Deteksi Parasit Malaria Menggunakan Metode Gray Level Co-Occurance Matrix (GLCM)
DOI:
https://doi.org/10.29407/stains.v3i1.4356Keywords:
Deteksi, malaria, sel darah merah, teksturAbstract
Malaria adalah salah satu penyakit menular paling mematikan di dunia. Deteksi dini malaria sangat penting untuk mencegah penyebaran penyakit ini. Tradisionalnya, deteksi malaria dilakukan dengan pemeriksaan mikroskopis darah. Namun, metode ini membutuhkan waktu dan usaha yang relatif lama. Dalam penelitian ini, deteksi parasit malaria dilakukan menggunakan metode Gray Level Co-occurrence Matrix (GLCM). Metode GLCM digunakan untuk mengekstraksi fitur tekstur dari gambar sel darah merah yang terinfeksi malaria. Fitur tekstur ini kemudian digunakan untuk melatih model klasifikasi. Hasil penelitian menunjukkan bahwa metode GLCM dapat digunakan untuk mendeteksi parasit malaria dengan akurasi 89%. Studi ini menyarankan bahwa metode GLCM memiliki potensi sebagai metode deteksi malaria yang lebih cepat dan akurat.
Downloads
References
Y.B. Utomo, G.W. Harsanto, “Penerapan Metode Certainty Factor Dan Naïve BayesUntuk Mendiagnosa Penyakit Akibat Gigitan Nyamuk” Generation Journal, Vol.4 No.2, 2020
P. Gascoyne, J. Satayavivad, M. Ruchirawat, “Microfluidic approaches to malaria detection”, Acta Tropica 89 (2004) 357–369.
N. Ain Banyal, Surianti, A. Rachmat Dayat, “Klasifikasi Citra Plasmodium Penyebab Penyakit Malaria Dalam Sel Darah Merah Manusia Dengan Menggunakan Metode Multi Class Support Vector Machine (Svm)”, ILKOM Jurnal Ilmiah Volume 8 Nomor 2 (Agustus 2016).
J. Bana Abraham, “Plasmodium Detection Using Simple CNN and Clustered GLCM Features”, Electrical and Information Engineering.
A. Negi, K. Kumar, and P. Chauhan, “Deep Learning-Based Image Classifier for Malaria Cell Detection”, Machine Learning for Healthcare Applications, 2021.
Q. Shandy, S.S. Panna, Y. Malago, “Penerapan Metode Grey Level Co-Occurrence Matriks (GLCM) dan K-Nearest Neighbor (K-NN) Untuk Mendeteksi Tingkat Kematangan Buah Belimbing Bintang”, Jurnal Nasional cosPhi, Vol. 3 No. 1, 2019
A. Prasetio, R. Rosnelly, Wanayumini, “Identification of Malaria Parasite Patterns With Gray Level Co-Occurance Matrix Algorithm (GLCM)”, Jurnal Rekayasa Sistem dan Teknologi Informasi, Vol. 6 No. 3 (2022) 359 – 369, 2022.
Qurina Firdaus,, et all, “Lung Cancer Detection Based On Ct-Scan Images with Detection Features Using Gray Level Conoccurrence Matrix (Glcm) and Support Vector Machine (Svm) Methods” Indonesian Journal of Electrical Engineering and Computer Science Vol. 16, 2020.
Downloads
Published
Issue
Section
License
Copyright (c) 2024 Ghina Briliana Fatin Octariana, Ahmad Arsyad Surgi Mukti, Krisna Dian Sukmana, Fitri Bimantoro

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Copyright on any article is retained by the author(s).
- The author grants the journal, right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work’s authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal’s published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.
- The article and any associated published material is distributed under the Creative Commons Attribution-ShareAlike 4.0 International License





