| dc.contributor.advisor | Marcelita, Faldiena | |
| dc.contributor.author | Khairullah, Hauzan Hanif | |
| dc.date.accessioned | 2026-07-30T07:09:56Z | |
| dc.date.available | 2026-07-30T07:09:56Z | |
| dc.date.issued | 2026 | |
| dc.identifier.uri | http://repository.ipb.ac.id/handle/123456789/176531 | |
| dc.description.abstract | Sistem pemantauan sedimentasi sungai secara manual dinilai kurang efisien dan memakan biaya operasional yang tinggi. Penelitian ini bertujuan mengembangkan Sistem Cerdas Informasi Sedimentasi (SiCita) berbasis Internet of Things (IoT) pada Sungai Ciliwung. Sistem ini memanfaatkan mikrokontroler ESP32-S3, sensor ultrasonik A01NYUB, serta sistem catu daya panel surya mandiri untuk memastikan operasional perangkat secara kontinu di luar ruangan. Data jarak yang dibaca oleh sensor dikonversi menjadi kedalaman air, lalu dianalisis menggunakan metode regresi linear sederhana pada sisi peladen. Pengujian lapangan dari Januari hingga April 2025 dengan 10.277 data valid menunjukkan tren penurunan kedalaman sebesar -0,0432 cm/hari, yang mengindikasikan pendangkalan sungai secara perlahan, meski belum signifikan secara statistik. Sistem juga berhasil menampilkan data pemantauan secara real-time melalui aplikasi mobile dan sukses mengirimkan peringatan dini melalui push notification Firebase ketika terjadi anomali muka air. | |
| dc.description.abstract | Conventional manual river sedimentation monitoring systems are considered inefficient and costly. This study aims to develop an Internet of Things
(IoT)-based Intelligent Sedimentation Information System (SiCita) for the Ciliwung River. The system utilizes an ESP32-S3 microcontroller, an A01NYUB ultrasonic sensor, and an independent solar panel power supply to ensure continuous outdoor operation. The measured distance data is converted into water depth and analysed using a simple linear regression method on the server side. Field testing from January to April 2025 with 10,277 valid data points showed a depth reduction trend of -0.0432 cm/day, indicating slow river shallowing, although it is not yet statistically significant. The system also successfully displays real-time monitoring data through a mobile application and reliably sends early warnings via Firebase push notifications when water level anomalies occur. | |
| dc.description.sponsorship | | |
| dc.language.iso | id | |
| dc.publisher | IPB University | id |
| dc.title | Pengembangan Sistem SiCita Berbasis IoT untuk Pemantauan Sedimentasi pada Sungai Ciliwung | id |
| dc.title.alternative | Development of IoT-Based SiCita System for Sedimentation Monitoring in the Ciliwung River | |
| dc.type | Tugas Akhir | |
| dc.subject.keyword | ESP32-S3 | id |
| dc.subject.keyword | internet of things | id |
| dc.subject.keyword | Regresi Linear | id |
| dc.subject.keyword | sedimentasi | id |
| dc.subject.keyword | Sungai Ciliwung | id |
| dc.subject.keyword | Ciliwung River | id |
| dc.subject.keyword | Linear Regression | id |
| dc.subject.keyword | sedimentation | id |
| dc.subtype | Undergraduate Theses | |
