ANALISIS KELAYAKAN GEDUNG PENDIDIKAN MENGGUNAKAN METODE LEARNING VECTOR QUANTIZATION DAN NAÏVE BAYES

Abstract

The building of the school building is a very important infrastructure in supporting the success of educational programs. The physical condition of school buildings that meet standards and is supported by adequate facilities becomes a benchmark for school quality or quality. School is a building that contains a means for students to demand knowledge given by teachers, but if the school building is damaged it will affect the process of teaching and learning activities so that it becomes less comfortable. But repairing or rehabilitating school buildings requires a large budget, while currently for schools there are no construction costs charged to students. So it is necessary to submit to the Education Office of West Java Province. The more schools that apply the more difficult it is to determine which schools are eligible to receive such assistance. This is what makes researchers want to analyze the damage to school building assets in the Branch of education office of Region X of West Java Province so that the decision on eligibility of school rehab recipients can be done quickly and accurately. Method: This study uses nae bayes algorithm method and sample data or secondary data used in analyzing damage to school building assets from the Branch of the Education Office region X. Results: The results of this study are in the form of results from the analysis of asset damage of pred education buildings. Renovation of BOS true Renovation of BOS 108 building funds, pred. Renovation of true Renovation of BOS 18 funds and true Renovation of 53 buildings. pred. Can't Help True Can't Help 47 Buildings, pred. Build New true Renovation funds from BOS 16 and true Build New 42. Performance of the application of Nae Bayes algorithm method in classifying building asset damage data resulted in an accuracy value of 88.03% Discussion: Analysis of asset damage of The Education Office Building area X can be used as a determination of the decision to provide renovation assistance or build a new building precisely and accurately. Keywords: Analysis, Data Mining, Classification, Nae Bayes, RapidMiner