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DETEKSI PENYALAHGUNAAN NARKOBA DENGAN METODE TWIN BOUNDED SVM

BAREKENG: Jurnal Ilmu Matematika dan Terapan · Vol. 15 No. 4 (2021) · pp. 753–760

DOI
10.30598/barekengvol15iss4pp753-760
ISSN
2615-3017, 1978-7227
Published
2021-12-01
Publisher
PATTIMURA UNIVERSITY
Language
eng

Abstract

Twin Bounded SVM (TB-SVM) is an improvement of the Twin SVM method and has advantages in classification problems compared to standard SVM. In this research, linear TB-SVM and nonlinear TB-SVM methods will be applied to detect drug use based on 23 symptoms experienced. The training and testing data is divided into three partition data schemes (60/40 scheme, 70/30 scheme and 80/20 scheme) in order to determine the best level of accuracy that can be obtained. The test results show that the nonlinear TB-SVM with the RBF kernel has a better accuracy rate than the linear TB-SVM, that is 80% at 60/40 scheme, 90% at 70/30 scheme, and 95% at 80/20 scheme.

Collaboration network of the authors

40 authors · 99 co-authorship links · 3 across different organisations · showing the 40 most connected

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  • Universitas Nahdlatul Ulama Surabaya
  • Institut Teknologi Sepuluh Nopember
  • Universitas Halu Oleo
  • Darul Ulum Islamic University
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Teguh HerlambangUniversitas Nahdlatul Ulama Surabaya6
Andy SuryowinotoAffiliation unknown4
Venn Yan Ishak IlwaruAffiliation unknown4
Zeth Arthur LeleuryAffiliation unknown4
Mozart W. TalakuaAffiliation unknown2
Fajar Annas SusantoAffiliation unknown2
Hendro NurhadiInstitut Teknologi Sepuluh Nopember2
Olumuyiwa James PeterAffiliation unknown2
Dorteus L. RahakbauwAffiliation unknown2
Elvinus R. PersulessyAffiliation unknown2
Venn Y. I. IlwaruAffiliation unknown1
Zeth A. LeleuryAffiliation unknown1
Tesa SumahAffiliation unknown1
H. AbrahamsAffiliation unknown1
Rethalina WaasAffiliation unknown1
Glevi E. MadoAffiliation unknown1
Henry W.M. PattyAffiliation unknown1
Sasmita HayotoAffiliation unknown1
Ronald John DjamiAffiliation unknown1
Jemsry E. LakotanyAffiliation unknown1
Y. SambonoAffiliation unknown1
Jesica TentuaAffiliation unknown1
Charlitta FhilyaAffiliation unknown1
Moch ZakariaAffiliation unknown1
Fredrylo Alberth Noel Joddy ApituleyAffiliation unknown1
Marlon S. Noya van DelsenAffiliation unknown1
Yunita Puspita SariAffiliation unknown1
Nurhidayah NurhidayahUniversitas Halu Oleo1
Firman YudiantoAffiliation unknown1
Dinita RahmaliaDarul Ulum Islamic University1
Abdul MuhithAffiliation unknown1
Nona Tjie SapuletteAffiliation unknown1
Monalissa E RijolyAffiliation unknown1
Mozart Winston TalakuaAffiliation unknown1
Muhammad Sawal BaitalAffiliation unknown1
Angga Adhitya WidjanarkoAffiliation unknown1
Yuliyanto Agung PrabowoAffiliation unknown1
Swine Enggelina LarubunAffiliation 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://ojs3.unpatti.ac.id/index.php/barekeng/oai, first harvested 5 Nov 2026, last seen 5 Nov 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">DETEKSI PENYALAHGUNAAN NARKOBA DENGAN  METODE TWIN BOUNDED SVM</dc:title>
	<dc:creator>Tomasouw, Berny Pebo</dc:creator>
	<dc:creator>Lesnussa, Yopi Andry</dc:creator>
	<dc:description xml:lang="en-US">Twin Bounded SVM (TB-SVM) is an improvement of the Twin SVM method and has advantages in classification problems compared to standard SVM. In this research, linear TB-SVM and nonlinear TB-SVM methods will be applied to detect drug use based on 23 symptoms experienced. The training and testing data is divided into three partition data schemes (60/40 scheme, 70/30 scheme and 80/20 scheme) in order to determine the best level of accuracy that can be obtained. The test results show that the nonlinear TB-SVM with the RBF kernel has a better accuracy rate than the linear TB-SVM, that is 80% at 60/40 scheme, 90% at 70/30 scheme, and 95% at 80/20 scheme.</dc:description>
	<dc:publisher xml:lang="en-US">PATTIMURA UNIVERSITY</dc:publisher>
	<dc:date>2021-12-01</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://ojs3.unpatti.ac.id/index.php/barekeng/article/view/4260</dc:identifier>
	<dc:identifier>10.30598/barekengvol15iss4pp753-760</dc:identifier>
	<dc:source xml:lang="en-US">BAREKENG: Jurnal Ilmu Matematika dan Terapan; Vol 15 No 4 (2021): BAREKENG: Jurnal Ilmu Matematika dan Terapan; 753-760</dc:source>
	<dc:source>2615-3017</dc:source>
	<dc:source>1978-7227</dc:source>
	<dc:source>10.30598/barekengvol15iss4year2021</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/4260/3582</dc:relation>
</oai_dc:dc>