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All Journal Journal of Fundamental Mathematics and Applications (JFMA)
Aditya Toraismaya
Program Studi Matematika, Universitas Kristen Satya Wacana
Decision Sciences, Operations Research & Management

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PRINCIPAL COMPONENT DAN K-MEANS CLUSTER ANALYSIS UNTUK DATA SPEKTRUM BLACK TEA GRADES GUNA PENILAIAN KUALITAS ALTERNATIF Aditya Toraismaya; Leopoldus Ricky Sasongko; Ferdy Semuel Rondonuwu
Journal of Fundamental Mathematics and Applications (JFMA) Vol 3, No 2 (2020)
Publisher : Diponegoro University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (881.229 KB) | DOI: 10.14710/jfma.v3i2.8663

Abstract

The aim of this article is to apply the method to measure or evaluate the sampling quality of black tea in determining its category or class based on the spectrum of the sampled data. The number of the spectrum of the variable of data reduced by the Principal Components Analysis (PCA) method becomes a new variable that will be classified later by using K-Means Clustering method. This research use 120 sample of tea from Fanning II (F-II), Pekoe Fanning (PFANN), and Broken Orange Pekoe Fannings (BOPF) with 90 sample used for training and 30 sample used for validation. The method and the analysis used in this research gave effective and efficient performance in measuring/evaluating the quality of the black tea sample to determine its class as it showed that the accuracy of K-Means Clustering results is larger than 50%.
Co-Authors Ferdy Semuel Rondonuwu Leopoldus Ricky Sasongko