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Journal : International Journal Engineering and Applied Technology (IJEAT)

SMART AGRICULTURE BASED IOT AND MOBILE APPS Somantri; Supriatna; Hardi Herdiana; Ujang Mulyana; Anggy Pradifta Junfithrana
INTERNATIONAL JOURNAL ENGINEERING AND APPLIED TECHNOLOGY (IJEAT) Vol. 3 No. 2 (2020): International Journal of Engineering and Applied Technology (IJEAT)
Publisher : Nusa Putra University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (881.361 KB) | DOI: 10.52005/ijeat.v3i2.62

Abstract

Agriculture is the main occupation in our country for many centuries. But now because of the migration of people from rural to urban areas there are barriers to agriculture. So to solve this problem we are using smart farming techniques using IOT. Smart farming is a developing concept, because the IOT Sensor is able to provide information about the agricultural sector and then act on user input. To develop this Intelligent Agricultural System, it uses the advantages of the latest technology such as Arduino, IOT and Wireless Sensor Networks. The paper aims to make use of the emerging technology namely IOT and the use of intelligent agricultural automation. Monitoring of environmental conditions is the main factor for increasing efficient crop yields. This feature includes the development of a system that can monitor temperature, humidity and is programmed via an android application.
Broken Road Detection Methods Comparison: A Literature Survey Indra Yustiana; Somantri; Dudih Gustian; Anggy Pradifta Junfithrana; Satish Kumar Damodar
INTERNATIONAL JOURNAL ENGINEERING AND APPLIED TECHNOLOGY (IJEAT) Vol. 5 No. 2 (2022): November 2022
Publisher : Nusa Putra University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52005/ijeat.v5i2.75

Abstract

Roads are infrastructure built to facilitate regional development. Good road conditions will certainly provide a sense of comfort for every vehicle that will pass through it. For that, care and attention to road conditions needs to be done. The occurrence of damage to the road will hinder the development process. Currently, detection of damaged roads is still done manually using human resource. It makes the detection process take quite a lot of time to determine how bad the damage is. So there needs a way to help improve time efficiency and accuracy in detecting damaged roads. One of them is by utilizing machine learning technology. In this paper, we will discuss what methodology can be use and their comparisons to be able to use appropriate and effective methodologies to detect cases of damaged roads