Open AccessLearning Vector Quantization Implementation to Predict the Provision of Assistance for Indonesian Telematics Services SMES
Author(s) -
Eneng Tita Tosida,
Fajar Delli Wihartiko,
Indra Lumesa
Publication year - 2018
Publication title -
international journal of engineering and technology
Language(s) - English
Resource type - Journals
ISSN - 2227-524X
DOI - 10.14419/ijet.v7i3.20.20576
Subject(s) - learning vector quantization , telematics , computer science , indonesian , machine learning , artificial intelligence , artificial neural network , census , vector quantization , data mining , telecommunications , medicine , linguistics , philosophy , population , environmental health
Implementation of Learning Vector Quantization (LVQ) Algorithm for classification of Indonesia telematics service is designed and created as a classification system to support the decision of grant aid for Small Medium Enterprises (SMEs). Based on the test results, the LVQ algorithm has the best accuracy (93.11%) when compared with ID3 algorithm (64%) and C45 (62%) for telematics data of National Census of Economic (Susenas 2006). The data is still valid and relevant for use in this research because in Indonesia census data is done every 10 years and there is no update of data until now. LVQ implementation results are applied to a web-based decision support system to predict the provision of assistance for Indonesian telematics services SMEs. Unlike the C45 and ID3 algorithms, the LVQ algorithm generates the weight of a neural network where it difficult to know which attributes are most influential for decision making. But in this study LVQ able to show good performance through the analysis of the relevance of existing conditions by comparing it with the weight value produced by the model that are implemented in a web-based decision support system