scholarly journals Klasifikasi Multi Output pada Harga Smartphone Menggunakan Learning Vector Quantization (LVQ) dan Backpropagation (BP)

2021 ◽  
Vol 6 (2) ◽  
pp. 14-19
Author(s):  
Dinita Rahmalia ◽  
Mohammad Syaiful Pradana ◽  
Teguh Herlambang
Keyword(s):  
Vector Quantization ◽  
Error Rate ◽  
Gradient Descent ◽  
Euclidean Distance ◽  
Learning Vector Quantization ◽  
Training Data ◽  
Price Range ◽  
Testing Data ◽  
Internal Storage ◽  
Simulation Results

There are many smartphones with various price sold in market. The price of smartphone is affected by some components such as weight, internal storage, memory (RAM), rear camera, front camera and brands. There are two methods for classifying price class of smartphone in market such as Learning Vector Quantization (LVQ) and Backpropagation (BP). From classifying price class of smartphone in market using LVQ and BP, there are the differences on the both of them. LVQ classifies price range of smartphone by euclidean distance of weight and data on its iteration. BP classifies price range of smartphone by gradient descent of target and output on its iteration. In multi output classification, one object may have multi output. Based on simulation results, BP gives the better accuracy and error rate in training data and testing data than LVQ.  

scholarly journals Identification of Disease on Leaves Soybean using Modified Otsu and Learning Vector Quantization Neural Networks

Kursor ◽  
2018 ◽  
Vol 9 (3) ◽  
Author(s):  
Candra Dewi ◽  
Muhammad Sa’idul Umam ◽  
Imam Cholissodin
Keyword(s):  
Vector Quantization ◽  
Threshold Value ◽  
Learning Vector Quantization ◽  
Training Data ◽  
Blue Color ◽  
Phakopsora Pachyrhizi ◽  
Average Value ◽  
Maximum Value ◽  
Otsu Method ◽  
Soybean Leaf

Disease of the soybean crop is one of the obstacles to increase soybean production in Indonesia. Some of these diseases usually are found in the leaves and resulted to the crop become unhealthy. This study aims to identify disease on soybean leaf through leaves image by applying the Learning Vector Quantization (LVQ) algorithm. The identification begins with preprocessing using modified Otsu method to get part of the diseases on the leaves with a certain threshold value. The next process is to identify the type of disease using LVQ. This process uses the minimum value, the maximum value and the average value of the red, green and blue color of the image. The testing conducted in this study is to identify two diseases called Peronospora manshurica (Downy Mildew) and phakopsora pachyrhizi (Karat). The result of testing by using 60 training data and the value of all recommendations parameters obtained the highest accuracy of identification is 95% %, but the more stable accuracy is 90%. This result shows that the method perform quite well identification of two mentioned disease.

Rate-Compatible Punctured Ring Convolutional Coded Continuous Phase Modulation

2012 ◽  
Vol 195-196 ◽  
pp. 217-220
Author(s):  
Lei Zhang ◽  
Man Ping Tong ◽  
Hong Bo Wang
Keyword(s):  
Bit Error Rate ◽  
Error Rate ◽  
Phase Modulation ◽  
Euclidean Distance ◽  
Convolutional Codes ◽  
Continuous Phase ◽  
Transmission Efficiency ◽  
Continuous Phase Modulation ◽  
Simulation Results ◽  
Squared Euclidean Distance

In this paper, continuous phase modulation (CPM) with rate-compatible punctured ring convolutional codes is investigated. Some typical schemes with maximum normalized minimum squared euclidean distance (NMSED) are searched and given. The performance of bit error rate for rate-compatible punctured ring convolutional coded CPM on AWGN channel is simulated, and simulation results show that this system can provide good performance of bit error rate and variable-rate capabilities. Furthermore, simulation results also prove that the transmission efficiency increases when code rate is decreasing.

scholarly journals PENERAPAN METODE LEARNING VECTOR QUANTIZATION UNTUK IDENTIFIKASI PENYAKIT PADI BERDASARKAN BENTUK BERCAK DAUN

2020 ◽  
Vol 6 (1) ◽  
pp. 28-35
Author(s):  
Ery Murniyasih ◽  
Luluk Suryani
Keyword(s):  
Vector Quantization ◽  
Error Rate ◽  
Learning Vector Quantization ◽  
Learning Rate ◽  
Rate Data ◽  
Target Error

Penelitian ini bertujuan : (1). Membuat suatu aplikasi untuk identifikasi jenis penyakit pada tanaman padi berdasarkan bentuk bercak daun padi.;(2). Menerapkan metode Learning Vector Quantization (LVQ) pada identifikasi penyakit tanaman padi. Pada tahapan learning dan testing pada LVQ citra diproses menjadi Grayscale, Thresholding, dan segmentasi. Di tahap pelatihan, metode LVQ digunakan untuk menentukan bobot, target error, max epoch, dan laju pelatihan (Learning rate). Data yang dijadikan sebagai input adalah citra identifikasi jenis penyakit tanaman padi berdasarkan bentuk bercak daun padi  yaitu dengan ukuran piksel 95x35 dan berekstensi BITMAP (.bmp). Standar keberhasilan sistem identifikasi ini adalah menghitung nilai Termination Error Rate dan tingkat keakuratan dalam identifikasi bentuk bercak daun. Dari simulasi ini diperoleh struktur Jaringan Syaraf Tiruan dengan jumlah nilai learning rate 0,02 dan jumlah epoch sebesar 5 kali. Sistem yang terbentuk mampu mengenali citra yang berisi bentuk bercak daun yang digunakan sebagai bobot dengan nilai keakuratan optimum yaitu 73,33% dengan komposisi penyakit bercak coklat (BC) 20 %, Blast  20 % dan cercak cercospora 33,33%.

Soft Learning Vector Quantization

2003 ◽  
Vol 15 (7) ◽  
pp. 1589-1604 ◽  
Author(s):  
Sambu Seo ◽  
Klaus Obermayer
Keyword(s):  
Vector Quantization ◽  
Gradient Descent ◽  
Distance Measure ◽  
Learning Rule ◽  
Gaussian Mixture ◽  
Classification Performance ◽  
Learning Vector Quantization ◽  
Additional Benefit ◽  
New Approach ◽  
New Methods

Learning vector quantization (LVQ) is a popular class of adaptive nearest prototype classifiers for multiclass classification, but learning algorithms from this family have so far been proposed on heuristic grounds. Here, we take a more principled approach and derive two variants of LVQ using a gaussian mixture ansatz. We propose an objective function based on a likelihood ratio and derive a learning rule using gradient descent. The new approach provides a way to extend the algorithms of the LVQ family to different distance measure and allows for the design of “soft” LVQ algorithms. Benchmark results show that the new methods lead to better classification performance than LVQ 2.1. An additional benefit of the new method is that model assumptions are made explicit, so that the method can be adapted more easily to different kinds of problems.

scholarly journals Cataract Disease Diagnosis System Using Artificial Neural Network Learning Vector Quantization Method

2020 ◽  
Vol 4 (2) ◽  
pp. 75-85
Author(s):  
Chrisani Waas ◽  
D. L. Rahakbauw ◽  
Yopi Andry Lesnussa
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Vector Quantization ◽  
Processing System ◽  
Disease Diagnosis ◽  
Learning Vector Quantization ◽  
Learning Rate ◽  
Training Data ◽  
Neural Network Learning ◽  
Artificial Neural

Artificial Neural Network (ANN) is an information processing system that has certain performance characteristics that are artificial representatives based on human neural networks. ANN method has been widely applied to help human performance, one of which is health. In this research, ANN will be used to diagnose cataracts, especially Congenital Cataracts, Juvenile Cataracts, Senile Cataracts and Traumatic Cataracts based on the symptoms of the disease. The ANN method used is the Learning Vector Quantization (LVQ) method. The data used in this research were 146 data taken from the medical record data of RSUD Dr. M. Haulussy, Ambon. The data consists of 109 data as training data and 37 data as testing data. By using learning rate (α) = 0.1, decrease in learning rate (dec α) = 0.0001 and maximum epoch (max epoch) = 5, the accuracy rate obtained is 100%.

scholarly journals SIMULASI PENGENALAN TULISAN MENGGUNAKAN LVQ (LEARNING VECTOR QUANTIZATION )

MATICS ◽  
2012 ◽  
Author(s):  
Fachrul Kurniawan ◽  
Hani Nurhayati
Keyword(s):  
Vector Quantization ◽  
Error Rate ◽  
Learning Vector Quantization ◽  
Learning Rate ◽  
Rate Data ◽  
Gray Scale ◽  
Target Error

<p>Proses simulasi yang dilakukan untuk menghasilkan sebuah sistem pengenalan meliputi beberapa tahap, yaitu tahap pengolahan citra dan  tahap pelatihan dan pengenalan. Tahap pengolahan citra dimulai dari Gray Scale,  Thresholding, segmentasi, dan normalisasi. Pengolahan citra diperlukan untuk memudahkan pengolahan data gambar sebelum masuk dalam tahap pelatihan. Kemudian tahap kedua adalah pelatihan dan pengenalan. Pada tahap ini, metode LVQ mulai digunakan dan menentukan bobot, target error, maxepoch, dan laju pelatihan (Learning rate). Data yang dijadikan sebagai input adalah citra huruf yang dinormalisasi sehingga berukuran 20x20 dan berekstensi bitmap (.bmp). Simulasi pengenalan tulisan ini dilakukan dalam beberapa tahapan sehingga bisa dengan membuat pengenalan menjadi lebih tajam. Tolak ukur keberhasilan sistem pengenalan tulisan tangan ini adalah dengan menghitung nilai Termination Error Rate dan tingkat keakuratan dalam pengenalan tanda tangan. Dari simulasi ini diperoleh struktur JST dengan jumlah nilai learning rate 0,003 nilai target error 0,00001 dan jumlah epoch sebesar 10.000 karena dalam rentang epoch 1000 sampai 10000 perubahan  epoch tidak mempengaruhi kinerja sistem. Sistem yang terbentuk mampu mengenali citra yang berisi huruf yang digunakan sebagai bobot dengan nilai keakuratan rata – rata sebesar  61,07% dan rata – rata keakuratan hasil pengenalan terhadap citra yang belum dilakukan pembelajaran sebesar 48,17%.</p><p> </p><strong>Kata Kunci</strong> : Pengolahan Citra, Learning Vector Quantization

scholarly journals PREDIKSI PENYAKIT TUBERCULOSIS PARU (TB PARU) MENGGUNAKAN METODE LEARNING VEKTOR QUANTIZATION (LVQ)

2018 ◽  
Vol 15 (1) ◽  
pp. 20-27
Author(s):  
A W Rahmadani ◽  
A I Jaya ◽  
N Nacong
Keyword(s):  
Pulmonary Disease ◽  
Vector Quantization ◽  
Medical Records ◽  
Disease Risk ◽  
Health Centers ◽  
Training Data ◽  
Shortness Of Breath ◽  
Pulmonary Tb ◽  
Testing Data ◽  
Weight Classification

Tuberculosis pulmonary (TB pulmonary) is a contagious disease that attacks the lungs that can spread through the air when a person active TB cough, sneeze or talk. This study aims to predict Tuberculosis pulmonary disease  using Learning Vector quantization based on data from the medical records of the health centers kamonji, Palu city. The study was conducted using 8 TB pulmonary disease risk factors which are age, gender, fever, long cough, cough, chest pain, shortness of breath, and decreased body weight. Classification is done by using 100 data consisting of 80 training data and 20 testing data. Results of the study showed that tested all the data correctly with rank of accuracy is 100%.

Penerapan Learning Vector Quantization Dalam Memprediksi Jumlah Rumah Tangga Miskin

2019 ◽  
Vol 5 (2) ◽  
pp. 118-127
Author(s):  
Harliana Harliana ◽  
Sodik Kirono
Keyword(s):  
Vector Quantization ◽  
Error Rate ◽  
Learning Vector Quantization ◽  
Learning Rate ◽  
Input Output ◽  
Data Set

Kemiskinan merupakan salah satu permasalahan penting yang masih terus dilakukan pengkajiannya oleh pemerintah daerah termasuk pemerintah Kabupaten Cirebon, berbagai upaya pemberian bantuan telah dilakukan, namun sayangnya masih ada beberapa rumah tangga miskin yang belum dapat keluar dari kemiskinan tersebut. Penelitian ini bertujuan untuk memprediksi rumah tangga miskin yang telah mendapatkan bantuan, apakah dapat keluar dari kemiskinannya ataukah tetap pada kelompok desil 1 (rumah tangga sangat miskin), desil 2 (rumah tangga miskin), desil 3 (rumah tangga hampir miskin) melalui algoritma LVQ. Algoritma LVQ merupakan salah satu algoritma klasifikasi yang mampu mengenali dan meniru input output yang telah ditentukan. Penelitian ini menggunakan 70 data set, 10 neuron inputan, 3 neuron keluaran, 100 MaxEpoh dan 0,05 learning rate (a) dalam melakukan prediksi. Dari 70 data set yang digunakan selanjutnya akan dipecah menjadi data training dan data testing. Berdasarkan 5 hasil pengujian yang telah dilakukan, didapatkan bahwa tingkat akurasi dan error rate akan berbanding lurus terhadap jumlah data training dan data testing yang ditentukan.

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