Alat Deteksi Suara Gergaji Mesin Sebagai Indikasi Awal Terjadinya Penebangan Menggunakan Metode Convolutional Neural Network

  • Ana Surya Ningrum Politeknik Perkapalan Negeri Surabaya
  • Adianto Politeknik Perkapalan Negeri Surabaya
  • Rini Indarti Politeknik Perkapalan Negeri Surabaya
  • Edy Setiawan Politeknik Perkapalan Negeri Surabaya
  • Afif Zuhri Arfianto Politeknik Perkapalan Negeri Surabaya
  • Zindhu Maulana Ahmad Putra Politeknik Perkapalan Negeri Surabaya
Abstract views: 123 , PDF downloads: 69
Keywords: convolutional neural network, data processing, lora rfm95, max4466 sound sensor, sound classification

Abstract

Illegal logging in Indonesia is no small problem, with illegal logging causing damage to forest resources in terms of quantity, quality and ecosystem. Many efforts have been taken by the Indonesian government, but it has not been effective in dealing with this problem, due to limited supervision. From this problem, a chainsaw sound detection system was designed as an early indication of logging activity. This system is equipped with four MAX4466 sound sensors using the Convolutional Neural Network method. This system also uses data processing so that the chainsaw sound can be recognized by the system specifically and can communicate remotely with the use of LoRa RFM95.  Thus, the system can identify the sound of the chainsaw with a maximum distance of 50 m, the success accuracy of the CNN model created reaches 97.5%, and can be integrated with WhatsApp in realtime.

References

A. Zulfadli, O. B. Kharisma, H. Simaremare, and E. Ismaredah, “PENDETEKSI PENEBANG LIAR MENGGUNAKAN SENSOR SUARA MAX4466 DI KAWASAN HUTAN,” Transmisi: Jurnal Ilmiah Teknik Elektro, vol. 25, no. 3, pp. 95–102, Aug. 2023, doi: 10.14710/transmisi.25.3.95-102.

A. Herlan, I. Fitri, and R. Nuraini, “Rancang Bangun Sistem Monitoring Data Sebaran Covid-19 Secara Real-Time menggunakan Arduino Berbasis Internet of Things (IoT),” Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi), vol. 5, no. 2, p. 206, Apr. 2021, doi: 10.35870/jtik.v5i2.212.

M. Hafiz Aziz, S. W. Jadmiko, and S. Yahya, “Prosiding The 12 th Industrial Research Workshop and National Seminar Bandung,” 2021.

I. Putu Gede Surya Angga Pranata, I. Made Suartika, and I. Wayan Arta Wijaya, “PERANCANGAN ALAT PENDETEKSI PENEBANG LIAR MENGGUNAKAN SENSOR SUARA BERBASIS IoT-RASPBERRY PI,” 2021.

P. Agustinus Mikael Rondo, “Quo Vadis Penegakan Hukum: Kewenangan Pemerintah Terhadap Lingkungan Hidup dalam Kasus Illegal Logging Di Indonesia,” Jurnal Syntax Transformation, vol. 3, no. 04, pp. 532–537, Apr. 2022, doi: 10.46799/jst.v3i4.545.

R. F. Fadhillah and R. Sumiharto, “Klasifikasi Suara Untuk Memonitori Hutan Berbasis Convolutional Neural Network,” IJEIS (Indonesian Journal of Electronics and Instrumentation Systems), vol. 13, no. 1, Apr. 2023, doi: 10.22146/ijeis.79536.

D. Candra Prasetyo, G. Andriana Mutiara, and R. Handayani, “SISTEM PENDETEKSI SUARAGERGAJI PADA ILLEGAL LOGGING.”

B. S. Dewa and I. H. Santoso, “Perancangan Dan Implementasi Alat Pendeteksi Kebisingan Kendaraan Bermotor Berbasis Internet Of Things Dengan Menggunakan Sensor KY-037 Dan Sensor MAX4466 The Design And Implementation Of Motor Vehicle Noise Detection Equipment Based On Internet Of Things Using KY-037 And MAX4466 Sensor,” vol. 8, no. 6, p. 3463, 2022.

P. Fiľo and O. Janoušek, “Differences in the Course of Physiological Functions and in Subjective Evaluations in Connection With Listening to the Sound of a Chainsaw and to the Sounds of a Forest,” Front Psychol, vol. 13, Feb. 2022, doi: 10.3389/fpsyg.2022.775173.

F. Fauziah, I. I. Tritoasmoro, and S. Rizal, “SISTEM KEAMANAN BERBASIS PENGENALAN SUARA SEBAGAI PENGAKSES PINTU MENGGUNAKAN METODE MEL FREQUENCY CEPSTRAL COEFFICIENT (MFCC) DOOR ENTRY USING VOICE RECOGNITION SECURITY SYSTEM WHILE UNTILIZING MEL FREQUENCY CEPSTRAL COEFFICIENT (MFCC),” 2021.

A. S. Irtawaty, M. Ulfah, and R. S. Fathmala, “Application Development of Male and Female Voice Differentiation Based on Gender, Age Range, Frequency Class Based on FFT and K-Means,” Jurnal Ecotipe (Electronic, Control, Telecommunication, Information, and Power Engineering), vol. 9, no. 1, pp. 32–39, Mar. 2022, doi: 10.33019/jurnalecotipe.v9i1.2836.

A. Firman Mawardi, A. Ubaidillah, and K. A. Wibisono, “Rancang Bangun Smart Dispenser Untuk Penyandang Tunanetra Menggunakan Pola Pengenalan Suara (Voice Recognition) Dengan Algoritma Fast Fourier Transform (FFT) Dan Autocorrelation,” 2020.

R. Yohanes Sipasulta, A. S. Lumenta, and S. RUA Sompie, “Simulasi Sistem Pengacak Sinyal Dengan Metode FFT (Fast Fourier Transform),” 2014.

D. T. Kusuma, “Fast Fourier Transform (FFT) Dalam Transformasi Sinyal Frekuensi Suara Sebagai Upaya Perolehan Average Energy (AE) Musik,” PETIR, vol. 14, no. 1, pp. 28–35, Oct. 2020, doi: 10.33322/petir.v14i1.1022.

V. Zilvan and F. Hensan Muttaqien, “Identifikasi Pembicara Menggunakan Algoritme VFI5 dengan MFCC sebagai Pengekstraksi Ciri,” 2011.

V. Karenina, M. F. Erinsyah, and D. S. Wibowo, “Klasifikasi Rentang Usia Dan Gender Dengan Deteksi Suara Menggunakan Metode Deep Learning Algoritma Cnn (Convolutional Neural Network),” Komputika : Jurnal Sistem Komputer, vol. 12, no. 2, pp. 75–82, Sep. 2023, doi: 10.34010/komputika.v12i2.10516.

PlumX Metrics

Published
2024-07-31