Success factors for citizen-based government decision making using K-means fuzzy learning vector quantization

<span>The Indonesian government often needs assistance in making citizen-based decisions, for example selecting work program plans. Residents have their criteria the forum to choose a plan. This study proposes K-means fuzzy learning vector quantization (FLVQ) methods select decision-making criteria. The FLVQ method has never been used assist decision-making. However, citizen can be success factor selection of begins with data collection from participants. results get 11 Then, carries out labeling and classification. addition process provide optimal results. Citizens give assessment freely. Then citizens is classified by FLVQ. classification obtained seven criteria, namely: i) urgency, ii) sustainability, iii) priority, vi) usability, v) prosperity, comfortability, vii) artistic. Governments use these make decisions about planned programs. algorithm was also evaluated using confusion matrix an accuracy 88% error 12%.</span>