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All Journal Syntax Literate: Jurnal Ilmiah Indonesia Jurnal Teknik Informatika (JUTIF)
Syaiful Haq Al Furuqi
Teknik Informatika, Universitas Pradita, Indonesia
Humanities Computer Science & IT Education Environmental Science Law, Crime, Criminology & Criminal Justice Social Sciences Other

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IOT-BASED REMOTE LIGHT CONTROL SYSTEM USING BOT TELEGRAM APPLICATION (CASE STUDY OF SMP AL WASHLIYAH CIREBON) Syaiful Haq Al Furuqi; Muhamad Abu Hafsin; Haryono Haryono
Jurnal Teknik Informatika (Jutif) Vol. 3 No. 6 (2022): JUTIF Volume 3, Number 6, December 2022
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20884/1.jutif.2022.3.6.313

Abstract

Advances in technology nowadays could make it easier for human beings in order to save time doing work that is commonly done. Smartphones are currently developing rapidly, and at the moment social media applications can be used to emerge as remote controllers which could manage electronic devices remotely. This study objectives to construct an IoT-based remote light control system using the telegram application. The consequences of this have a look at are predicted to save the user's time to turn on or turn off the lights remotely. Users only need to open the telegram application, then select the bot constructed to control the lights, and be asked to press the available commands. The consequences of this study take a look at the telegram bot application can turn the lights on and off properly, this has a delay less than 60 seconds according to the connection used, for every command that is pressed from the telegram bot utility.
Comparative Analysis Of DCGAN And WGAN Syaiful Haq Al Furuqi; Handri Santoso
Syntax Literate Jurnal Ilmiah Indonesia
Publisher : Syntax Corporation

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (159.024 KB) | DOI: 10.36418/syntax-literate.v7i10.9754

Abstract

In current years, image recognition is increasingly being used, however, it becomes much less correct if there is not plenty data available while training data. Generative Adversarial Networks (GAN) can assist to create new data that is nearly similar to the original data to help the training process while the original data isn't always much in order that the training process might be more accurate. GAN is currently growing and there are an increasing number of types, along with the Deep Convolutional GAN (DCGAN) and Wasserstein GAN (WGAN) algorithms. This study analyzes the comparison among DCGAN and WGAN which objectives to offer a decision approximately which algorithm is better to use. Based on the research results, DCGAN is simpler however there are still drawbacks, particularly the Mode collapse and vanishing gradient, at the same time as WGAN can remedy those shortcomings however the process is slower.
Co-Authors Handri Santoso Haryono Haryono Muhamad Abu Hafsin