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Deteksi Kebakaran Dalam Ruangan Menggunakan Internet Of Things Gunawan; Hoendarto, Genrawan; Tendean, Sandi
INTEKSIS Vol 12 No 1: Mei 2025
Publisher : LPPM Universitas Widya Dharma Pontianak

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66003/inteksis.v12i1.10540

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

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.

Design of Diabetes Prediction Interface Using E-ss and Classification Tree Algorithm Venecia, Venecia; Hoendarto, Genrawan; Darmanto, Tony
Jutisi : Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Vol 14, No 3: Desember 2025
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/jutisi.v14i3.3370

Diabetes was a chronic disease that continued to increase globally, making early detection essential to reduce long-term complications. This study aimed to develop a desktop-based diabetes prediction system that provided fast and simple classification results for medical personnel and individual users. The system used the entropy-based subset selection (E-ss) method to choose the most relevant attributes and a classification tree to classify the risk. The dataset from the National Institute of Diabetes and Digestive and Kidney Diseases, contained 768 patient records with attributes such as number of pregnancies, glucose level, blood pressure, and other risk factors. The E-ss process produced three attributes with the highest information scores, namely body mass index (BMI), blood pressure, and triceps skinfold thickness. These three attributes were then used as input to the classification tree model to generate diabetes risk predictions. Cross-validation testing showed an accuracy of up to 78.95%. These findings indicated that E-ss feature reduction helped maintain prediction performance while improving computational efficiency. This system was expected to serve as a practical and reliable diagnostic tool.

A Hybrid Federated-Edge Learning Framework with Dynamic Model Pruning for Real-Time Anomaly Detection in Smart Manufacturing Networks Genrawan Hoendarto; Thommy Willay; Pavan Kumar
Global Science: Journal of Information Technology and Computer Science Vol. 1 No. 3 (2025): September: Global Science: Journal of Information Technology and Computer Scien
Publisher : International Forum of Researchers and Lecturers

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70062/globalscience.v1i3.173

The rapid advancement of intelligent systems has accelerated the adoption of data-driven solutions across diverse industries, creating an increasing need for models that are both efficient and privacy-preserving. While traditional centralized machine learning approaches offer strong predictive capabilities, they often struggle with challenges related to data privacy, network latency, and computational inefficiency-especially in distributed environments with heterogeneous devices. To address these limitations, recent research has explored hybrid learning frameworks that integrate federated learning, edge computing, and dynamic model optimization techniques. These hybrid approaches enable models to process and learn from data closer to the source while maintaining stringent privacy requirements by keeping raw data localized. Additionally, the incorporation of pruning strategies, adaptive model compression, or multimodal data fusion contributes to improved speed, scalability, and accuracy in real-time inference tasks. Such frameworks have demonstrated notable promise in settings characterized by high data volume, operational complexity, and the necessity for fast anomaly detection or decision-making. However, despite these advancements, several challenges remain, including synchronization delays across edge nodes, variability in hardware capabilities, and the need for more efficient aggregation algorithms. Future developments may involve leveraging next-generation pruning techniques, energy-aware edge scheduling, decentralized orchestration protocols, or the integration of digital twin technologies to further enhance performance. Overall, hybrid distributed learning frameworks represent an important evolution toward more intelligent, secure, and autonomous computational ecosystems capable of supporting the next wave of smart applications.

Sistem Kendali Otomatis Ruang Kelas Berbasis ESP32 Pada Universitas Widya Dharma Pontianak Liando, Felix; Hoendarto, Genrawan; tjen, jimmy
INTEKSIS Vol 12 No 1: Mei 2025
Publisher : LPPM Universitas Widya Dharma Pontianak

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66003/inteksis.v12i1.10539

Seiring dengan perkembangan teknologi, Building Automation telah menjadi aspek penting dalam pengelolaan bangunan. Dengan otomatisasi yang canggih, kebutuhan akan pengelolaan manual semakin berkurang. Dalam konteks ini, teknologi Internet of Things (IoT) memainkan peran krusial dengan menyediakan solusi yang inovatif untuk mewujudkan konsep Building Automation. Pada Universitas Widya Dharma Pontianak (UWDP), salah satu masalah yang sering dihadapi adalah ketergantungan pada pengontrolan manual perangkat seperti air conditioner (AC) dan lampu. Seringkali setelah selesainya kegiatan perkuliahan, perangkat seperti AC dan lampu tidak dimatikan oleh pengguna ruangan. Oleh karena itu, IoT dapat menjadi salah satu solusi untuk mengatasi masalah ini. Dengan diterapkannya sistem kendali otomatis ini, diharapkan proses pengontrolan secara manual terhadap perangkat lampu dan AC dapat diminimalisir. Sistem ini memungkinkan pengalihan kendali perangkat menuju pengontrolan yang lebih efisien dan praktis melalui aplikasi mobile berbasis Android yang telah dirancang secara khusus. Setelah diterapkannya sistem ini, pengguna dapat mengontrol perangkat lampu dan AC sesuai dengan preferensi pengguna secara real-time dimanapun dan kapanpun, sehingga dapat meningkatkan kenyamanan dan kemudahan pengelolaan perangkat di ruang kelas tanpa perlu hadir secara langsung di dalam ruang kelas.

Deteksi Kebakaran Dalam Ruangan Menggunakan Internet Of Things Gunawan; Hoendarto, Genrawan; Tendean, Sandi
INTEKSIS Vol 12 No 1: Mei 2025
Publisher : LPPM Universitas Widya Dharma Pontianak

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66003/inteksis.v12i1.10540

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.

Integrated Digital Twin and Physics Informed Machine Learning Model for Real Time Performance Prediction of Industrial Mechanical Systems Irlon Irlon; Siti Shofiah; Helmi Wibowo; Erick Fernando; Genrawan Hoendarto; Mursalim Mursalim
International Journal of Mechanical, Industrial and Control Systems Engineering Vol. 2 No. 2 (2025): June :IJMICSE: International Journal of Mechanical, Industrial and Control Syst
Publisher : Asosiasi Riset Ilmu Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/ijmicse.v2i2.404

Background: The rapid advancement of digital technologies in the Industry 4.0 era has transformed industrial mechanical systems into highly interconnected and data driven environments through the integration of sensors, the Internet of Things (IoT), data analytics, and cyber physical systems. This increasing complexity requires more adaptive and accurate monitoring and prediction methods than conventional simulation approaches, which often face limitations in capturing real time dynamic system behavior. Objective: This study aims to develop a predictive performance model for industrial mechanical systems by integrating Digital Twin technology with Physics Informed Machine Learning in order to improve monitoring accuracy and support predictive maintenance strategies. Methods: This research adopts a data driven modeling and simulation approach by developing a digital representation of an industrial mechanical system that is connected to real time sensor data. The prediction model is constructed using a Physics Informed Neural Network (PINN), which integrates operational data with physical principles governing system dynamics. The research process includes the development of a Digital Twin model, integration of sensor data, training of the PINN model, model validation using experimental data, and evaluation of prediction performance using statistical metrics. Results: The results indicate that the integration of Digital Twin technology and PINN significantly improves the prediction accuracy of industrial mechanical system performance compared with conventional simulation methods and purely data driven machine learning models. The proposed model is capable of representing system dynamics more consistently, accurately following sensor data patterns, and providing strong potential for supporting machine condition monitoring and predictive maintenance strategies in modern industrial environments.

Rancang Bangun Keamanan Rumah Berbasis IOT Dengan Sensor Pir Dan Kamera IOT Viktorius Ando Saputra; Genrawan Hoendarto; Ricky I. Ndaumanu
INTEKSIS Vol 12 No 2: November 2025
Publisher : LPPM Universitas Widya Dharma Pontianak

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66003/inteksis.v12i2.10594

The rising threat of crimes, such as theft, necessitates a home security system capable of delivering rapid and accurate alerts. This research aims to develop an integrated and responsive Internet of Things (IoT)-based home security system. The method used involves designing a prototype that utilizes an ESP32-CAM module as the central controller, supported by a Passive Infrared (PIR) sensor for motion detection and an MC-38 magnetic sensor for door access detection. The research results show that the developed system successfully integrates all components. When a sensor detects suspicious activity, the system is capable of automatically capturing visual evidence and sending real-time notifications to the homeowner's mobile device via a WiFi network. In conclusion, this system offers a practical, efficient, and affordable security solution. The integration of multiple sensors with real-time visual notifications is proven to enhance vigilance and provide better protection against potential threats in residential environments.

UI/UX Design for New Student Admission Queue System Using the Design Thinking Method Antonius Antonius; Genrawan Hoendarto; Tony Darmanto; Sandi Tendean
International Journal of Multidisciplinary Sciences and Arts Vol. 5 No. 2 (2026): International Journal of Multidisciplinary Sciences and Arts, Article April 202
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/ijmdsa.v5i2.8347

The new student admission process (PMB) often faces queue management issues, as prospective students must wait in line to submit documents, verify data, and consult staff. This study designed a user interface (UI) for a PMB queueing system at Universitas Widya Dharma Pontianak (UWDP) using the Design Thinking approach. The UI was created with Figma as an interactive prototype and later developed into a web-based application. Design Thinking consists of five stages: empathize, define, ideate, prototype, and test to ensure the solution meets user needs. During empathize, interviews and observations were conducted with students and the PMB committee to identify queue-related problems. The findings were turned into user personas and problem statements in the define stage. Next, the ideate stage produced prioritized solution ideas, followed by a user interface prototype in Figma. The resulting prototype visualizes the queueing process from registration to student call-outs, featuring remote queue number retrieval, and the real-time queue display. The prototype was tested using the System Usability Scale (SUS) questionnaire with 30 prospective students. The average SUS score was 78.5, classified as "acceptable" with a grade B (Good). This indicates that the prototype has good acceptance and meets user needs in terms of ease of use and clarity of queue information. This research contributes to the development of digital queueing systems in higher education and enriches the literature on applying Design Thinking to new student admissions.

Rancang Bangun Keamanan Rumah Berbasis IOT Dengan Sensor Pir Dan Kamera IOT Viktorius Ando Saputra; Genrawan Hoendarto; Ricky I. Ndaumanu
INTEKSIS Vol 12 No 2: November 2025
Publisher : LPPM Universitas Widya Dharma Pontianak

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66003/inteksis.v12i2.10594

The rising threat of crimes, such as theft, necessitates a home security system capable of delivering rapid and accurate alerts. This research aims to develop an integrated and responsive Internet of Things (IoT)-based home security system. The method used involves designing a prototype that utilizes an ESP32-CAM module as the central controller, supported by a Passive Infrared (PIR) sensor for motion detection and an MC-38 magnetic sensor for door access detection. The research results show that the developed system successfully integrates all components. When a sensor detects suspicious activity, the system is capable of automatically capturing visual evidence and sending real-time notifications to the homeowner's mobile device via a WiFi network. In conclusion, this system offers a practical, efficient, and affordable security solution. The integration of multiple sensors with real-time visual notifications is proven to enhance vigilance and provide better protection against potential threats in residential environments.

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