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Kisron Kisron
Politeknik Elektronika Negeri Surabaya
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering

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Improved Performance of Trash Detection and Human Target Detection Systems using Robot Operating System (ROS) Kisron Kisron; Bima Sena Bayu Dewantara; Hary Oktavianto
Jurnal Rekayasa Elektrika Vol 17, No 2 (2021)
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1282.865 KB) | DOI: 10.17529/jre.v17i2.20805

Abstract

In a visual-based real detection system using computer vision, the most important thing that must be considered is the computation time. In general, a detection system has a heavy algorithm that puts a strain on the performance of a computer system, especially if the computer has to handle two or more different detection processes. This paper presents an effort to improve the performance of the trash detection system and the target partner detection system of a trash bin robot with social interaction capabilities. The trash detection system uses a combination of the Haar Cascade algorithm, Histogram of Oriented Gradient (HOG) and Gray-Level Coocurrence Matrix (GLCM). Meanwhile, the target partner detection system uses a combination of Depth and Histogram of Oriented Gradient (HOG) algorithms. Robotic Operating System (ROS) is used to make each system in separate modules which aim to utilize all available computer system resources while reducing computation time. As a result, the performance obtained by using the ROS platform is a trash detection system capable of running at a speed of 7.003 fps. Meanwhile, the human target detection system is capable of running at a speed of 8,515 fps. In line with the increase in fps, the accuracy also increases to 77%, precision increases to 87,80%, recall increases to 82,75%, and F1-score increases to 85,20% in trash detection, and the human target detection system has also improved accuracy to 81%, %, precision increases to 91,46%, recall increases to 86,20%, and F1-score increases to 88,42%.
Improved Performance of Trash Detection and Human Target Detection Systems using Robot Operating System (ROS) Kisron Kisron; Bima Sena Bayu Dewantara; Hary Oktavianto
Jurnal Rekayasa Elektrika Vol 17, No 2 (2021)
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17529/jre.v17i2.20805

Abstract

In a visual-based real detection system using computer vision, the most important thing that must be considered is the computation time. In general, a detection system has a heavy algorithm that puts a strain on the performance of a computer system, especially if the computer has to handle two or more different detection processes. This paper presents an effort to improve the performance of the trash detection system and the target partner detection system of a trash bin robot with social interaction capabilities. The trash detection system uses a combination of the Haar Cascade algorithm, Histogram of Oriented Gradient (HOG) and Gray-Level Coocurrence Matrix (GLCM). Meanwhile, the target partner detection system uses a combination of Depth and Histogram of Oriented Gradient (HOG) algorithms. Robotic Operating System (ROS) is used to make each system in separate modules which aim to utilize all available computer system resources while reducing computation time. As a result, the performance obtained by using the ROS platform is a trash detection system capable of running at a speed of 7.003 fps. Meanwhile, the human target detection system is capable of running at a speed of 8,515 fps. In line with the increase in fps, the accuracy also increases to 77%, precision increases to 87,80%, recall increases to 82,75%, and F1-score increases to 85,20% in trash detection, and the human target detection system has also improved accuracy to 81%, %, precision increases to 91,46%, recall increases to 86,20%, and F1-score increases to 88,42%.
Tempat Sampah Pintar dengan Sistem Monitoring berbasis Cloud dan Pemilihan Rute Tercepat Saifudin Usman; Kisron Kisron
Smart Comp :Jurnalnya Orang Pintar Komputer Vol 12, No 1 (2023): Smart Comp: Jurnalnya Orang Pintar Komputer
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/smartcomp.v12i1.4589

Abstract

Tingginya jumlah penduduk di Indonesia memicu tingkat konsumsi produk-produk seperti makanan, minuman, dan lain-lain yang sangat tinggi. Dampak nyata dari tingginya tingkat konsumsi tersebut akan mengakibatkan sisa produk/barang yang dikategorikan sebagai limbah sangat tinggi. Dari banyaknya sampah yang dihasilkan, para penanggung jawab kebersihan perlu melakukan inovasi sistem pengelolaan sampah agar tidak menjadi bahaya bagi kehidupan masyarakat. Dalam makalah ini, inovasi dibuat tentang tempat sampah pintar dengan sistem pemantauan waktu nyata dan perencanaan jalur tercepat untuk pengumpulan sampah. Untuk mengetahui volume sampah yang digunakan dipasang sensor ultrasonik pada tutup tempat sampah. Sistem monitoring ini menggunakan konsep internet of things dengan media komunikasi wireless. Penyimpanan data yang digunakan dalam penelitian ini adalah cloud computing Amazon Web Service EC2, sedangkan metode yang digunakan untuk membuat jalur tercepat adalah Algoritma Genetika. Hasil yang didapatkan sensor ultrasonik dapat menentukan volume sampah dengan tingkat kesalahan pembacaan sebesar 6,08%. Sistem ini juga dilengkapi dengan sleep-scheduling pada sensor-node, dimana node tidak selalu melakukan pengukuran dan mengirim data ke server. Penggunaan algoritma genetika dalam menentukan jalur tercepat untuk pengambilan sampah bagi petugas pengambil sampah memberikan hasil yang baik dengan selisih waktu 10 menit.
Co-Authors Bima Sena Bayu Dewantara Hary Oktavianto Saifudin Usman