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All Journal TEKNIK Jurnal Pendidikan Teknologi dan Kejuruan Indonesian Journal of Electrical Engineering and Informatics (IJEEI) ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika JOIV : International Journal on Informatics Visualization JST ( Jurnal Sains Terapan ) JURNAL INTEGRASI JOURNAL OF APPLIED INFORMATICS AND COMPUTING J-Dinamika: Jurnal Pengabdian Kepada Masyarakat JEECAE (Journal of Electrical, Electronics, Control, and Automotive Engineering) Jurnal Teknik Mesin dan Mekatronika (Journal of Mechanical Engineering and Mechatronics) Jurnal Rekayasa elektrika The Indonesian Journal of Computer Science Journal of Electrical and Intelligent Systems (JEIS)
Hary Oktavianto
Departemen Teknik Elektro, Politeknik Elektronika Negeri Surabaya
Automotive Engineering Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Energy Engineering Materials Science & Nanotechnology Mathematics Mechanical Engineering Social Sciences Transportation Other

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Comparative Study of CNN Architectures for Real-Time Audio-Based Car Accident Detection on Edge Devices Ilahi, Ahmada Haiz Zakiyil; Irwansyah, Arif; Oktavianto, Hary
JOIV : International Journal on Informatics Visualization Vol 9, No 3 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.3.2985

Abstract

Traffic accidents often result in fatalities for both drivers and bystanders. Traditionally, accident information relies heavily on community reports, which can delay the provision of victim assistance. To address this issue, a system capable of detecting accidents responsively in various weather conditions and traffic densities is necessary. One approach involved using audio analysis techniques to evaluate collision sounds. Thus, this study proposed an audio classification system for detecting car accidents using Convolutional Neural Networks (CNNs). The system’s performance was evaluated on personal computers and edge devices, such as the Raspberry Pi 4 and NVIDIA Jetson Nano, to compare inference times and power consumption. To enhance the dataset, segmentation and augmentation techniques were applied before converting the audio data into a 2D Mel-spectrogram. The dataset was then trained and assessed with four CNN architectures: custom sequential, custom with shared input layer, transfer learning EfficientNetB0, and transfer learning MobileNetV2. Both original and Lite models were deployed on experimental devices. Results showed that the custom CNN model had faster inference times across devices in both original and lite forms, though it had a 4% increase in the false positive rate. The Lite MobileNetV2 model recorded the fastest inference time on edge devices at 86 ms. Jetson Nano exhibited faster inference times compared to Raspberry Pi 4. However, Raspberry Pi 4 showed a minor increase in power consumption of 0.6 watts during inference. In future work, this system can be tested in real-time environments using embedded systems to evaluate its robustness against noise and varying environmental conditions.
Prototipe Sistem Keamanan Kunci Pintu Rumah Otomatis Dengan Pengenalan Wajah Berbasis IoT Niam Tamami; Achmad Rizky Ramadhani; Hary Oktavianto; Rifqi Nabila Zufar
The Indonesian Journal of Computer Science Vol. 14 No. 6 (2025): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v14i6.4998

Abstract

Dalam era digital yang semakin berkembang, teknologi telah mengubah sistem keamanan dengan menggantikan kunci pintu konvensional dengan kunci pintu pintar yang canggih. Teknologi seperti pengenalan wajah, sidik jari, dan sensor gerak memberikan tingkat keamanan yang lebih tinggi dibandingkan dengan kunci tradisional. Kunci pintu konvensional rentan terhadap risiko pembobolan, kehilangan, atau duplikasi oleh pelaku kejahatan. Sebagai solusi, penulis ingin menciptakan kunci pintu pintar yang dapat membuka pintu dengan pengenalan wajah, Personal Identification Number (PIN), dan melalui Telegram bot. Pengenalan wajah pada kunci pintu pintar memastikan bahwa hanya pemilik atau orang yang terdaftar yang dapat membuka pintu. Fitur tambahan berupa penggunaan PIN memberikan lapisan keamanan ekstra. Jika PIN yang salah dimasukkan sebanyak tiga kali, alarm akan berbunyi untuk memberikan peringatan. Selain itu, jika sistem pengenalan wajah tidak dapat mengenali wajah yang sedang dipindai, kamera akan mengambil gambar dan mengirimkannya ke Telegram bot di smartphone pengguna. Dengan penerapan kunci pintu pintar ini, diharapkan keamanan dan kemudahan akses yang lebih baik dibandingkan dengan kunci pintu konvensional. Hasil pengujian menunjukkan bahwa sistem ini dapat bekerja dengan akurasi pengenalan wajah sebesar 79% dan respon alarm bekerja secara real-time melalui integrasi Telegram bot.
Development of a UWB-Based Trilateration System for Multi-Mobile Node Indoor Localization Hary Oktavianto; Haniif Mulya Wicaksana; Audra Annisa Zhafirah; Mohammad Syafrudin; Prima Kristalina; Bambang Sumantri
Journal of Electrical and Intelligent Systems Vol. 1 No. 1 (2026): April
Publisher : Politeknik Elektronika Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.68129/jeis.v1i1.39

Abstract

Accurate indoor localization is essential for navigation and coordination in multi-agent systems, particularly in environments where Global Positioning System (GPS) signals are unavailable. While ultrawideband (UWB)-based trilateration has been widely studied, most existing works focus on single-node localization and do not explicitly address scalability in terms of computational cost and processing time. This study proposes a scalable multi-mobile node localization framework based on UWB trilateration, with a key contribution in demonstrating linear computational growth with respect to the number of mobile nodes. The system employs UWB DWM1000 modules and the Symmetrical Double-Sided Two-Way Ranging (SDS-TWR) method to estimate distances between mobile nodes and anchor nodes, followed by onboard trilateration for position estimation. Experimental validation is conducted using up to four simultaneous mobile nodes within a 10×10 m indoor environment. The results show that the proposed system maintains centimeter-level accuracy, with RMSE values of 10.08 cm, 11.46 cm, 12.25 cm, and 9.13 cm for nodes 1 to 4, respectively. More importantly, the processing time increases consistently from 55 ms (one node) to 115 ms (four nodes), exhibiting an approximately constant incremental cost of 20 ms per additional node, which confirms the linear scalability of the proposed approach. These findings highlight that the proposed system not only achieves reliable localization accuracy but also ensures predictable and efficient computational performance, making it suitable for real-time multi-node applications such as robot swarm coordination and collaborative autonomous systems.
Automatic Control of Oxygen Flow for Hypoxemia Therapy Based on Fuzzy Method Rika Rokhana; Santi Anggraini; Retno Sukmaningrum; Hary Oktavianto; Paulus Susetyo Wardana; Agrippina Waya Rahmaning; Moch. Rochmad; Kemalasari; Hendhi Hermawan Efendi; Zainal Arief
Journal of Electrical and Intelligent Systems Vol. 1 No. 1 (2026): April
Publisher : Politeknik Elektronika Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.68129/jeis.v1i1.46

Abstract

Hypoxemia is a serious condition that requires oxygen transfusion. Indiscriminate oxygen administration is a poor strategy that can increase organ damage and even death. This paper describes a system for automatically controlling airflow of an oxygen tubes to a patient based on blood oxygen saturation and respiratory rate measurements. The MAX30102 sensor is used to measure oxygen saturation levels, and the MAX9814 module is used to determine respiratory rate. Both sensor outputs are processed by an STM32F411 microcontroller, and then sent wirelessly to an Arduino Uno microcontroller, which implements the fuzzy logic controller to control oxygen flow. The fuzzy output is used to activate a motor servo that controls the oxygen tube valve opening. The valve opening width (in degrees) is divided into 5 categories. Communication between the microcontroller and the valve actuator uses a 433MHz wireless RF module. The device test results revealed an MAE of 0.40% for oxygen saturation measurements compared to standard hospital measuring instruments and an MAE of 0.47% for respiratory rate measurements compared to manual measurements. Overall system testing produced a valve opening with an MAE of 0.56% compared to simulation results using MATLAB.
Co-Authors Abdillah Aziz Muntashir Abdullah Aziz Muntashir Achmad Purnomo Wijaya Achmad Rizky Ramadhani Agrippina Waya Rahmaning Amang Sudarsono, Amang angga wahyu aditya Apriyanto, R. Akbar Nur APRIYANTO, RADEN AKBAR NUR Audra Annisa Zhafirah Aulia, Fira Bagus Nugraha Deby Ariyadi Bambang Sumantri BASUKI, GAMAR Bima Sena Bayu Dewantara Dadet Pramadihanto Era Purwanto Fakhruddin, Hanif Hasyier GAMAR BASUKI Gamar Basuki Gigih Prabowo Handri Toar HANIF HASYIER FAKHRUDDIN Hanif Hasyier Fakhruddin Haniif Mulya Wicaksana Hendhi Hermawan Efendi Hendriawan, Akhmad Idris Winarno Ilahi, Ahmada Haiz Zakiyil Irwansyah, Arif Izzulhaq, Muhammad Arya Kadek Reda Setiawan Suda Kemalasari Kisron Kisron Mentari Putri Jati Moch. Rochmad MOCHAMAD ARI BAGUS NUGROHO Mohammad Syafrudin Niam Tamami Niam Tamami PAULUS SUSETYO WARDANA Prima Kristalina R. Akbar Nur Apriyanto R. Akbar Nur Apriyanto R. Akbar Nur Apriyanto R. Oktav Yama Hendra Ramadhan, Ibnu Cipta Retno Sukmaningrum Ridwan Ridwan Rifqi Amalya Fatekha RIFQI NABILA ZUFAR Rika Rokhana Rusli, Muhammad Rizani Rusminto Tjatur Widodo Santi Anggraini Setiawan Suda, Kadek Reda Sri Muntiah Andriami Wahjoe Tjatur Sesulihatien Zainal Arief