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ANALISIS PENGARUH VARIASI MATERIAL DYE ALAMI TERHADAP PERFORMA KELISTRIKAN DYE SENSITIZED SOLAR CELL (DSSC) BERBASIS PLATFORM ORANGE DATA MINING

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The electrical performance of Dye Sensitized Solar Cells (DSSC) is significantly influenced by the characteristics of the dye material used as a sensitizer. Variations in natural dye materials produce differences in light absorption capability and electrical characteristics, which directly affect power conversion efficiency. This study aims to analyze the effect of natural dye material variations on the electrical performance of DSSC and to identify the most influential parameters using a Machine Learning approach based on the Orange Data Mining platform. The dataset consists of 16 DSSC samples tested using a solar simulator to obtain electrical parameters such as open circuit voltage (Voc), short circuit current density (Jsc), fill factor (FF), output power (Pout), and efficiency (η), as well as UV-Vis spectrophotometer measurements to obtain dye absorbance values. The analysis stages include data preprocessing, normalization, modeling using the K-Means clustering algorithm, and evaluation of clustering results. The results show that clustering based on efficiency and absorbance successfully categorizes DSSC performance into three groups: low, medium, and high performances clusters.

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Detail Information

Series Title

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Call Number

T1965422026

Publisher

Indralaya : Prodi Teknik Arsitektur, Fakultas Teknik Universitas Sriwijaya., 2026

Collation

xvi, 48 hlm.; ilus.; 29 cm

Language

Indonesia

ISBN/ISSN

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Classification

621.312 440 7

Content Type

Text

Media Type

unmediated

Carrier Type

-

Edition

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Subject(s)

Machine Learning
Prodi Teknik Arsitektur

Specific Detail Info

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Statement of Responsibility

KA

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