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Journal : Journal of Applied Smart Electrical Network and System (JASENS)

Integrasi Sistem ESP32 dengan Pemantauan Cuaca Menggunakan Sensor Meteorologi Prayoga, Yusma'el Khammi; Arfianto, Afif Zuhri; Riananda, Dimas Pristovani; Muhammad Khoirul Hasin; Adianto; Ryan Yudha Adhitya
Journal of Applied Smart Electrical Network and Systems Vol 6 No 01 (2025): Vol 06, No. 01 June 2025
Publisher : Indonesian Society of Applied Science (ISAS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52158/jasens.v6i01.1149

Abstract

This study proposes an ESP32-based system integration for real-time weather monitoring using meteorological sensors including wind speed, wind direction, and rainfall sensors. ESP32 is chosen as the main platform because of its capability in wireless communication (Wi-Fi and Bluetooth) and its efficiency in processing sensor data with low power consumption. This system combines meteorological sensors to measure wind speed, wind direction, and rainfall, which are then displayed directly on the Nextion screen. The collected data will be updated in real-time, providing easily accessible information. The purpose of this study is to develop an effective and integrated weather monitoring system. The test results show that this system can collect and display data accurately on Nextion, providing an efficient and practical weather monitoring solution.
Simulasi Deteksi Marka Jalan Menggunakan Canny Edge Detection untuk Navigasi Kendaraan Otonom Akbara, Febrian; Ii Munadhif; Mohammad Abu Jami'in; Ryan Yudha Adhitya; Imam Sutrisno
Journal of Applied Smart Electrical Network and Systems Vol. 6 No. 2 (2025): Vol. 6 No. 02 (2025): Vol 06, No. 02 Desember 2025
Publisher : Indonesian Society of Applied Science (ISAS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52158/1a6jvn12

Abstract

This study develops an automatic steering control system based on image processing using the Canny Edge Detection method. The system is implemented on a small-scale autonomous vehicle prototype, utilizing Raspberry Pi 5 as the main processor and a Pi Camera as the visual sensor. Video frames are processed through several stages, including color conversion, Grayscale, Gaussian blur, Edge Detection, Region of Interest (RoI), and lane center estimation. The results show an average lane detection accuracy of 96% with responsive steering control, indicating the system's potential for lightweight autonomous vehicle navigation.