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This Author published in this journals
All Journal Journal of Information Technology and Computer Engineering International Journal of Advanced Science Computing and Engineering
Cipto Prabowo
Politeknik Negeri Padang
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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Identification of Bioethanol Quality for Motorcycle Fuel Angraini, Tuti; Aidha, Zas Ressy; Anton; Kurniadi, Dedi; Prabowo, Cipto
International Journal of Advanced Science Computing and Engineering Vol. 5 No. 3 (2023)
Publisher : SOTVI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/ijasce.5.3.173

Abstract

The availability of crude oil as a raw material for vehicle fuel is dwindling and limited in nature. One of the renewable energies worth developing is bioethanol, which is one of the alternative fuels that can be used as a biofuel and can be processed from plants containing starch and glucose. In this research, the entire bioethanol identification system in a sugar cane drip distillation apparatus was examined. The distillation process using MQ3 and MQ135 sensors resulted in an alcohol percentage of 42% and 46%. The maximum temperature measured by a thermocouple during distillation was 88°C, while the minimum temperature recorded was 87°C. This study utilized a backpropagation artificial neural network method to identify the detected bioethanol. The architecture of the artificial neural network included 2 input nodes, 4 neuron nodes, and 2 output nodes. The training and testing results showed that the formed backpropagation was able to identify and differentiate the detection of bioethanol according to the given inputs with a success rate of 88.86% for detected bioethanol and 94.86% for undetected bioethanol..
A review of Image Processing Technique for Monitoring The Growth and Health of Cows Zurnawita, Zurnawita; Prabowo, Cipto; Kurnia, Rahmadi; Elfitri, Ikhwana
JITCE (Journal of Information Technology and Computer Engineering) Vol. 7 No. 01 (2023)
Publisher : Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jitce.7.01.8-18.2023

Abstract

In general, monitoring of animal growth and health is done directly by farmers (invasive measurement methods) which can cause cows to be injured or experience stress. To avoid this, several studies have been conducted on non-invasive methods using image processing technology. In this study, we systematically reviewed several works of literature to identify and synthesize published articles on image processing technology and image processing applications related to weight estimation and individual cattle identification. Analysis of image processing technologies used for weight estimation and individual cattle identification is the main objective of this article. Articles were searched through several databases and studies that met the inclusion criteria were analyzed and used in the review. The studies were divided into three main themes: image processing technologies, applications using image processing, and image processing research on cattle growth and health. It can be concluded that deep learning approaches are increasingly being studied, tested and considered as a viable and promising approach to monitor cattle weight and health in several aspects
Co-Authors Angraini, Tuti Anton dedi kurniadi Ikhwana Elfitri Rahmadi Kurnia Zas Ressy Aidha Zurnawita Zurnawita