The use of data mining classification technique to fill in structural positions in bogor local government

Abstract

The human resources of Bogor local government are managed by human resources and training division, which is called Badan Kepegawaian Pendidikan dan Pelatihan (BKPP). BKPP form a team called Badan Pertimbangan Jabatan dan Kepangkatan (Baperjakat), which are responsible for promoting, rotating and dismissing local government employees from structural positions below the Echelon IIA positions. Baperjakat have problems on constructing the draft of structural government positions. These processes were done manually, even though BKPP have a human resources information systems called SIMPEG. The main purpose of this research is to identify patterns to fill in structural positions in Bogor Local Government. 62 Classifications algortithms were tested using 3 data mining tools with 7 data sets and 7 human resources attributes to identify filling structural position patterns. The classification process yields Classification Rule with Unbiased Interaction Selection and Estimation (CRUISE) as the best algorithm in echelon class. Its average accuracy is 95.7% for each echelon level.