DOI: https://doi.org/10.14710/jfma.v3i2.8663

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

Aditya Toraismaya  -  Program Studi Matematika, Universitas Kristen Satya Wacana, Indonesia
*Leopoldus Ricky Sasongko  -  Program Studi Matematika, Universitas Kristen Satya Wacana, Indonesia
Ferdy Semuel Rondonuwu  -  Program Studi Matematika, Universitas Kristen Satya Wacana, Indonesia

Citation Format:
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%.
Fulltext View|Download
Keywords: Data Spektrum, K-Means Clustering, PCA, Teh Hitam

Article Metrics:

Article Info
Section: FUNDAMENTAL MATHEMATICS AND APPLICATIONS
Language : EN
Recent articles
SOME RESULT OF NON-COPRIME GRAPH OF INTEGERS MODULO n GROUP FOR n A PRIME POWER SOLVING THE 106 YEARS OLD 3^k POINTS PROBLEM WITH THE CLOCKWISE-ALGORITHM HASIL TAMBAH LANGSUNG SUATU (R,S)-MODUL More recent articles
  1. Pusdatin, "Outlook Teh Komoditas Pertanian Subsektor Perkebunan," 27 Desember 2016. [Online]. Available: http://epublikasi.setjen.pertanian.go.id/arsip-outlook/75-outlook-perkebunan/417-outlook-teh-2016. [Diakses: 3 April 2020]
  2. D. A. Wibowo, "Prosedur Pengujian dan Sertifikasi Mutu Teh untuk Ekspor pada Balai Pengujian dan Sertifikasi Mutu Barang (BPSMB) di Surakarta," 25 July 2013. [Online]. Available: https://eprints.uns.ac.id/9557/. [Diakses: 12 November 2019]
  3. R. O. Anindya, J. Muninggar dan F. S. Rondonuwu, "Indonesian Black Tea Classification Using Fourier-Transform Near-Infrared Spectroscopy and a Principal Component Analysis," in International Conference on Mathematics, Science, and Education, Malang, 2018
  4. O. Ye Rodionova, A.V. Titova, K.S. Balyklova, A.L. Pomerantsev, "Detection Of Counterfeit And Substandard Tablets Using Non-Invasive NIR And Chemometrics - A Conceptual Framework For A Big Screening System," Talanta, vol. 205, 2019, 120150
  5. S. Samadia, S. Wajizaha, dan A. A. Munawar, "Rapid and Simultaneous Determination of Feed Nutritive Values by Means of Near Infrared Spectroscopy," Tropical Animal Science Journal, vol. 41, no. 2, pp. 121-127, 2018
  6. Y. Ozaki, C. Huck, dan K. Bec, "Near-IR Spectroscopy and Its Applications," Molecular and Laser Spectroscopy, Elsevier, 2017, pp. 11-38
  7. M. E. N. M. Ruah, N. F. Rasaruddin, S. S. Fong, M. Z. Jaafar, "Data Pre-processing Methods of FT-NIR Spectral Data for the Classification Cooking Oil," American Institute of Physics Conference Series, Vol. 1635, no. 1, pp. 890-897, 2014
  8. F. Ismawan, "Hasil Ekstraksi Algoritma Principal Component Analysis (PCA) untuk Pengenalan Wajah dengan Bahasa Pemograman Java Eclipse IDE," Jurnal Sisfotek Global, vol. 5, no. 1, 2015
  9. S. P. Mishra, et al, "Multivariate Statistical Data Analysis Principal Component Analysis (PCA)," International Journal of Livestock Research, vol. 7, no. 5, 2017
  10. M. Z. Nasution, "Penerapan Principal Component Analysis (PCA) Dalam Penentuan Faktor Dominan Yang Mempengaruhi Prestasi Belajar Siswa," Jurnal Teknologi Informasi, vol. 3, no.1, 2019
  11. J. Ma, Y. Yuan, "Dimension Reduction Of Image Deep Feature Using PCA," Journal of Visual Communication and Image Representation, vol. 63, August 2019, 102578
  12. F. M. Kusuma, dan A. Wibowo, "Principal Component Analysis (PCA) Untuk Mengatasi Multikolinieritas Terhadap Faktor Angka Kejadian Pneumonia Balita Di Jawa Timur Tahun 2014," Jurnal Biometrika dan Kependudukan, vol. 6, no. 2, 2017
  13. Husaini, Huzaeni, dan Fahmi, "Algoritma Principal Componen Analysis Dalam Pemrosesan Sinyal Electrokardiogram," Jurnal Infomedia, vol. 3, no. 1, 2018
  14. D. R. Hidalgo, B. B. Corte´s, dan E. C. Bravo, "Dimensionality Reduction Of Hyperspectral Images Of Vegetation And Crops Based On Self-Organized Maps,", Information Processing in Agriculture, in press. Available: https://www.sciencedirect.com/science/article/pii/S2-21431732030189X
  15. S. Ayesha, M. K. Hanif, dan R. Talib, "Overview And Comparative Study Of Dimensionality Reduction Techniques For High Dimensional Data," Information Fusion, vol. 59, pp. 44-58, 2020
  16. A. Ahmad, dan S. S. Khan, "InitKmix-A Novel Initial Partition Generation Algorithm For Clustering Mixed Data Using K-Means-Based Clustering," Juli 2020. [Online]. Available: https://doi.org/10.13140/RG.2.2.21979.62244. [Diakses: 9 November 2020]
  17. A. Kaur, S. K. Pal, dan A. P. Singh, "Hybridization Of Chaos And Flower Pollination Algorithm Over K-Means For Data Clustering," Applied Soft Computing Journal, 2019, 105523
  18. S. Tayfura, N. Alvera, S. Abdib, S. Saatcı, A. Ghiamid, "Characterization of concrete matrix/steel fiber de-bonding in an SFRC beam: Principal component analysis and k-mean algorithm for Clustering AE data," Engineering Fracture Mechanics, vol. 194, pp. 73-85, 2018
  19. O. R. Kristyaningrum, A. Setiawan, dan L. R. Sasongko, "Analisis Pengelompokan K-Means untuk Data Bivariat Laju Kunjungan dan Rasio Rujukan," Jurnal Riset dan Aplikasi Matematika, vol. 2, no. 1, 2018
  20. J. O. Ong, "Implementasi Algoritma K-Means Clustering untuk Menentukan Strategi Marketing President University," Jurnal Ilmiah Teknik Industri, vol. 12, no. 1, 2013

Last update:

No citation recorded.

Last update:

No citation recorded.