Skripsi

SISTEM PENDUKUNG KEPUTUSAN PEMILIHAN SMARTWATCH MENGGUNAKAN VIKOR DENGAN OPTIMASI GENETIC ALGORITHM (GA)

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The development of wearable technology has increased the use of smartwatches as devices that support daily activities and health monitoring. However, the large number of product choices with conflicting specifications and technical criteria makes the smartwatch selection process complex. This study aims to develop a web-based Decision Support System (DSS) for smartwatch selection by integrating the VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method and the Genetic Algorithm (GA). GA is used to adaptively optimize the criteria weights, thereby reducing subjectivity in the weighting process, while VIKOR is used to rank the alternatives based on the compromise solution concept. The software was developed using the Rational Unified Process (RUP) method, the Python programming language, and the Streamlit framework. The system was tested using 20 smartwatch alternatives obtained from a Kaggle dataset and evaluated based on seven technical criteria. The GA optimization results showed that the algorithm reached convergence at the 74th generation with the best fitness value of 0.598776. The optimal weights were then applied to the VIKOR calculation, which identified the Samsung Galaxy Watch 7 as the best alternative with the lowest VIKOR Index (Q) value of 0.0164. Based on the tabulated assessments of six respondents, the Spearman rank correlation coefficients were 0.8602, 0.8571, 0.8481, 0.7293, 0.7383, and 0.8211, with an average value of 0.8090. This value falls within the very strong relationship category, indicating that the ranking generated by the system has a high level of agreement with the preferences of the respondents. Therefore, the integration of GA and VIKOR is capable of producing objective, measurable, and representative smartwatch selection recommendations based on user assessments.

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Detail Information

Series Title

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Call Number

T2010422026

Publisher

Indralaya : Prodi Teknik Informatika, Fakultas Ilmu Komputer Universitas Sriwijaya., 2026

Collation

xv, 126 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
Genetic Algorithm (GA)

Specific Detail Info

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Statement of Responsibility

MI

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