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Journal : International Journal of Advanced Science Computing and Engineering

Fiber to The Home (FTTH) Network Design with Addition of Optical Distribution Point (ODP) Using the Branching Method Asril, Aprinal Adila; Maria, Popi; Lifwarda, -; Antonisfia, Yul; Hadi, Ronal
International Journal of Advanced Science Computing and Engineering Vol. 5 No. 2 (2023)
Publisher : SOTVI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/ijasce.5.2.136

Abstract

Optical fiber is a transmission medium that uses light as a signal conductor. In order for optical fibers to be used and the benefits are felt, a network architecture is needed, namely FTTH. FTTH consists of active devices such as OLT and ONT as well as passive devices consisting of ODC, Closure, and ODP. In this design, it uses OLS as a light signal transmitter and produces input power. However, over time and the increase in population capacity and the number of access services available, of course, there are more and more requests for the installation of optical networks in customers' homes. Therefore, the most available FTTH device is ODP in order to be able to withdraw cables to customers' homes and continue to expand FTTH. Therefore, this study will discuss the addition of new ODP with the branching method. Using OPM as a measuring tool and calculating the power link budget to find out that the resulting attenuation value is no more than 28dB.
Design and Development of a Coffe Blending Device with Carbon Monoxide (CO) Level Identification Based on Artificial Neural Networks Antonisfia, Yul; Susanti, Roza; Efendi; Anderson, Sir; Anisa, Fitri
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.5.3.171

Abstract

Coffee is categorized into three types Robusta, Arabica, and Liberica. The roasting process is the most crucial step in developing the aroma, flavor, and underlying color that determine coffee quality. The coffee roasting process produces complex aroma compounds that impart the desired taste and aroma characteristics of coffee. This research aims to design a coffee content detection tool by determining the carbon monoxide (CO) level in Arabica, Robusta, and Liberica coffee. The research uses the Backpropagation artificial neural network method with 2 hidden layers, including 4 input layers and 3 output layers, to identify the tested coffee varieties. The highest carbon monoxide (CO) levels were found in Arabica Special coffee, with an ADC level of 662 carbon monoxide gas. The lowest carbon monoxide (CO) levels were detected in Liberica coffee, with an ADC level of 105 carbon monoxide gas. Coffee identification was carried out using an artificial neural network method with a success rate of 98% for Liberica coffee, 100% for Arabica coffee, and 98% for Robusta coffee.