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All Journal Dinamik Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Prosiding Seminar Nasional Sains Dan Teknologi Fakultas Teknik Jurnal Algoritma Telematika : Jurnal Informatika dan Teknologi Informasi Format : Jurnal Imiah Teknik Informatika RABIT: Jurnal Teknologi dan Sistem Informasi Univrab JIKO (Jurnal Informatika dan Komputer) Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control CogITo Smart Journal JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jiko (Jurnal Informatika dan komputer) JSI (Jurnal sistem Informasi) Universitas Suryadarma JISKa (Jurnal Informatika Sunan Kalijaga) JAMINFOKOM DECODE: Jurnal Pendidikan Teknologi Informasi Media Publikasi Promosi Kesehatan Indonesia (MPPKI) Jurnal Algoritma
Rusydi Umar
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Languange, Linguistic, Communication & Media Library & Information Science Public Health Other

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Journal : JOURNAL OF APPLIED INFORMATICS AND COMPUTING

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Klasifikasi Citra Menggunakan Convolutional Neural Network dan K Fold Cross Validation Ari Peryanto; Anton Yudhana; Rusydi Umar
Journal of Applied Informatics and Computing Vol 4 No 1 (2020): Juli 2020
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1169.96 KB) | DOI: 10.30871/jaic.v4i1.2017

Abstract

Image classification is a fairly easy task for humans, but for machines it is something that is very complex and is a major problem in the field of Computer Vision which has long been sought for a solution. There are many algorithms used for image classification, one of which is Convolutional Neural Network, which is the development of Multi Layer Perceptron (MLP) and is one of the algorithms of Deep Learning. This method has the most significant results in image recognition, because this method tries to imitate the image recognition system in the human visual cortex, so it has the ability to process image information. In this research the implementation of this method is done by using the Keras library with the Python programming language. The results showed the percentage of accuracy with K = 5 cross-validation obtained the highest level of accuracy of 80.36% and the highest average accuracy of 76.49%, and system accuracy of 72.02%. For the lowest accuracy obtained in the 4th and 5th testing with an accuracy value of 66.07%. The system that has been made has also been able to predict with the highest average prediction of 60.31%, and the highest prediction value of 65.47%.
A Komparasi Image Matching Menggunakan Metode K-Nearest Neightbor (KNN) dan Support Vector Machine (SVM) Rusydi Umar; Imam Riadi; Dewi Astria Faroek
Journal of Applied Informatics and Computing Vol 4 No 2 (2020): Desember 2020
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v4i2.2226

Abstract

Image matching is the process of finding digital images that have a degree of similarity. matching images using the classification method. In measuring image matching, the images used are original logo images and manipulated logo images. Comparison of classification algorithms from the two methods namely K-Nearest Neighbor (KNN) and Support Vector Machine with Sequential Minimal Optimization (SMO) optimization used to calculate matches based on accuracy values. The K-Nearest Neighbor (KNN) classification method is based on proximity or K calculations while the Support Vector Machine (SVM) classification method measures the distance between the hyperplane and the nearest data. Image match values are measured by Precision, Recall, F1-Score, and Accuracy. The image matching steps start from the preparation of data processing, extraction of HSV color features and shapes, then the classification stage. Digital images are used as many as 10 images consisting of one original logo and 9 manipulated logos. In the classification testing stage, using the WEKA application by applying the 10-fold cross-validation method. From the results of tests conducted that the closest k-neighbor (KNN) classification method is 80% and has a k = 0.889 which is quite good in measuring proximity, while the SVM classification method is 70%. The results of this image matching comparison can be concluded that the K-Nearest Neighbor classification method works better than SVM for image matching.
Co-Authors Abdul Fadlil Abdullah Hanif Abdullah Hanif Achmad Dito Achmad Noerkhaerin Putra Ahmad Fajar Sidiq Anthon Yudhana Anton Yudhana Arief Budiman Arief Setyo Nugroho Dewi Astria Faroek Dewi Soyusiawaty Elfatiha, Muhammad Ihya Aulia Erviana, Vera Yuli Fadhilah Dhinur Aini Fadlillah Mukti Ayudewi Gema Kharismajati Imam Riadi Kgs Muhammad Rizky Alditra Utama Muhammad Noor Fadillah Mustafa Mustafa Oktomi Wijaya Peryanto, Ari Rahmat Ardila Dwi Yulianto Rauhulloh Ayatulloh Khomeini Noor Bintang Rio Anggara Rochmat Mantofani Sarjimin Sarjimin Sri Rahayu Astari sunardi sunardi Tri Lestari