Pengembangan Alat Pemantau Ketinggian Air dan Curah Hujan Menggunakan Sensor Ultrasonik serta Hall Effect.

Abstract

Curah hujan dapat memengaruhi ketinggian air sungai sehingga diperlukan sistem pemantauan otomatis jarak jauh. Penelitian ini bertujuan mengembangkan alat pemantauan curah hujan dan ketinggian air berbasis Internet of Things serta menganalisis hubungan kedua parameter. Penelitian kuantitatif dilakukan melalui perancangan, implementasi, kalibrasi, pengujian, pengumpulan data, dan regresi linear sederhana. Sistem menggunakan ESP32 S3, sensor ultrasonik JSN-SR04T, dan sensor hall effect KY-003 pada mekanisme tipping bucket. Data dikirimkan setiap lima menit ke ThingSpeak dan Telegram. Kalibrasi sensor ultrasonik menghasilkan Mean Absolute Percentage Error 1,75%, sedangkan satu pulsa tipping bucket setara dengan 0,52 mm curah hujan. Sebanyak 90 data dikumpulkan di Sungai Ciluar pada 17–27 April 2026. Persamaan regresi yang diperoleh ialah Y= 79,3182 - 0,1587X, dengan R^2= 0,0335 dan p= 0,0843. Hubungan kedua parameter sangat lemah dan tidak signifikan. Sistem menyediakan data pemantauan otomatis untuk analisis lanjutan.

Kata kunci: curah hujan, hall effect, internet of things, ketinggian air, regresi linear

Rainfall may affect river water levels; therefore, an automated remote monitoring system is required. This study aimed to develop an Internet of Things-based rainfall and water level monitoring device and analyze the relationship between both parameters. A quantitative approach included design, implementation, calibration, testing, data collection, and simple linear regression. The system used an ESP32 S3, a JSN-SR04T ultrasonic sensor, and a KY-003 hall effect sensor with a tipping bucket mechanism. Data were transmitted to ThingSpeak and Telegram every five minutes. Ultrasonic sensor calibration resulted in a Mean Absolute Percentage Error of 1,75%, while one tipping bucket pulse represented 0,52 mm of rainfall. A total of 90 observations were collected at the Ciluar River from 17 to 27 April 2026. The regression model was Y= 79,3182 - 0,1587X, with R^2= 0,0335 and p= 0,0843. The relationship was very weak and not significant. The system provides automatic monitoring data for further analysis.

Keywords: hall effect, internet of things, linear regression, rainfall, water level