Identifikasi Plasmodium falciparum Berbasis Citra Mikroskopis Apusan Darah Tipis dengan Menggunakan Metode Kohonen's Self-Organizing Feature Map

Junita, Rahmadana (2019) Identifikasi Plasmodium falciparum Berbasis Citra Mikroskopis Apusan Darah Tipis dengan Menggunakan Metode Kohonen's Self-Organizing Feature Map. Other thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Malaria adalah salah satu penyakit yang berbahaya. Pada tahun 2015, WHO (World Health Organization) mencatat ada sekitar 429.000 kasus yang menyebabkan kematian, dan 99% dari kasus tersebut disebabkan oleh parasit Plasmodium falciparum. Dalam diagnosanya, salah satu hal yang dilakukan adalah pengamatan mikroskopis pada sel darah merah oleh tenaga medis, yang masih dilakukan secara manual. Pengolahan citra digital telah banyak dilakukan dalam upaya membantu kinerja medis. Dalam penelitian ini, pengolahan citra digunakan untuk mengidentifikasi keberadaan parasit Plasmodium falciparum pada citra mikroskopis sel darah merah. Metode Kohonen's Self-Organizing Feature Map digunakan dalam identifikasi citra mikroskopis yang nantinya akan menghasilkan keputusan apakah sel darah tersebut terjangkit parasit Plasmodium falciparum atau tidak. Dari 200 citra yang diuji coba, dihasilkan keakuratan sebesar 86,5%.
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Malaria is a dangerous disease. In 2015, the WHO (World Health Organization) recorded around 429,000 cases that resulted in death, and 99% of these cases were caused by the parasite Plasmodium falciparum. In its diagnosis, one of the methods used is microscopic observation of red blood cells by medical personnel, which is still done manually. Digital image processing has been widely used in efforts to support medical performance. In this study, image processing was used to identify the presence of the Plasmodium falciparum parasite in microscopic images of red blood cells. The Kohonen's Self-Organizing Feature Map method is used in the identification of microscopic images, which ultimately produces a decision on whether the blood cells are infected with the Plasmodium falciparum parasite or not. Of the 200 images tested, it produced an accuracy of 86.5%.

Item Type:	Thesis (Other)
Uncontrolled Keywords:	Plasmodium falciparum, identifikasi, citra mikroskopis, Self Organizing Map
Subjects:	Q Science > QA Mathematics > QA76.87 Neural networks (Computer Science)
Divisions:	Faculty of Mathematics, Computation, and Data Science > Mathematics > 44201-(S1) Undergraduate Thesis
Depositing User:	Rahmadana Yunita
Date Deposited:	05 Aug 2026 08:01
Last Modified:	05 Aug 2026 08:01
URI:	http://repository.its.ac.id/id/eprint/70285

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