Features of Household Solid Waste Object Recognition on Garbage Collector Robot (GACOBOT)
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Keywords
Abstract
Solid waste or garbage is one of the problems that must be faced by the world's population so that life becomes more harmonious. Through a series of studies, a Garbage Collector Robot (GACOBOT) was created which is expected to help humans overcome this problem in terms of garbage collection. By adding a feature in the form of object recognition, the waste can be sorted by type so that it can be grouped and processed further. In this research, using the Support Vector Machine (SVM) classification method based on the feature extraction of the Histogram of Oriented Gradients (HOG) as the main method. Researchers used 14 pieces of data as training data and 10 pieces of data as test data. From the results of the tests that have been carried out, it has been obtained a success rate of 100% that the system has succeeded in separating waste into 2 types, namely plastic bag waste and glass bottle waste.
References
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Wahyono, Sri. Pengolahan sampah organik dan aspek sanitasi. Jurnal Teknologi Lingkungan, 2001, 2.2.
Demirbas, Ayhan. Waste management, waste resource facilities and waste conversion processes. Energy Conversion and Management, 2011, 52.2: 1280-1287.
Naryono, Eko; Soemarno, Soemarno. Perancangan sistem pemilahan, pengeringan dan pembakaran sampah organik rumah tangga. The Indonesian Green Technology Journal, 2013, 2.1: 27-36.
Poon, Chi Sun; Ann, T. W.; NG, L. H. On-site sorting of construction and demolition waste in Hong Kong. Resources, conservation and recycling, 2001, 32.2: 157-172.
Ekvall, Staffan; Jensfelt, Patric; Kragic, Danica. Integrating active mobile robot object recognition and slam in natural environments. In: 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE, 2006. p. 5792-5797.
Song, Shuran; Zhang, Linguang; Xiao, Jianxiong. Robot in a room: Toward perfect object recognition in closed environments. CoRR, abs/1507.02703, 2015.
Prasetyo, Aditya PP, et al. Simulasi Robot Manipulator 4 DOF Sebagai Media Pembelajaran dalam Kasus Robot Menulis Huruf. Jurnal Nasional Teknik Elektro, 2016, 5.3: 339-349.
Prasetyo, Aditya PP, et al. Garbage Collector Robot (GACOBOT) Design for Dry Waste Distribution. In: Journal of Physics: Conference Series. IOP Publishing, 2020. p. 012103.
Szeliski, Richard. Computer vision: algorithms and applications. Springer Science & Business Media, 2010.
Platt, John, et al. Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. Advances in large margin classifiers, 1999, 10.3: 61-74.
Chang, Chih-Chung; Lin, Chih-Jen. LIBSVM: a library for support vector machines. ACM transactions on intelligent systems and technology (TIST), 2011, 2.3: 1-27.
Dalal, Navneet; Triggs, Bill. Histograms of oriented gradients for human detection. In: 2005 IEEE computer society conference on computer vision and pattern recognition (CVPR'05). Ieee, 2005. p. 886-893.
Gunawan, Wahyu, et al. GACOBOT Navigation System for Distribution Solid Waste to Temporary Dumpsite. In: Sriwijaya International Conference on Information Technology and Its Applications (SICONIAN 2019). Atlantis Press, 2020. p. 282-288.
Endra, Robby Yuli, et al. Deteksi Objek Menggunakan Histogram Of Oriented Gradient (Hog) Untuk Model Smart Room. Explore: Jurnal Sistem informasi dan telematika (Telekomunikasi, Multimedia dan Informatika), 2018, 9.2.
Zhou, Wei, et al. Histogram of oriented gradients feature extraction from raw bayer pattern images. IEEE Transactions on Circuits and Systems II: Express Briefs, 2020, 67.5: 946-950.
Prasetyo, Aditya PP, et al. Implementasi Electrinic Nose Dan Support Vector Machine Pada Aplikasi Olfactory Mobile Robot Dalam Mengenali Gas. Jurnal Nasional Teknik Elektro, 2018, 7.1: 69-79.
Purnamawan, I. Ketut. Support vector machine pada information retrieval. Jurnal Pendidikan Teknologi dan Kejuruan, 2015, 12.2: 139-146.
Fletcher, Tristan. Support vector machines explained. Tutorial paper, 2009, 1-19.