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All Journal Jurnal Informatika dan Teknik Elektro Terapan JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Bulletin of Information Technology (BIT) Green Intelligent Systems and Applications Informatics and Enginering Dedication SENTRI: Jurnal Riset Ilmiah MDP Student Conference Jurnal Ilmiah Multidisiplin Indonesia IPSSJ
Eka Puji Widiyanto
Universitas Multi Data Palembang
Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Physics Public Health Social Sciences Other

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ANALISIS KINERJA MODEL YOLOv8 UNTUK MONITORING KEPATUHAN PENGGUNAAN SEPATU SAFETY PADA PETUGAS PEMADAM KEBAKARAN Stephen Setyawan; Eka Puji Widiyanto
Jurnal Informatika dan Teknik Elektro Terapan Vol. 13 No. 3S1 (2025)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v13i3S1.8065

Abstract

Compliance with Personal Protective Equipment (PPE), such as safety shoes, is a crucial challenge in high-risk work environments, including for firefighters. Negligence in PPE usage is a leading cause of workplace accidents. This study aims to analyze the performance of the YOLOv8 object detection model in a real-time monitoring system designed to detect the use of safety shoes. The research method includes system design, image dataset collection, YOLOv8 model training, and performance evaluation using standard metrics. The performance analysis shows excellent model performance, achieving a precision of 97%, recall of 94.9%, and a mean Average Precision (mAP) of 97.5%. Furthermore, functional testing of the system resulted in a 90% user satisfaction rate. These results indicate that YOLOv8 is an effective and reliable method for automated monitoring and has great potential to minimize workplace accidents caused by negligence in PPE use.
PENGENALAN TEKNOLOGI RFID DALAM SISTEM ABSENSI OTOMATIS BERBASIS KARTU FLAZZ DI SMA XAVERIUS 3 PALEMBANG Suparto, Adrian; Clement, Michael Joy; Laksana, Jovansa Putra; Pratama, Brilliant Chandra; Feliansyah, Fernando; Pribadi, Muhammad Rizky; Widiyanto, Eka Puji
FORDICATE Vol 4 No 3 (2025): November 2025
Publisher : Universitas Multi Data Palembang, Fakultas Ilmu Komputer dan Rekayasa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/fordicate.v4i3.11704

Abstract

Abstrak: Kegiatan pengabdian kepada masyarakat ini bertujuan untuk memperkenalkan konsep dan implementasi sistem absensi otomatis berbasis teknologi RFID di lingkungan sekolah menengah. Tujuan utama kegiatan adalah memberikan pemahaman praktis kepada siswa mengenai cara kerja sistem absensi tanpa kontak dan manfaatnya dalam meningkatkan efisiensi administrasi. Metode yang digunakan meliputi sosialisasi langsung, penyampaian materi visual, serta demonstrasi aplikasi prototipe yang dikembangkan menggunakan antarmuka berbasis web dan pemindai kartu RFID. Hasil kegiatan menunjukkan respons positif dari siswa terhadap penggunaan teknologi tersebut. Demonstrasi berhasil memperlihatkan proses pencatatan kehadiran secara otomatis menggunakan kartu RFID dan bagaimana data disimpan dalam basis data lokal. Meskipun sistem belum diadopsi oleh pihak sekolah, aplikasi ini menunjukkan potensi sebagai solusi digital yang dapat diimplementasikan di masa mendatang. Kegiatan ini memberikan manfaat edukatif sekaligus mendorong kesadaran akan pentingnya transformasi digital dalam tata kelola sekolah.
Pengembangan Sistem Pemantauan Kebakaran Real-Time dan Peringatan Dini Menggunakan Teknologi LoRa pada Kawasan Perkotaan Rahman, Abdul; Widiyanto, Eka Puji
SENTRI: Jurnal Riset Ilmiah Vol. 4 No. 12 (2025): SENTRI : Jurnal Riset Ilmiah, Desember 2025
Publisher : LPPM Institut Pendidikan Nusantara Global

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55681/sentri.v4i12.5196

Abstract

Fires in urban areas require reliable monitoring and early warning systems to reduce potential losses. This study aims to evaluate the performance of components that build an urban fire monitoring and early warning system, consisting of the DHT22 temperature and humidity sensor, MQ-2 smoke sensor, 5-channel IR flame detector, and LoRa E220-900T22D communication module as the main components of the IoT-based system. The testing methods include measuring the accuracy of temperature and humidity using the DHT22, smoke detection tests with MQ-2, fire detection distance tests with the IR flame detector, and communication range tests of LoRa in urban environments. The results show that the DHT22 provides high accuracy with an average of 98.67% for humidity and 97.82% for temperature. The MQ-2 consistently detects varying smoke concentrations, while the IR flame detector perfectly detects fire at a distance of 90 cm on all channels and only on certain channels at 300 cm. The LoRa module demonstrates an effective range of 1–2 km with relatively high reliability, though performance decreases beyond 3 km due to physical obstacles and signal interference. Overall, the system can be effectively implemented for urban fire monitoring and early warning, although additional strategies such as denser gateway placement or mesh architecture are required to improve communication stability.
Implementation of Organic and Inorganic Waste Detection Modeling in School Waste Bins Using Yolov11 Feriyanto, Feriyanto; Eka Puji Widiyanto
Jurnal Ilmiah Multidisiplin Indonesia (JIM-ID) Vol. 5 No. 02 (2026): Jurnal Ilmiah Multidisplin Indonesia (JIM-ID), February 2026
Publisher : Sean Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Waste management in the school environment poses a significant challenge due to the high volume of mixed organic and inorganic waste, which hinders the recycling process. The utilization of object detection technology can offer a solution. However, previous studies employed older YOLO architectures, which still have room for improvement. This research aims to implement a detection model to differentiate between organic and inorganic waste within the school environment, with a focus on the implementation of the YOLOv11 architecture. The method used is a Convolutional Neural Network (CNN) featuring the YOLOv11 architecture, utilizing a public dataset from Kaggle that is divided into 7 waste classes. The research stages include image preprocessing, image augmentation, and dataset partitioning using Stratified K-Fold Cross Validation. The model’s performance will be evaluated using mean Average Precision (mAP), precision, recall, and F1-score metrics. Subsequently, the model will be developed into a desktop-based system application. The result of this study are expected to provide an accurate and efficient waste detection model to assist in recognizing the types of waste present in the school environment.
An Image Processing-Based Fire Detection System Using Orange Pi 4A with Internet of Things Integration in Indoor Environments Pratiwi, Safeti Intan; Puji Widiyanto, Eka
Green Intelligent Systems and Applications Volume 6 - Issue 1 - 2026
Publisher : Tecno Scientifica Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53623/gisa.v6i1.1009

Abstract

Fire hazards in indoor industrial environments require fast and reliable detection systems, as conventional sensor-based methods often suffer from delayed responses and high false-alarm rates. This study proposes a low-cost, Internet of Things-integrated visual fire detection system based on the YOLOv11 deep learning model implemented on an Orange Pi 4A. The system integrates an IP camera for visual acquisition, real-time detection, and automatic data logging through a MySQL-based monitoring platform. Experiments were conducted in a 3 × 3 m indoor environment using candle, stove, and burning fires at various camera distances. System performance was evaluated using confidence score, bounding box pixel area, and recall based on True Positive and False Negative classifications. Candle flames were reliably detected up to 100 cm with recall values of 90.24%–100% and pixel areas below 5,000 px, while stove flames achieved recall above 93% at 50–100 cm with pixel areas of 11,144–42,525 px. Burning fires maintained high performance up to 300 cm, reaching confidence values above 0.70 and recall rates of 78.94%–100% with pixel areas exceeding 44,000 px. The results indicate that detection reliability is primarily influenced by apparent flame size rather than camera distance. Overall, the proposed system demonstrates strong feasibility as an embedded, IoT-integrated fire detection solution for early warning in indoor industrial environments, although limitations remain in detecting small flames under low-resolution and low-light conditions.
Design and Implementation of a Multi-Node Gas Sensor-Based Indoor Air Quality Monitoring and Control System Alkan Dawasoka, Siti Milda; Puji Widiyanto, Eka
Green Intelligent Systems and Applications Volume 6 - Issue 1 - 2026
Publisher : Tecno Scientifica Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53623/gisa.v6i1.1012

Abstract

:  Air quality monitoring was a crucial aspect of maintaining occupational health and safety, particularly in industrial environments. This study proposed the design and implementation of an Internet of Things (IoT)-based indoor air quality monitoring system capable of measuring environmental parameters in real time. The system integrated an ENS160 gas sensor and an AHT21 temperature–humidity sensor with a Wemos D1 Mini microcontroller. Sensor data were transmitted via the MQTT protocol to an Orange Pi 4A server and visualized using a Node-RED dashboard. The monitored parameters included Total Volatile Organic Compounds (TVOC), equivalent CO₂ (eCO₂), temperature, and humidity. Experimental evaluation demonstrated that the system responded proportionally to different pollutant exposure levels. Under high NH₃ exposure (100%), TVOC values reached a maximum of 12,697 ppb with an average of 5,037 ppb, clearly exceeding the hazardous threshold (>200 ppb). At moderate exposure (50%), the average TVOC decreased to 2,106 ppb, while at low exposure (10%), the average value remained within the safe range at 84 ppb. For eCO₂ testing, cigarette smoke exposure produced a peak value of 11,524 ppm with an average of 1,663 ppm, indicating hazardous conditions (>1000 ppm). Statistical analysis using mean and standard deviation confirmed that sensor stability improved at lower pollutant concentrations. The proposed system successfully provided stable real-time monitoring, threshold-based classification, and automatic mitigation control, demonstrating its feasibility for intelligent indoor air quality management in industrial workspaces.
Co-Authors Abdul Rahman alek bayu kurniawan Alkan Dawasoka, Siti Milda Clement, Michael Joy Dedy Hermanto Fahrezi, Muhammad Azril Feliansyah, Fernando Feriyanto Feriyanto Hafiz Irsyad Hartati, Ery Hendra Kurniawan Kelly, Angel Khrisnaldi Wijaya Laksana, Jovansa Putra Muhammad Rizky Pribadi Pratama, Brilliant Chandra Pratiwi, Safeti Intan Ricky Ricky Sa'adat, Fadhil Stephen Setyawan Suparto, Adrian Therino Elevan, Rivaldo William Wijaya