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All Journal Jurdimas (Jurnal Pengabdian Kepada Masyarakat) Royal TEKTRIKA - Jurnal Penelitian dan Pengembangan Telekomunikasi, Kendali, Komputer, Elektrik, dan Elektronika Jurnal Elkolind : Jurnal Elektronika dan Otomasi Industri Bulletin of Community Engagement SAGA: Journal of Technology and Information Systems Jurnal 7 Samudra Politeknik Pelayaran Surabaya Jurnal Teknologi Maritim Jurnal Cakrawala Maritim
Mohammad Basuki Rahmat
Politek Perkapalan ITS
Religion Arts Humanities Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Library & Information Science Mechanical Engineering Social Sciences Transportation Other

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Deteksi Objek di Lapangan pada Robot Sepakbola Beroda Menggunakan Metode YOLOV5 Dzil, Fadhli; Agus Khumaidi; Mohammad Basuki Rahmat; Joko Endrasmono4; Mat Syai’in; Dimas Pristovani Riananda
Jurnal Elektronika dan Otomasi Industri Vol. 11 No. 2 (2024): Vol. 11 No.2 (2024) : Jurnal Elkolind Vol.11, No. 2, 2024 (Juli 2024)
Publisher : Program Studi Teknik Elektronika Politeknik Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33795/elkolind.v11i2.5235

Abstract

Penelitian ini bertujuan untuk mendeteksi objek seperti bola, robot, dan gawang pada robot sepak bola beroda dengan menggunakan metode You Only Look Once Version 5 (YOLOv5). Metode ini dipilih karena memiliki kemampuan deteksi yang cepat dan akurasi yang lebih tinggi dibandingkan versi YOLO sebelumnya. Dataset yang digunakan mencakup 4555 gambar yang terbagi menjadi data pelatihan dan validasi. Pelatihan model YOLOv5 dilakukan dengan parameter-parameter tertentu seperti ukuran gambar 416x416 piksel, ukuran batch 16, jumlah epoch 1000, dan parameter lainnya. Hasil pelatihan menunjukkan presisi sebesar 0,919 relatif terhadap recall, dengan recall tertinggi mencapai 0,97 pada tingkat kepercayaan 0,00. Kurva F1 mencapai nilai puncak rata-rata 0,89 pada keyakinan 0,467, sementara presisi rata-rata tertinggi adalah 1,00 pada keyakinan 0,925. Akurasi keseluruhan sistem deteksi objek mencapai 75,5%. Penelitian ini membuktikan bahwa metode YOLOv5 dapat diterapkan secara efektif untuk mendeteksi objek pada robot sepak bola beroda dan memberikan kinerja yang memadai.
Accessing Ping Sonar Echosounder Produksi ROVMAKER pada Mikrokontroler dan Data Noise Reduction dengan Kalman Filter Adam Maulana, Adam Maulana; Joko Endrasmono; Zindhu Maulana Ahmad Putra; Lilik Subiyanto; Mohammad Basuki Rahmat; M.Khoirul Hasin; Isa Rachman; Agus Khumaidi; Yuning Widiarti; Ryan Yudha Adhitya; Dimas Pristovani Riananda
Jurnal Elektronika dan Otomasi Industri Vol. 11 No. 1 (2024): Jurnal Elkolind Vol. 11 No.1 (Mei 2024)
Publisher : Program Studi Teknik Elektronika Politeknik Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33795/elkolind.v11i1.5389

Abstract

Two-thirds of Indonesia's territory is waters that have a high risk of flooding. Bathymetric surveys are important for flood management because they provide depth and topography data to determine the need for dredging of a water area. Traditional bathymetric surveys produce less than optimal coverage due to limited ship movements. This research aims to develop an automatic bathymetry system using a ping sonar echosounder sensor integrated with a USV (Unmanned Surface Vehicle). The ping sonar echosounder produced by ROVMAKER can generally only be accessed using the built-in software from the ROVMAKER manufacturer and can now be integrated with the microcontroller system on the USV to increase the efficiency of bathymetric system performance. This research also uses the Kalman filter method to reduce sensor reading noise. Validation of the suitability of the sensor shows an average error of 2.94% at a depth of 1400mm and 2.195% at a depth of 2200mm, which shows that the sensor is classified as suitable for use. The optimal noise reduction in the Kalman filter experiment is at a variance ratio (R/Q) of 1000 with an RMSE of 5.05mm at a depth of 1400mm and 3.99mm at a depth of 2200mm. This system has been proven to increase the accuracy of bathymetric data and system access efficiency.
Co-Authors Achmad Ainnurzein Adam Maulana, Adam Maulana Adi, Muhammad Adimassyah Adianto Adianto Agus Khumaidi Agus Nurcahyo Anggara Trisna Nugraha Arfianto, Afif Zuhri Arif Purnomo Aji Asri, Purwidi Auliya, Prativi Khilyatul Bella Naziel Iqmalia Binti Mualifatul Rosydah Budianto C. I. Sutrisno Dewi Rizani Ruwahida Dian Asa Utari Didik Sukoco Dimas Fanny Kusuma Dimas Pristovani Riananda Dinda Pramanta Dody Hidayat Dzil, Fadhli Farizi Rachman Fitroh Resmi Hendro Agus Widodo Ilyasa' Maula Basharon Isa Rachman Ivan Azwar Septiadi Joesianto Eko Poetro Joko Endrasmono Joko Endrasmono4 Lanang Ilham Kelana Lilik Subiyanto Lutfi Wicaksono M.Khoirul Hasin Mat Syai’in Mey Rohma Dhani Mohammad Abu Jamiin Muhamad Nasir Muhammad Fikri Fathurrohman Muhammad Khoirul Hasin Muhammad Naufal Maulana Pranowo Sidi Raimundus Risa Arnestanta Rama Arya Sobhita Rini Indarti Ryan Yudha Adhitya Septaviola Dini Utami Trio Andika Putra Urip Mudjiono Wahyudi, Asyal Achnan Yuning Widiarti Zindhu Maulana Ahmad Putra