Sistem Monitoring dan Kontrol Kualitas Air Akuarium Berbasis Internet of Things untuk Meningkatkan Akurasi dan Otomatisasi Pemeliharaan

Abstract

Kualitas air akuarium meliputi suhu, pH, Total Dissolved Solids (TDS), dan turbidity, perlu dipantau dan dikendalikan secara presisi untuk menjaga kesehatan ikan, namun pemantauan yang masih dilakukan secara manual belum memungkinkan pemantauan jarak jauh maupun otomatisasi.. Penelitian ini merancang sistem monitoring dan kontrol berbasis ESP32 dan IoT menggunakan sensor DS18B20, pH, TDS, dan turbidity melalui modul ADS1115, dengan penyimpanan data pada Supabase dan dashboard berbasis Next.js. Akurasi sensor dievaluasi terhadap alat ukur referensi selama 10 hari menggunakan Mean Error, MAE, RMSE, MAPE, dan standar deviasi error. Hasil menunjukkan sensor suhu, pH, dan TDS berakurasi sangat tinggi (MAPE 0,953%; 0,677%; 3,389%), sedangkan turbidity berakurasi baik (MAPE 12,62%). Kontrol otomatis pompa pengurasan dan pengisian air tervalidasi 100% terhadap 960 data pengujian, dengan seluruh data terpantau secara real-time melalui dashboard web. Sistem yang dikembangkan terbukti akurat dan andal untuk mendukung pemeliharaan akuarium secara otomatis dan efisien.

Aquarium water quality covering temperature, pH, Total Dissolved Solids (TDS), and turbidity must be precisely monitored and controlled to maintain fish health, but monitoring that is still done manually does not yet allow for remote access or automation. This study designed an IoT-based monitoring and control system on the ESP32 microcontroller, using DS18B20, pH, TDS, and turbidity sensors via an ADS1115 module, with data storage on Supabase and a Next.js dashboard. Sensor accuracy was evaluated against a reference instrument over 10 days using Mean Error, MAE, RMSE, MAPE, and standard deviation of error. Results show the temperature, pH, and TDS sensors achieved very high accuracy (MAPE of 0.953%, 0.677%, and 3.389%), while turbidity achieved good accuracy (MAPE 12.62%). The automatic drainage and refill pump control was validated with 100% consistency across 960 test data points, with all data viewable in real time via the web dashboard. The developed system proved accurate and reliable for automated, efficient aquarium maintenance.