PEMANFAATAN ARDUINO DAN SENSOR KY-038 UNTUK MEMBEDAKAN SUARA MESIN CHAINSAW DAN MESIN LAIN DI AREA PEMBALAKAN LIAR

Hendri; Mutmainnah Muchtar; Sarimuddin

doi:10.23960/jitet.v13i3.6797

Authors

Hendri Universitas Sembilanbelas November Kolaka
Mutmainnah Muchtar http://orcid.org/0000-0002-1423-5375
Sarimuddin Universitas Sembilanbelas November Kolaka

DOI:

https://doi.org/10.23960/jitet.v13i3.6797

Abstract Views: 64 File Views: 58

Keywords:

chainsaw, sound detection, KNN, KY-038, illegal logging

Abstract

Illegal logging is a significant environmental issue in Indonesia, particularly in the tropical forests of Southeast Sulawesi, which threatens biodiversity and contributes to global climate change. Manual monitoring of illegal activities in remote areas is often ineffective, necessitating innovative and real-time solutions for early detection. This study aims to develop an early detection system to distinguish the sound of chainsaws commonly used in illegal logging activities from other machine sounds such as RX King motorcycles and ketinting boats. The KY-038 sound sensor connected to an Arduino was used to capture environmental sounds, and the obtained data was classified using the K-Nearest Neighbors (KNN) algorithm. Experiments were conducted by collecting training data and testing the system with sound samples from each machine. The results showed that the developed sound detection system could classify the sounds of chainsaws, RX King motorcycles, and ketinting boats with good performance. With the optimal k value in KNN, the average classification accuracy reached 90%. This system can be used as an effective monitoring tool for the early detection of illegal logging activities, contributing to the conservation of tropical forests.

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References

H. Setiawan, I. N. S. Jaya, and N. Puspaningsih, “MODEL SPASIAL DEFORESTASI DI KABUPATEN KONAWE UTARA DAN KONAWE PROVINSI SULAWESI TENGGARA ( Deforestation Spatial Model In North Konawe And Konawe Districts South East Sulawesi Province ),” Media Konservasi, vol. 20, no. 2, pp. 166–176, 2015.

S. Sarimuddin, M. Muchtar, Y. P. Pasrun, L. A. F. Hasidu, and R. Riska, “Penentuan Tingkat Kesehatan Komunitas Mangrove Secara Otomatis Menggunakan Otsu Thresholding,” Jurnal Informatika dan Rekayasa Perangkat Lunak, vol. 6, no. 1, pp. 30–39, 2024.

S. Marwah, “Potential of karbon stock in Tanjung Peropa Wildlife Reserve Forests in the implementation of INDC and initiatives of local mitigation,” Ecogreen, vol. 2, no. 2, pp. 115–122, 2016.

E. Olteanu, V. Suciu, S. Segarceanu, I. Petre, and A. Scheianu, “Forest Monitoring System Through Sound Recognition,” 2018 12th International Conference on Communications, COMM 2018 - Proceedings, pp. 75–80, 2018, doi: 10.1109/ICComm.2018.8430163.

A. Srisuphab, N. Kaakkurivaara, P. Silapachote, K. Tangkit, P. Meunpong, and T. Sunetnanta, “Illegal logging listeners using IoT networks,” IEEE Region 10 Annual International Conference, Proceedings/TENCON, vol. 2020-Novem, pp. 1277–1282, 2020, doi: 10.1109/TENCON50793.2020.9293935.

A. Bansal and N. K. Garg, “Environmental Sound Classification using Hybrid Ensemble Model,” Procedia Comput Sci, vol. 218, pp. 418–428, 2022, doi: 10.1016/j.procs.2023.01.024.

E. Tsalera, A. Papadakis, and M. Samarakou, “Monitoring, profiling and classification of urban environmental noise using sound characteristics and the KNN algorithm,” Energy Reports, vol. 6, no. June, pp. 223–230, 2020, doi: 10.1016/j.egyr.2020.08.045.

M. P. Lukman, . J., and Y. F. Y. Rieuwpassa, “Sistem Lampu Otomatis Dengan Sensor Gerak, Sensor Suhu Dan Sensor Suara Berbasis Mikrokontroler,” Jurnal RESISTOR (Rekayasa Sistem Komputer), vol. 1, no. 2, pp. 100–108, 2018, doi: 10.31598/jurnalresistor.v1i2.305.

N. A. J. Gnamele, Y. B. Ouattara, T. A. Kobea, G. Baudoin, and J. M. Laheurte, “KNN and SVM classification for chainsaw sound identification in the forest areas,” International Journal of Advanced Computer Science and Applications, vol. 10, no. 12, pp. 531–536, 2019, doi: 10.14569/ijacsa.2019.0101270.

L. Czúni and P. Z. Varga, “Time Domain Audio Features for Chainsaw Noise Detection Using WSNs,” IEEE Sens J, vol. 17, no. 9, pp. 2917–2924, 2017, doi: 10.1109/JSEN.2017.2670232.

D. C. Prasetyo, G. A. Mutiara, and R. Handayani, “Chainsaw Sound and Vibration Detector System for Illegal Logging,” Proceedings - 2018 International Conference on Control, Electronics, Renewable Energy and Communications, ICCEREC 2018, no. December 2018, pp. 93–98, 2018, doi: 10.1109/ICCEREC.2018.8712091.

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, 2023, doi: 10.22146/ijeis.79536.

T. M. Aide, C. Corrada-Bravo, M. Campos-Cerqueira, C. Milan, G. Vega, and R. Alvarez, “Real-time bioacoustics monitoring and automated species identification,” PeerJ, vol. 2013, no. 1, pp. 1–19, 2013, doi: 10.7717/peerj.103.

P. Hukum, L. Hidup, D. Kehutanan Kementerian, and D. Kehutanan, “DIREKTORAT JENDERAL.”

Sarimuddin, Cara Mudah Kuasai Mikrokontroler Arduino Teori Dan Praktek, vol. 1, no. 1. Purbalingga: EUREKA MEDIA AKSARA, 2023.

M. P. Lukman, . J., and Y. F. Y. Rieuwpassa, “Sistem Lampu Otomatis Dengan Sensor Gerak, Sensor Suhu Dan Sensor Suara Berbasis Mikrokontroler,” Jurnal RESISTOR (Rekayasa Sistem Komputer), vol. 1, no. 2, pp. 100–108, 2018, doi: 10.31598/jurnalresistor.v1i2.305.

Sarimuddin, Cara Mudah Kuasai Mikrokontroler Arduino Teori Dan Praktek, vol. 1, no. 1. Purbalingga: EUREKA MEDIA AKSARA, 2023.

Hasmawati, M. Muchtar, and J. Nangi, “APLIKASI PREDIKSI PENJUALAN BARANG MENGGUNAKAN METODE K-NEAREST NEIGHBOR (KNN) (STUDI KASUS TUMAKA MART),” semanTIK, vol. 3, no. 2, pp. 151–160, 2017.

M. Muchtar and R. A. Muchtar, “Perbandingan Metode KNN dan SVM Dalam Klasifikasi Kematangan Buah Mangga Berdasarkan Citra HSV dan Fitur Statistik,” Jurnal Informatika dan Teknik Elektro Terapan, vol. 12, no. 2, pp. 876–884, 2024.

M. Muchtar and R. A. Muchtar, “Integrasi fitur warna, tekstur dan renyi fraktal untuk klasifikasi penyakit daun kentang menggunakan linear discriminant analysis,” Jurnal MNEMONIC, vol. 7, no. 1, pp. 77–84, 2024.