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All Journal JSI: Jurnal Sistem Informasi (E-Journal) InfoTekJar (Jurnal Nasional Informatika dan Teknologi Jaringan) Jurnal Komputer Terapan Seminar Nasional Teknologi Informasi Komunikasi dan Industri ILKOM Jurnal Ilmiah IJITEE (International Journal of Information Technology and Electrical Engineering) Jurnal Nasional Teknik Elektro dan Teknologi Informasi Technologica Jurnal Kecerdasan Buatan dan Teknologi Informasi
Warnia Nengsih
Politeknik Caltex Riau
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Library & Information Science Mathematics Other

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Journal : ILKOM Jurnal Ilmiah

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Classification of cendrawasih birds using convolutional neural network (CNN) keras recognition Warnia Nengsih; Ardiyanto Ardiyanto; Ayu Putri Lestari
ILKOM Jurnal Ilmiah Vol 13, No 3 (2021)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v13i3.865.259-265

Abstract

Classification is part of predictive modeling and supervised learning. This method is used to determine the data class based on the previous value. In solving certain cases, there are various classification methods with varying degrees of accuracy. Convolutional Neural Network (CNN) is part of the Multilayer Perceptron (MLP) for processing two-dimensional data. CNN is also part of the Deep Neural Network and is applied to image objects. From several sources, it is stated that the classification process using images is not properly implemented in this MLP. Of course, this will result in the accuracy of the method in handling certain cases. In this study, the object classification process uses hard recognition to determine the accuracy value of the method using the object of the bird of paradise. From the results of this study, a training model was conducted using 10 ephocs with an accuracy value of 0.0850 while a loss value of 2.5658. So these results indicate that MLP can successfully complete the classification process using images.
Comparative Analysis to Determine the Best Accuracy of Classification Methods Warnia Nengsih; Yuli Fitrisia; Mardhiah Fadhli
ILKOM Jurnal Ilmiah Vol 14, No 2 (2022)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v14i2.1128.134-141

Abstract

The classification method is one of the methods of supervised learning and predictive learning. This method can be used to detect an object in the image presented, whether it is in accordance with the existing object in the training phase. There are several classification methods used, including Support Vector Machine (SVM), K-Nearest Neighbors (K-NN) and Decision Tree. To determine the accuracy in detecting these objects, it is necessary to measure the accuracy of each classification method used. The object that becomes simulation in this research is the object image of Guava and Pear fruit. Testing using confusion matrix. The results showed that the Support Vector Machine (SVM) method was able to detect with an accuracy of 98.09%. Then the K-Nearest Neighbors (K-NN) method with an accuracy of 98.06%, then the Decision Tree method with an accuracy of 97.57%. From the results of the accuracy test, it can be concluded that basically these three classification methods have good accuracy with a difference of 0.49% and the overall average accuracy of the classification of the three methods is 97.89%
Co-Authors Ardiyanto Ardiyanto Ayu Putri Lestari Dini Hidayatul Qudsi Dini Nurmala Sari Hajijah Noor Istianah Muslim M. Mahrus Zein Mardhiah Fadhli, Mardhiah mutia sari zulvi Nazifa Hayati Nurmalasari, Dini Ria Anjelina Agustin Syefrida Yulina Yuli Fitrisia