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All Journal JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Knowledge Engineering and Data Science JURTEKSI JATI (Jurnal Mahasiswa Teknik Informatika) Journal of Intelligent Decision Support System (IDSS) Jurnal Pengabdian Masyarakat IPTEK IJETH
Mahardika Abdi Prawira Tanjung
Universitas Muhammadiyah Sumatera Utara, Medan, Indonesia
Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Engineering Languange, Linguistic, Communication & Media

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Journal : JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING

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Numerical Analysis of Variations Distance Formulas on K Nearest Neighbors In Classifying Malaria Parasite Blood Cells Taufik Ismail Simanjuntak; Juliansyah Putra Tanjung; Mahardika abdi prawira tanjung; Cut Try Utari; Muhathir Muhathir
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol 6, No 1 (2022): Issues July 2022
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v6i1.5464

Abstract

Malaria is one of the numerous acute and chronic diseases. Even malaria can pose a threat to a person's safety. The original cause of malaria was an infection with a protozoan of the genus Plasmodium, which was transmitted by the bite of a mosquito. This Anopheles mosquito parasite infects red blood cells throughout the body, resulting in an enlarged spleen. This research aims to make it easier for physicians to classify blood images as malaria-infected or not. If the input is a blood image, then SURF Feature Extraction will be used to extract the blood image. We therefore obtained weight results based on the extraction results. The weighted results generated by the SURF extraction process will be classified using the KNN Algorithm to determine whether or not an individual is infected with malaria. This study's tests compared various distance formulas utilized by the KNN classification method. Comparing the results of malaria blood image classification using the KNN classification method with variations in the distance formula, it is evident in table 7 that correlation is the optimal distance formula for malaria parasite blood cells recognition, followed by cosine. According to the results of KNN's tests, it is not optimal at classifying blood images containing malaria, but these results are categorized as good
Co-Authors Abdurrafy Hasibuan, Rifky Adhitya Syahputra, Muhammad Agus Suryadi Alhamidy Alhamidy Andi Zulherry Cut Try Utari Dimas Fadhlurrohman Fachri, Barany Farid Akbar Siregar Febrianti, Eka Lia Firahmi Rizky Frainskoy Rio Naibaho Hazidar, Al-Hamidy Isman Efendi Limbong Izzuddin Mubarok, Muhammad Jannah, Aulia Juliansyah Putra Tanjung Lely Fitri Hasibuan Muhammad Ardiansyah Muhammad Haris Muhathir Muhathir Ricky Ramadhan Harahap Syafii, Rahmad Syahdilan, Akbar Taufik Ismail Simanjuntak Zuli Agustina Gultom