scholarly journals Identifikasi Penyakit Daun Jeruk Siam Menggunakan K-Nearest Neighbor

2021 ◽  
Vol 1 (2) ◽  
pp. 133-140
Author(s):  
Rifqi Hakim Ariesdianto ◽  
Zilvanhisna Emka Fitri ◽  
Abdul Madjid ◽  
Arizal Mujibtamala Nanda Imron
Keyword(s):  
Computer Vision ◽  
Nearest Neighbor ◽  
K Nearest Neighbor ◽  
Xanthomonas Axonopodis

Jeruk siam adalah salah satu jeruk local yang mempunyai nilai jual yang tinggi di Indonesia. Tahun 2020, tingkat produksi jeruk siam mengalami penurunan menjadi 712.585 ton di Jawa Timur. Salah satu faktor utama yang menyebabkan menurunnya tingkat produksi jeruk siam yaitu serangan penyakit pada daun jeruk siam. Dua penyakit yang sering menyerang daun jeruk siam adalah penyakit kanker yang disebabkan oleh patogen Xanthomonas axonopodis pv.citri dan penyakit ulat peliang. Selama ini, pengamatan pada penyakit daun jeruk siam dilakukan secara manual menggunakan mata sehingga penentuan penyakit tersebut bersifat subyektif. Untuk mengatasi masalah tersebut dibuatlah sistem otomatis identifikasi daun jeruk siam sehat dan daun jeruk siam terserang penyakit dengan bantuan teknik computer vision. Tahapan penelitian yaitu pengumpulan citra daun jeruk, konversi warna, ekstraksi fitur warna dan tekstur serta klasifikasi K-Nearest Neighbor (KNN). Parameter fitur yang digunakan yaitu fitur warna GB, fitur tekstur (ASM, entropi dan kontras). Metode KNN mampu mengklasifikasi dan mengidentifikasi penyakit daun jeruk siam dengan akurasi sebesar 70% dengan variasi nilai K = 21.

Automatic Defect Detection and Classification of Terminals in a Bussed Electrical Center Using Computer Vision

2016 ◽  
pp. 241-266
Author(s):  
Osslan Osiris Vergara Villegas ◽  
Vianey Guadalupe Cruz Sánchez ◽  
Humberto de Jesús Ochoa Domínguez ◽  
Jorge Luis García-Alcaraz ◽  
Ricardo Rodriguez Jorge
Keyword(s):  
Computer Vision ◽  
Defect Detection ◽  
Nearest Neighbor ◽  
Region Of Interest ◽  
Morphological Operation ◽  
K Nearest Neighbor ◽  
Electrical Center ◽  
Salt And Pepper ◽  
Intelligent Computer

In this chapter, an intelligent Computer Vision (CV) system, for the automatic defect detection and classification of the terminals in a Bussed Electrical Center (BEC) is presented. The system is able to detect and classify three types of defects in a set of the seven lower pairs of terminals of a BEC namely: a) twisted; b) damaged and c) missed. First, an environment to acquire a total of 56 training and test images was created. After that, the image preprocessing is performed by defining a Region Of Interest (ROI) followed by a binarization and a morphological operation to remove small objects. Then, the segmentation stage is computed resulting in a set of 12-14 labeled zones. A vector of 56 features is extracted for each image containing information of area, centroid and diameter of all terminals segmented. Finally, the classification is performed using a K-Nearest Neighbor (KNN) algorithm. Experimental results on 28 BEC images have shown an accuracy of 92.8% of the proposed system, allowing changes in brightness, contrast and salt and pepper noise.

scholarly journals Classification of Mango Fruit Quality Based on Texture Characteristics of GLCM (Gray Level Co-Occurrence Matrices) with Algorithm K-NN (K-Nearest Neighbors)

2019 ◽  
Vol 20 (1) ◽  
pp. 31
Author(s):  
Wahyu Wijaya Widiyanto ◽  
Eko Purwanto ◽  
Kusrini Kusrini
Keyword(s):  
Computer Vision ◽  
Nearest Neighbor ◽  
Nearest Neighbors ◽  
Gray Level ◽  
Nearest Neighbour ◽  
K Nearest Neighbor ◽  
Mango Fruit ◽  
K Nearest Neighbors ◽  
Texture Characteristics

Proses klasifikasi kualitas mutu buah mangga dengan cara konvensional menggunakan mata manusia memiliki kelemahan di antaranya membutuhkan tenaga lebih banyak untuk memilah, anggapan mutu kualitas buah mangga antar manusia yang berbeda, tingkat konsistensi manusia dalam menilai kualitas mutu buah mangga yang tidak menjamin valid karena manusia dapat mengalami kelelahan. Penelitian ini bertujuan untuk klasifikasi kualitas mutu buah mangga ke dalam tiga kelas mutu yaitu kelas Super, A, dan B dengan computer vision dan algoritma k-Nearest Neighbor. Hasil pengujian menggunakan jumlah k tetangga 9 menunjukan tingkat akurasi sebesar 88,88%.Kata-kata kunci— Klasifikasi, GLCM, K-Nearest Neighbour, Mangga

scholarly journals Implementasi K-Nearest Neighbor untuk Klasifikasi Bunga Dengan Ekstraksi Fitur Warna RGB

2020 ◽  
Vol 7 (6) ◽  
pp. 1129
Author(s):  
Lia Farokhah
Keyword(s):  
Neural Networks ◽  
Computer Vision ◽  
Convolutional Neural Networks ◽  
Nearest Neighbor ◽  
Nearest Neighbour ◽  
K Nearest Neighbor ◽  
Color Features ◽  
Knn Classification ◽  
Total Data ◽  
Shape Characteristics

<p class="Abstrak">Era computer vision merupakan era dimana komputer dilatih untuk bisa melihat, mengidentifikasi dan mengklasifikasi seperti kecerdasan manusia. Algoritma klasifikasi berkembang dari yang paling sederhana seperti K-Nearest Neighbor (KNN) sampai Convolutional Neural Networks. KNN merupakan algoritma klasifikasi yang paling sederhana dalam mengklasifikasikan sebuah gambar kedalam sebuah label. Metode ini mudah dipahami dibandingkan metode lain karena mengklasifikasikan berdasarkan jarak terdekat dengan objek lain (tetangga). Tujuan penelitian ini untuk membuktikan kelemahan metode KNN dan ekstraksi fitur warna RGB dengan karakteristik tertentu. Percobaan pertama dilakukan terhadap dua objek dengan kemiripan bentuk tetapi dengan  warna yang  mencolok di salah satu sisi objek. Percobaan kedua terhadap dua objek yang memiliki perbedaan karakteristik bentuk meskipun memiliki kemiripan warna. Empat objek tersebut adalah bunga coltsfoot, daisy, dandelion dan matahari. Total data dalam dataset adalah 360 data. Dataset memiliki tantangan variasi sudut pandang, penerangan, dan  gangguan dalam latar. Hasil menunjukkan bahwa kolaborasi metode klasifikasi KNN dengan ekstraksi fitur warna RGB memiliki kelemahan terhadap percobaan pertama dengan akurasi 50-60% pada K=5. Percobaan kedua memiliki akurasi sekitar 90-100% pada K=5. Peningkatan akurasi, precision dan recall terjadi ketika menaikkan jumlah K yaitu dari K=1menjadi K=3 dan K=5.</p><p><strong>Kata kunci</strong>: k-nearest neighbour, RGB, kelemahan, kemiripan, bunga</p><p class="Judul2" align="left"> </p><p class="Judul2"> </p><p class="Judul"><em>IMPLEMENTATION OF K-NEAREST NEIGHBOR FOR FLOWER CLASSIFICATION WITH EXTRACTION OF RGB COLOR FEATURES</em></p><p class="Judul"><em>The era of computer vision is an era where computers are trained to be able to see, identify and classify as human intelligence. Classification algorithms develop from the simplest such as K-Nearest Neighbor (KNN) to Convolutional Neural Networks. KNN is the simplest classification algorithm in classifying an image into a label. This method is easier to understand than other methods because it classifies based on the closest distance to other objects (neighbors). The purpose of this research is to prove the weakness of the KNN method and the extraction of RGB color features for specific characteristics. The first  experiment on two objects with similar shape but with sharp color on one side of the object. The second experiment is done on two objects that have different shape characteristics even having a similar colors. The four objects are coltsfoot, daisy, dandelion and sunflower. Total data in the dataset is 360 data. The dataset has the challenge of varying viewpoints, lighting and background noise. The results show that the collaboration of the KNN classification method with RGB color feature extraction has weakness in the first experiment with the level of accuracy about 50-60% at K = 5. The second experiment has an accuracy of around 90-100% at K = 5. Increased accuracy, precision and recall occur when increasing the amount of K, from K = 1 to K = 3 and K = 5.</em></p><p class="Judul2"> </p><p class="Judul2" align="left"> </p><strong>Keywords</strong>: <em>k-nearest neighbour</em>, RGB, <em>weakness, similar, flower</em>

Automatic Measurement of Traffic State Parameters Based on Computer Vision for Intelligent Transportation Surveillance

2017 ◽  
Vol 32 (04) ◽  
pp. 1855003 ◽  
Author(s):  
Jianqiang Ren ◽  
Chunhong Zhang ◽  
Lingjuan Zhang ◽  
Ning Wang ◽  
Yue Feng
Keyword(s):  
Computer Vision ◽  
Traffic Congestion ◽  
Nearest Neighbor ◽  
Automatic Measurement ◽  
Intelligent Transportation ◽  
Vehicle Classification ◽  
Feature Points ◽  
K Nearest Neighbor ◽  
Spatial Image ◽  
Traffic State

Online automatic measurement of traffic state parameters has important significance for intelligent transportation surveillance. The video-based monitoring technology is widely studied today but the existing methods are not satisfactory at processing speed or accuracy, especially for traffic scenes with traffic congestion or complex road environments. Based on technologies of computer vision and pattern recognition, this paper proposes a novel measurement method that can detect multiple parameters of traffic flow and identify vehicle types from video sequence rapidly and accurately by combining feature points detection with foreground temporal-spatial image (FTSI) analysis. In this method, two virtual detection lines (VDLs) are first set in frame images. During working, vehicular feature points are extracted via the upstream-VDL and grouped in unit of vehicle based on their movement differences. Then, FTSI is accumulated from video frames via the downstream-VDL, and adhesive blobs of occlusion vehicles in FTSI are separated effectively based on feature point groups and projection histogram of blob pixels. At regular intervals, traffic parameters are calculated via statistical analysis of blobs and vehicles are classified via a K-nearest neighbor (KNN) classifier based on geometrical characteristics of their blobs. For vehicle classification, the distorted blobs of temporary stopped vehicles are corrected accurately based on the vehicular instantaneous speed on the downstream-VDL. Experiments show that the proposed method is efficient and practicable.

scholarly journals Artificial Intelligence in Automated Sorting in Trash Recycling

2018 ◽  
Author(s):  
Bernardo S. Costa ◽  
Aiko C. S. Bernardes ◽  
Julia V. A. Pereira ◽  
Vitoria H. Zampa ◽  
Vitoria A. Pereira ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Computer Vision ◽  
Support Vector Machine ◽  
Random Forest ◽  
Nearest Neighbor ◽  
Image Database ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Process Waste

A computer vision approach to classify garbage into recycling categories could be an efficient way to process waste. This project aims to take garbage waste images and classify them into four classes: glass, paper, metal and, plastic. We use a garbage image database that contains around 400 images for each class. The models used in the experiments are Pre-trained VGG-16 (VGG16), AlexNet, Support Vector Machine (SVM), K-Nearest Neighbor (KNN) and, Random Forest (RF). Experiments showed that our models reached accuracy around 93%.

scholarly journals Plant Disease Diagnosis and Classification by Computer Vision using Statistical Texture Feature Extraction Technique and K Nearest Neighbor Classification

2019 ◽  
Vol 9 (2) ◽  
pp. 2336-2341
Keyword(s):  
Computer Vision ◽  
Plant Growth ◽  
Nearest Neighbor ◽  
Control Method ◽  
Texture Feature ◽  
Disease Diagnosis ◽  
Disease Detection ◽  
K Nearest Neighbor ◽  
Key Points ◽  
Insect Attack

Pest attack and infectious diseases has become more common in the field of agriculture in the recent times. It has become a challenging task to identify the infection or the insect that destructs the plant growth and production. Diagnosing the disease or the insect attack on the plants in the early stage will safe guard the plant growth and the production rate. Timely intervention of technology that deals with disease detection and control method can protect the plants from usage of harmful pesticides. The higher dosage of pesticides impacts the health of human as well as other creatures like birds and animals which directly or indirectly consumes the plant or get in touch with the plants in different circumstances. A Computer vision technique which combines the Digital Image processing and Machine Learning methodology has been proposed to provide pest management solution. The disease detection is based on the statistical texture feature analysis and it is classified using K nearest neighbor classifier. Statistical PCA is combined with SIFT method to extract the key points, which eliminates the non-operational key points and SFTA is used to extract the texture. The system has achieved better result in identifying and differentiating the infection and insect attack on multiple plant taxonomy. The implementation has been performed using MATLAB.

Machine Learning Verdict of EEG Signals in Brain Computer Interface

2018 ◽  
pp. 429-441
Author(s):  
M. Jeyanthi ◽  
C. Velayutham
Keyword(s):  
Nearest Neighbor ◽  
Technology Development ◽  
Vital Role ◽  
Svm Classifier ◽  
K Nearest Neighbor ◽  
Data Mining Technique ◽  
Data Set ◽  
Eeg Data ◽  
Irrelevant Attributes

In Science and Technology Development BCI plays a vital role in the field of Research. Classification is a data mining technique used to predict group membership for data instances. Analyses of BCI data are challenging because feature extraction and classification of these data are more difficult as compared with those applied to raw data. In this paper, We extracted features using statistical Haralick features from the raw EEG data . Then the features are Normalized, Binning is used to improve the accuracy of the predictive models by reducing noise and eliminate some irrelevant attributes and then the classification is performed using different classification techniques such as Naïve Bayes, k-nearest neighbor classifier, SVM classifier using BCI dataset. Finally we propose the SVM classification algorithm for the BCI data set.

PENENTUAN DAERAH PRIORITAS PELAYANAN AKTA KELAHIRAN DENGAN METODE K-NN DAN K-MEANS

2020 ◽  
Vol 17 (1) ◽  
pp. 319-328
Author(s):  
Ade Muchlis Maulana Anwar ◽  
Prihastuti Harsani ◽  
Aries Maesya
Keyword(s):  
Nearest Neighbor ◽  
Information Gain ◽  
Birth Certificate ◽  
Population Data ◽  
Community Services ◽  
Birth Certificates ◽  
Similar Data ◽  
K Nearest Neighbor ◽  
Civil Registration ◽  
The Family

Population Data is individual data or aggregate data that is structured as a result of Population Registration and Civil Registration activities. Birth Certificate is a Civil Registration Deed as a result of recording the birth event of a baby whose birth is reported to be registered on the Family Card and given a Population Identification Number (NIK) as a basis for obtaining other community services. From the total number of integrated birth certificate reporting for the 2018 Population Administration Information System (SIAK) totaling 570,637 there were 503,946 reported late and only 66,691 were reported publicly. Clustering is a method used to classify data that is similar to others in one group or similar data to other groups. K-Nearest Neighbor is a method for classifying objects based on learning data that is the closest distance to the test data. k-means is a method used to divide a number of objects into groups based on existing categories by looking at the midpoint. In data mining preprocesses, data is cleaned by filling in the blank data with the most dominating data, and selecting attributes using the information gain method. Based on the k-nearest neighbor method to predict delays in reporting and the k-means method to classify priority areas of service with 10,000 birth certificate data on birth certificates in 2019 that have good enough performance to produce predictions with an accuracy of 74.00% and with K = 2 on k-means produces a index davies bouldin of 1,179.

Optimizing Error Rate in Intrusion Detection System Using Artificial Neural Network Algorithm

2018 ◽  
Vol 6 (9) ◽  
pp. 152
Author(s):  
S. Vijaya Rani ◽  
G. N. K. Suresh Babu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Intrusion Detection ◽  
Error Rate ◽  
Learning Process ◽  
Nearest Neighbor ◽  
Detection System ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Artificial Neural

The illegal hackers  penetrate the servers and networks of corporate and financial institutions to gain money and extract vital information. The hacking varies from one computing system to many system. They gain access by sending malicious packets in the network through virus, worms, Trojan horses etc. The hackers scan a network through various tools and collect information of network and host. Hence it is very much essential to detect the attacks as they enter into a network. The methods  available for intrusion detection are Naive Bayes, Decision tree, Support Vector Machine, K-Nearest Neighbor, Artificial Neural Networks. A neural network consists of processing units in complex manner and able to store information and make it functional for use. It acts like human brain and takes knowledge from the environment through training and learning process. Many algorithms are available for learning process This work carry out research on analysis of malicious packets and predicting the error rate in detection of injured packets through artificial neural network algorithms.

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