Room occupancy classification using multilayer perceptron
##plugins.themes.bootstrap3.article.sidebar##
##plugins.themes.bootstrap3.article.main##
Tóm tắt
A room that should be comfortable for humans can create a sense of absence and appear diseases and other health problems. These rooms can be from boarding rooms, hotels, office rooms, even hospital rooms. Room occupancy prediction is expected to help humans in choosing the right room. Occupancy prediction has been evaluted with various statistical classification models such as Linier Discriminat Analysis LDA, Classification And Regresion Trees (CART), and Random Forest (RF). This study proposed learning approach to classification of room occupancy with multi layer perceptron (MLP). The result shows that a proper MLP tuning paramaters was able estimate the occupancy with 88.2% of accuracy
##plugins.themes.bootstrap3.displayStats.downloads##
##plugins.themes.bootstrap3.article.details##
Chuyên mục
Tác phẩm này được cấp phép theo Giấy phép Creative Commons Ghi công-Chia sẻ tương tự 4.0 Quốc tế.
Cách trích dẫn
Tài liệu tham khảo
C. Doxiadis, “The Formation of the Human Room,” JSTOR, vol. 33, no. 196, pp. 218–229, 1972.
J. C. Mandey and J. I. Kindangen, “Studi Kenyamanan Panas dan Hubungannya dengan Tingkat Produktivitas di Ruang Kantor,” J. Lingkung. Binaan Indones., vol. 6, no. 3, pp. 188–194, 2017.
J. Wulandari and M. Ernawati, “Efek Iklim Kerja Panas Pada Respon Fisiologis Tenaga Kerja Di Ruang Terbatas,” Indones. J. Occup. Saf. Heal., vol. 6, no. 2, p. 207, 2018.
N. Keenlyside and D. Dommenget, “The Fingerprint ofGlobal Warming in the Tropical Pacific,” Adv. Atmos. Sci., vol. 33, no. 4, pp. 433–441, 2016.
D. Zhang, Z. Wang, S. Li, and H. Zhang, “Impact of land urbanization on carbon emissions in urban agglomerations of the middle reaches of the yangtze river,” Int. J. Environ. Res. Public Health, vol. 18, no. 4, pp. 1–21, 2021.
S. Ritohardoyo and I. Sadali, “Kesesuaian Keberadaan Rumah Tidak Layak,” Tata Loka, vol. 19, pp. 291–305, 2017.
S. Amilia and Iriyani, “Pengaruh Lokasi, Harga dan Fasilitas terhadap Keputusan Sewa Kamar Kost Mahasiswa Fakultas Ekonomi Universitas Samudra,” J. Manaj. dan Keuang., vol. 8, no. 3, pp. 267–280, 2020.
P. Herlia Pramitasari, S. Tri Harjanto, and M. Nelza Mulki Iqbal, “Karakteristik Bangunan Hijau Pada Rumah Susun Umum Di Daerah Beriklim Tropis Lembab,” Pawon J. Arsit., vol. 4, no. 02, pp. 95–108, 2020.
O. I. Abiodun, A. Jantan, A. E. Omolara, K. V. Dada, N. A. E. Mohamed, and H. Arshad, “State-of-the-art in artificial neural network applications: A survey,” Heliyon, vol. 4, no. 11, p. e00938, 2018.
W. L. Mao, Suprapto, C. W. Hung, and T. W. Chang, “Nonlinear system identification using BBO-based multilayer perceptron network method,” Microsyst. Technol., vol. 27, no. 4, pp. 1497–1506, 2021.
Y. Karaki and N. Ivanov, “Hyperparameters of Multilayer Perceptron with Normal Distributed Weights,” Pattern Recognit. Image Anal., vol. 30, no. 2, pp. 170–173, 2020.
S. Panghal and M. Kumar, “Multilayer Perceptron and Chebyshev Polynomials Based Neural Network for Solving Emden–Fowler Type Initial Value Problems,” Int. J. Appl. Comput. Math., vol. 6, no. 6, pp. 1–12, 2020.
A. Anton, N. F. Nissa, A. Janiati, N. Cahya, and P. Astuti, “Application of Deep Learning Using Convolutional Neural Network (CNN) Method For Women’s Skin Classification,” Sci. J. Informatics, vol. 8, no. 1, pp. 144–153, 2021.
R. Muhammad Ehsan, S. P. Simon, and P. R. Venkateswaran, “Day-ahead forecasting of solar photovoltaic output power using multilayer perceptron,” Neural Comput. Appl., vol. 28, no. 12, pp. 3981–3992, 2017.
P. Gupta and N. K. Sinha, Neural Networks for Identification of Nonlinear Systems: An Overview. Academic Press, 2000.