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All Journal Jurnal ELTEK Journal of Communication Systems, Networks, and Security (JCSNS)
Nailul Muna
Politeknik Elektronika Negeri Surabaya
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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Implementasi sistem penyemprotan herbisida cerdas berbasis wireless sensor network untuk pertanian presisi Nailul Muna; Norma Ningsih; Nanang Syahroni; Ekananda Sulistyo Putra; Muhammad Naufal Asyam Muflih; Muhamad Nur Afriza Farkhan
JURNAL ELTEK Vol. 24 No. 1 (2026): April 2026
Publisher : Politeknik Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33795/eltek.v24i1.9865

Abstract

Penelitian ini mengembangkan sistem penyemprotan herbisida otomatis berbasis Internet of Things (IoT) dan Wireless Sensor Network (WSN) untuk mendukung pertanian presisi. Sistem terdiri atas dua node penyemprotan yang dilengkapi Raspberry Pi 5, ESP32, IP Camera, sensor ultrasonik, sensor waterflow, relay, dan pompa penyemprot. Raspberry Pi digunakan sebagai edge processing unit, sedangkan ESP32 berfungsi sebagai pengendali sensor dan aktuator. Sistem menggunakan SSD-MobileNet-V2 FPNLite untuk mendeteksi keberadaan gulma sebagai mekanisme pendukung pengambilan keputusan penyemprotan otomatis. Seluruh data monitoring dikirimkan ke Firebase dan divisualisasikan melalui website secara real-time. Hasil pengujian menunjukkan bahwa rata-rata akurasi sensor ultrasonik setelah kalibrasi mencapai 97,53% pada Node 1 dan 97,59% pada Node 2, sedangkan sensor waterflow mencapai 95,94% pada Node 1 dan 97,43% pada Node 2. Pengujian komunikasi data menunjukkan rata-rata latency sebesar 1,9 detik. Selain itu, seluruh perangkat keras dan website monitoring berhasil beroperasi dengan baik selama pengujian sistem. Hasil penelitian menunjukkan bahwa sistem mampu mengintegrasikan proses monitoring, komunikasi data, dan penyemprotan herbisida secara otomatis.   ABSTRACT An automatic herbicide spraying hardware system based on the Internet of Things (IoT) and Wireless Sensor Network (WSN) was developed to support precision agriculture. The system consists of two spraying nodes equipped with Raspberry Pi 5, ESP32, IP Camera, ultrasonic sensor, waterflow sensor, relay, and spraying pump. Raspberry Pi functions as the edge processing unit, while ESP32 serves as the sensor and actuator controller. The system utilizes SSD-MobileNet-V2 FPNLite to detect weeds as a supporting mechanism for automatic spraying decision-making. All monitoring data are transmitted to Firebase and visualized through a real-time monitoring website. Experimental results show that the average error of the calibrated ultrasonic sensor reached 2.47% on Node 1 and 2.41% on Node 2, while the waterflow sensor achieved 4.06% on Node 1 and 2.57% on Node 2. Data communication testing showed an average latency of 1.9 seconds. In addition, all hardware components and the monitoring website operated properly during system testing. The experimental results demonstrate that the developed system is capable of integrating monitoring, data communication, and automatic herbicide spraying processes.
GPS-Based Proximity Detection System Between Ship and Buoy for Marine Navigation Muhammad Edo Listiawan; Nihayatus Sa'adah; Nailul Muna; Karimatun Nisa'; Faridatun Nadziroh
Journal of Communication Systems, Networks, and Security Vol. 1 No. 1 (2026): April
Publisher : Politeknik Elektronika Negeri Surabaya

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Abstract

Maritime navigation safety remains a major concern, especially for small vessels that lack advanced navigation aids such as radar or AIS. This study proposes a proximity detection system between a ship and a buoy using GPS data, LoRa communication, and ThingsBoard visualization. The ship node periodically transmits its coordinates through LoRa. In contrast, the buoy node receives the ship position, reads its own GPS coordinates, calculates the separation distance using the Haversine formula, and determines the system status as SAFE or ALERT based on a 50 m threshold. The processed telemetry is then forwarded to a gateway and visualized in real time on ThingsBoard. Experiments were conducted at distances of 10-100 m under two scenarios, with and without an obstacle. In the open-area condition, the system achieved an average percentage error of 3.42%, a mean absolute error (MAE) of 1.82 m, and a root mean square error (RMSE) of 2.47 m. Under obstacle conditions, the average percentage error increased to 24.07%, with MAE and RMSE values of 6.36 m and 6.93 m, respectively. Communication performance remained stable, with a 200-230 ms delay, RSSI values in the good category, and packet loss between 0% and 10%. These results show that the proposed system can provide an affordable early-warning solution for ship-buoy proximity monitoring.
Co-Authors Ekananda Sulistyo Putra Faridatun Nadziroh Karimatun Nisa' Muhamad Nur Afriza Farkhan Muhammad Edo Listiawan Muhammad Naufal Asyam Muflih Nanang Syahroni Nihayatus Sa'adah Ningsih, Norma