Segmentasi Dan Perhitungan Sel Darah Putih Menggunakan Operasi Morfologi Dan Transformasi Watershed

Alham, Dwi Syamsuifin; Herumurti, Darlis

doi:10.19184/isj.v4i2.13347

Cited by 3 publications

(3 citation statements)

References 7 publications

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“…Terdapat beberapa jenis operasi yang sering digunakan, yaitu dilation, erotion, closing, dan opening [11]. Dalam prakteknya, operasi morfologi seperti dilation, erotion, closing, dan opening dapat diimplementasikan menggunakan persamaan (3), ( 4), ( 5), dan (6) [12].…”

Section: Operasi Morfologiunclassified

Klasifikasi Tanaman Obat Berdasarkan Citra Daun Menggunakan Backpropagation Neural Networks

Nurhikam,

Agung,

Rohman

et al. 2024

J. Inf. dan Komp.

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Indonesia terkenal dengan kekayaan hayati yang melimpah, termasuk ribuan spesies tumbuhan yang sebagian besar memiliki nilai pengobatan dalam pengobatan tradisional. Meskipun pengobatan tradisional merupakan bagian dari warisan budaya Indonesia dan digunakan oleh generasi sebelumnya, penggunaan obat tradisional saat ini mengalami penurunan akibat kurangnya informasi yang tersedia. Hal ini mengakibatkan banyak tumbuhan obat di Indonesia, termasuk yang memiliki potensi pengobatan, tidak dimanfaatkan secara optimal. Untuk meningkatkan pemahaman dan pemanfaatan obat tradisional, penelitian ini menggunakan teknologi Jaringan Saraf Tiruan (Artificial Neural Networks, ANN), khususnya metode Backpropagation Neural Networks (BNN), untuk mengklasifikasikan jenis tanaman obat berdasarkan citra daun. Metode ini didasarkan pada model matematika yang terinspirasi dari cara kerja otak manusia dan mampu mempelajari pola dari data yang diberikan. Pada penelitian ini, dilakukan pengklasifikasian citra daun herbal menggunakan algoritma BNN dengan ekstraksi ciri berdasarkan bentuk. Dataset yang digunakan terdiri dari 40 citra yang dibagi menjadi 4 kelas, dengan masing-masing kelas memiliki 10 citra. Kelas-kelas tersebut mencakup daun lengkuas, daun kersen, dan daun sirih. Proses pengklasifikasian melibatkan segmentasi citra, operasi morfologi, serta ekstraksi ciri menggunakan parameter metric dan eccentricity. Model ini diimplementasikan melalui antarmuka pengguna grafis (GUI) menggunakan perangkat lunak Matlab, sehingga mempermudah pengujian. Hasil evaluasi menunjukkan bahwa model yang dikembangkan mampu mengklasifikasikan citra daun herbal dengan akurasi keseluruhan mencapai 93,75%.

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Section: Operasi Morfologiunclassified

Klasifikasi Tanaman Obat Berdasarkan Citra Daun Menggunakan Backpropagation Neural Networks

Nurhikam,

Agung,

Rohman

et al. 2024

J. Inf. dan Komp.

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“…The segmentation stage in the CT Scan brain image is very important because the more accurate the segmentation results to separate objects and backgrounds, the more information that can be built and developed from the calculation stage to stroke identification. [14]…”

Section: Image Segmentationmentioning

confidence: 99%

Application Of Dynamic Segmentation In Stroke Detection Software With ANN

Audytra

Supardi

Abdiansah

2022

International Journal of New Media Technology

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One way to find out whether there is a stroke is to do a CT scan . But the results of the examination with a new CT scan can be obtained in quite a long time. In addition, sometimes there are differences of opinion between doctors and radiologists regarding what is seen from the results of the examination. This research was conducted to produce a software that can later be integrated with the existing system on the CT Scan tool so that it can immediately be known whether or not stroke is present from the CT Scan results. In this study, a dynamic image segmentation method is implemented, namely the watershed transformation method which will later produce regions as a feature for the stroke detection process carried out with the backpropagation algorithm. From experiments conducted on CT scan images of the brain, this method can detect stroke well. The results obtained are 100% for training data and 90% for test data.

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“…This study uses HSV color space segmentation (Hue, Saturation, and Value) to represent actual color values [5]. The results of the segmentation were carried out with feature extraction using the GLCM method.…”

Section: Introductionmentioning

confidence: 99%

Identification of White Blood Cells Using Machine Learning Classification Based on Feature Extraction

Musliman

Fadlil

Yudhana

2021

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In various disease diagnoses, one of the parameters is white blood cells, consisting of eosinophils, basophils, neutrophils, lymphocytes, and monocytes. Manual identification takes a long time and tends to be subjective depending on the staff's experience, so the automatic identification of white blood cells will be faster and more accurate. White blood cells are identified by examining a colored blood smear (SADT) and examined under a digital microscope to obtain a cell image. Image identification of white blood cells is determined through HSV color space segmentation (Hue, Saturation Value) and feature extraction of the Gray Level Cooccurrence Matrix (GLCM) method using the Angular Second Moment (ASM), Contrast, Entropy, and Inverse Different Moment (IDM) features. The purpose of this study was to identify white blood cells by comparing the classification accuracy of the K-nearest neighbor (KNN), Naïve Bayes Classification (NBC), and Multilayer Perceptron (MLP) methods. The classification results of 100 training data and 50 white blood cell image testing data. Tests on the KNN, NBC, and MLP methods yielded an accuracy of 82%, 80%, and 94%, respectively. Therefore, MLP was chosen as the best classification model in the identification of white blood cells.

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Segmentasi Dan Perhitungan Sel Darah Putih Menggunakan Operasi Morfologi Dan Transformasi Watershed

Cited by 3 publications

References 7 publications

Klasifikasi Tanaman Obat Berdasarkan Citra Daun Menggunakan Backpropagation Neural Networks

Klasifikasi Tanaman Obat Berdasarkan Citra Daun Menggunakan Backpropagation Neural Networks

Application Of Dynamic Segmentation In Stroke Detection Software With ANN

Identification of White Blood Cells Using Machine Learning Classification Based on Feature Extraction

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