Klasifikasi Golongan Darah Menggunakan Artificial Neural Networks Berdasarkan Histogram Citra
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 identificati...
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| Main Authors: | , , |
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| Format: | EJournal Article |
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IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.,
2021-10-31.
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| Online Access: | Get Fulltext |
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| LEADER | 02113 am a22003013u 4500 | ||
|---|---|---|---|
| 001 | IJEIS_UGM_64049_32416 | ||
| 042 | |a dc | ||
| 100 | 1 | 0 | |a Syafaah, Lailis |e author |
| 100 | 1 | 0 | |e contributor |
| 700 | 1 | 0 | |a Hidayat, Yudawan |e author |
| 700 | 1 | 0 | |a Setyawan, Novendra |e author |
| 245 | 0 | 0 | |a Klasifikasi Golongan Darah Menggunakan Artificial Neural Networks Berdasarkan Histogram Citra |
| 260 | |b IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia., |c 2021-10-31. | ||
| 500 | |a https://jurnal.ugm.ac.id/ijeis/article/view/64049 | ||
| 520 | |a 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%. | ||
| 540 | |a Copyright (c) 2021 IJEIS (Indonesian Journal of Electronics and Instrumentation Systems) | ||
| 540 | |a http://creativecommons.org/licenses/by-sa/4.0 | ||
| 546 | |a eng | ||
| 690 | |a Electronics; Instrumentation | ||
| 690 | |a Blood Classification; RGB; Image Histogram; Artificial Neural Network | ||
| 655 | 7 | |a info:eu-repo/semantics/article |2 local | |
| 655 | 7 | |a info:eu-repo/semantics/publishedVersion |2 local | |
| 655 | 7 | |2 local | |
| 786 | 0 | |n IJEIS (Indonesian Journal of Electronics and Instrumentation Systems); Vol 11, No 2 (2021): Oktober; 133-142 | |
| 786 | 0 | |n 2460-7681 | |
| 786 | 0 | |n 2088-3714 | |
| 787 | 0 | |n https://jurnal.ugm.ac.id/ijeis/article/view/64049/32416 | |
| 856 | 4 | 1 | |u https://jurnal.ugm.ac.id/ijeis/article/view/64049/32416 |z Get Fulltext |