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Journal : EPI International Journal of Engineering

Monitoring and Predicting Water Quality in Swimming Pools Apriandy Angdresey; Lanny Sitanayah; Vandri Josua Abram Sampul
EPI International Journal of Engineering Vol 3 No 2 (2020): Volume 3 Number 2, August 2020
Publisher : Center of Techonolgy (COT), Engineering Faculty, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25042/epi-ije.082020.05

Abstract

Water quality in public swimming pools affects human health. While changing the water too soon is wasteful, postponing changing the dirty water is not hygiene. In this paper, we propose an Internet of Things-based wireless system to monitor and predict water quality in public swimming pools. Our system utilizes an Arduino Uno, an ESP8266 ESP-01 WiFi module, a DS18B20 temperature sensor, a pH sensor, and a turbidity sensor. We predict the water quality using a data mining prediction model, namely the decision tree Iterative Dichotomiser 3 algorithm. We show by experiment that our sensor node and the wireless monitoring system work correctly. We also show by simulation using Weka that we can get 100% accuracy with a kappa statistical value of 1 and 0% error rate.
A Low Cost Vehicle Counting System Based On The Internet of Things Lanny Sitanayah; Apriandy Angdresey; Jeri Wahyu Utama
EPI International Journal of Engineering Vol 4 No 1 (2021): Volume 4 Number 1, February 2021
Publisher : Center of Techonolgy (COT), Engineering Faculty, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25042/epi-ije.022021.03

Abstract

In urban areas where land for parking is very limited, drivers often waste time, fuel, and emissions circling around without information if unoccupied parking spaces are available or not. In this paper, we design and implement a low-cost wireless system to count the number of cars and motorcycles in a parking lot. The system consists of two sensor devices, which are installed at an entrance gate and an exit gate of a parking lot. Each device has a NodeMCU ESP8266, an HC-SR04 ultrasonic sensor, and an MPU-9266 accelerometer. We use REST API as the web service to connect sensor devices and users, who will access the parking information using a web browser. The C4.5 algorithm is utilized to construct a decision tree to classify detected objects as cars, motorcycles, or people. We show by experiment that our sensor devices and the wireless monitoring system work correctly.