Sistem Deteksi Nyamuk
Abstrak
Abstrak-Penyakit yang ditularkan oleh nyamuk seperti demam berdarah dengue (DBD), malaria, dan Zika merupakan ancaman serius bagi kesehatan masyarakat, khususnya di wilayah tropis seperti Indonesia. Upaya pengendalian populasi nyamuk selama ini masih menghadapi tantangan dalam hal efektivitas dan efisiensi. Oleh karena itu, penelitian ini mengembangkan sebuah sistem deteksi nyamuk berbasis. Internet of Things (IoT) dan machine learning yang mampu mendeteksi dan menganalisis aktivitas nyamuk secara realtime. Sistem ini memanfaatkan kombinasi sensor suara, suhu, kelembapan, serta kadar karbon dioksida untuk mendeteksi kondisi lingkungan yang mendukung aktivitas nyamuk. Deteksi populasi dilakukan melalui suara letupan yang dihasilkan saat nyamuk tersengat perangkap elektrik, yang kemudian diolah oleh mikrokontroler ESP32 dan dikirimkan ke. web dashboard. Data yang terkumpul digunakan untuk prediksi tren populasi nyamuk melalui algoritma. machine learning, sehingga mendukung pengambilan keputusan dalam upaya mitigasi wabah penyakit. Hasil implementasi menunjukkan bahwa sistem ini mampu memberikan informasi akurat secara. realtime, mudah digunakan, serta memiliki potensi untuk diterapkan secara luas di berbagai lingkungan. Kata kunci-sistem deteksi nyamuk, internet of things, machine learning, esp32, perangkap elektrik, sensor suara.
Referensi
K. A. Farajollahi, D. C. Price, J. M. Williams, and M. P. Nelder, “Emerging mosquito-borne diseases,” Annu. Rev. Entomol., vol. 57, no. 1, pp. 245–264, 2012.
P. Georgiades et al., “Machine Learning Modeling of Aedes albopictus Habitat Suitability in the 21st Century,” Insects, vol. 14, no. 5, p. 447, May 2023.
P. Georgiades et al., “Machine Learning Modeling of Aedes albopictus Habitat Suitability in the 21st Century,” Insects, vol. 14, no. 5, p. 447, May 2023.
J. Aira, T. O. Montes, F. M. Delicado, and D. Vezzani, “MosquIoT: A System Based on IoT and Machine
Learning for the Monitoring of Aedes aegypti (Diptera: Culicidae),” arXiv preprint, Jan. 2024.
W.-L. Liu et al., “An IoT-based smart mosquito trap system embedded with real-time mosquito image processing by neural networks for mosquito surveillance,” Front. Bioeng. Biotechnol., vol. 11, Jan.
A. C. Kinney, R. Barrera, and J. Lega, “Rapid and accurate mosquito abundance forecasting with Aedes-AI neural networks,” arXiv preprint, Aug. 2024.
E. Rosita, W. Hidayat, and W. Yuliani, “UJI VALIDITAS DAN RELIABILITAS KUESIONER PERILAKU PROSOSIAL,” FOKUS (Kajian Bimbingan & Konseling dalam Pendidikan), vol. 4, no. 4, p. 279, Jul. 2021, doi: 22460/fokus.v4i4.7413.
O. Mudele et al., “Modeling the Temporal Population Distribution of Aedes aegypti Mosquito using Big Earth Observation Data,” arXiv preprint, Nov. 2019.
N. Zhao et al., “Machine learning and dengue forecasting: Comparing random forests and artificial neural networks for predicting dengue burden at national and sub-national scales in Colombia,” PLoS Negl. Trop. Dis., vol. 14, no. , e0008056, Sep. 2020.
I. Fahrurrozi et al., “Integrating random forest model and internet of things-based sensor for smart poultry farm monitoring system,” Indonesian Journal of Electrical Engineering and Computer Science, vol. 33, no. 2, pp. –1292, 2025.
M. A. Al-Emran and M. Shaalan, “Framework for Evaluating the Usability and User Experience of Mobile Applications,” J. Comput. Sci., vol. 15, no. 6, pp. 817– , 2019.
S. K. Khraisat et al., “The role of usability and user experience evaluation in mobile application success: A literature review,” Journal of Theoretical and Applied
Information Technology, vol. 98, no. 13, pp. 2500–2513,
R. Kumar, P. Singh, and A. Sharma, “Random Forest
Algorithm for Disease Prediction in IoT-Based
Healthcare Systems,” IEEE Access, vol. 10, pp. 65432- , 2022.
M. L. Fernandez, J. A. Torres, and C. P. Cruz, “Machine Learning Approaches for Dengue Outbreak
Prediction,” PLoS Negl. Trop. Dis., vol. 15, no. 8, e0009709, Aug. 2021.
T. Nguyen, H. Kim, and Y. Lee, “Random ForestBased Mosquito Species Classification Using Acoustic
Signals,” Ecological Informatics, vol. 68, p. 101554,
M. Pal, P. Choudhury, and S. Ghosh, “Design and implementation of IoT-based health monitoring system using Flask framework,” Materials Today: Proceedings, vol. 62, no. 4, pp. 2210-2216, 2022.
C. T. Salawali, A. H. P. Dillah, and M. R. Ramadhan, "Penerapan Internet of Things (IoT) dalam Sistem
Kontrol-Monitoring Berbasis ESP32 dan Sensor," Jurnal
BUSITI, vol. 4, no. 3, pp. 123-130, Aug. 2023. [Online].
Available: https://jurnal.fikom.umi.ac.id/index.php/BUS
ITI/article/viewFile/1855/pdf



