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Abstract
Pengoperasian Air Conditioner (AC) secara statis tanpa mempertimbangkan okupansi ruangan dapat menyebabkan pemborosan energi. Penelitian ini merancang sistem kendali adaptif AC berbasis YOLOv4-Tiny yang mendeteksi jumlah penghuni secara real-time dan mengubah setelan AC melalui pemancar inframerah. Penelitian menggunakan metode Research and Development dengan model ADDIE. Raspberry Pi 4 menjalankan deteksi visual dan antarmuka LCD, sedangkan ESP32 mengeksekusi kendali inframerah dan fungsi keselamatan. Pengujian dilakukan di ruang kelas tertutup seluas 25 m² melalui uji fungsional, jarak dan sudut inframerah, akurasi deteksi pada pagi, siang, dan sore hari, waktu respons, respons termal, serta konsumsi energi. Hasil menunjukkan seluruh fungsi bekerja dengan baik dan akurasi deteksi rata-rata mencapai 98,13%. Sistem merespons perubahan mode suhu dalam 5,44-5,50 detik dan mematikan AC dalam 10,38 detik. Suhu aktual memiliki deviasi 0,6-1,9 °C terhadap setpoint. Selama empat jam pengujian, konsumsi energi turun dari 3,120 kWh menjadi 1,715 kWh atau hemat 45,03%. Sistem ini layak digunakan sebagai kendali AC non-invasif berbasis okupansi pada ruang kelas.
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Copyright (c) 2026 Amalia Resky Hasniati, Endah Fitriani, Nina Paramytha, Deni Erlansyah
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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References
J. H. Ham, B. S. Kim, I. W. Bae, and J. Joe, "Occupant-detection-based individual control of four-way air conditioner for sustainable building energy management," Sustainability, vol. 16, no. 17, p. 7404, 2024, doi: 10.3390/su16177404.
W. Zhang, J. Calautit, P. W. Tien, Y. Wu, and S. Wei, "Deep learning models for vision-based occupancy detection in high occupancy buildings," Journal of Building Engineering, vol. 98, 2024, doi: 10.1016/j.jobe.2024.111355.
D. Feng et al., "Fry counting models based on attention mechanism and YOLOv4-Tiny," IEEE Access, vol. 10, pp. 132363-132375, 2022, doi: 10.1109/ACCESS.2022.3230909
X. Zhao, L. Wang, Y. Zhang, X. Han, M. Deveci, and M. Parmar, "A review of convolutional neural networks in computer vision," Artificial Intelligence Review, vol. 57, no. 4, 2024, doi: 10.1007/s10462-024-10721-6.
I. N. Sukarma, B. Anakottapary, I. G. K. S. Budarsa, and I. K. Parti, "Air conditioner operation using the Internet of Things," Jurnal Penelitian Pendidikan IPA, vol. 11, no. 4, pp. 575-582, 2025, doi: 10.29303/JPPIPA.V11I4.10943.
A. A. Prasetyo, R. Arifin, and R. I. Vidyastri, "Sistem otomatisasi AC berbasis IoT untuk pengendalian suhu ruangan secara optimal," SinarFe7, vol. 7, no. 1, pp. 270-280, 2025.
Y. M. Obeidat and A. M. Alqudah, "An embedded system based on Raspberry Pi for effective electrocardiogram monitoring," Applied Sciences, vol. 13, no. 14, 2023, doi: 10.3390/app13148273.
H. Bordbar, F. Alinejad, K. Conley, T. Ala-Nissila, and S. Hostikka, "Flame detection by heat from the infrared spectrum: Optimization and sensitivity analysis," Fire Safety Journal, vol. 133, 2022, doi: 10.1016/j.firesaf.2022.103673.
S. Indarwati, S. M. B. Respati, and D. Darmanto, "Kebutuhan daya pada air conditioner saat terjadi perbedaan suhu dan kelembaban," Jurnal Ilmiah Momentum, vol. 15, no. 1, 2019, doi: 10.36499/jim.v15i1.2666.
A. Q. Ayun, U. Dwita, and M. Thoriq, "Penerapan metode fuzzy Mamdani dalam menentukan suhu optimal pada pendingin ruangan AC di ruang kelas," Journal Technology of Computer, vol. 2, no. 1, 2025.
S. Ariyani, M. Sudarma, and P. A. Wicaksana, "Analysis of functional suitability and usability in sales order procedure to determine management information system quality," INTENSIF, vol. 5, no. 2, pp. 234-248, 2021, doi: 10.29407/INTENSIF.V5I2.155373.
ASHRAE, ANSI/ASHRAE Standard 55-2021 Thermal Environmental Conditions for Human Occupancy. Atlanta, GA, USA: ASHRAE, 2021.
Badan Standardisasi Nasional, SNI 03-6572-2001 Tata Cara Perancangan Sistem Ventilasi dan Pengondisian Udara pada Bangunan Gedung. Jakarta, Indonesia: BSN, 2001.