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Journal : TELKOMNIKA (Telecommunication Computing Electronics and Control)

Advertisement billboard detection and geotagging system with inductive transfer learning in deep convolutional neural network Romi Fadillah Rahmat; Dennis Dennis; Opim Salim Sitompul; Sarah Purnamawati; Rahmat Budiarto
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 17, No 5: October 2019
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v17i5.11276

Abstract

In this paper, we propose an approach to detect and geotag advertisement billboard in real-time condition. Our approach is using AlexNet’s Deep Convolutional Neural Network (DCNN) as a pre-trained neural network with 1000 categories for image classification. To improve the performance of the pre-trained neural network, we retrain the network by adding more advertisement billboard images using inductive transfer learning approach. Then, we fine-tuned the output layer into advertisement billboard related categories. Furthermore, the detected advertisement billboard images will be geotagged by inserting Exif metadata into the image file. Experimental results show that the approach achieves 92.7% training accuracy for advertisement billboard detection, while for overall testing results it will give 71,86% testing accuracy.
File Reconstruction in Digital Forensic Opim Salim Sitompul; Andrew Handoko; Romi Fadillah Rahmat
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 16, No 2: April 2018
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v16i2.8230

Abstract

File recovery is one of the stages in computer forensic investigative process to identify an acquired file to be used as digital evident. The recovery is performed on files that have been deleted from a file system. However, in order to recover a deleted file, some considerations should be taken. A deleted file is potentially modified from its original condition because another file might either partly or entirely overriding the file content. A typical approach in recovering deleted file is to apply Boyer-Moore algorithm that has rather high time complexity in terms of string searching. Therefore, a better string matching approach for recovering deleted file is required. We propose Aho-Corasick parsing technique to read file attributes from the master file table (MFT) in order to examine the file condition. If the file was deleted, then the parser search the file content in order to reconstruct the file. Experiments were conducted using several file modifications, such as 0% (unmodified), 18.98%, 32.21% and 9.77%. From the experimental results we found that the file reconstruction process on the file system was performed successfully. The average successful rate for the file recovery from four experiments on each modification was 87.50% and for the string matching process average time on searching file names was 0.32 second.