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A Novel Human STR Similarity Method using Cascade Statistical Fuzzy Rules with Tribal Information Inference

International Journal of Electrical and Computer Engineering (IJECE) · Vol. 6 No. 6 (2016)

DOI
10.11591/ijece.v6i6.pp3103-3111
ISSN
2088-8708, 2088-8708
Published
2016-12-01
Publisher
Institute of Advanced Engineering and Science
License
creativecommons.org/licenses/by-nc/4.0

Abstract

A novel human STR (Short Tandem Repeat) similarity method using cascade statistical fuzzy rules with tribal information inference is proposed. The proposed method consists of two cascade Fuzzy Inference Systems (FIS). The first FIS is to discriminate the tribal similarity, and the second FIS is to calculate the STR similarity. By using the allele marker’s statistical distribution probability density function as the membership function in the Fuzzy Rules of the first FIS, the new method makes it possible to tell the tribal similarity between two STR profiles. A 727 data acquired from tribal groups of Indonesia is used to examine the method produced promising result, being able to indicate higher tribal similarity score within a tribal group and lower similarity between tribal groups. In the light of Indonesia’s diverse tribal groups, these properties are able to be leveraged as a new way to improve the versatility of existing DNA matching algorithm.

Collaboration network of the authors

9 authors · 16 co-authorship links · 2 across different organisations

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  • University of Indonesia
  • Institut Teknologi Sepuluh Nopember
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
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Co-authorOrganisationJoint articles
Maria Susan AnggreainyUniversity of Indonesia2
Belawati H. WidjajaUniversity of Indonesia2
Putut T. WidodoUniversity of Indonesia2
Bilqis AmaliahInstitut Teknologi Sepuluh Nopember1
Chastine FatichahInstitut Teknologi Sepuluh Nopember1
Shinta PuspasariAffiliation unknown1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

This record comes from Crossref (what the publisher deposited), because the journal's own OAI-PMH endpoint could not be harvested.

Metadata source: Crossref API (cadangan, berdasarkan ISSN) https://api.crossref.org/journals/2088-8708/works, first harvested 27 Okt 2026, last seen 27 Okt 2026.
Metadata as oai_dc, converted from Crossref
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