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Comparison of KNN and SVM Algorithms in Facial Image Recognition Using Haar Wavelet Feature Extraction

Neneng Rachmalia Feta

To process all the pixels in the face image, feature extraction can be performed using the Haar Wavelet method so that it processes identifiers with lower dimensions. However, a classification algorithm must separate the distance between classes with mini... see more

facial image recognition | feature extraction | Haar Wavelet | SVM algorithm | KNN algorithm

Jurnal Riset Informatika , 2023

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Extreme learning machine with feature extraction using GLCM for phosphorus deficiency identification of cocoa plants Basri Basri(1); Muhammad Assidiq(2); Harli A. Karim(3); Andi Nuraisyah(4) (1) Universitas Al Asyariah Mandar (2) Universitas Al Asyariah Mandar (3) Universitas Al Asyariah Mandar (4) Universitas Al Asyariah Mandar () Corresponding Author

This study aims to analyze the implementation of the Extreme Learning Machine (ELM) Algorithm with Gray Level Co-Occurrence Matrix (GLCM) as an Image Feature Extraction method in identifying phosphorus deficiency in cocoa plants based on leaf characterist... see more

Extreme Learning Machine | GLCM feature extraction | Phosphorus deficiency | Cocoa.

ILKOM Jurnal Ilmiah , 2022

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Identification of Pneumonia using The K-Nearest Neighbors Method using HOG Fitur Feature Extraction

Nurul Khairina,Theofil Tri Saputra Sibarani,Rizki Muliono,Zulfikar Sembiring,Muhathir Muhathir

Pneumonia is a wet lung disease. Pneumonia is generally caused by viruses, bacteria or fungi. Not infrequently Pneumonia can cause death. The K-Nearest Neighbors method is a classification method that uses the majority value from the closest k value categ... see more

Disease Identification | Pneumonia | K-Nearest Neighbors | HOG Extraction | HOG Features

JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING , 2022

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Evaluasi Ekstraksi Fitur GLCM dan LBP Menggunakan Multikernel SVM untuk Klasifikasi BatikEvaluation of GLCM and LBP Feature Extraction Using Multicernel SVM for Classification of Batik

Pulung Nurtantio Andono, Eko Hari Rachmawanto

Batik as one of Indonesia's cultural heritages has various types, motifs and colors. A batik may have almost the same motif with a different color or vice versa, therefore it requires a classification of batik motifs. In this study, a printed batik was us... see more

Batik | classification | SVM | KNN | feature extraction

Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) , 2021

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Good Morning to Good Night Greeting Classification Using Mel Frequency Cepstral Coefficient (MFCC) Feature Extraction and Frame Feature Selection

Heriyanto Heriyanto

Purpose:Select the right features on the frame for good accuracyDesign/methodology/approach:Extraction of Mel Frequency Cepstral Coefficient (MFCC) Features and Selection of Dominant Weight Normalized (DWN) FeaturesFindings/result:The accuracy results sho... see more

extraction of features | features | frames | cepstral coefficient | linear

Telematika , 2021

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K-MEANS CLUSTERING FOR EGG EMBRYO'S DETECTION BASED-ON STATISTICAL FEATURE EXTRACTION APPROACH OF CANDLING EGGS IMAGE

Shoffan Saifullah

This research discusses the detection of embryonic eggs using the k-means clustering method based on statistical feature extraction. The processes that occur in detection are image acquisition, image enhancement, feature extraction, and identification/det... see more

Eggs embryo | GLCM | K-means Clustering | Statistical Feature Extraction

SINERGI , 2021

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Feature Extraction of Tobacco Leaf Based on Discrete Cosine Transform (DCT)

Rosida Vivin Nahari, Arda Surya Editya, Riza Alfita

The success of the tobacco leaf classification process is very dependent on the extraction of tobacco leaf features. Several stages of digital image processing can improve the ability to identify the best quality tobacco automatically through extracting l... see more

Tobacco leaf | DCT | feature extraction | YCbCr | Classification

JOURNAL OF APPLIED INFORMATICS AND COMPUTING , 2020

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Predicting breast cancer recurrence using principal component analysis as feature extraction: an unbiased comparative analysis

(1) Zuhaira Muhammad Zain (College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, Saudi Arabia) (2) Mona Alshenaifi (College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, Saudi Arabia) (3) Abeer Aljaloud (College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, Saudi Arabia) (4) Tamadhur Albednah (College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, Saudi Arabia) (5) Reham Alghanim (College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, Saudi Arabia) (6) Alanoud Alqifari (College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, Saudi Arabia) (7) Amal Alqahtani (College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, Saudi Arabia)

Breast cancer recurrence is among the most noteworthy fears faced by women. Nevertheless, with modern innovations in data mining technology, early recurrence prediction can help provide relief from these fears. Although medical information is typically co... see more

Breast cancer recurrence | Data Mining | Feature Extraction | Machine Learning | Principal Component Analysis

International Journal of Advances in Intelligent Informatics , 2020

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Utilization of Optical Character Recognition and Text Feature Extraction to Build a Workforce Complaint DatabasePemanfaatan Optical Character Recognition Dan Text Feature Extraction Untuk Membangun Basisdata Pengaduan Tenaga Kerja

Yan Puspitarani, Yenie Syukriyah

The examination of complaints of labor violations is part of the main activity of the labor inspection section within the Department of Manpower. Monitors will examine companies that are considered to have violated labor laws based on a letter of complain... see more

OCR | text feature extraction | database

Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) , 2020

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