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Klasifikasi Golongan Darah Menggunakan Artificial Neural Networks Berdasarkan Histogram Citra
https://doi.org/10.22146/ijeis.64049 Lailis Syafaah(1), Yudawan Hidayat(2), Novendra Setyawan(3*)
(1) Program Studi Teknik Elektro, Fakultas Teknik, Universitas Muhammadiyah Malang, Malang
(2) Program Studi Teknik Elektro, Fakultas Teknik, Universitas Muhammadiyah Malang, Malang
(3) Program Studi Teknik Elektro, Fakultas Teknik, Universitas Muhammadiyah Malang, Malang
(*) Corresponding Author
Abstract
Blood type in the medical world can be divided into 4 groups, namely A, B, AB and O. To be able to find out the blood type, a blood type test must be done. So far, human blood type detection is still done manually to observe the agglutination process. This research applies a blood type identification process using image processing. This system works by reading the blood type card image that has been filled with blood samples, then it will be processed through a histogram process to get the minimum and maximum RGB values and pixel locations which are then classified by Artificial Neural Networks (ANN) to determine the blood type from the training results and data matching. From the test results using 12 samples, it was found that the average error in blood type identification was 16.67%.
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A. Oktari and N. D. Silvia, “Pemeriksaan Golongan Darah Sistem ABO Metode Slide dengan Reagen Serum Golongan Darah A, B, O,” J. Teknol. Lab., vol. 5, no. 2, pp. 49–54, 2016.
A. Pudji, “PENENTUAN GOLONGAN DARAH DENGAN PENGOLAHAN CITRA,” J. TEKNOKES, vol. 8, no. 1, 2013.
A. B. W. Putra, D. S. B. Utomo, and M. D. Rahmawan, “Verifikasi Golongan Darah Manusia Berbasis Citra Dijital Menggunakan Logika Fuzzy,” JST (Jurnal Sains Ter., vol. 4, no. 1, pp. 23–32, 2018.
F. R. Hariri, “Klasifikasi Jenis Golongan Darah Menggunakan Fuzzy C-Means Clustering (FCM) dan Learning Vector Quantization (LVQ),” MATICS, vol. 10, no. 1, pp. 26–29, 2018.
S. Kusmaryanto, “Jaringan Saraf Tiruan Backpropagation untuk Pengenalan Wajah Metode Ekstraksi Fitur Berbasis Histogram,” J. EECCIS, vol. 8, no. 2, pp. 193–198, 2014.
N. Setyawan, N. Mardiyah, K. Hidayat, and Z. Has, “Object Detection of Omnidirectional Vision Using PSO-Neural Network for Soccer Robot,” in 2018 5th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI), 2018, pp. 117–121.
M. Lestandy, L. Syafaah, and A. Faruq, “Klasifikasi pendonor darah potensial menggunakan pendekatan algoritme pembelajaran mesin,” J. Teknol. dan Sist. Komput., vol. 8, no. 3, pp. 217–221, 2020.
N. Setyawan, M. Nasar, and N. Mardiyah, “Jaya-Neural Network for Server Room Temperature Forecasting Through Sensor Network,” in 2019 International Electronics Symposium (IES), 2019, pp. 428–431.
A. Faruq, S. S. Abdullah, A. Marto, C. M. C. Razali, and S. F. M. Hussein, “Flood Forecasting using Committee Machine with Intelligent Systems: a Framework for Advanced Machine Learning Approach,” in IOP Conference Series: Earth and Environmental Science, 2020, vol. 479, no. 1, p. 12039.
L. V Fausett, Fundamentals of neural networks: architectures, algorithms and applications. Pearson Education India, 2006.
DOI: https://doi.org/10.22146/ijeis.64049
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