Identification of Malaria Parasite Patterns With Gray Level Co-Occurance Matrix Algorithm (GLCM)

  • Annas Prasetio University Of Potensi Utama Medan
  • Rika Rosnelly University Of Potensi Utama Medan
  • Wanayumini University Of Potensi Utama Medan
Keywords: Parasites, Malaria, GCLM, Extraction, Patterns, Identification.

Abstract

The results of the test using 5 data of malaria parasite test imagery found that image 1 has an average accuracy value of the energy of 0.55627, homogeneity average of 0.8371, PSNR of 6.1336db, and MSE of 0.24358. Image 2 has an average energy accuracy value of 0.22274, an average Homonegity of 0.98532, a PSNR of 6.1336db, and an MSE of 0.24358. Image 3 has an energy average accuracy value of 0.28735, a Homonegity average accuracy value of 0.9793, a PSNR of 6.133db, and an MSE of 0.24358. Image 4 has an energy average accuracy value of 0.32907 and an average homogeneity accuracy value of 0.97073, PSNR 6.133db, and MSE 0.24358. Image 5 has an average accuracy value of 0.74102, Homonegity average of 0.99844, PSNR of 6.133db, and MSE of 0.4358. Image 6 has an accuracy value of 0.34758 energy, an average accuracy value of homogeneity of 0.99129, a PNSR of 6.133db, and an MSE of 0.24358. Obtained the rule if the average value of energy > = 0.50 then the pattern of malaria parasites is very clear, namely Image 1 and image 5 with a pattern of malaria parasites is very clear.

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Published
2022-06-30
How to Cite
Annas Prasetio, Rika Rosnelly, & Wanayumini. (2022). Identification of Malaria Parasite Patterns With Gray Level Co-Occurance Matrix Algorithm (GLCM). Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 6(3), 359 - 369. https://doi.org/10.29207/resti.v6i3.3850
Section
Artikel Teknologi Informasi