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OPTIMASI FUZZY TSUKAMOTO DENGAN PARTICLE SWARM OPTIMIZATION (PSO) UNTUK SCREENING TINGKAT KECANDUAN NARKOBA

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The increasing problem of drug abuse has made it difficult for families to determine whether noticeable changes in a person’s behavior are normal or early signs of addiction. To address this challenge, this study develops an addiction-screening system using the Fuzzy Tsukamoto method combined with Particle Swarm Optimization (PSO). The system assesses four main indicators—physical, psychological, social, and mental-health related changes—based on expert input from rehabilitation counselors. Test results show that the basic Fuzzy Tsukamoto model achieved an accuracy of 50.00%, while the PSO-optimized model improved accuracy to 76.67%, representing an improvement of 26.67%. This improvement demonstrates the effectiveness of PSO in refining membership function parameters, enabling the system to produce more accurate and consistent classifications, particularly for moderate and severe addiction categories. Overall, the proposed system can serve as an accessible early-stage screening tool for families and rehabilitation staff before further professional assessment.

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Detail Information

Series Title

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Call Number

T1897612025

Publisher

: Prodi Teknik Informatika, Fakultas Ilmu Komputer Universitas Sriwijaya., 2025

Collation

xiii, 96 hlm.; ilus.; tab.; 29 cm

Language

Indonesia

ISBN/ISSN

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Classification

006.307

Content Type

Text

Media Type

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Carrier Type

-

Edition

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Subject(s)

Prodi Teknik Informatika
Fuzzy Logic--Proses Data

Specific Detail Info

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Statement of Responsibility

MI

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