scholarly journals Klasifikasi Jenis Golongan Darah Menggunakan Fuzzy C-Means Clustering (FCM) dan Learning Vector Quantization (LVQ)

MATICS ◽  
2018 ◽  
Vol 10 (1) ◽  
pp. 26
Author(s):  
Fajar Rohman Hariri
Keyword(s):  
Vector Quantization ◽  
Learning Vector Quantization ◽  
The Body ◽  
Blood Type ◽  
Test Results ◽  
Font Size ◽  
Optimal Result ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering ◽  
Index Terms

<p class="Text"><strong><em><span style="font-size: 9.0pt; line-height: 105%;">Abstract</span></em></strong><strong><span style="font-size: 9.0pt; line-height: 105%;">—</span></strong> <strong><span style="font-size: 9.0pt; line-height: 105%;">Blood is an important part of the body. Blood is divided into several groups A, B, O, and AB. Conventionally, detect blood group by dripping anti-A serum and anti-B serum into the blood to be recognized and direct measurement of the serum droplet reaction. This study will compare the processes that use segmentation and  without using segmentation to know the various segmentation information in introduction of human blood type image. From the test results that segmentation increase accuracy of recognition between 10% -24% of each test. By using JST Learning Vector Quantization (LVQ) as a classifier and Fuzzy C-Mean as segmentation, the optimal result on the system averages 92% to 98%..</span></strong></p><p class="MsoNormal"> </p><p class="IndexTerms"><em>Index Terms</em>—Blood, Segmentation, Classification</p><p class="MsoNormal"> </p><p class="Abstract"><em>Abstrak</em>–- Darah merupakan salah satu bagian penting dalam tubuh. Darah dibedakan menjadi beberapa golongan yaitu A, B, O, dan AB. Secara konvensional, mendeteksi golongan darah dengan cara meneteskan serum anti-A dan serum anti-B ke darah yang akan dikenali kemudian melakukan pengamatan langsung terhadap reaksi tetesan serum tersebut.  Penelitian ini akan membandingkan antara proses pengenalan yang menggunakan segmentasi dengan proses pengenalan tanpa menggunakan segmentasi untuk mengetahui seberapa besar pengaruh metode segmentasi dalam pengenalan citra golongan darah manusia. Dari hasil pengujian didapatkan bahwa dengan adanya metode segmentasi akurasi system pengenalan bertambah antara 10%-24% setiap uji coba. Dengan menggunakan JST Learning Vector Quantization (LVQ) sebagai pengklasifikasi dan Fuzzy C-Mean sebagai segmentasi citra darah dapat diperoleh hasil yang optimal pada sistem pengenala golongan darah manusia dengan prosentase keberhasilan rata rata 92% hingga 98%.</p><p class="MsoNormal"> </p><p class="IndexTerms"><a name="PointTmp"><em>Kata Kunci</em>—Darah, Segmentasi, Klasifikasi </a></p><div><table width="637" cellspacing="0" cellpadding="0"><tbody><tr><td style="padding: 9.35pt;" align="left" valign="top" height="181"><p class="Authors" style="margin-bottom: .0001pt; mso-element: frame; mso-element-frame-width: 468.75pt; mso-element-frame-height: 117.05pt; mso-element-wrap: no-wrap-beside; mso-element-anchor-horizontal: page; mso-element-left: 85.2pt; mso-element-top: 43.85pt; mso-height-rule: exactly;"><strong><span style="font-size: 24.0pt; mso-font-kerning: 14.0pt;">Klasifikasi</span></strong><strong><span style="font-size: 24.0pt; mso-font-kerning: 14.0pt; mso-ansi-language: IN;" lang="IN"> Jenis Golongan Darah Menggunakan</span></strong><strong></strong><strong><span style="font-size: 24.0pt; mso-font-kerning: 14.0pt;">Fuzzy C-Means Clustering (FCM) dan Learning Vector Quantization (LVQ)</span></strong></p></td></tr></tbody></table></div><!--[if !supportTextWrap]--><br clear="ALL" /> <!--[endif]-->

scholarly journals Analysis of Copy Move Forgery Detection Process Using Fuzzy C Means Based DeepLearning Algorithm in Digital Image

2021 ◽  
Author(s):  
Parameswaran Nampoothiri ◽  
Sugitha N
Keyword(s):  
Digital Image ◽  
Test Results ◽  
Forgery Detection ◽  
Fuzzy C Means ◽  
Digital World ◽  
Technological Advances ◽  
Fuzzy C Means Clustering ◽  
Photo Editing ◽  
Copy Move Forgery Detection ◽  
Further Development

Abstract Technological advances in the digital world have led to a tremendous growth in the popularity of digital photography in all walks of life. However, photo editing software tools are easy to use and make photo manipulation a breeze. Therefore, there is a need to find the wrong part of the image. Therefore, this work focuses on finding false images used using the copying process, better known as Copy Move Forgery Detection (CMFD). A copy of Motof spoofing basically means to hide or duplicate a place in a region by attaching certain parts of the same image to it. Initially, digital input images are pre-processed with a Gaussian filter, which is used to blur the image and reduce noise. After further development, a collection of Multi-kernel Fuzzy C-means clustering (MKFCM) was developed to classify images into multiple groups and depending on the various features, the features were extracted using the SIFT algorithm. Finally, with the help of an in-depth reading method, part of the illegal images are found. Test results show that this method is effective and efficient in detecting digital image deception and its functionality and the proposed method is shown in false images.

scholarly journals Identifikasi Barcode pada Gambar yang Ditangkap Kamera Digital Menggunakan Metode JST

2013 ◽  
Vol 7 (2) ◽  
pp. 121
Author(s):  
Salman Aliaji ◽  
Agus Harjoko
Keyword(s):  
Vector Quantization ◽  
Digital Camera ◽  
Block Size ◽  
Modern Society ◽  
Learning Vector Quantization ◽  
Test Results ◽  
Training Process ◽  
Identification Process ◽  
Photo Identification ◽  
Time Required

AbstrakDewasa ini hampir setiap produk konsumen memiliki label barcode. Namun alat pembaca barcode jenis laser memiliki kelemahan karena tidak dapat mengenali barcode yang mengalami goresan atau noise. Namun telah dikembangkan teknik lain dengan memanfaatkan kamera digital untuk identifikasi barcode. JST telah banyak digunakan untuk identifikasi berbagai macam pola. Proses identifikasi barcode dalam JST terdiri dari proses training dan proses identifikasi. Proses training menggunakan metode LVQ (Learning Vector Quantization). Proses identifikasi terdiri dari beberapa tahap, yaitu akuisisi citra, preprocessing, locating barcode, proses pengujian dan verifikasi. Berdasarkan hasil pengujian metode LVQ dapat digunakan untuk identifikasi foto barcode dengan kinerja yang baik. Hasil pengujian menunjukkan tingkat akurasi sebesar 73,6 % dari 72 citra yang diuji dengan waktu rata-rata adalah 0.5 detik. Sementara waktu yang dibutuhkan untuk menemukan lokasi barcode adalah sekitar 6 detik menggunakan blok dengan ukuran 32x32 pixel. Kata kunci— Barcode, Learning Vector Quantization, Jaringan Syaraf Tiruan AbstrakIn today’s modern society, almost every consumer product has a barcode label. But the barcode reader with laser type has the disadvantage of not being able to recognize the barcode has a scratch or noise. However, other techniques have been developed by using a digital camera for barcode identification. ANN has been widely used for identification of various patterns. Barcode identification process consists of the ANN training process and the identification process. Training process using the LVQ (Learning Vector Quantization). Identification process consists of several stages: image acquisition, preprocessing, locating barcode, testing and verification process. Based on test results LVQ method can be used for photo identification barcode with good performance. The test results showed an accuracy of 73.6% rate of 72 images were tested with an average time is 0.5 seconds. While the time required to find the location of the barcode is about 6 seconds using a block size of 32x32 pixels. Keyword— Barcode, Learning Vector Quantization, Artificial Neural Network

scholarly journals Tutorial sobre Fuzzy-c-Means e Fuzzy Learning Vector Quantization: Abordagens Híbridas para Tarefas de Agrupamento e Classificação

2012 ◽  
Vol 19 (1) ◽  
pp. 120 ◽  
Author(s):  
Sarajane Marques Peres ◽  
Thiago Rocha ◽  
Helton H. Biscaro ◽  
Renata Cristina B. Madeo ◽  
Clodis Boscarioli
Keyword(s):  
Vector Quantization ◽  
Learning Vector Quantization ◽  
Fuzzy C Means ◽  
Fuzzy Learning

Implementation Learning Vector Quantization(LVQ) for Chronic Kidney Disease Classification

2020 ◽  
Vol 9 (2) ◽  
pp. 241
Author(s):  
I Gst Bgs Bayu Adi Pramana ◽  
I Made Widiartha ◽  
Luh Gede Astuti
Keyword(s):  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
Vector Quantization ◽  
Waste Product ◽  
Learning Vector Quantization ◽  
The Body ◽  
Disease Classification ◽  
Optimal Learning ◽  
Fully Functioning

Chronic kidney disease is a disruption in the function of the kidney organs. When the kidneys are no longer fully functioning, the body is filled with water and a waste product called uremia. As a result, the body or legs will experience swelling and feel tired quickly because the body needs clean blood. Therefore, impaired kidney function should not be underestimated because it can be fatal. Researchers have conducted research related to the classification of kidney disease to find out what symptoms can cause kidney disease. One method that can be used for classification is the Learning Vector Quantization (LVQ) method. In this study, the LVQ algorithm was applied to classify chronic kidney disease. From the research results, the highest accuracy is 81.667% with the optimal learning rate is 0.002.

Noise Fuzzy Learning Vector Quantization

2010 ◽  
Vol 439-440 ◽  
pp. 367-371
Author(s):  
Xiao Hong Wu ◽  
Bin Wu ◽  
Jie Wen Zhao
Keyword(s):  
Vector Quantization ◽  
Noisy Data ◽  
Learning Vector Quantization ◽  
Experimental Results ◽  
Fuzzy C Means ◽  
Learning Rates ◽  
Sensitivity Problem ◽  
Noise Clustering ◽  
Fuzzy Learning ◽  
Better Than

Fuzzy learning vector quantization (FLVQ) benefits from using the membership values coming from fuzzy c-means (FCM) as learning rates and it overcomes several problems of learning vector quantization (LVQ). However, FLVQ is sensitive to noises because it is a FCM-based algorithm (FCM is sensitive to noises). Here, a new fuzzy learning vector quantization model, called noise fuzzy learning vector quantization (NFLVQ), is proposed to handle the noises sensitivity problem of FLVQ. NFLVQ integrates LVQ and generalized noise clustering (GNC), uses the membership values from GNC as learning rates and clusters data containing noisy data better than FLVQ. Experimental results show the better performances of NFLVQ.

Hybrid revised weighted fuzzy c-means clustering with Nelder-Mead simplex algorithm for generalized multisource Weber problem

2018 ◽  
Vol 31 (6) ◽  
pp. 908-924
Author(s):  
Tarik Kucukdeniz ◽  
Sakir Esnaf
Keyword(s):  
Cost Minimization ◽  
Simplex Algorithm ◽  
Data Sets ◽  
Weber Problem ◽  
Test Results ◽  
Content Type ◽  
Facility Location Problems ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering ◽  
And Performance

PurposeThe purpose of this paper is to propose hybrid revised weighted fuzzy c-means (RWFCM) clustering and Nelder–Mead (NM) simplex algorithm, called as RWFCM-NM, for generalized multisource Weber problem (MWP).Design/methodology/approachAlthough the RWFCM claims that there is no obligation to sequentially use different methods together, NM’s local search advantage is investigated and performance of the proposed hybrid algorithm for generalized MWP is tested on well-known research data sets.FindingsTest results state the outstanding performance of new hybrid RWFCM and NM simplex algorithm in terms of cost minimization and CPU times.Originality/valueProposed approach achieves better results in continuous facility location problems.

Rancang Bangun Aplikasi Pendeteksi Penyakit Ginjal Kronis dengan Menggunakan Algoritma C4.5

2017 ◽  
Vol 9 (1) ◽  
pp. 10-18
Author(s):  
Robi Rianto ◽  
Ni Made Satvika Iswari
Keyword(s):  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
Decision Tree ◽  
Kidney Diseases ◽  
Detection System ◽  
Waste Product ◽  
The Body ◽  
Test Results ◽  
Tree Detection ◽  
Index Terms

Kidneys are two bean-shaped organs, each about the size of a fist. They are located just below the rib cage, one on each side of the spine. Kidneys are vital organs contained in the human body and serve to filter the blood of metabolic waste product and throw it in the form of urine. Given the changing conditions in the body can affect the kidneys, causing a decrease in the function of these organ and lead to chronic kidney disease. In an article on the website of the National Institute of Diabetes and Digestive and Kidney Diseases (2014) argued that chronic kidney disease is a silent disease, in which patients appear normal and show no symptomp but the test results stating the patient’s kidney function had decreased. Based on the above information, the research on how to detect chronic kidney disease will be conducted. Applications built on the basis of desktop and using Decision Tree algorithm C4.5. The trial was conducted to determine how much the level of accuracy that can be generated by the application. The testing process is done by using cross-validation and based on the results already calculated this application has an accuracy of 91.50% at the time of the decision tree is made without using preprocess menu. Index Terms— C4.5, chronic kidney disease, decision tree, detection system.

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