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Comparison of Probabilistic Neural Network (PNN) and k-Nearest Neighbor (k-NN) Algorithms for Diabetes Classification

Recursive Journal of Informatics · Vol. 1 No. 2 (2023) · pp. 73–82

DOI
10.15294/rji.v1i2.66078
ISSN
2986-6588, 2963-5551
Published
2023-09-29
Publisher
Universitas Negeri Semarang
Language
eng

Abstract

Purpose: This study aims to compare algorithms to determine the accuracy of the algorithm and determine the speed of the algorithm used for diabetes classification. Methods: There are two algorithms used in this study, namely Probabilistic Neural Network (PNN) and k-Nearest Neighbor (k-NN). The data used is the Pima Indians Diabetes Database. The data contains 768 data with 8 attributes and 1 target class, namely 0 for no diabetes and 1 for diabetes. The dataset has been divided into 80% training data and 20% testing data. Result: Accuracy is obtained after implementing k-fold cross validation with a value of k = 4. The accuracy results show that the k-Nearest Neighbor algorithm is superior and has better quickness compared to the Probabilistic Neural Network. The k-Nearest Neighbor algorithm obtains an accuracy of 74.6% for all features and 78.1% for four features Novelty: The novelty of this paper is optimizing and improving accuracy which is implemented with by focusing on data preprocessing, feature selection and k-fold cross validation in the classification algorithm

Collaboration network of the authors

40 authors · 166 co-authorship links · 11 across different organisations · showing the 40 most connected

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  • Institut Teknologi Kalimantan
  • State University of Semarang
  • Tun Hussein Onn University of Malaysia
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Alamsyah AlamsyahInstitut Teknologi Kalimantan7
Much Aziz MuslimTun Hussein Onn University of Malaysia3
Endang SugihartiState University of Semarang3
Amalia Ika WulandariAffiliation unknown3
Riza ArifudinState University of Semarang2
Chris Jeremy Verian SitorusInstitut Teknologi Kalimantan2
Muhammad Uswah PawaraInstitut Teknologi Kalimantan2
Rodlian Jamal IkhwaniInstitut Teknologi Kalimantan2
Suardi SuardiAffiliation unknown2
Amalia Ika WulandariInstitut Teknologi Kalimantan2
Suardi SuardiInstitut Teknologi Kalimantan2
Muhamad Faishol HakimAffiliation unknown2
Meidika Bagus SaputroAffiliation unknown2
Lisa AriyantiAffiliation unknown2
Budi PrasetiyoState University of Semarang1
Jumanto JumantoState University of Semarang1
Rofik RofikState University of Semarang1
Charles Sambera KalaAffiliation unknown1
Niti Gede KumandangInstitut Teknologi Kalimantan1
Arif Nur IskandarAffiliation unknown1
Taufik HidayatAffiliation unknown1
Taufik HidayatInstitut Teknologi Kalimantan1
Arif Nur IskandarInstitut Teknologi Kalimantan1
Abrari Noor HasmiAffiliation unknown1
Abrari Noor HasmiInstitut Teknologi Kalimantan1
Asram Dio PratamaInstitut Teknologi Kalimantan1
Rodlian Jamal IkhwaniAffiliation unknown1
Irvan SetiawanInstitut Teknologi Kalimantan1
Destyariani Liana PutriAffiliation unknown1
Alan Dicky Swans SaragihAffiliation unknown1
Anggoronadhi DianiswaraAffiliation unknown1
Destyariani Liana PutriInstitut Teknologi Kalimantan1
Alan Dicky Swans SaragihInstitut Teknologi Kalimantan1
Anggoronadhi DianiswaraInstitut Teknologi Kalimantan1
Wira SetiawanAffiliation unknown1
Muhammad Uswah PawaraAffiliation unknown1
Andi Mursid Nugraha ArifuddinAffiliation unknown1
Samsu Dlukha NurcholiqAffiliation 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.unnes.ac.id/sju/index.php/rji/oai, first harvested 6 Nov 2026, last seen 6 Nov 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">Comparison of Probabilistic Neural Network (PNN) and k-Nearest Neighbor (k-NN) Algorithms for Diabetes Classification</dc:title>
	<dc:creator>Azzahrah, Diah Siti Fatimah</dc:creator>
	<dc:creator>Alamsyah, Alamsyah</dc:creator>
	<dc:description xml:lang="en-US">Purpose: This study aims to compare algorithms to determine the accuracy of the algorithm and determine the speed of the algorithm used for diabetes classification.
Methods: There are two algorithms used in this study, namely Probabilistic Neural Network (PNN) and k-Nearest Neighbor (k-NN). The data used is the Pima Indians Diabetes Database. The data contains 768 data with 8 attributes and 1 target class, namely 0 for no diabetes and 1 for diabetes. The dataset has been divided into 80% training data and 20% testing data.
Result: Accuracy is obtained after implementing k-fold cross validation with a value of k = 4. The accuracy results show that the k-Nearest Neighbor algorithm is superior and has better quickness compared to the Probabilistic Neural Network. The k-Nearest Neighbor algorithm obtains an accuracy of 74.6% for all features and 78.1% for four features
Novelty: The novelty of this paper is optimizing and improving accuracy which is implemented with by focusing on data preprocessing, feature selection and k-fold cross validation in the classification algorithm</dc:description>
	<dc:publisher xml:lang="en-US">Universitas Negeri Semarang</dc:publisher>
	<dc:date>2023-09-29</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.unnes.ac.id/sju/rji/article/view/66078</dc:identifier>
	<dc:identifier>10.15294/rji.v1i2.66078</dc:identifier>
	<dc:source xml:lang="en-US">Recursive Journal of Informatics; Vol 1 No 2 (2023): September 2023; 73-82</dc:source>
	<dc:source>2986-6588</dc:source>
	<dc:source>2963-5551</dc:source>
	<dc:source>10.15294/rji.v1i2</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journal.unnes.ac.id/sju/rji/article/view/66078/25386</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2023 Recursive Journal of Informatics</dc:rights>
</oai_dc:dc>