Klasifikasi Kadar Kolesterol Menggunakan Ekstraksi Ciri Moment Invariant dan Algoritma K-Nearest Neighbor (KNN)
Abstract
Cholesterol is a fat that is mostly formed by the body itself, especially in the liver. Cholesterol is very useful for the body but will be very dangerous if it has excessive levels. The impact of excessive cholesterol is the emergence of deadly diseases such as heart disease, stroke and poor blood circulation. In this study, one of the medical sciences that can be used to detect cholesterol levels is Iridology. This iridology itself can be applied in computer science which is often referred to as Digital Image Processing. In this case, the feature recognition method will be used using Moment Invariant feature extraction and the K-Nearest Neighbor Algorithm. Where the data used is the Dataset from Ubiris V1. With the resulting accuracy of 84,8485%.
Downloads
References
Murray, Robert K. 2009. Biokimia Harper. Jakarta : EGC.
Lyli, S. Sulistyawati. (2017). Penyakit Tidak Menular. Profil Penyakit Tidak Menular 2016. Jakarta : Kementerian Kesehatan RI.
Rani, H. A. D., Supriyati, E., & Khotimah, T. (2014). Deteksi Iris Mata untuk Menentukan Kelebihan Kolesterol menggunakan Ekstraksi Ciri Moment Invariant dengan K-Means Clustering. Prosiding SNATIF, 287-292.
Kurniawan, C. T. (2017). SISTEM PENDUKUNG KEPUTUSAN PENGELOMPOKAN STATUS KADAR KOLESTEROL MENGGUNAKAN ALGORITMA K-NEAREST NEIGHBOR (K-NN) DI PUSKESMAS ARJUNO. JATI (Jurnal Mahasiswa Teknik Informatika), 1(1), 135-143.
Waluya, D. P., Suhartono, E., & Safitri, I. Deteksi Kolesterol Menggunakan Citra Mata Berbasis Local Binary Pattern (LBP) Dan Support Vector Machine (SVM) Detection Of Cholesterol Using Eye Picture Based Local Binary Pattern (LBP) And Support Vector Machine (SVM).
Enjelica, R. (2019). Deteksi Kelebihan Kolesterol melalui Citra Iris Mata dengan Metoda Discrete Wavelet Transform dan Klasifikasi K-Nearest Neighbor. Universitas Telkom, Bandung.
Sari, Y. A., & Arwan, A. (2018). Seleksi Fitur Information Gain untuk Klasifikas i Penyakit Jantung Menggunakan Kombinasi Metode K-Nearest Neighbor dan Naïve Bayes Human Detection and Tracking View project Smart Nutritio n Box View project. Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer e-ISSN, 2548, 964X.
Kusumadewi, S. (2003). Artificial Intelligence (Teknik dan Aplikasinya). Yogyakarta: Graha Ilmu.
Andono, Pulung Nurtantio, Konsep Pengolahan Citra Digital, Ed. 1. Yogyakarta: Andi, (2015).
Whidhiasih, Retno Nugroho et al. 2013. Klasifikasi Buah Belimbing Berdasarkan Citra Red-Green-Blue Menggunakan KNN dan LDA. Jurnal Penelitian Ilmu Komputer Universitas Pakuan Bogor : 29-35.
Fahma ST MSc, 2007, Perancangan Algoritma Pengolahan Citra Mata Menjadi Citra Polar Iris Sebagai Bentuk Antara Sistem Biometrik, Universitas Sumatra Utara, Medan.
Farida Sachran, 2005, Iridology: A Complete Guide To Diagnosing Through the Iris And To Related Forms of Treatment, New York, USA.
Copyright (c) 2021 Sekar Arum Nurhusni, Riza Ibnu Adam, Carudin Carudin
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:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License (Attribution-ShareAlike 4.0 International (CC BY-SA 4.0) ) that allows others to share the work with an acknowledgement 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 acknowledgement 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 (See The Effect of Open Access).