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Kota yogyakarta,
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INDONESIA
Jurnal Informatika
Published by Universitas Ahmad Dahlan
ISSN : 19780524     EISSN : 25286374     DOI : 10.26555
Core Subject : Science,
Computer Science & IT
Arjuna Subject : -
Articles 5 Documents
 1 
Search results for , issue "Vol 15, No 2 (2021): May 2021" : 5 Documents clear
Image denoising using wavelet thresholding and median filter based Raspberry pi Rusul Sabah; Ruzelita Ngadiran; Dalal Abdulmohsin Hammood
Jurnal Informatika Vol 15, No 2 (2021): May 2021
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jifo.v15i2.a20609

Abstract

The goal of any denoising technique is to remove noise from an image which is the first step in any image processing. The noise removal method should be applied watchful manner. Otherwise, artifacts can be introduced, which may blur the image. In this work, three levels of Gaussian noise are used for adding noise on the original image (σ=10, σ=50, σ =100) and also (σ=15, σ=20, σ=25) to compare with existing work and analysis with it to test embedded system with a median filter. Performance evaluation of the median filter, wavelet threshold denoising techniques is provided. The techniques used are the median filter and wavelet threshold used to remove noise based on raspberry pi with Python. Four methods to remove noise images are used. (Median Filter, Wavelet Thresholding) MF, WT, MF before and after WT. The results showed the camera image was better than the other after tested all the methods with Gaussian noise σ=10. On the other hand, the other images were better than the camera images for the Gaussian levels 50 and 100. The results were good in the median filter in wavelet threshold based on Raspberry Pi, which is compared with most of the images butter in the median filter.
Improved data storage performance based modified-SPEED algorithm Mamun Bin Ibne Reaz; Araf Farayez
Jurnal Informatika Vol 15, No 2 (2021): May 2021
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jifo.v15i2.a20610

Abstract

With the rising demand for smart devices and smart home systems, automation and activity prediction has become a vital aspect of people's everyday lives. Researchers have focused on developing approaches that detect user activity patterns and used them to predict future actions. One such system is Modified Sequence Prediction via Enhanced Episode Discovery (M-SPEED), which uses spatiotemporal daily life activities to analyze user behaviors. However, the low accuracy of this algorithm can be a limiting factor inefficient activity prediction. Also, the computational overhead of run time and memory causes this algorithm to show poor performance in large datasets. This research focuses on modifying the M-SPEED algorithm to improve its capability to run on a larger dataset while at the same time improving run time. The accuracy is also improved to make it more effective in real-world applications. Proof of algorithm efficiency is provided to ensure system validity, and simulation is carried out on real-life data. The results demonstrate a 66.69% improvement in cumulative memory efficiency, 37% faster run time, and 8.22% better accuracy confirming the proposal's effectiveness
Prediction of standard penetration test value on cohesive soil using artificial neural networks Soewignjo Agus Nugroho; Hendra Fernando; Reni Suryanita
Jurnal Informatika Vol 15, No 2 (2021): May 2021
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jifo.v15i2.a19822

Abstract

Soil investigation is the main key in starting construction. Standard Penetration Test (SPT) and Cone Penetration Test (CPT) are field tests often used to estimate soil parameters for foundation design purposes. The SPT value (N-SPT) shows a correlation between the CPT value and other soil parameters. At present, there have been many conventional correlations examining these correlations, but the nonlinear nature of the soil due to very complex soil formations means that this correlation cannot be used in all situations. This research aimed to predict the value of SPT on cohesive soil based on CPT test data and soil physical properties using artificial neural network capabilities using the Backpropagation algorithm, and the activation function was bipolar sigmoid. This study used 284 data from several places in Sumatra Island, Indonesia, with data input were tip resistance, shaft resistance, effective overburden pressure, percentage of liquid limit, plastic limit, sand, silt, and clay. The results showed that the training data of RMSE was 3.441, MAE and R2 were 0.9451 and 2.318, respectively while test data showed RMSE, MAE, R2 were 2.785, 2.085, and 0.9792, respectively. It means that the proposed artificial neural network NN_Nspt(C) was promising to predict the N-SPT value with a minimum error value and a strong regression equation.
Device-to-device (D2D) reliable transmission in the internet of things Tanweer Alam
Jurnal Informatika Vol 15, No 2 (2021): May 2021
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jifo.v15i2.a20275

Abstract

D2D stands for device-to-device communication, which is likely to perform a major impact in future mobile communications because it offers ultra-low latency for end user’s direct conversation. Throughout minimizing latency, increasing strength and improved transmission efficiency, and expanding telecommunication services, D2D services are seen as a successful innovation for emerging mobile communications. The D2D networking makes a unique contribution to the wireless world by simplifying data transfer among devices connected. D2D networking makes use of adjacent two nodes to maximize the use of existing infrastructure, low latency, boost throughput and expand service functionality. Within wireless networks, D2D communication is described as immediate interaction among two mobile devices without passing through the access point or network infrastructure. The fully integrated wireless communication would be built by integrating D2D and the Internet of Things. D2D enables the larger number of devices to be paired at a higher bandwidth frequency and with minimum latency. Building a new reliable framework for D2D communication of smart devices can be an important framework for improving the reliability of communication. Internet of Things is the process of communicating and sharing information between nearby devices. But there are many challenges to secure and reliable communication. Amongst the major concerns for wireless transmission has been identified as communication trust, and overcoming this issue could lead to sustained expansion in the usage and popularity of the Internet of Things. The proposed study develops a system for providing internet access to a network of smart devices connected to the internet of things. The significant contributions link the latest findings that incorporate the interaction framework's stability and provides secure internet networking for connected devices.
Proposal of Image generation model using cGANs for sketching faces Nguyen Phat Huu; Nguyet Giap Thi
Jurnal Informatika Vol 15, No 2 (2021): May 2021
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jifo.v15i2.a20576

Abstract

The transition from sketches to realistic images of human faces has an important application in criminal investigation science to find criminals as depicted by witnesses. However, due to the difference between the sketch image and the real face image in terms of image detail and color, it is challenging and takes time to transform from hand-drawn sketches to actual faces. To solve this problem, we propose an image generation model using the conditional generative adversarial network with autoencoder (cGANs-AE) model to generate synthetic samples for variable length and multi-feature sequence datasets. The goal of the model is to learn how to encode a dataset that reduces its vector size. Using a vector with reducing the dimension, the autoencoder will have to recreate the image similar to the original image. The autoencoder aims to produce output as input and focus only on the essential features. Raw sketches over the cGANS create realistic images that quickly and easily make the sketch images raw images. The results show that the model achieves high accuracy of up to 75%, and PSNR is 25.5 dB that is potentially applicable for practice with only 606 face images. The performance of our proposed architecture is compared with other solutions, and the results show that our proposal obtains competitive performance in terms of output quality (25.5 dB) and efficiency (above 75%).

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