Sistem Penentuan Insentif Karyawan Pada PT Remco Dengan Teknik Data Mining

Mandasari, Apriyanti (2014) Sistem Penentuan Insentif Karyawan Pada PT Remco Dengan Teknik Data Mining. Other thesis, Sekolah Tinggi Teknik Musi.

	Text IF-2014-310001-cover.pdf Download (46kB)
	Text IF-2014-310001-abstract.pdf Download (131kB)
	Text IF-2014-310001-tableofcontent.pdf Download (345kB)
	Text IF-2014-310001-chapter1.pdf Download (496kB)
	Text IF-2014-310001-chapter2.pdf Restricted to Repository staff only Download (1MB)
	Text IF-2014-310001-chapter3.pdf Restricted to Repository staff only Download (2MB)
	Text IF-2014-310001-chapter4.pdf Restricted to Repository staff only Download (1MB)
	Text IF-2014-310001-conclusion.pdf Download (85kB)
	Text IF-2014-310001-reference.pdf Download (271kB)
	Text IF-2014-310001-summary_id.pdf Restricted to Repository staff only Download (495kB)
	Other (pdf) IF-2014-310001-complete.PDF Restricted to Repository staff only Download (2MB)

Abstract

Every companies have the employees where they have destination for get salary every months for still alive. Employee has the important role for companies. The processing for choose employee it’s not easy, actually giving the incentives do by the leader ordinary. Incentives earned based criterias from company. Criterias made with one reason namely give incentive fairly. The criterias such as absent, perform, attitude and how many long their work. Without criterias for valuation, probably the incentive it’s not precise. On this experiment will be built decision support system of determining incentive employee using naive bayes algorithm. The calculation naive bayes based history data. History data was gotten by observation and interview. This system give suggestion about the incentive for company. With the built this system give easier and effieciency of time when the process in it. This experiment using waterfall method along with SQL server 2000 and Visual Basic 2008. The resulting sytsem tested with 50 data employee at PT Remco on Januari 2014, the incentives exact with the resulting namely 38 data are right dan 12 data is not match so that the conclusion decision support system using naïve bayes algorithm having the accuracy 60% with the error path 40%.

Actions (login required)

View Item