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All Journal Jurnal EECCIS Jurnal Elkolind : Jurnal Elektronika dan Otomasi Industri Prosiding Seminar Teknologi Kebumian dan Kelautan (SEMITAN Journal of Applied Smart Electrical Network and System (JASENS)
Mat Syai’in
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Control & Systems Engineering Earth & Planetary Sciences Electrical & Electronics Engineering Energy Engineering

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Analisis Sistem Kendali Anti-Sway pada Gantry Crane Menggunakan Linear Quadratic Regulator (LQR) Dan PID Naufal Hibban, Naufal Hibban; Mat Syai’in; Aulia Rahma Annisa; Imam Sutrisno; Didik Sukoco; Ii Munadhif
Jurnal Elektronika dan Otomasi Industri Vol. 12 No. 2 (2025): Jurnal Elkolind Vol 12 No 2 (Juli 2025)
Publisher : Program Studi Teknik Elektronika Politeknik Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33795/elkolind.v12i2.7265

Abstract

Penelitian ini membandingkan efektivitas metode kendali Proportional-Integral-Derivative (PID) dan Linear Quadratic Regulator (LQR) dalam meredam ayunan beban (sway) pada sistem gantry crane dua dimensi. Dengan simulasi MATLAB menggunakan variasi beban 0,1 hingga 1,3 kg, hasil menunjukkan bahwa kendali LQR mampu menjaga kestabilan posisi troli dengan waktu naik (rise time) antara 1,56 hingga 1,96 detik dan overshoot rendah sekitar 2,66%–3,24%, serta berhasil mengurangi overshoot ayunan beban dari 37,41% menjadi 28,18%. Sebaliknya, kendali PID meskipun menunjukkan rise time dan overshoot lebih kecil secara numerik, mengalami ketidakstabilan pada grafik respons posisi yang mengindikasikan divergensi sistem secara fisik. Temuan ini menegaskan keunggulan LQR dalam mengendalikan gantry crane dengan dinamika kompleks dan variasi beban, sehingga lebih cocok untuk aplikasi industri yang menuntut kestabilan dan keamanan tinggi.
Implementation of YOLOv5s for Automatic Waste Category Classification in Digital Waste Bank Systems Rinanto, Noorman; Mat Syai’in; Agus Khumaidi; Muhammad Khoirul Hasin; Lilik Subiyanto; Vivin Setiani; Firstama Yusuf Noor; Harun Ismail
Jurnal EECCIS (Electrics, Electronics, Communications, Controls, Informatics, Systems) Vol. 20 No. 1 (2026)
Publisher : Faculty of Engineering, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/jeeccis.v20i1.1894

Abstract

The increasing volume of organic waste in campuses or households demands innovative solutions for waste management and classification. This study proposes an automated classification system based on deep learning using the YOLOv5s algorithm to detect 14 categories of inorganic waste in real-time. The dataset consists of over 3.500 labeled images, annotated via Makesense.ai and augmented using Roboflow. The model was trained on Google Collaboratory for 100 epochs using the YOLOv5s architecture and evaluated based on precision, recall, F1-score, and mean Average Precision (mAP). Training result show mAP@0.5 approaching 100% and mAP@0.5:0.95 around 85%, with an average confidence score of 88.30% during real-time testing using a webcam. These findings demonstrate that YOLOv5s can accurately and efficiently classify waste objects, offering strong potential for integration into digital waste bank systems to enhance the efficiency and transparency of waste management processes.
Co-Authors Achmad Nawawi Adi Rahmad Ramadhan Agus Khumaidi Agus Khumaidi Ahmad Putra Alfiatin Nuriyah Aulia Rahma Annisa Boedi Herijono DANIA KIKI SANDIYA Didik Sukoco Dimas Pristovani Riananda Dzil, Fadhli Firstama Yusuf Noor Harun Ismail Ii Munadhif Imam Sutrisno Isa Rachman Jefry Setyo Fambudi Joko Endrasmono Joko Endrasmono4 Lilik Subiyanto Minto Basuki Mohammad Basuki Rahmat Muhammad Khoirul Hasin Mustika Kurnia Mayangsari Naufal Hibban, Naufal Hibban Pingky, Ginda Pingky Ramadhani Putra, Zindhu Maulana Ahmad Rinanto, Noorman Rizal Indrawan Ryan Yudha Adhitya Ryan Yudha Aditya Samudra Rozzak Arrachman Soelistijono, Rachmad Tri Tiara Erly Syah Putri Tusila, Fonda Jiwa Arkananta Tusila Vivin Setiani Wahyudi, Asyal Achnan Zindhu Maulana Ahmad Putra