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Open access Jurnal SINTA 2

CPU and eGPU Support System Based on Naive Bayes Classification

IJCCS (Indonesian Journal of Computing and Cybernetics Systems) · Vol. 15 No. 2 (2021) · pp. 165–176

DOI
10.22146/ijccs.63689
ISSN
2460-7258, 1978-1520
Published
2021-04-30
Publisher
IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

Central Processing Unit (CPU) and External Graphics Processing Unit (eGPU) technology are known as overclocks which aim to make the device exceed the benchmarks set by the device maker. Until now there is no determination to rank the two hardware within certain limits such as hardware price range and year-by-year. Therefore, it is necessary to process the ranking of the hardware using Simple Additive Weighting (SAW) to obtain a ranking range and determine the weight per type of hardware analyzed. It can be classified using Naïve Bayes to determine results of criteria combination between two hardware to determine possible criteria into "not good" and "good". This classification used to determine probability criteria of choosing a combination of CPU and eGPU hardware. The results of this study are getting the best CPU and eGPU every year using SAW and then classifying it for pricing. In testing conducted on application of Naïve Bayes using 80% of training data has 2776 data and 20% of testing data has 695 data that will be tested for accuracy, precision, recall, and F1-score. For results of tests that have been carried out get 0.78 accuracy results, precision 1, Recall 0.764, and F1-Score 0.866.

Keywords

Collaboration network of the authors

28 authors · 57 co-authorship links · 8 across different organisations

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • Universitas Amikom Yogyakarta
  • State University of Malang
  • Universitas AKPRIND Indonesia
  • Institut Teknologi Sepuluh Nopember
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Ahmad Fikri IskandarUniversitas Amikom Yogyakarta4
Anggit Dwi HartantoUniversitas Amikom Yogyakarta4
Andi SunyotoUniversitas Amikom Yogyakarta2
Agung Budi PrasetyoUniversitas Amikom Yogyakarta2
Catur Riyono Heri WibowoUniversitas Amikom Yogyakarta1
HanafiAffiliation unknown1
Agung Budi PrasetioUniversitas Amikom Yogyakarta1
Kartikasari Kusuma AgustiningsihUniversitas Amikom Yogyakarta1
Muhammad Altoumi AlsyaibaniUniversitas Amikom Yogyakarta1
Kartika Candra KiranaInstitut Teknologi Sepuluh Nopember1
Slamet WibawantoState University of Malang1
Ahmad HamdanState University of Malang1
Veny Cahya HarditaUniversitas Amikom Yogyakarta1
Emha Taufiq LuthfiUniversitas Amikom Yogyakarta1
Wahyu Krisna HidayatUniversitas Amikom Yogyakarta1
Agung Prasetio BudiUniversitas Amikom Yogyakarta1
Kusrini KusriniUniversitas Amikom Yogyakarta1
Arief SetyantoUniversitas Amikom Yogyakarta1
Mardi Siswo UtomoAffiliation unknown1
Mei KurniawanAffiliation unknown1
M. SuyantoAffiliation unknown1
KusriniAffiliation unknown1
Aiman MukhlishahAffiliation unknown1
Erwin SyahrudinUniversitas Amikom Yogyakarta1

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.ugm.ac.id/ijccs/oai, first harvested 26 Okt 2026, last seen 26 Okt 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">CPU and eGPU Support System Based on Naive Bayes Classification</dc:title>
	<dc:creator>Ardiansyah, Mursyid</dc:creator>
	<dc:creator>Hidayat, Wahyu</dc:creator>
	<dc:creator>Utami, Ema</dc:creator>
	<dc:creator>Raharjo, Suwanto</dc:creator>
	<dc:subject xml:lang="en-US">Computer Science</dc:subject>
	<dc:subject xml:lang="en-US">Simple Additive Weighting; Naïve Bayes; Classification</dc:subject>
	<dc:description xml:lang="en-US">Central Processing Unit (CPU) and External Graphics Processing Unit (eGPU) technology are known as overclocks which aim to make the device exceed the benchmarks set by the device maker. Until now there is no determination to rank the two hardware within certain limits such as hardware price range and year-by-year. Therefore, it is necessary to process the ranking of the hardware using Simple Additive Weighting (SAW) to obtain a ranking range and determine the weight per type of hardware analyzed. It can be classified using Naïve Bayes to determine results of criteria combination between two hardware to determine possible criteria into "not good" and "good". This classification used to determine probability criteria of choosing a combination of CPU and eGPU hardware. The results of this study are getting the best CPU and eGPU every year using SAW and then classifying it for pricing. In testing conducted on application of Naïve Bayes using 80% of training data has 2776 data and 20% of testing data has 695 data that will be tested for accuracy, precision, recall, and F1-score. For results of tests that have been carried out get 0.78 accuracy results, precision 1, Recall 0.764, and F1-Score 0.866.</dc:description>
	<dc:publisher xml:lang="en-US">IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.</dc:publisher>
	<dc:contributor xml:lang="en-US"></dc:contributor>
	<dc:date>2021-04-30</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
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	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journal.ugm.ac.id/ijccs/article/view/63689</dc:identifier>
	<dc:identifier>10.22146/ijccs.63689</dc:identifier>
	<dc:source xml:lang="en-US">IJCCS (Indonesian Journal of Computing and Cybernetics Systems); Vol 15, No 2 (2021): April; 165-176</dc:source>
	<dc:source>2460-7258</dc:source>
	<dc:source>1978-1520</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journal.ugm.ac.id/ijccs/article/view/63689/31156</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2021 IJCCS (Indonesian Journal of Computing and Cybernetics Systems)</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
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