Application of Artificial Neural Network Method as A Detection of Blood Fat Disorders in Images of Complete Blood Examination

Based on various epidemiological studies, it is stated that blood lipids are the main risk factor for atherosclerosis that leads to coronary heart disease. In patients with blood lipid disorders, red blood cells undergo deformability so that their shape is flatter than normal red blood cells, which are round. The research entitled Application of Artificial Neural Network Method as Detection of Blood Fat Abnormalities in Image of Complete Blood Examination Results was conducted to help facilitate laboratory examinations. This research hopes that it will provide appropriate early detection to support the expert diagnosis. This research consists of two stages. The first stage is digital image processing to obtain area, perimeter, and eccentricity features. These three features will be used as input to the Backpropagation Neural Network program as the second stage. At this stage, blood lipid abnormalities are detected from features that have been obtained from image processing. The accuracy of detecting blood lipid abnormalities with ANN Backpropagation is 85%.

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PENERAPAN ARTIFICIAL NEURAL NETWORK UNTUK KLASIFIKASI FERTILITAS TELUR ITIK MENGGUNAKAN RASPBERRY PI

BUANA ILMU ◽

10.36805/bi.v3i1.460 ◽

2018 ◽

Vol 3 (1) ◽

Author(s):

Jamaludin Indra

Keyword(s):

Neural Network ◽

Image Processing ◽

Artificial Neural Network ◽

Raspberry Pi ◽

Detection Accuracy ◽

Neural Network Method ◽

Network Method ◽

Artificial Neural Network Method ◽

Artificial Neural ◽

Artificial Neural Network Ann

ABSTRAK Artificial Neural Network (ANN) telah banyak diterapkan pada berbagai bidang, salah satunya penerapan pada bidang peternakan. Penetasan menggunakan mesin penetas telur, proses pengklasifikasian embrio telur menjadi sangat penting dalam proses penetasan untuk membedakan antara yang layak, berdasarkan adanya perkembangan embrio yang dapat dilanjutkan dalam proses inkubasi atau tidak layak (fertile atau infertile), dalam penelitian ini menyajikan klasifikasi menggunakan teknik pengolahan citra digital menggunakan metode artificial neural network yang diaplikasikan pada Raspberry Pi sebagai pemroses gambar dan menampilkan hasil klasifikasi. Dengan metode artificial neural network dan penggunaan Raspberry Pi mampu mencapai akurasi pendeteksian 95%. Kata kunci: Artificial Neural Network, Pengolahan Citra Digital, Embrio , Klasifikasi, Telur . ABSTRACT Artificial Neural Network (ANN) has been widely applied in various fields, one of which is the application in the field of animal husbandry. Hatching using an egg incubator machine, the classification process of egg embryos is very important in the hatching process to distinguish between the appropriate, based on the embryonic development that can be continued in the process of incubation or inadequate (fertile or infertile), in this study presents classification using image processing techniques digital uses the artificial neural network method that is applied to the Raspberry Pi as an image processor and displays the classification results. With the artificial neural network method and the use of Raspberry Pi it is expected to be able to achieve 90% detection accuracy. Key word : Artificial Neural Network, Digital Image Processing, Embriyo, Calssification, Egg.

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