Adam, Abdillah Putra (2025) Sistem Monitoring Level Air Pelarut Pada Solvent Tank Dalam Budidaya Kepiting Bakau Vertikal Terintegrasi Aplikasi Mobile. Other thesis, Institut Teknologi Sepuluh Nopember.
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Abstract
Sistem kontrol dan monitoring kualitas air merupakan elemen penting dalam budidaya kepiting bakau vertikal untuk memastikan kondisi air yang optimal bagi kepiting. Penelitian ini mengembangkan sistem monitoring level air pelarut pada solvent tank yang terintegrasi dengan aplikasi mobile. Sistem ini dirancang untuk monitoring level air setiap solvent tank air asin, air tawar, air asam, dan air basa guna mengetahui level air pelarut yang digunakan untuk mengondisikan air budidaya kepiting agar sesuai dengan kondisi air yang dibutuhkan oleh kepiting yaitu temperatur 25-35°C, pH 7.5-9.0, salinitas 22-28 ppt. Penelitian ini dilengkapi prediksi amonia menggunakan Artificial Neural Network (ANN) dengan menggunakan 3 input berupa nilai pH, salinitas dan temperatur. Model ANN dilatih menggunakan dataset berdasarkan validator amonia untuk meningkatkan akurasi prediksi dibandingkan penelitian sebelumnya, selain itu sistem prediksi amonia menggunakan ANN berhasil dibuat dengan eror sebesar 10,47%. Hasil pengujian menunjukkan bahwa sistem mampu memberikan data akurat mengenai level air di setiap solvent tank serta memprediksi kadar amonia dengan eror yang lebih rendah dibandingkan metode sebelumnya yaitu 12%. Dengan implementasi sistem ini, budidaya kepiting bakau dapat dilakukan secara lebih efisien, mengurangi risiko kekurangan air pelarut pada solvent tank, serta meningkatkan keberlangsungan hidup kepiting.
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Water quality control and monitoring systems are essential elements in vertical mud crab farming to ensure optimal water conditions. This research developed a monitoring system for solvent water levels in solvent tanks integrated with a mobile application. The system is designed to monitor water levels in each solvent tank containing saline water, freshwater, acidic water, and alkaline water, to determine the solvent water levels used to condition the crab farming water according to the required parameters: temperatur 25-35°C, pH 7.5-9.0, and salinity 22-28 ppt. This research includes ammonia prediction using an Artificial neural network (ANN) with three input parameters: pH, salinity, and temperatur. The ANN model was trained using a dataset based on an ammonia validator to improve prediction accuracy compared to previous studies. Additionally, the ammonia prediction system using ANN achieved an error rate of 10.47%. The testing results demonstrated that the system could provide accurate data on water levels in each solvent tank and predict ammonia levels with a lower error rate compared to the previous method, which had an error rate of 12%. With the implementation of this system, mud crab farming can be conducted more efficiently, reducing the risk of solvent water shortages in solvent tanks and improving the survival rate of crabs
| Item Type: | Thesis (Other) |
|---|---|
| Uncontrolled Keywords: | Sistem Monitoring, Artificial neural network, Aplikasi Mobile, Sensor Ultrasonik US-016, Budidaya Kepiting Bakau Monitoring System, Artificial neural network, Mobile Application, Ultrasonic Sensor US-016, Mud Crab Farming. |
| Subjects: | Q Science > Q Science (General) > Q325.5 Machine learning. Support vector machines. Q Science > QC Physics > QC100.5 Measuring instruments (General) Q Science > QC Physics > QC271.8.C3 Calibration Q Science > QC Physics > QC271 Temperature measurements T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK3070 Automatic control T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7878 Electronic instruments |
| Divisions: | Faculty of Vocational > Instrumentation Engineering |
| Depositing User: | Abdillah Putra Adam |
| Date Deposited: | 31 Jul 2025 07:20 |
| Last Modified: | 31 Jul 2025 07:28 |
| URI: | http://repository.its.ac.id/id/eprint/123241 |
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