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IMPLEMENTASI KEAMANAN RUMAH PINTAR BERBASIS IOT DENGAN ESP32, SENSOR GERAK, KAMERA DAN NOTIFIKASI REAL-TIME MELALUI BLYNK & TELEGRAM Yosua Miha; Yohanes Suban Belutowe; Tri Ana Setyariny Tri Ana Setyariny
METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi Vol. 12 No. 2 (2026): Volume 12 Nomor 2 Tahun 2026
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/mtk.v12i2.6044

Abstract

This study addresses the slow response of conventional home security systems, which typically only record footage without providing immediate alerts during suspicious activity. The system utilizes ESP32 and ESP32-CAM microcontrollers as the core units, alongside a Passive Infrared (PIR) sensor, magnetic door sensor, MQ-2 smoke sensor, DHT22 temperature sensor, and vibration sensor for detection. The research methodology encompasses hardware design, software development using the Arduino IDE, integration with the Blynk and Telegram platforms, and functional testing of all system components. Implementation results confirm the successful construction of the system and the effective integration of all components. Testing demonstrated that the PIR sensor detects motion up to 5 meters away, triggering the ESP32-CAM to capture an image (with a 4 second delay) and send a notification containing the image to Telegram (with a delay of less than 1 second). The magnetic door sensor operates without delay when the door closes but exhibits a 1.5 second delay when the door opens, while notifications to Blynk are sent instantly. The MQ-2 sensor registers values ​​below 200 in the absence of smoke and consistently exceeds 200 upon smoke detection, triggering a notification to Blynk. The DHT22 sensor does not send notifications at normal temperatures (28°C) but triggers an alert when the temperature exceeds 30°C. The vibration sensor sends notifications only when vibration is detected. All sensor data is processed by the ESP32 and transmitted to Blynk and Telegram via Wi-Fi. This research enhances security and environmental monitoring for weapon storage facilities, improving oversight through real-time integration with Blynk and Telegram