Sistem Pengendalian Lalat Otomatis Berbasis IoT dan Deep Learning dengan Optimasi Frekuensi

Authors

  • I Gusti Made Ngurah Desnanjaya Rekayasa Sistem Komputer, Fakultas Teknologi Informatika, institut Bisnis dan Teknologi Indonesia https://orcid.org/0000-0002-8811-6395
  • Nyoman Sarasuartha Mahajaya Rekayasa Sistem Komputer, Fakultas Teknologi Informatika, institut Bisnis dan Teknologi Indonesia
  • Anak Agung Surya Pradhana Rekayasa Sistem Komputer, Fakultas Teknologi Informatika, institut Bisnis dan Teknologi Indonesia
  • I Made Aditya Nugraha Jurusan Mekanisasi Perikanan, Politeknik Kelautan dan Perikanan Kupang

Keywords:

IoT, deep learning, ultrasonic, fly repellent, LSTM

Abstract

This research develops an IoT-based fly repellent system using AD9850 module and piezo speaker, combined with adaptive ultrasonic frequency optimization through artificial intelligence (AI). The system is built using ESP32, integrates DHT22 sensors, and is controlled via the Blynk IoT platform. Field data are collected over seven days, with hourly records of fly count, temperature, humidity, and device status. A Long Short-Term Memory (LSTM) model predicts fly populations and recommends optimal frequencies (40/60 kHz) automatically. Results show that activating 60 kHz frequency reduced the average fly count by up to 65%. The AI model achieves R²=0.96 and RMSE=1.8 flies/hour. The integrated IoT-AI solution reduces manual intervention, increases repellent effectiveness, and supports the realization of a hygienic environment powered by smart technology.

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Published

2025-04-30

How to Cite

Desnanjaya, I. G. M. N., Mahajaya, N. S., Pradhana, A. A. S., & Nugraha, I. M. A. (2025). Sistem Pengendalian Lalat Otomatis Berbasis IoT dan Deep Learning dengan Optimasi Frekuensi. Jurnal RESISTOR (Rekayasa Sistem Komputer), 8(1), 8–17. Retrieved from https://ejournal.instiki.ac.id/index.php/jurnalresistor/article/view/1935

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