Penerapan Non-Linier Support Vector Machine pada Penggunaan Alat Kontrasepsi di Provinsi Maluku Utara - Muhamad Budiman Johra (Mathematics Department, Faculty of Science and Technology, UIN Sunan Ampel Surabaya, 2018)
The objective of BKKBN is to reduce the rate of population growth because the high population growth rate causes a high population quantity as well. According to the Departemen Kesehatan RI (2013), married women aged 15-49 years who are not use contraseption mostly in eastern Indonesia, one of them is Provinsi Maluku Utara. According to BKKBN Provinsi Maluku Utara, the birth rate increased from 57.4 to 57.9. This happens because many KB participants are drop out, contraceptive failure and side effects, the need for family planning is served 9.1 in 2007 to 8.5 in 2012 with a target of 5 in 2014. Therefore, it important to know determinant factors that affect women to use contraceptives. There are several methods in the classification, one of which is the Support Vector Machine (SVM). SVM has advantages over other classification methods because the Support Vector Machine not only minimizes errors in the trainset, but also has a high generalization capability. This is reflected in maximal margin selection. This study shows the Support Vector Machine can describe the decision of women to use contraception or not. The best kernel in this study is a radial base kernel with cost 1 and gamma 0.14286.