ANALISIS PENGELOMPOKAN DATA PERILAKU CYCLIC VOLTAMMETRY PADA SUPERKAPASITOR BERBASIS GRAPHENE OKSIDA MENGGUNAKAN ALGORITMA K-MEANS

YURDIANSYAH, MELKY and Jauhari, Jaidan and Supardi, Julian (2022) ANALISIS PENGELOMPOKAN DATA PERILAKU CYCLIC VOLTAMMETRY PADA SUPERKAPASITOR BERBASIS GRAPHENE OKSIDA MENGGUNAKAN ALGORITMA K-MEANS. Undergraduate thesis, Sriwijaya University.

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Abstract

The need for renewable energy is a pressure to develop further developments in overcoming the problem of depleting fossil fuel energy. There are an pressure to do some research on renewable energy or energy storage technologies. This research proposes a computational approach with unsupervised learning method in investigating cyclic voltammetry behavior using K-Means Clustering algorithm. This study applies the elbow method, silhouette coefficient, and Davies Bouldin Index in determining the optimal number of clusters in the data. As a result, the silhouette coefficient shows a better performance than the elbow method in determining the optimal cluster in the data and the application of the optimal cluster on the data is able to show the visualization of cyclic voltammetry behavior through descriptive analysis.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Machine Learning, Unsupervised Learning, Clustering, K-Means, Elbow Method, Silhouette Coefficient, Davies-Bouldin Index, Data Mining, Superkapasitor, Cyclic Voltammetry
Subjects:	Q Science > QA Mathematics > QA8.9-QA10.3 Computer science. Artificial intelligence. Computational complexity. Data structures (Computer scienc. Mathematical Logic and Formal Languages T Technology > TP Chemical technology > TP1-1185 Chemical technology > TP159.C3.A348 Catalysts. TECHNOLOGY & ENGINEERING / Material Science
Divisions:	09-Faculty of Computer Science > 55201-Informatics (S1)
Depositing User:	Melky Yurdiansyah
Date Deposited:	05 Sep 2022 08:47
Last Modified:	05 Sep 2022 08:47
URI:	http://repository.unsri.ac.id/id/eprint/78350

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