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A comparison between deep learning, naïve bayes and random forest for the application of data mining on the admission of new students

IAES International Journal of Artificial Intelligence · Vol. 10 No. 2 (2021) · hlm. 324–331

DOI
10.11591/ijai.v10.i2.pp324-331
ISSN
2252-8938, 2089-4872
Terbit
2021-06-01
Penerbit
Institute of Advanced Engineering and Science
Bahasa
eng
Lisensi
creativecommons.org/licenses/by-sa/4.0

Abstrak

The process of admitting new students at Universitas Islam Negeri Raden Fatah each year produces a lot of new student data. So that there is an accumulation of student data continuously. The purpose of this study is to compare deep learning, naïve bayes, and random forest on the admission of new students as well as being one of the bases for making decisions to determine the promotion strategy of each study program. The data mining method used is knowledge discovery in database (KDD). The tools used are rapid miner. The attributes used are student ID number, name, program study, faculty, gender, place of birth, date of birth, year of entry, school origin, national examination, type of payment, and nominal payment. The new student data used from 2016 to 2019 was an 18.930 item. The results of this study used deep learning bayes results resulted in an accuracy value of 52.65%, naïve bayes results resulted in an accuracy value of 99.79%, and random forest results resulted in an accuracy value of 44.65%.

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26 penulis · 103 tautan kepenulisan bersama · 50 lintas organisasi

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  • Universitas Bina Darma
  • Sriwijaya University
  • Universitas Labuhanbatu
  • Universitas Muhammadiyah Palembang
  • Universitas Islam Negeri Raden Fatah Palembang
  • Universitas 17 Agustus 1945 Jakarta
  • Universitas Multi Data Palembang
  • Afiliasi tidak diketahui
  • Organisasi berbeda
  • Organisasi sama
  • Afiliasi tidak diketahui
  • Garis lebih tebal = lebih banyak artikel bersama · lingkaran lebih besar = lebih banyak artikel
Tampilkan sebagai tabel
Rekan penulisOrganisasiArtikel bersama
Ria AndryaniUniversitas Bina Darma3
Rezki SyaputraUniversitas Bina Darma2
Rahmat RamadanUniversitas 17 Agustus 1945 Jakarta2
Latius HermawanSriwijaya University2
JemakmunAfiliasi tidak diketahui1
Mukran RoniAfiliasi tidak diketahui1
Prihambodo Hendro SaksonoUniversitas Bina Darma1
Deni ErlansyahUniversitas Bina Darma1
Ferdi AdityaUniversitas Bina Darma1
Padel Mohammad AgamUniversitas Bina Darma1
Ahmad SyaziliUniversitas Labuhanbatu1
Ahmad RusliUniversitas Labuhanbatu1
Dedek JulianUniversitas Bina Darma1
Sunda ArianaUniversitas Bina Darma1
Hastari MayritaUniversitas Bina Darma1
Desy ArisandyUniversitas Bina Darma1
Mohamad FaroziUniversitas Bina Darma1
Deris StiawanSriwijaya University1
Rizma Adlia SyakurahSriwijaya University1
Indri RamayantiUniversitas Muhammadiyah Palembang1
Meilinda MeilindaSriwijaya University1
Muhammad FahmiUniversitas Muhammadiyah Palembang1
Muhammad Qurhanul RizqieSriwijaya University1
Dedy HermantoUniversitas Multi Data Palembang1

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Sumber metadata: OAI-PMH (metadata jurnal) http://ijai.iaescore.com/index.php/IJAI/oai, pertama di-harvest 8 Nov 2026, terakhir terlihat 8 Nov 2026.
Metadata asli (oai_dc) 
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	<dc:title xml:lang="en-US">A comparison between deep learning, naïve bayes and random forest for the application of data mining on the admission of new students</dc:title>
	<dc:creator>Nurhachita, Nurhachita</dc:creator>
	<dc:creator>Negara, Edi Surya</dc:creator>
	<dc:subject xml:lang="en-US"></dc:subject>
	<dc:subject xml:lang="en-US">Data mining; Deep learning; Naïve bayes; New students; Random forest</dc:subject>
	<dc:description xml:lang="en-US">The process of admitting new students at Universitas Islam Negeri Raden Fatah each year produces a lot of new student data. So that there is an accumulation of student data continuously. The purpose of this study is to compare deep learning, naïve bayes, and random forest on the admission of new students as well as being one of the bases for making decisions to determine the promotion strategy of each study program. The data mining method used is knowledge discovery in database (KDD). The tools used are rapid miner. The attributes used are student ID number, name, program study, faculty, gender, place of birth, date of birth, year of entry, school origin, national examination, type of payment, and nominal payment. The new student data used from 2016 to 2019 was an 18.930 item. The results of this study used deep learning bayes results resulted in an accuracy value of 52.65%, naïve bayes results resulted in an accuracy value of 99.79%, and random forest results resulted in an accuracy value of 44.65%.</dc:description>
	<dc:publisher xml:lang="en-US">Institute of Advanced Engineering and Science</dc:publisher>
	<dc:contributor xml:lang="en-US"></dc:contributor>
	<dc:date>2021-06-01</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
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	<dc:identifier>https://ijai.iaescore.com/index.php/IJAI/article/view/20835</dc:identifier>
	<dc:identifier>10.11591/ijai.v10.i2.pp324-331</dc:identifier>
	<dc:source xml:lang="en-US">IAES International Journal of Artificial Intelligence (IJ-AI); Vol 10, No 2: June 2021; 324-331</dc:source>
	<dc:source>2252-8938</dc:source>
	<dc:source>2089-4872</dc:source>
	<dc:source>10.11591/ijai.v10.i2</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ijai.iaescore.com/index.php/IJAI/article/view/20835/13105</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2021 Institute of Advanced Engineering and Science</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
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