Model Perencanaan Bidang Minat Bagi Siswa SMA Berbasis Algoritma Subtractive Clustering - Fitriyadi Fitriyadi; Bahar Bahar (Jutisi : Jurnal Ilmiah Teknik Informatika dan Sistem Informasi, 2015)
Based on the results of several studies on Specialization Department that has been done on High School / Vocational, many students do not have adequate ability in the area of interest that has been chosen, giving rise to drop out. This is caused because one can choose areas of interest. Models such as the       K-Means Clustering and FCM and classification models have been widely tested in the process of specialization. However, the nature of the hard classification algorithms are forcing each data to be members of a target group (cluster) of certain pre-defined at the beginning of the process, so that the accuracy of the cluster / classification was not optimal. This paper describes the use of subtractive clustering algorithm to form a number of clusters (groups) are naturally in the case of Interests Determination Division at High School / Vocational. The result of the determination of the areas of interest using subtractive Clustering Algorithm on a High School, showed that the number of clusters (groups) formed areas of interest exceeds the number of areas of interest that have been assigned by the school management, the accuracy rate of cluster formation by 89%.Keywords:  Specialization in High School, Hard Classification Algorithm, Subtractive Clustering Algorithms