Comparison of the Classification Data Mining Methods to Identify Civil Servants in Indonesian Social Insurance Company

Adityan Iguh Sasmito, Yova Ruldeviyani

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Indonesian civil servants already have social security; however, the benefits' value has not sufficed life necessities in retirement. Indonesian social insurance company provides additional insurance products for civil servants, yet only 7 percent of civil servants are interested. Improved marketing by identifying civil servants through data mining will help boost product sales. Data mining uses the CRISP-DM approach, starting from understanding business processes, civil servant data, data preparation, and modeling to evaluation. Data mining techniques use classification with three algorithms: Decision Tree, Naive Bayes, and Neural Network. Data mining results show six influential attributes of civil servants, including sex, the number of children, age, remaining working period, marital status, and years of service. The neural network algorithm has better performance with an accuracy value of 71.7%, a F1-score value of 73.4%, a precision value of 69.7%, a recall value of 77.6%, and an AUC value of 79.1%.

Original languageEnglish
Title of host publication2020 3rd International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2020
EditorsFerry Wahyu Wibowo
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages111-116
Number of pages6
ISBN (Electronic)9781728184067
DOIs
Publication statusPublished - 10 Dec 2020
Event3rd International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2020 - Yogyakarta, Indonesia
Duration: 10 Dec 2020 → …

Publication series

Name2020 3rd International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2020

Conference

Conference3rd International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2020
Country/TerritoryIndonesia
CityYogyakarta
Period10/12/20 → …

Keywords

  • civil servants
  • classification
  • CRISP-DM
  • data mining
  • insurance

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