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All Journal Teknik dan Ilmu Komputer Jurnal Ilmiah Matrik JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI METIK JURNAL Green Intelligent Systems and Applications MDP Student Conference jurnal informasi teknologi dan sistem
Genrawan Hoendarto
Universitas Widya Dharma Pontianak
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Electrical & Electronics Engineering Library & Information Science

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Deteksi Kebakaran Dalam Ruangan Menggunakan Internet Of Things Gunawan; Hoendarto, Genrawan; Tendean, Sandi
INTEKSIS Vol 12 No 1: Inteksis Vol. 12 No. 1
Publisher : LPPM Universitas Widya Dharma Pontianak

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/zenodo.15621215

Abstract

Penelitian ini bertujuan untuk mengimplementasikan teknologi Internet of Things (IoT) dalam mendeteksi kebakaran secara otomatis di dalam ruangan. Penerapan sistem deteksi kebakaran berbasis IoT bertujuan untuk meningkatkan respons terhadap insiden kebakaran dengan memberikan peringatan dini. Sistem ini menggunakan sensor suhu, sensor asap, dan modul komunikasi untuk mendeteksi parameter yang menunjukkan potensi kebakaran. Data yang diperoleh dari sensor dikirimkan secara real-time melalui jaringan IoT ke platform monitoring dan notifikasi, yang memungkinkan pengguna mendapatkan informasi melalui perangkat seluler. Metode penelitian melibatkan perancangan perangkat keras, pengembangan perangkat lunak, dan pengujian sistem di lingkungan simulasi. Hasil pengujian menunjukkan bahwa sistem dapat mendeteksi potensi kebakaran dengan cepat dan mengirimkan peringatan secara cepat. Implementasi teknologi ini memberikan kontribusi signifikan dalam mengurangi risiko kebakaran dan kerugian material, terutama di lingkungan yang memerlukan pengawasan ketat. Kesimpulan menunjukkan bahwa teknologi IoT menawarkan solusi inovatif dan efisien untuk mendukung sistem deteksi kebakaran berbasis teknologi modern.
Design an Electricity Consumption Prediction Information System Using the Monte Carlo-Based Regression Tree Method Ng, Junira Merrylin; Hoendarto, Genrawan; Willay, Thommy
Green Intelligent Systems and Applications Volume 5 - Issue 2 - 2025
Publisher : Tecno Scientifica Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53623/gisa.v5i2.910

Abstract

Electricity became an essential component in every industry and was widely used in organizations and households. Improper handling of electricity consumption resulted in unnecessary energy loss and increased costs. The objective of this study was to develop an online electricity consumption prediction information system that was efficient, reliable, and capable of rapid forecasting. The system used IoT sensor data from Universitas Widya Dharma Pontianak, and the Monte Carlo based Regression Tree (MCRT) method was employed to mitigate the unpredictability of the data. Feature selection was conducted using Monte Carlo simulation to identify the most important features, which in this case were the year, month, and day, and these were used in the regression tree model. The developed system was able to provide estimations of hourly and daily energy consumption and the associated costs based on the MCRT model. The MCRT model predicted daily energy consumption with an accuracy of 91.61%, outperforming the Monte Carlo simulation (85.39%) and the Regression Tree method (84.29%). The results demonstrated that the MCRT model was the most efficient in capturing non-linear relationships and regression patterns in the energy consumption data. The constructed system featured an easy-to-use web interface that captured real-time data inputs and visualized predicted consumption for operational use. The system was suitable for public and private sectors, as well as educational and household applications. This approach improved effectiveness in energy management and streamlined resource allocation decision-making. The study highlighted the potential of integrating the Internet of Things (IoT) with predictive analytics to provide actionable, reliable, and precise energy management and monitoring services.
Co-Authors Armita, Angella Balbo, Florensius Bobby Reza Chang, Calvin Clesya, Clesya Didik Setiyadi Ferdynand, Hosea Gunawan Hoga Saragih Jimmy Tjen Kartono , Liando, Felix Ng, Junira Merrylin Sigan, Agustinus Sudjalmo, Sugito Susana Lin Tendean, Sandi Tjen, Jimmy Tony Darmanto Vicni Iskandar Willay, Thommy