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Penerapan Metode Exhaustive Chi-Square Automatic Interaction Detection pada Klasifikasi Penderita Diabetes dan Non Diabetes

ESTIMASI: Journal of Statistics and Its Application · Vol. 6 No. 1 (2025) · pp. 79–88

DOI
10.20956/ejsa.v6i1.33079
ISSN
2721-379X, 2721-3803
Published
2025-02-17
Publisher
Hasanuddin University
Language
en

Abstract

Classification is a process of grouping an object into a certain category. One of classification method is the Exhaustive Chi-Square Automatic Interaction Detection (CHAID). The Exhaustive CHAID method is a classification method for categorical data by forming a classification tree. The classification tree interprets predictor variables that have a significant effect on the response variable based on the chi-square test. The purpose of this study was to obtain classification results for diabetics and non-diabetics using the Exhaustive CHAID method. The response variable used is the blood sugar level and the predictor variables consist of systolic blood pressure, diastolic blood pressure, length of sleep, working style, level of knowledge about diabetes, abdominal circumference, hereditary history of diabetes, age, exercise habits, and body mass index. The classification results show that the factors that have a significant influence at the 5% level are a hereditary history of diabetes, abdominal circumference, level of knowledge about diabetes, and diastolic blood pressure. Apart from that, the accuracy value of the Exhaustive CHAID classification tree is quite good, namely 86% based on the confusion matrix.

Keywords

Collaboration network of the authors

32 authors · 84 co-authorship links

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  • Universitas Halu Oleo
  • Affiliation unknown
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Co-authorOrganisationJoint articles
Erna Tri HerdianiAffiliation unknown2
La Podje TalangkoAffiliation unknown2
Andi Kresna JayaAffiliation unknown2
Rachmawati HasidUniversitas Halu Oleo1
Aminudin Mane KandariAffiliation unknown1
Rizki AdiputraAffiliation unknown1
Risma RismaAffiliation unknown1
Dewi Santika Upa P.Affiliation unknown1
Ririn AriantiAffiliation unknown1
Muhammad IdmanAffiliation unknown1
Raupong RaupongAffiliation unknown1
Fitri AnnisaAffiliation unknown1
Nasrah SirajangAffiliation unknown1
Mukrimin AdamAffiliation unknown1
Anisa Haura Salsa Fatih YusufAffiliation unknown1
Georgina Maria TinungkiAffiliation unknown1
Hendriete Tiur Marowi WaibusiAffiliation unknown1
Rael Hofni TandirerungAffiliation unknown1
Amalia AndrianingrumAffiliation unknown1
Siswanto SiswantoAffiliation unknown1
Andi Isna YunitaAffiliation unknown1
Muhammad FadilAffiliation unknown1
A.Muthiah Nur AngrianyAffiliation unknown1
Rini JamalAffiliation unknown1
Andi M Alfin BasoAffiliation unknown1
Andi FebriyantiAffiliation unknown1
Rahmiati RahimAffiliation unknown1
Rabina YunusAffiliation unknown1
Nuvida RafAffiliation 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.

Metadata source: OAI-PMH (metadata jurnal) https://journal.unhas.ac.id/index.php/ESTIMASI/oai, first harvested 2 Nov 2026, last seen 2 Nov 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en">Penerapan Metode Exhaustive Chi-Square Automatic Interaction Detection pada Klasifikasi Penderita Diabetes dan Non Diabetes</dc:title>
	<dc:creator xml:lang="en">Nurhidayatullah, Nurhidayatullah</dc:creator>
	<dc:creator xml:lang="en">Sahriman, Sitti</dc:creator>
	<dc:creator xml:lang="en">Nirwan, Nirwan</dc:creator>
	<dc:subject xml:lang="en">Classification</dc:subject>
	<dc:subject xml:lang="en">Classification Tree</dc:subject>
	<dc:subject xml:lang="en">Chi-Square Test</dc:subject>
	<dc:subject xml:lang="en">Exhaustive Chi-Square Automatic Interaction Detection</dc:subject>
	<dc:subject xml:lang="en">Diabetes</dc:subject>
	<dc:description xml:lang="en">Classification is a process of grouping an object into a certain category. One of classification method is the Exhaustive Chi-Square Automatic Interaction Detection (CHAID). The Exhaustive CHAID method is a classification method for categorical data by forming a classification tree. The classification tree interprets predictor variables that have a significant effect on the response variable based on the chi-square test. The purpose of this study was to obtain classification results for diabetics and non-diabetics using the Exhaustive CHAID method. The response variable used is the blood sugar level and the predictor variables consist of systolic blood pressure, diastolic blood pressure, length of sleep, working style, level of knowledge about diabetes, abdominal circumference, hereditary history of diabetes, age, exercise habits, and body mass index. The classification results show that the factors that have a significant influence at the 5% level are a hereditary history of diabetes, abdominal circumference, level of knowledge about diabetes, and diastolic blood pressure. Apart from that, the accuracy value of the Exhaustive CHAID classification tree is quite good, namely 86% based on the confusion matrix.</dc:description>
	<dc:publisher xml:lang="en">Hasanuddin University</dc:publisher>
	<dc:date>2025-02-17</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journal.unhas.ac.id/index.php/ESTIMASI/article/view/33079</dc:identifier>
	<dc:identifier>10.20956/ejsa.v6i1.33079</dc:identifier>
	<dc:source xml:lang="en">ESTIMASI: Journal of Statistics and Its Application; Vol. 6, No. 1, Januari, 2025 : Estimasi; 79-88</dc:source>
	<dc:source>2721-379X</dc:source>
	<dc:source>2721-3803</dc:source>
	<dc:language>en</dc:language>
	<dc:relation>https://journal.unhas.ac.id/index.php/ESTIMASI/article/view/33079/12553</dc:relation>
</oai_dc:dc>