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All Journal Sistemasi: Jurnal Sistem Informasi Jurnal Rekayasa Material, Manufaktur & Energi Adimas : Jurnal Pengabdian Kepada Masyarakat Applied Technology and Computing Science Journal Jurnal Teknik Elektro dan Komputasi (ELKOM) International Journal of Science, Engineering and Information Technology Journal Geuthee of Engineering and Energy TANJAK : Jurnal Pengabdian Kepada Masyarakat Jurnal Teknik Elektro dan Komputasi (ELKOM)
Adiratna Ciptaningrum
Politeknik Negeri Madiun
Aerospace Engineering Agriculture, Biological Sciences & Forestry Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Mechanical Engineering Social Sciences

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A Dual-Stage Hybrid Vision Framework Using YOLOv8n-Canny Edge Detection for Real-Time Railway Trespassing and Intrusion Monitoring Adiratna Ciptaningrum; Mohammad Erik Echsony; R. Akbar Nur Apriyanto
TEKNOLOGI DITERAPKAN DAN JURNAL SAINS KOMPUTER Vol 8 No 2 (2025): December
Publisher : Universitas Nahdlatul Ulama Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33086/atcsj.v8i2.8579

Abstract

Intrusion and trespasser detection on railway tracks is a crucial safety measure to prevent accidents and maintain operational reliability. This study proposes a hybrid vision-based approach that integrates YOLOv8n, a lightweight real-time object detection model, with the Canny edge detection algorithm to identify and classify unauthorized objects and individuals on railway tracks. In this context, intrusions refer to inanimate objects such as rocks, fallen trees, or construction materials obstructing the tracks, whereas trespassers refer to humans or other living beings engaging in unauthorized activities near or on the railway line. YOLOv8n is employed as a single-stage detector to localize and classify objects, while Canny edge detection is applied to enhance object contours and improve shape-based differentiation between intrusion and trespasser categories. Experimental results show an average accuracy of 52.37%, indicating moderate detection performance. Although the accuracy remains limited, the findings demonstrate the potential of combining deep learning and traditional image processing techniques to develop an automated monitoring system that supports railway safety and surveillance applications. Further optimization of the dataset, model tuning, and feature enhancement are recommended to improve detection performance.
Battery Management System Design Using Buck–Boost Converter with PID Control: Desain Sistem Manajemen Baterai Menggunakan Buck–Boost Converter dengan Kendali PID R. Akbar Nur Apriyanto; Mohammad Erik Echsony; Ibra Satriatama; Andhika Putra Widyadharma; Imam Junaedi; Adiratna Ciptaningrum; R. Gaguk Pratama Yudha; Rossanti Dwi Hapsari; Haykal Puguh Pratama
Jurnal Teknik Elektro dan Komputasi (ELKOM) Vol. 8 No. 1 (2026): Jurnal Teknik Elektro dan Komputasi (ELKOM)
Publisher : Universitas Muhammadiyah Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32528/elkom.v8i1.4213

Abstract

Sistem Manajemen Baterai (Battery Management System/BMS) merupakan komponen utama dalam menjaga efisiensi, keamanan, dan umur panjang baterai, khususnya pada aplikasi seperti kendaraan listrik dan sistem penyimpanan energi. Penelitian ini merancang BMS yang dilengkapi dengan Buck–Boost Converter berbasis kontrol Proportional-Integral-Derivative (PID) untuk mengoptimalkan kinerja baterai, dengan tujuan menghasilkan tegangan keluaran yang stabil sesuai dengan nilai referensi yang diinginkan. Konverter dirancang agar dapat beroperasi pada mode penurun tegangan (buck) maupun penaik tegangan (boost) sesuai dengan kondisi masukan, dan sistem dikembangkan menggunakan dua pendekatan kontrol: open-loop dan closed-loop. Hasil pengujian menunjukkan bahwa metode open-loop tidak dapat secara konsisten mencapai tegangan keluaran target sebesar 15 V, sehingga memerlukan penyesuaian duty cycle sebesar 5%, sedangkan metode closed-loop dengan kontrol PID berhasil mencapai error tegangan kurang dari 5%, yang menunjukkan kestabilan yang baik terhadap gangguan dan perubahan beban. Selain itu, penerapan kontrol PID pada konfigurasi Buck Converter berhasil menurunkan tegangan dari 18 V menjadi 15 V dengan error sekitar 3%.
Co-Authors Alfiyani, Risma Apriyanto, R. Akbar Nur Arum, Widya Sekar Bi Asngali Dimas Nur Prakoso Faikhsan, Edo Zulmi Fita Sari, Fita Haykal Puguh Pratama I Putu Eka Widyadharma Ibra Satriatama Ida Ayu Putu Sri Widnyani Imam Junaedi Jamil, Zhulfi Fadhillah Mudmainah, Putri Hana Widyaning Nadia, Hana Jasmine Pangestu, Djadug Suryo Pratama, R. Gaguk Putri, Larissa Kartika R. Akbar Nur Apriyanto R. Gaguk Pratama Yudha R. Oktav Yama Hendra Rahayu Mekar Bisono, Rahayu Mekar Rossanti Dwi Hapsari Saskya, Lorensa Firda Sembiring, Rinawati Syah Putra, Muhammad Dio Wahyu Pribadi Wicaksono, Darma Arif Wicaksono, Ramadhana Eka Wida Yuliar Rezika, Wida Yuliar Yudha, R. Gaguk Pratama Yudhiatama, Galang Dafa