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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%. Kata Kunci: Spectrum data, K-Means Clustering, PCA, black tea. Abstrak. Tujuan dari makalah ini adalah mengaplikasikan metode untuk mengukur atau menilai kualitas sampel teh hitam untuk menentukan kategori atau kelas mereka berdasarkan sampel data spektrum. Jumlah variabel data spektrum dikurangi dengan metode Principal Components Analysis (PCA) menjadi variabel baru yang kemudian akan dikelompokkan dengan metode K-Means Clustering. Penelitian ini menggunakan 120 sampel teh dari Fanning II (F-II), Pekoe Fanning (PFANN), dan Broken Orange Pekoe Fannings (BOPF) dengan 90 sampel sebagai data pelatihan dan 30 sampel sebagai data validasi. Metode dan analisis yang dilakukan dalam penelitian ini cukup efektif dan efisien untuk mengukur/menilai kualitas sampel teh hitam untuk menentukan kelasnya karena memiliki tingkat keakuratan hasil K-Means Clustering diatas 50%. Kata Kunci: Data Spektrum, K-Means Clustering, PCA, Teh Hitam.

PRINCIPAL COMPONENT DAN K-MEANS CLUSTER ANALYSIS UNTUK DATA SPEKTRUM BLACK TEA GRADES GUNA PENILAIAN KUALITAS ALTERNATIF