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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) · hlm. 73–82

DOI
10.15294/c363d161
ISSN
2986-6588, 2963-5551
Terbit
2023-09-29
Penerbit
Universitas Negeri Semarang
Bahasa
eng

Abstrak

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

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  • Institut Teknologi Kalimantan
  • State University of Semarang
  • Tun Hussein Onn University of Malaysia
  • Afiliasi tidak diketahui
  • Organisasi berbeda
  • Organisasi sama
  • Afiliasi tidak diketahui
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Tampilkan sebagai tabel
Rekan penulisOrganisasiArtikel bersama
Alamsyah AlamsyahInstitut Teknologi Kalimantan7
Much Aziz MuslimTun Hussein Onn University of Malaysia3
Endang SugihartiState University of Semarang3
Amalia Ika WulandariAfiliasi tidak diketahui3
Riza ArifudinState University of Semarang2
Chris Jeremy Verian SitorusInstitut Teknologi Kalimantan2
Muhammad Uswah PawaraInstitut Teknologi Kalimantan2
Rodlian Jamal IkhwaniInstitut Teknologi Kalimantan2
Suardi SuardiAfiliasi tidak diketahui2
Amalia Ika WulandariInstitut Teknologi Kalimantan2
Suardi SuardiInstitut Teknologi Kalimantan2
Muhamad Faishol HakimAfiliasi tidak diketahui2
Meidika Bagus SaputroAfiliasi tidak diketahui2
Lisa AriyantiAfiliasi tidak diketahui2
Budi PrasetiyoState University of Semarang1
Jumanto JumantoState University of Semarang1
Rofik RofikState University of Semarang1
Charles Sambera KalaAfiliasi tidak diketahui1
Niti Gede KumandangInstitut Teknologi Kalimantan1
Arif Nur IskandarAfiliasi tidak diketahui1
Taufik HidayatAfiliasi tidak diketahui1
Taufik HidayatInstitut Teknologi Kalimantan1
Arif Nur IskandarInstitut Teknologi Kalimantan1
Abrari Noor HasmiAfiliasi tidak diketahui1
Abrari Noor HasmiInstitut Teknologi Kalimantan1
Asram Dio PratamaInstitut Teknologi Kalimantan1
Rodlian Jamal IkhwaniAfiliasi tidak diketahui1
Irvan SetiawanInstitut Teknologi Kalimantan1
Destyariani Liana PutriAfiliasi tidak diketahui1
Alan Dicky Swans SaragihAfiliasi tidak diketahui1
Anggoronadhi DianiswaraAfiliasi tidak diketahui1
Destyariani Liana PutriInstitut Teknologi Kalimantan1
Alan Dicky Swans SaragihInstitut Teknologi Kalimantan1
Anggoronadhi DianiswaraInstitut Teknologi Kalimantan1
Wira SetiawanAfiliasi tidak diketahui1
Muhammad Uswah PawaraAfiliasi tidak diketahui1
Andi Mursid Nugraha ArifuddinAfiliasi tidak diketahui1
Samsu Dlukha NurcholiqAfiliasi tidak diketahui1

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Sumber metadata: OAI-PMH (metadata jurnal) https://journal.unnes.ac.id/journals/rji/oai, pertama di-harvest 6 Nov 2026, terakhir terlihat 8 Nov 2026.
Metadata asli (oai_dc) 
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	<dc:title xml:lang="en">Comparison of Probabilistic Neural Network (PNN) and k-Nearest Neighbor (k-NN) Algorithms for Diabetes Classification</dc:title>
	<dc:creator xml:lang="en">Azzahrah, Diah Siti Fatimah</dc:creator>
	<dc:creator xml:lang="en">Alamsyah, Alamsyah</dc:creator>
	<dc:subject xml:lang="en">Data Mining </dc:subject>
	<dc:subject xml:lang="en">Probabilistic Neural Network</dc:subject>
	<dc:subject xml:lang="en">k-Nearest Neighbor</dc:subject>
	<dc:subject xml:lang="en">Feature Selection</dc:subject>
	<dc:subject xml:lang="en">K-Fold Cross Validation</dc:subject>
	<dc:description xml:lang="en">Purpose:&amp;nbsp;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:&amp;nbsp;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:&amp;nbsp;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:&amp;nbsp;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">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:type xml:lang="en">Peer-reviewed Article</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journal.unnes.ac.id/journals/rji/article/view/35216</dc:identifier>
	<dc:identifier>10.15294/c363d161</dc:identifier>
	<dc:source xml:lang="en">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>10.15294/8v9k4e37</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journal.unnes.ac.id/journals/rji/article/view/35216/6446</dc:relation>
</oai_dc:dc>