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All Journal G-Tech : Jurnal Teknologi Terapan Indonesian Community Journal
Ferry Faisal
Politeknik Negeri Pontianak, Indonesia
Agriculture, Biological Sciences & Forestry Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Energy Engineering Environmental Science Public Health Social Sciences Other

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Penerapan Smart Laboratory Berbasis Internet of Things untuk Meningkatkan Keamanan dan Keselamatan di Laboratorium Komputer Universitas Muhammadiyah Pontianak Fitri Wibowo; Suheri Suheri; Muhammad Hasbi; Neny Firdyanti; Budianingsih Budianingsih; Ferry Faisal; Yasir Arafat
I-Com: Indonesian Community Journal Vol 4 No 1 (2024): I-Com: Indonesian Community Journal (Maret 2024)
Publisher : Fakultas Sains Dan Teknologi, Universitas Raden Rahmat Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33379/icom.v4i1.3961

Abstract

The Informatics Engineering Laboratory at Universitas Muhammadiyah Pontianak currently has not been equipped with monitoring system, posing a potential risk to user safety. This community service initiative aims to enhance security by implementing a smart laboratory system through the integration of Internet of Things (IoT) technology. The approach involves creating IoT sensor nodes and providing training to laboratory users. These sensor nodes, equipped with various sensors, monitor conditions like door status, smoke, and air quality. Data from these sensors is transmitted to a server and accessible via a web dashboard, enabling real-time online monitoring from any location. Consequently, the laboratory's monitoring capabilities are significantly enhanced, ensuring the safety of users. The implementation of an IoT-based smart laboratory system is crucial for safeguarding both the facility and its occupants, emphasizing the importance of technological advancements in maintaining a secure laboratory environment.
Privacy-Focused AIoT: Implementing an Offline Voice Assistant for Smart Building Management Using Local LLMs Fitri Wibowo; Suheri Suheri; Ferry Faisal; Freska Rolansa
G-Tech: Jurnal Teknologi Terapan Vol 10 No 2 (2026): G-Tech, Vol. 10 No. 2 April 2026
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/g-tech.v10i2.9342

Abstract

Voice assistants are increasingly used for smart building control, yet cloud-based architectures raise privacy risks and become unavailable during internet outages. This study designs and evaluates a fully offline AIoT voice assistant for smart building management using local speech and language models. The system employs an edge audio node (Raspberry Pi Zero 2W with ReSpeaker 2-Mics Pi HAT) and a local GPU server running containerized microservices for speech-to-text (Whisper), intent understanding and action planning (Ollama-hosted LLMs), and text-to-speech (Piper). Building devices and sensors are integrated through Home Assistant, enabling voice-driven control and monitoring without sending audio or interaction logs to external services. Experiments in a laboratory smart-building testbed evaluate speech recognition robustness under varying noise levels, LLM command understanding accuracy and memory footprint, and end-to-end IoT task execution. The speech subsystem achieves a Word Error Rate of 5–20% depending on background noise. Across 33 IoT entities, the assistant reaches a 96.67% execution success rate with an average response time of 5.5 s. Among the evaluated local models, Qwen3 8B achieves the highest intent-to-action accuracy (Acc_I2A=100% on an oracle-text command test set with N=43) with 6.8 GB memory use. The results demonstrate that privacy-preserving and resilient voice interaction for smart building management is feasible using current local LLM stacks.
Co-Authors Budianingsih Budianingsih Fitri Wibowo Fitri Wibowo Freska Rolansa Muhammad Hasbi Neny Firdyanti Suheri Suheri Yasir Arafat