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All Journal Journal of Student Research Exploration
Salsabila, Halimah
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Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering

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Analysis of quality of service (QoS) wi-fi network in UNNES digital center building using wireshark Rianto, Nur Aziz Kurnia; Salsabila, Halimah; Jumanto, Jumanto
Journal of Student Research Exploration Vol. 1 No. 1: January 2023
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/josre.v1i1.108

Abstract

The need for the internet is a very absolute target in today's all-digital era. The traffic of information that is so dense and always dynamic every second makes everyone want speed in capturing information circulating. The speed in gathering information in this all-digital era cannot be separated from the internet and networks. UNNES Digital Center is one of the facilities owned by Semarang State University which is used as a digital-based learning center to support the realization of the Smart Digital Campus. The availability of qualified network services at the UNNES Digital Center is needed to support the all-digital-based student learning process. This research was done to find out how fast and good the quality of the internet network provided by the UNNES Digital Center is. In the research conducted, the network analysis step uses the Quality of Service (QoS) method. In obtaining research data that will be used as a basis for analyzing throughput, packet loss, delay, and network jitter, Wireshark software is used as a tool. The research results show that the quality of the Digital Center's internet network is very good and very adequate for digital learning activities. This is evidenced by a network throughput value of 6122.37 /kbits/s, a packet loss value of 0.7%, a delay of 214 ms with a moderate or quite good value and jitter = 0.511 ms.
Detection and prediction of rice plant diseases using convolutional neural network (CNN) method Pahlawanto, Reyhan Dzaki Sheva; Salsabila, Halimah; Pratiwi, Kusuma Ratna
Journal of Student Research Exploration Vol. 2 No. 1: January 2024
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/josre.v2i1.254

Abstract

Rice is a basic staple food in many Asian countries and is generally irreplaceable. Rice accounts for almost half of Asia food expenditure. Rice is too a crop that is prone to plant disease. It can appear and cause a decline in the quality of rice. However, constant monitoring of the rice fields can prevent the infection of the disease. Therefore, detection and prediction of rice plant diseases is one of the topics that will be discussed in this research. The purpose of this research is to help farmers to quickly pinpoint the disease of rice plants and take care of it properly. The methods used in this paper is researching and redesigning the previous attempt to hopefully make it better and more accurate. We will be using Convolutional Neural Network (CNN) models VGG16 as our algorithm. The results are that our proposed method has more accuracy than previous research using a similar dataset. The novelty of this paper is the increased accuracy of rice plant disease detection.
Co-Authors Jumanto Jumanto, Jumanto Pahlawanto, Reyhan Dzaki Sheva Pratiwi, Kusuma Ratna Rianto, Nur Aziz Kurnia