa
title	The Classification of Children Gadget Addiction: The Employment of Learning Vector Quantization 3
title_short	The Classification of Children Gadget Addiction: The Employment of Learning Vector Quantization 3
title_full	The Classification of Children Gadget Addiction: The Employment of Learning Vector Quantization 3
title_fullStr	The Classification of Children Gadget Addiction: The Employment of Learning Vector Quantization 3
title_full_unstemmed	The Classification of Children Gadget Addiction: The Employment of Learning Vector Quantization 3
title_sort	classification of children gadget addiction: the employment of learning vector quantization 3
publisher	UIR Press
publishDate	2020
url	https://doaj.org/article/f1d163a066da4ac2948de66622df5144
work_keys_str_mv	AT okfalisaokfalisa theclassificationofchildrengadgetaddictiontheemploymentoflearningvectorquantization3 AT elviabudianita theclassificationofchildrengadgetaddictiontheemploymentoflearningvectorquantization3 AT musairfan theclassificationofchildrengadgetaddictiontheemploymentoflearningvectorquantization3 AT hidayatirusnedy theclassificationofchildrengadgetaddictiontheemploymentoflearningvectorquantization3 AT saktiotosaktioto theclassificationofchildrengadgetaddictiontheemploymentoflearningvectorquantization3 AT okfalisaokfalisa classificationofchildrengadgetaddictiontheemploymentoflearningvectorquantization3 AT elviabudianita classificationofchildrengadgetaddictiontheemploymentoflearningvectorquantization3 AT musairfan classificationofchildrengadgetaddictiontheemploymentoflearningvectorquantization3 AT hidayatirusnedy classificationofchildrengadgetaddictiontheemploymentoflearningvectorquantization3 AT saktiotosaktioto classificationofchildrengadgetaddictiontheemploymentoflearningvectorquantization3
_version_	1718444936836153344

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

Ejemplares similares