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All Journal Jurnal Teknologi Informasi dan Komunikasi Jurnal Ilmiah Sistem Informasi
Anggono, Sigit Umar
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Computer Science & IT Education Electrical & Electronics Engineering Engineering Mechanical Engineering

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COMPARATIVE ANALYSIS OF POWER CONSUMPTION AND REAL-TIME PERFORMANCE BETWEEN ESP32 AND RASPBERRY PI PICO W IN IOT-BASED TEMPERATURE MONITORING SYSTEMS Supriyanto, Supriyanto; Anggono, Sigit Umar
JURNAL TEKNOLOGI INFORMASI DAN KOMUNIKASI Vol. 16 No. 1 (2025): MARET
Publisher : UNIVERSITAS STEKOM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jtikp.v16i1.1147

Abstract

Power efficiency and real-time performance are two essential aspects in the development of Internet of Things (IoT) systems, especially for temperature monitoring applications operated continuously with limited energy resources. This study aims to compare two widely used microcontroller platforms—ESP32 and Raspberry Pi Pico W—in the context of an IoT-based temperature monitoring system using the DHT22 sensor and Wi-Fi communication. A quantitative experimental approach was employed, including measurement of average power consumption, system response time, and initial booting time over more than 100 operation cycles. The results show that Raspberry Pi Pico W consumes significantly less power (90 mA) than ESP32 (160 mA). However, ESP32 outperformed in real-time response, recording an average latency of 240 ms, while Pico W recorded 310 ms. Pico W also demonstrated faster boot time (950 ms) compared to ESP32 (1500 ms), which is beneficial in deep sleep scenarios. These findings highlight a trade-off between power efficiency and system responsiveness. This study contributes practically by providing a technical basis for selecting suitable microcontroller platforms based on application-specific needs. The results may serve as a reference for developing energy-efficient and responsive embedded systems, particularly in agriculture, healthcare, and smart home sectors.
Peluang dan Tantangan Sistem Pelatihan Berbasis AI dalam Meningkatkan Keterampilan Tenaga Kerja di Era Digital Anggono, Sigit Umar; Supriyanto, Supriyanto
Jurnal Ilmiah Sistem Informasi Vol. 4 No. 2 (2025): Mei : Jurnal Ilmiah Sistem Informasi
Publisher : LPPM Universitas Sains dan Teknologi Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/6mbmzk12

Abstract

The rapid evolution of digital technology has reshaped workforce training paradigms, with Artificial Intelligence (AI) emerging as a transformative tool. This study investigates the opportunities and challenges of implementing AI-based training systems to enhance workforce skills in the digital era. Data were collected through surveys, in-depth interviews, and focus group discussions involving industry practitioners, AI developers, and workers. The results indicate a training effectiveness average of 70.2% (SD = 9.8) and satisfaction levels averaging 75.4% (SD = 8.5). Logistic regression analysis revealed a significant positive relationship between AI adoption and skill improvement, with a success probability of 68.9%. Despite these promising outcomes, challenges such as limited digital infrastructure, low digital literacy, and resistance to technology adoption persist, particularly in developing regions. This study provides strategic recommendations for inclusive digital transformation, emphasizing collaborative efforts between governments, private sectors, and educational institutions. These findings contribute to theoretical and practical knowledge for advancing AI-based training systems and fostering a sustainable, equitable workforce transformation.
Co-Authors Supriyanto Supriyanto