Classification of Broadleaf Weeds Using a Combination of K-Nearest Neighbor (KNN) and Principal Component Analysis (PCA)

Palm oil is one of the leading commodities in Indonesia. Oil palm yields can be influenced by several factors, one of which is proper weed control. Uncontrolled weeds can damage oil palm plantations. To be able to manage and control weeds, especially large leaf weeds, it is necessary to know the types of weeds. However, not all farmers have knowledge about the types of weeds. For that we need a system that can help identify broadleaf weeds based on leaf images using image processing. So this study aims to build a large leaf weed classification system using a combination of the K-Nearest Neighbor (KNN) and Principal Component Analysis (PCA) algorithms. PCA is used as feature extraction based on the characteristics formed from each spatial property. PCA can be used to reduce and retain most of the relevant information from the original features according to the optimal criteria. The results of the information will then be used by KNN for learning by paying attention to the closest distance from the object. Based on the test results, the developed model is able to produce an accuracy of 90%. Principal Component Analysis (PCA) and K-Nearest Neighbor (KNN) algorithms can be used in the classification process properly. Accuracy results are strongly influenced by the amount of training data and test data as well as the quality of the image used.

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Application of Fuzzy K-Nearest Neighbor (FKNN) to Detect the Parkinson’s Disease

InPrime: Indonesian Journal of Pure and Applied Mathematics ◽

10.15408/inprime.v1i1.12827 ◽

2019 ◽

Vol 1 (1) ◽

Author(s):

L.N. Desinaini ◽

Azizatul Mualimah ◽

Dian C. R. Novitasari ◽

Moh. Hafiyusholeh

Keyword(s):

Machine Learning ◽

Parkinson’S Disease ◽

Principal Component Analysis ◽

Parkinson's Disease ◽

Nearest Neighbor ◽

Principal Component ◽

Component Analysis ◽

Training Data ◽

K Nearest Neighbor ◽

Positive Data

AbstractParkinson’s disease is a neurological disorder in which there is a gradual loss of brain cells that make and store dopamine. Researchers estimate that four to six million people worldwide, are living with Parkinson’s. The average age of patients is 60 years old, but some are diagnosed at age 40 or even younger and the worst thing is some patients are late to find out that they have Parkinson's disease. In this paper, we present a diagnosis system based on Fuzzy K-Nearest Neighbor (FKNN) to detect Parkinson’s disease. We use Parkinson’s disease dataset taken from UCI Machine Learning Repository. The first step is normalize the Parkinson’s disease dataset and analyze using Principal Component Analysis (PCA). The result shows that there are four new factors that influence Parkinson’s disease with total variance is 85.719%. In classification step, we use several percentage of training data to classify (detect) the Parkinson's disease i.e. 50%, 60%, 70%, 75%, 80% and 90%. We also use k = 3, 5, 7, and 9. The classification result shows that the highest accuracy obtained for the percentage of training data is 90% and k = 5, where 19 are correctly classified i.e. 14 positive data and 5 negative data, while 1 positive data is classified incorrectly.Keywords: Parkinson's disease; Fuzzy K-Nearest Neighbor; Principal Component Analysis. AbstrakPenyakit Parkinson merupakan kelainan sel saraf pada otak yang menyebabkan hilangnya dopamin pada otak. Para peneliti mengestimasi bahwa, empat sampai enam juta orang di dunia, menderita Parkinson. Penyakit ini rata-rata diderita oleh pasien berusia 60 tahun, namun beberapa orang terdeteksi saat berusia 40 tahun atau lebih muda dan hal terburuk adalah seseorang terlambat untuk mendeteksinya. Di dalam artikel ini, kami menyajikan sistem diagnosa penyakit Parkinson menggunakan metode Fuzzy K-Nearest Neighbor (FKNN). Kami menggunakan Data uji yang diperoleh dari UCI Machine Learning Repository yang telah banyak diterapkan pada masalah klasifikasi. Tahapan pertama yang kami lakukan adalah menormalisasi data kemudian menganalisisnya menggunakan Analisis Komponen Utama (Principal Component Analysis). Hasil Analisis Komponen Utama menunjukkan bahwa terdapat empat factor baru yang mempengaruhi penyakit Parkinson dengan variansi total 87,719%. Pada tahap klasifikasi, kami menggunakan beberapa prosentase data latih untuk mendeteksi penyakit yaitu 50%, 60%, 70%, 75%, 80% and 90%. Selain itu, kami menggunakan beberapa nilai k yaitu 3, 5, 7, and 9. Hasil menunjukkan bahwa klasifikasi dengan akurasi tertinggi diperoleh untuk 90% data latih dengan k = 5, dimana 19 diklasifikasikan secara tepat yaitu 14 data positif dan 5 data negatif, sedangkan satu data positif tidak diklasifikasikan dengan tepat.Keywords: penyakit Parkinson; Fuzzy K-Nearest Neighbor; Analisis Komponen Utama.

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Pears Classification Using Principal Component Analysis and K-Nearest Neighbor

SinkrOn ◽

10.33395/sinkron.v4i2.10502 ◽

2020 ◽

Vol 4 (2) ◽

pp. 34

Author(s):

Moh. Arie Hasan ◽

Arief Setya Budi

Keyword(s):

Principal Component Analysis ◽

Western Europe ◽

Nearest Neighbor ◽

Principal Component ◽

Component Analysis ◽

Training Data ◽

Morphological Operations ◽

K Nearest Neighbor ◽

Tropical Climates

Pears is a fruit that is widely available in tropical climates such as in western Europe, Asia, Africa and one of them is Indonesia. There are many types of pears in Indonesia. Types of pears can be distinguished from the color, size, and shape. But it is still difficult for ordinary people to get to know the types of pears. This is what gave rise to the idea to conduct research related to image processing to classify three types of pears namely abate, red and william pears in order to help determine the type of pears. The pear type classification process is done by verify the image of pears based on existing training data. The research method used consisted of preprocessing image segmentation with morphological operations and feature extraction into Principal Component Analysis (PCA). The classification algorithm used is K-Nearest Neighbor (KNN). The use of adequate training data will further improve the classification of types of pears. The final results of this study amounted to 87.5%.

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Comparison of PCA and 2DPCA Accuracy with K-Nearest Neighbor Classification in Face Image Recognition

Scientific Journal of Informatics ◽

10.15294/sji.v6i1.18553 ◽

2019 ◽

Vol 6 (1) ◽

pp. 64-72

Author(s):

Sri Sutarti ◽

Anggyi Trisnawan Putra ◽

Endang Sugiharti

Keyword(s):

Principal Component Analysis ◽

Covariance Matrix ◽

Nearest Neighbor ◽

Principal Component ◽

Component Analysis ◽

Face Image ◽

Training Data ◽

K Nearest Neighbor ◽

Testing Data ◽

Pca Method

Face recognition is a special pattern recognition for faces that compare input image with data in database. The image has a variety and has large dimensions, so that dimension reduction is needed, one of them is Principal Component Analysis (PCA) method. Dimensional transformation on image causes vector space dimension of image become large. At present, a feature extraction technique called Two-Dimensional Principal Component Analysis (2DPCA) is proposed to overcome weakness of PCA. Classification process in 2DPCA using K-Nearest Neighbor (KNN) method by counting euclidean distance. In PCA method, face matrix is changed into one-dimensional matrix to get covariance matrix. While in 2DPCA, covariance matrix is directly obtained from face image matrix. In this research, we conducted 4 trials with different amount of training data and testing data, where data is taken from AT&T database. In 4 time testing, accuracy of 2DPCA+KNN method is higher than PCA+KNN method. Highest accuracy of 2DPCA+KNN method was obtained in 4th test with 96.88%. while the highest accuracy of PCA+KNN method was obtained in 4th test with 89.38%. More images used as training data compared to testing data, then the accuracy value tends to be greater.

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Analisis Metode Pengenalan Wajah Two Dimensial Principal Component Analysis (2DPCA) dan Kernel Fisher Discriminant Analysis Menggunakan Klasifikasi KNN (K- Nearest Neighbor)

Jurnal Teknologi dan Rekayasa Manufaktur ◽

10.48182/jtrm.v2i2.30 ◽

2020 ◽

Vol 2 (2) ◽

pp. 29-38

Author(s):

Abdur Rohman Harits Martawireja ◽

Hilman Mujahid Purnama ◽

Atika Nur Rahmawati

Keyword(s):

Principal Component Analysis ◽

Discriminant Analysis ◽

Cross Validation ◽

Nearest Neighbor ◽

Principal Component ◽

Component Analysis ◽

K Nearest Neighbor ◽

Fisher Discriminant Analysis ◽

Fisher Discriminant ◽

Kernel Fisher Discriminant Analysis

Pengenalan wajah manusia (face recognition) merupakan salah satu bidang penelitian yang penting dan belakangan ini banyak aplikasi yang menerapkannya, baik di bidang komersil ataupun di bidang penegakan hukum. Pengenalan wajah merupakan sebuah sistem yang berfungsikan untuk mengidentifikasi berdasarkan ciri-ciri dari wajah seseorang berbasis biometrik yang memiliki keakuratan tinggi. Pengenalan wajah dapat diterapkan pada sistem keamanan. Banyak metode yang dapat digunakan dalam aplikasi pengenalan wajah untuk keamanan sistem, namun pada artikel ini akan membahas tentang dua metode yaitu Two Dimensial Principal Component Analysis dan Kernel Fisher Discriminant Analysis dengan metode klasifikasi menggunakan K-Nearest Neigbor. Kedua metode ini diuji menggunakan metode cross validation. Hasil dari penelitian terdahulu terbukti bahwa sistem pengenalan wajah metode Two Dimensial Principal Component Analysis dengan 5-folds cross validation menghasilkan akurasi sebesar 88,73%, sedangkan dengan 2-folds validation akurasi yang dihasilkan sebesar 89,25%. Dan pengujian metode Kernel Fisher Discriminant dengan 2-folds cross validation menghasilkan akurasi rata rata sebesar 83,10%.

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Discrimination of Chinese Liquors Based on Electronic Nose and Fuzzy Discriminant Principal Component Analysis

Foods ◽

10.3390/foods8010038 ◽

2019 ◽

Vol 8 (1) ◽

pp. 38 ◽

Cited By ~ 2

Author(s):

Xiaohong Wu ◽

Jin Zhu ◽

Bin Wu ◽

Chao Zhao ◽

Jun Sun ◽

...

Keyword(s):

Principal Component Analysis ◽

Feature Extraction ◽

Electronic Nose ◽

Nearest Neighbor ◽

Principal Component ◽

Component Analysis ◽

K Nearest Neighbor ◽

Knn Classifier ◽

Extraction Algorithm ◽

Leave One Out

The detection of liquor quality is an important process in the liquor industry, and the quality of Chinese liquors is partly determined by the aromas of the liquors. The electronic nose (e-nose) refers to an artificial olfactory technology. The e-nose system can quickly detect different types of Chinese liquors according to their aromas. In this study, an e-nose system was designed to identify six types of Chinese liquors, and a novel feature extraction algorithm, called fuzzy discriminant principal component analysis (FDPCA), was developed for feature extraction from e-nose signals by combining discriminant principal component analysis (DPCA) and fuzzy set theory. In addition, principal component analysis (PCA), DPCA, K-nearest neighbor (KNN) classifier, leave-one-out (LOO) strategy and k-fold cross-validation (k = 5, 10, 20, 25) were employed in the e-nose system. The maximum classification accuracy of feature extraction for Chinese liquors was 98.378% using FDPCA, showing this algorithm to be extremely effective. The experimental results indicate that an e-nose system coupled with FDPCA is a feasible method for classifying Chinese liquors.

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Facial recognition using two-dimensional principal component analysis and k-nearest neighbor: a case analysis of facial images

Journal of Physics Conference Series ◽

10.1088/1742-6596/1567/3/032028 ◽

2020 ◽

Vol 1567 ◽

pp. 032028

Author(s):

E Sugiharti ◽

A T Putra ◽

Subhan

Keyword(s):

Principal Component Analysis ◽

Nearest Neighbor ◽

Facial Recognition ◽

Principal Component ◽

Case Analysis ◽

Component Analysis ◽

Two Dimensional ◽

K Nearest Neighbor ◽

Facial Images

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Multi-mode operation of principal component analysis with k-nearest neighbor algorithm to monitor compressors for liquefied natural gas mixed refrigerant processes

Computers & Chemical Engineering ◽

10.1016/j.compchemeng.2017.05.029 ◽

2017 ◽

Vol 106 ◽

pp. 96-105 ◽

Cited By ~ 19

Author(s):

Daegeun Ha ◽

Usama Ahmed ◽

Hahyung Pyun ◽

Chul-Jin Lee ◽

Kye Hyun Baek ◽

...

Keyword(s):

Principal Component Analysis ◽

Natural Gas ◽

Nearest Neighbor ◽

Principal Component ◽

Component Analysis ◽

Liquefied Natural Gas ◽

K Nearest Neighbor ◽

Nearest Neighbor Algorithm ◽

K Nearest Neighbor Algorithm ◽

Multi Mode

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Analysis of Accuracy Improvement in K-Nearest Neighbor using Principal Component Analysis (PCA)

Journal of Physics Conference Series ◽

10.1088/1742-6596/1566/1/012062 ◽

2020 ◽

Vol 1566 ◽

pp. 012062

Author(s):

A H Lubis ◽

P Sihombing ◽

E B Nababan

Keyword(s):

Principal Component Analysis ◽

Nearest Neighbor ◽

Principal Component ◽

Component Analysis ◽

K Nearest Neighbor ◽

Accuracy Improvement

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Identification of Baby's Feet Using Principal Component Analysis (PCA) Method Character Extraction with K-Nearest Neighbor (KNN) Classification in Matlab Application

Jurnal Ilmiah Merpati (Menara Penelitian Akademika Teknologi Informasi) ◽

10.24843/jim.2021.v09.i03.p02 ◽

2021 ◽

Vol 9 (3) ◽

pp. 200

Author(s):

Geyge Andika Lesmana ◽

I Nyoman Piarsa ◽

I Made Suwija Putra

Keyword(s):

Principal Component Analysis ◽

Nearest Neighbor ◽

Principal Component ◽

Component Analysis ◽

Identification System ◽

Analysis Method ◽

K Nearest Neighbor ◽

Biometric Recognition ◽

Principal Component Analysis Method ◽

Soles Of The Feet

Biometric recognition systems or human identification are very important in security access for identification and verification systems. The biometric recognition system can be used as an identification system based on the characteristics possessed by the body part of each individual. The soles of the feet can be used for identification because the soles of the feet have certain and unique characteristics which include major lines, protrusions, small dots, single points, and textures. The introduction of biometrics in babies is still conventional, which is a standard operating procedure such as attaching bracelets on baby's feet and imprinting or inking on the soles of baby's feet which are affixed to paper and are very vulnerable to the risk of damage or loss of data, there is a need for a system that can store data automatically digital and able to do the baby identification process. The Principal Component Analysis method is used for the extraction process of the characteristics of the baby's feet. The classification uses the K-Nearest Neighbor (K-NN) method with the euclidean distance approach. Tests were carried using 120 images of baby feet, there are 20 classes, each class contains 3 images of the right foot and 3 images of the foot of the left foot, and a dataset of 280 training images. The highest accuracy result obtained in system testing is 91% with a computation time of 5.63 seconds using the Principal Component Analysis method with the K-Nearest Neighbor (K-NN) classification.Keywords: Footprint, Feature Extraction, Principal Component Analysis, K-Nearest Neighbor.

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