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Clustering the Addiction Levels of Drug Users Using Fuzzy C-Mean

Indonesian Journal of Electical Engineering and Informatics (IJEEI) · Vol. 13 No. 1 (2025) · hlm. 226–238

DOI
10.52549/ijeei.v13i1.6008
ISSN
2089-3272, 2089-3272
Terbit
2025-03-23
Penerbit
IAES Indonesian Section
Bahasa
en

Abstrak

Recently, the number of drugs abused, such as Narcotics, Psychotropics, and Addictive Substances have been linear increased with the drug users. The increasing number of these cases triggers the difficulties for rehabilitation associations in diagnosticating the abuse level for medical and health prevention. Herein, data mining with a Fuzzy C-Mean clustering approach is employed to delve 506 drug users’ addiction into three clusters by considering several indicators including age, urine test, duration of use, physical effects, and psychological effects. As a result, 215 data are recorded in clusters 1 as high optimum addiction, 105, and 186 data in clusters 2, and 3 as medium and regular addiction levels, respectively. The Silhouette Coefficient, Calinski-Harabasz Index, and Davies-Bouldin Index algorithms reveal high potential values to indicate the proper achievement of this clustering structural test. A clustering software has been successfully developed and tested to aid the calculation and analysis. Hence, the rehabilitation associations in Riau province as end-user of this case are aided in identifying the addiction level of drug users in order to ensure the proper therapeutic prevention and curative action.

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  • Riau University
  • Universitas Islam Negeri Sultan Syarif Kasim Riau
  • L. N. Gumilyov Eurasian National University
  • Dulaty University
  • Universiti Teknologi MARA
  • Arab Open University
  • M. Kh Dulati Taraz State University
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Rekan penulisOrganisasiArtikel bersama
Saktioto SaktiotoRiau University6
Romi Fadli SyahputraUniversitas Muhammadiyah Riau2
Syamsudhuha SyamsudhuhaRiau University2
Haryana Mohd HairiUniversiti Teknologi MARA2
Shukor Sanim Mohd FauziUniversiti Teknologi MARA2
Septian Tri NugrahaChina University of Petroleum, Beijing2
Ayagoz MukhanovaL. N. Gumilyov Eurasian National University1
Valentina E. MakhatovaKhalel Dosmukhamedov Atyrau University1
Madiyar BaitemirovL. N. Gumilyov Eurasian National University1
Tleugaisha OspanovaL. N. Gumilyov Eurasian National University1
Azamat AmirovAbylkas Saginov Karaganda Technical University1
Assemgul KaipovaAstana Medical University1
Ulzhan MakhazhanovaAfiliasi tidak diketahui1
Dedi IrawanRiau University1
Dadang Syarif Sihabudin SahidCaltex Riau Polytechnic1
Wahyu CandraRiau University1
Vepy AsyanaRiau University1
Faisal SaeedUniversity of Technology Malaysia1
Mahyarni MahyarniUniversitas Islam Negeri Sultan Syarif Kasim Riau1
Wresni AnggrainiUniversitas Islam Negeri Sultan Syarif Kasim Riau1
Taofeeq Durojaye MoshoodUniversiti Malaysia Pahang Al-Sultan Abdullah1
Defrianto DefriantoRiau University1
Andika ThoibahRiau University1
Yan SoerbaktiRiau University1
Dedi IrawanRiau University1
Rina AmeliaUniversitas Sumatera Utara1
Lola OktaviaUniversitas Islam Negeri Sultan Syarif Kasim Riau1
Pizaini PizainiUniversitas Islam Negeri Sultan Syarif Kasim Riau1
Rahmad AbdillahUniversitas Islam Negeri Sultan Syarif Kasim Riau1

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Sumber metadata: OAI-PMH (metadata jurnal) http://section.iaesonline.com/index.php/IJEEI/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">Clustering the Addiction Levels of Drug Users Using Fuzzy C-Mean</dc:title>
	<dc:creator xml:lang="en">Okfalisa, Okfalisa</dc:creator>
	<dc:creator xml:lang="en">Suliandri, Pramulia</dc:creator>
	<dc:creator xml:lang="en">Alwesabi, Ola A.</dc:creator>
	<dc:creator xml:lang="en">Tassuov, Bolat</dc:creator>
	<dc:creator xml:lang="en">Saule, Raimbayeva</dc:creator>
	<dc:creator xml:lang="en">Vitaliy, Kurdyukov</dc:creator>
	<dc:creator xml:lang="en">Zulkifli, Zahidah</dc:creator>
	<dc:creator xml:lang="en">Handayani, Lestari</dc:creator>
	<dc:subject xml:lang="en">Drug Addiction</dc:subject>
	<dc:subject xml:lang="en">Clustering</dc:subject>
	<dc:subject xml:lang="en">Data Mining</dc:subject>
	<dc:subject xml:lang="en">Fuzzy C-Means</dc:subject>
	<dc:subject xml:lang="en">Silhouette Coefficient Index</dc:subject>
	<dc:description xml:lang="en">Recently, the number of drugs abused, such as Narcotics, Psychotropics, and Addictive Substances have been linear increased with the drug users. The increasing number of these cases triggers the difficulties for rehabilitation associations in diagnosticating the abuse level for medical and health prevention. Herein, data mining with a Fuzzy C-Mean clustering approach is employed to delve 506 drug users’ addiction into three clusters by considering several indicators including age, urine test, duration of use, physical effects, and psychological effects. As a result, 215 data are recorded in clusters 1 as high optimum addiction, 105, and 186 data in clusters 2, and 3 as medium and regular addiction levels, respectively. The Silhouette Coefficient, Calinski-Harabasz Index, and Davies-Bouldin Index algorithms reveal high potential values to indicate the proper achievement of this clustering structural test. A clustering software has been successfully developed and tested to aid the calculation and analysis. Hence, the rehabilitation associations in Riau province as end-user of this case are aided in identifying the addiction level of drug users in order to ensure the proper therapeutic prevention and curative action. </dc:description>
	<dc:publisher xml:lang="en">IAES Indonesian Section</dc:publisher>
	<dc:date>2025-03-23</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://section.iaesonline.com/index.php/IJEEI/article/view/6008</dc:identifier>
	<dc:identifier>10.52549/ijeei.v13i1.6008</dc:identifier>
	<dc:source xml:lang="en">Indonesian Journal of Electrical Engineering and Informatics (IJEEI); Vol. 13 No. 1: March 2025; 226-238</dc:source>
	<dc:source>2089-3272</dc:source>
	<dc:source>2089-3272</dc:source>
	<dc:language>en</dc:language>
	<dc:relation>https://section.iaesonline.com/index.php/IJEEI/article/view/6008/991</dc:relation>
</oai_dc:dc>