Recommendation of prospective construction service providers in Government Procurement using decision tree

Yustina, Yustina, Hariyadi, Mokhamad Amin ORCID: https://orcid.org/0000-0001-9327-7604 and Crysdian, Cahyo ORCID: https://orcid.org/0000-0002-7488-6217 (2024) Recommendation of prospective construction service providers in Government Procurement using decision tree. International Journal of Advances in Data and Information Systems, 5 (1). pp. 39-48. ISSN 2721-3056

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Abstract

The determination of prospective construction service providers using thedirect procurement method is the authority of the Goods/ Services Procurement Officer.Administrative requirements are an important factor in selecting prospective construction service providers.The use of thedecision tree method in this study is to find out, determine, and analyse the variables that influence the assessment of the feasibility of prospective construction service providers, and get an accuracy valueinproviding an assessment of the feasibility of prospective construction service providers.The data used in this study are 153 datasets consisting of 13 variables.The existing variables are divided into basic variables and additional variables.The basic variables consist of 5 variables, namely experts, work experience, quality of work, winning tenders and contract value.While the additional variables consist of 8 variables namely business entity status, business entity form, business entity NPWP, business entity domicile, business entity qualification, type of business licence, percentage of work and construction services business licence.By using the decision tree method, the accuracy on the basic variable is 84.84%.The addition of additional variables to thebasic variables resulted in an accuracy of 90.91%.This shows that by adding additional variables the accuracy results are higher than using only the basic variables.

Item Type:	Journal Article
Keywords:	Procurement of Goods/Services; Construction Service Provider; Decision Tree
Subjects:	08 INFORMATION AND COMPUTING SCIENCES > 0801 Artificial Intelligence and Image Processing > 080105 Expert Systems
Divisions:	Faculty of Technology > Department of Informatics Engineering
Depositing User:	Mokhamad Amin Hariyadi
Date Deposited:	30 Oct 2024 11:33

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