ANALISA KETINGGIAN MUKA AIR MENGGUNAKAN STATISTIK
DESKRIPTIF DAN INFERENSIAL UNTUK MENJAGA KETERSEDIAAN
AIR DI WADUK SERI OP1
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Institusion
INSTITUT TEKNOLOGI DIRGANTARA ADISUTJIPTO
Author
KURNIA PUTRI, PUJA
Subject
Q Science (General)
Datestamp
2024-06-05 09:03:33
Abstract :
One of the processes of watering pineapple plants at PT Great Giant Pineapple (GGP) is by utilizing a reservoir as a water storage area when the dry season arrives. So far, in knowing the condition of the reservoir water, PT GGP employees have to go into the field which results in inefficient work in time placement due to the very long distance between the office and the reservoir or other reservoirs. For this reason, a system that can measure the water level of the reservoir was created which can help PT GGP employees in determining the conditions for when the reservoir should carry out the process of adding water if the reservoir experiences drought and water reduction when the reservoir overflows. This measurement system was successfully built and worked well by utilizing Internet of Things (IoT) technology which consists of an HC-SR04 Ultrasonic sensor and an ESP32 microcontroller. The results of this research from the analysis of the water level show that the accuracy of the water level data between manual data and IoT data is 98.46% and the precision is 0%. The results of the time velocity accuracy test results in the reservoir water level between the time velocity data on the IoT and the time velocity data on the website of 88.86%. The data generated by this system are declared valid and consistent according to validity and reliability tests. In inferential statistical analysis using the Independent Sample T-Test, it shows that there is no significant (real) difference between manual data and IoT data
One of the processes of watering pineapple plants at PT Great Giant Pineapple (GGP) is by utilizing a reservoir as a water storage area when the dry season arrives. So far, in knowing the condition of the reservoir water, PT GGP employees have to go into the field which results in inefficient work in time placement due to the very long distance between the office and the reservoir or other reservoirs. For this reason, a system that can measure the water level of the reservoir was created which can help PT GGP employees in determining the conditions for when the reservoir should carry out the process of adding water if the reservoir experiences drought and water reduction when the reservoir overflows. This measurement system was successfully built and worked well by utilizing Internet of Things (IoT) technology which consists of an HC-SR04 Ultrasonic sensor and an ESP32 microcontroller. The results of this research from the analysis of the water level show that the accuracy of the water level data between manual data and IoT data is 98.46% and the precision is 0%. The results of the time velocity accuracy test results in the reservoir water level between the time velocity data on the IoT and the time velocity data on the website of 88.86%. The data generated by this system are declared valid and consistent according to validity and reliability tests. In inferential statistical analysis using the Independent Sample T-Test, it shows that there is no significant (real) difference between manual data and IoT data
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