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Journal : jurnal teknik informatika dan sistem informasi

Monitoring Kualitas Air Tambak Udang Menggunakan NodeMCU, Firebase, dan Flutter Ramadhan, Harry Pratama; Kartiko, Condro; Prasetiadi, Agi
Jurnal Teknik Informatika dan Sistem Informasi Vol 6 No 1 (2020): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v6i1.2365

Abstract

Abstract — Based on the prior study, some shrimp ponds went bankrupt due to pond water quality monitoring is still not good. Many shrimps get sick and die for water quality monitoring still relies on laboratory checks and is rarely done because of financial problems. The purpose of this study is to develop a monitoring system of shrimp pond water quality especially for vannamei shrimp using an Internet of Things (IoT)-based device with a data logging method. The system role is to monitor the water condition, record sensor data, and provide water quality status of shrimp ponds based on water movement, turbidity of water, and water temperature. The data logger device uses a microcontroller named NodeMCU ESP8266 and two sensors namely the LDR sensor and the water temperature sensor dallas 18b20. The devices are connected to the internet and send all water quality monitoring data to Google's database service called Firebase. The results of the water quality monitoring can be accessed through an Android-based monitoring application that is built using Flutter framework which contains information. Keywords— Flutter Android; Internet of Things; Monitoring System; Water Quality
Penerapan Estimasi Posisi dan Tracking Wajah Pada Sistem Presensi Mahasiswa Pratama, Afrillebar Putra; Prasetiadi, Agi; Usada, Elisa
Jurnal Teknik Informatika dan Sistem Informasi Vol 6 No 2 (2020): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v6i2.2730

Abstract

The current presence system can be done with a computerized system, one of which is the face biometric system. This study focuses on the application of position estimation and tracking based on clustering on people's faces to determine the position in three dimensions. Position estimation can be obtained by making a kernel that is ready to be used to predict three-dimensional coordinates of faces based on two-dimensional coordinates of two images. Position estimation can be done by utilizing the Machine Learning algorithm family. In this study, Least Absolute Shrinkage and Selection Operators (LASSO) is used to perform the position estimation. Meanwhile, clustering in this study uses the K-Means algorithm. Based on the test results, the kernel error obtained in estimating the face location is 9.23 cm. The tracking accuracy of an object based on clustering is 100%.