PEMODELAN MULTIVARIATE ADAPTIVE REGRESSION SPLINES (MARS) PADA FAKTOR-FAKTOR RESIKO ANGKA KESAKITAN DIARE (Studi Kasus : Angka Kesakitan Diare Di Jawa Tengah, Jawa Timur Dan Daerah Istimewa Yogyakarta Tahun 2011) - Wasis Wicaksono; Yuciana Wilandari; Suparti Suparti (Jurnal Gaussian, 2014)
Diarrhea morbidity is a number of diarrhea suffers in specific region in period of one year per 1000 populations. Diarrhea morbidity is the impact from some factors such as environment, education, socioeconomic, nutrition and foods. Environmental factors that can affect the morbidity of diarrhea include the percentage of families who have a healthy latrine and percentage of households using clean water. For educational factors include the average length of school and literacy rates. On socio-economic factors include the percentage of poor and average people per household. While the food and nutritional factors are the percentage TUPM (Public Places and Food Management) healthy.Diarrhea morbidity can be pressed by analyzing those factors so that the prevention can be devised. Regression curve is used to draw the relationship of response variable and predictor variable and mostly approached by parametric regression, where the curve design is known (such as linear, quadratic and cubic). If curve design is unknown, then regression curve can be derived by approaching using non parametric regression. Multivariate Adaptive Regression Spline (MARS) is one of  nonparametric regression method that can be used on high dimension data. the best MARS model is derived by combination of Minimal Observation (MO), Maximum Basic Function (BF), and Maximal Interaction (MI) through trial and error. MARS model to predict diarrhea morbidity in Central Java, East Java and Yogyakarta is MARS (MO=2;BF=28;MI=3) and equation is  =  -0.526742 + 0.264444 * BF2 + 12.2382 * BF5 - 7.76719 * BF15 + 4.96445 * BF17