| dc.contributor.advisor | Wicaksono, Aditya | |
| dc.contributor.advisor | Nasir, Muhammad | |
| dc.contributor.author | Nurrohman, Yana | |
| dc.date.accessioned | 2025-08-01T03:03:31Z | |
| dc.date.available | 2025-08-01T03:03:31Z | |
| dc.date.issued | 2025 | |
| dc.identifier.uri | http://repository.ipb.ac.id/handle/123456789/166352 | |
| dc.description.abstract | Inovasi teknologi dibutuhkan dalam sistem hidroponik untuk meningkatkan efisiensi pengelolaan nutrisi dan pemantauan lingkungan. Penelitian ini merancang sistem monitoring dan kontrol berbasis Internet of Things (IoT) yang dilengkapi dengan model kontrol cerdas Adaptive Neuro-Fuzzy Inference System (ANFIS). Fokus perancangan mencakup integrasi sensor TDS, pH, suhu (DS18B20), dan ultrasonik (HC-SR04), komunikasi real-time antara ESP32 dan website melalui Firebase, serta pemrosesan data menggunakan ANFIS di server Python. Sistem ini dikembangkan untuk melakukan akuisisi data, pengambilan keputusan, dan kontrol otomatis pompa nutrisi dan air. Hasil pengujian menunjukkan performa sistem berbasis ANFIS untuk mengontrol PPM yang menjanjikan dalam menjaga kestabilan nutrisi larutan, dengan tingkat akurasi yang lebih baik dibandingkan kontrol konvensional. | |
| dc.description.abstract | Technological innovation is needed in hydroponic systems to improve the efficiency of nutrient management and environmental monitoring. This study designs a monitoring and control system based on the Internet of Things (IoT), equipped with an intelligent control model using the Adaptive Neuro-Fuzzy Inference System (ANFIS). The design focuses on integrating TDS, pH, temperature (DS18B20), and ultrasonic (HC-SR04) sensors, real-time communication between the ESP32 microcontroller and a website via Firebase, and data processing using ANFIS on a Python server. The system is developed to perform data acquisition, decision-making, and automatic control of nutrient and water pumps. Test results show that the ANFIS-based system demonstrates promising performance in controlling PPM and maintaining nutrient solution stability, with higher accuracy compared to conventional control methods. | |
| dc.description.sponsorship | | |
| dc.language.iso | id | |
| dc.publisher | IPB University | id |
| dc.title | Analisis Efektivitas Algoritma Adaptive Neuro-Fuzzy Inference System untuk Kontrol PPM pada Sistem Hirdroponik Berbasis IoT | id |
| dc.title.alternative | Effectiveness Analysis of the Adaptive Neuro-Fuzzy Inference System Algorithm for PPM Control in an IoT-Based Hydroponic System | |
| dc.type | Tugas Akhir | |
| dc.subject.keyword | ANFIS | id |
| dc.subject.keyword | ESP32 | id |
| dc.subject.keyword | hidroponik | id |
| dc.subject.keyword | internet of things | id |
| dc.subject.keyword | ppm | id |
