Computing and Information Processing Letters最新文献

Chicken Egg Detection Based-on Image Processing Concept: A Review 基于图像处理概念的鸡蛋检测综述

Computing and Information Processing Letters Pub Date : 2021-11-18 DOI: 10.31315/cip.v1i1.6129

S. Saifullah, Rabbimov Ilyos Mehriddinovich, L. K. Tolentino

{"title":"Chicken Egg Detection Based-on Image Processing Concept: A Review","authors":"S. Saifullah, Rabbimov Ilyos Mehriddinovich, L. K. Tolentino","doi":"10.31315/cip.v1i1.6129","DOIUrl":"https://doi.org/10.31315/cip.v1i1.6129","url":null,"abstract":"The concept of image processing has been implemented and developed in various fields, including the poultry industry. The focus in development is on egg detection. Detection is not only in the concept of object detection but also in other things such as weight prediction, egg physical characteristics, to embryo detection. This staged process starts from the image acquisition process, preprocessing, segmentation up to identifying or detecting eggs. This article provides details about the concept of image processing in detecting chicken eggs based on a review of previous studies. The studies discussed the basic concepts of image processing in detecting chicken eggs and their technical application. Based on image processing’s basic concept, there are four main parts: image acquisition, preprocessing, segmentation, and identification or classification. The acquisition process is carried out with a variety of tools that can capture images to be processed. The result of the acquisition is preprocessed by one or more methods that can improve image quality. After that, the image segmentation process is used to determine the object to be detected. Image segmentation can be used as a reference for objects processed by feature extraction. The feature extraction aims to provide certain fertile (embryonic) characteristics and unfertile (non-embryonic) egg images. The identification process is precise which objects are detected and not. The concept of segmentation and identification/classification can be implemented in computer-based applied applications. Besides, these methods are still developing and improving their accuracy and implementation in the poultry industry.","PeriodicalId":406895,"journal":{"name":"Computing and Information Processing Letters","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126968008","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Classification of mango plants based on leaf shape using GLCM and K-nearest neighbor methods 基于GLCM和k -最近邻的芒果叶片形状分类

Computing and Information Processing Letters Pub Date : 2021-11-18 DOI: 10.31315/cip.v1i1.6124

Bona Joshua Hutasoit, Herry Sofyan, F. R. Kodong

引用次数: 1

Identification of beef and pork using gray level co-occurrence matrix and probabilistic neural network 基于灰度共生矩阵和概率神经网络的牛肉和猪肉识别

Computing and Information Processing Letters Pub Date : 2021-11-18 DOI: 10.31315/cip.v1i1.6126

Clarita Magdalena, H. Rustamaji, B. Yuwono

{"title":"Identification of beef and pork using gray level co-occurrence matrix and probabilistic neural network","authors":"Clarita Magdalena, H. Rustamaji, B. Yuwono","doi":"10.31315/cip.v1i1.6126","DOIUrl":"https://doi.org/10.31315/cip.v1i1.6126","url":null,"abstract":"Objective: Identify images of beef and pork using texture feature extraction Gray Level Co-Occurrence Matrix and Probabilistic Neural Network classification algorithm.Design/method/approach: Apply texture feature extraction to Gray Level Co-Occurrence Matrix and Probabilistic Neural Network Classifier to perform classification.Results: From the test results with k-fold cross-validation and confusion matrix, it shows that feature extraction of Gray Level Co-Occurrence Matrix and Probabilistic Neural Network Classifier get an average accuracy of 87%, precision 83%, and recall 90%.Authenticity/state of the art: In this study, several scenarios were tested, namely the effect of using resize, brightness, and rotate values. Using a resize value of 256 x 256 pixels from the test results got the best accuracy of 87%. The brightness test of 20% affects the accuracy rate of 86% on increasing brightness and 90% on reducing brightness. In contrast, the test on the rotated image does not affect the accuracy results. The average accuracy obtained is 87%. The data in this study were obtained by collecting primary data on images of beef and pork in several markets in Denpasar.","PeriodicalId":406895,"journal":{"name":"Computing and Information Processing Letters","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129953273","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Classification of prospective borrowing customers to reduce the risk of bad deposits in sharia cooperatives using the FK-NNC method 使用FK-NNC方法对潜在借款客户进行分类，以降低伊斯兰教合作社的不良存款风险

Computing and Information Processing Letters Pub Date : 2021-11-18 DOI: 10.31315/cip.v1i1.6125

Annisa Reza Dhiya'atulhaq, O. S. Simanjuntak, H. Heriyanto

{"title":"Classification of prospective borrowing customers to reduce the risk of bad deposits in sharia cooperatives using the FK-NNC method","authors":"Annisa Reza Dhiya'atulhaq, O. S. Simanjuntak, H. Heriyanto","doi":"10.31315/cip.v1i1.6125","DOIUrl":"https://doi.org/10.31315/cip.v1i1.6125","url":null,"abstract":"Objective: Assisting cooperatives in determining the classification of prospective financing members to reduce non-performing deposits in sharia cooperativesDesign/method/approach: The Fuzzy K-Nearest Neighbor in Every Class method is used to classify prospective financing members. System development using the waterfall method.Results: Based on the implementation and the results of tests carried out using the confusion matrix, the results show that using the Fuzzy K-Nearest Neighbor in Every Class method can classify prospective financing members with an average accuracy rate of 80% with a value of k=1 to k=10. Stable accuracy results of 80%. It shows that adding k theory to the Fuzzy K-Nearest Neighbor in Every Class method can improve the theory of assigning k values to the previous method, namely K-Nearest Neighbor and Fuzzy K-Nearest Neighbor.Authenticity/state of the art: Based on previous research carried out, the research themes and characteristics are relatively the same, but in the research conducted, there are differences in terms of the methods used, case study data, preprocessing data, and research outputs. Previous research with the same object, namely the classification of cooperative customers by applying the K-Nearest Neighbor method by determining two classes of classification results, namely traffic jams and smooth, while this study will apply the development of the K-Nearest Neighbor method using the Fuzzy K-Nearest Neighbor in Every method. The class with the output specifies three outcomes: crash, sometimes crash, and smooth. This study uses the data preprocessing stage with fuzzification and data transformation techniques using the min-max normalization method. In contrast, the previous research used the z score normalization method.","PeriodicalId":406895,"journal":{"name":"Computing and Information Processing Letters","volume":"128 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124617872","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Implementation of the c4.5 decision tree learning algorithm for sentiment analysis in e-commerce application reviews on google play store c4.5决策树学习算法在google play商店电子商务应用评论情感分析中的实现

Computing and Information Processing Letters Pub Date : 2021-11-18 DOI: 10.31315/cip.v1i1.6128

Asrina Yuni Ikhsanti, Yulianti Fauziah, R. Perwira

引用次数: 1