RANCANG BANGUN SISTEM KONTROL SUHU DAN KELEMBAPAN PADA INKUBATOR BAYI BERBASIS LOGIKA FUZZY

RIVALDY FAZRI, . (2026) RANCANG BANGUN SISTEM KONTROL SUHU DAN KELEMBAPAN PADA INKUBATOR BAYI BERBASIS LOGIKA FUZZY. Sarjana thesis, UNIVERSITAS NEGERI JAKARTA.

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Abstract

Bayi prematur memerlukan lingkungan dengan suhu 33–35°C dan kelembapan 60–80% RH yang stabil, namun inkubator manual umumnya memiliki akurasi rendah (±2–5°C) dan kurang adaptif terhadap perubahan lingkungan. Penelitian ini merancang sistem kendali suhu dan kelembapan inkubator bayi berbasis Logika Fuzzy yang terintegrasi IoT, menggunakan ESP32, sensor DHT22, serta aktuator PTC heater, modul Peltier, dan ultrasonic humidifier. Dua model ANFIS Sugeno orde-1 dirancang untuk kendali pemanasan dan pendinginan, masing-masing dengan 2 input, 3 fungsi keanggotaan Gaussian, dan 9 aturan fuzzy hasil pelatihan hybrid learning. Sistem dilengkapi mode manual serta dashboard web berbasis Firebase untuk monitoring real-time. Hasil pengujian menunjukkan akurasi sensor DHT22 dengan error rata-rata 2,39%, serta model termal inkubator berorde satu dengan dead time 80 detik dan konstanta waktu 473,4 detik. Pada pengujian setpoint 33–35°C, mode ANFIS lebih responsif mencapai target suhu dibanding mode manual yang cenderung stabil di bawah setpoint, meskipun menghasilkan osilasi suhu dan kelembapan yang lebih besar. Sistem juga mampu memulihkan suhu setelah diberikan gangguan eksternal. Hasil ini menunjukkan ANFIS meningkatkan responsivitas kendali dibanding kontrol manual, namun masih memerlukan penyempurnaan data latih dan strategi aktuasi untuk menekan osilasi. ****** Premature infants require a stable environment of 33–35°C and 60–80% RH, yet manual incubators typically offer low accuracy (±2–5°C) and poor adaptability. This study designs a Fuzzy Logic-based, IoT-integrated temperature and humidity control system for baby incubators using an ESP32, DHT22 sensor, and PTC heater, Peltier module, and ultrasonic humidifier actuators. Two first-order Sugeno ANFIS models were built for heating and cooling control, each with 2 inputs, 3 Gaussian membership functions, and 9 fuzzy rules trained via hybrid learning. The system includes a manual mode and a Firebase-based web dashboard for real-time monitoring. Testing showed a DHT22 average reading error of 2.39% and a first-order thermal model with an 80-second dead time and a 473.4-second time constant. At setpoints of 33–35°C, ANFIS mode reached target temperatures more responsively than manual mode, which stabilized below setpoint, though with larger temperature and humidity oscillations. The system also recovered temperature after external disturbances. These results indicate ANFIS improves control responsiveness over manual control, though further refinement of training data and actuation strategy is needed to reduce oscillation.

Item Type:	Thesis (Sarjana)
Additional Information:	1). Rafiuddin Syam, M.Eng., Ph.D. 2). Churnia Sari, S.T., M.T.
Subjects:	Ilmu Kedokteran > Keperawatan > Kesehatan Anak Sains > Sains, Ilmu Pengetahuan Alam Sains > Ilmu Bumi > Biologi Teknologi dan Ilmu Terapan > Teknik Elektronika
Divisions:	FT > D IV Teknologi Rekayasa Otomasi
Depositing User:	Users 34624 not found.
Date Deposited:	06 Aug 2026 03:36
Last Modified:	06 Aug 2026 03:36
URI:	http://repository.unj.ac.id/id/eprint/68712

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