Classification thalassemia data using fuzzy kernel C-Means (FKCM) method

Zuherman Rustam, Febrisa Dhewi Ramadhany, Titin Siswantining, Fajar Subroto, Aditya Suryansyah

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)


This study will investigate thalassemia. Thalassemia is a disease of blood disorders in red blood cells caused by genetic factors that cause red blood cells (hemoglobin) are not able to function properly. This disease causes a lot of death because it can not be cured, so to prevent the occurrence of thalassemia can do a prenatal test or early detection to determine people who have thalassemia. This study will separate a number of data using thalassemia classification technique to differentiate patients with thalassemia and normal patients by using the Fuzzy Kernel C-Means method. The results obtained show that FKCM has far better performance compared to FCM in classifying thalassemia data. FCM has an accuracy rate of 100% with an execution time of 0.80 seconds, while FKCM has an accuracy rate of 100% with an execution time of 0.19 seconds.

Original languageEnglish
Pages (from-to)20-27
Number of pages8
JournalInternational Journal of Advanced Science and Technology
Issue number8 Special Issue
Publication statusPublished - 8 Oct 2019


  • Accuracy
  • Classification
  • Fuzzy Kernel C-Means
  • Thalassemia


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