Detection of Room Cleanliness Based on Digital Image Processing using SVM and NN Algorithm
DOI:
10.33395/sinkron.v7i3.11479Keywords:
Classification, Image Processing, Neural Network, Room Cleanliness, Support Vector MachineAbstract
A clean environment can prevent us from disease and can increase productivity. A neat and clean room arrangement can affect health, avoiding the possibility of stress, lethargy, and depression. The room recognition process based on its neatness is carried out through a process of matching and comparing the images that are used as training and testing sets. Technological developments make it possible to detect room conditions through image. Detection uses image processing by classifying images into 2 categories, clean and messy. It has been widely used in various fields, one of which is hospitality. In determining the clean room and messy room has problems due to image quality, different lighting, and image similarity. This study aims to detect clean and messy spaces by comparing the Support Vector Machine and Neural Network methods on a dataset of 199 images. Based on the test, the highest accuracy classification value was 98.0% for the Neural Network method with an AUC of 0.999
Downloads
References
Abu, M. A., Indra, N. H., Rahman, A. H. A., Sapiee, N. A., & Ahmad, I. (2019). A study on Image Classification based on Deep Learning and Tensorflow. International Journal of Engineering Research and Technology, 12(4), 563–569.
Balaji, K., & Lavanya, K. (2019). Medical Image Analysis With Deep Neural Networks. In Deep Learning and Parallel Computing Environment for Bioengineering Systems (Issue 1, pp. 75–97). Elsevier Inc. https://doi.org/10.1016/b978-0-12-816718-2.00012-9
Cherry, K. (2021). What a Messy Room Says About You. Dotdash Media, Inc. https://www.verywellmind.com/psychology-of-a-messy-room-4171244
Dharmali, M. J., Lioner, T., & Susilo, V. V. (2021). Sistem Klasifikasi Kerapihan Kamar Hotel Menggunakan Convolutional Neural Network (CNN). Computatio: Journal of Computer Science and Information Systems, 5(2), 61–72.
Giovany, S., Putra, A., Hariawan, A. S., Wulandhari, L. A., & Irwansyah, E. (2019). Indonesian Food Image Recognition Using Convolutional Neural Network. In Springer (Issue May). Springer International Publishing. https://doi.org/10.1007/978-3-030-19810-7_21
Hidayat, R., Agustiani, S., Wildah, S. K., Mustopa, A., & Safitri, R. A. (2021). Penerapan Metode Pembelajaran Menggunakan Ekstraksi Fitur dan Algoritma Klasifikasi untuk Identifikasi Pengenalan Iris. Jurnal Teknik Komputer AMIK BSI, 7(2), 151–157. https://doi.org/10.31294/jtk.v4i2
Islam, M. T., Rahman, S., Siddique, B. M. N. K., & Jabid, T. (2018). Food Image Classification with Convolutional Neural Network. ICIIBMS 2018, Track 2: Artificial Intelligent, Robotics, and Human-Computer Interaction, Bangkok, Thailand Food, 257–262. https://doi.org/10.1007/978-3-030-70542-8_18
Kamavisdar, P., Saluja, S., & Agrawal, S. (2013). A Survey on Image Classification Approaches and Techniques. International Journal of Advanced Research in Computer and Communication Engineering, 2(1), 1005–1009. www.ijarcce.com
Muhathir, M., Santoso, M. H., & Larasati, D. A. (2021). Wayang Image Classification Using SVM Method and GLCM Feature Extraction. Journal of Informatics and Telecommunication Engineering, 4(2), 373–382. https://doi.org/10.31289/jite.v4i2.4524
Othman, K. M., & Rad, A. B. (2019). An indoor room classification system for social robots via integration of CNN and ECOC. Applied Sciences (Switzerland), 9(3). https://doi.org/10.3390/app9030470
Abu, M. A., Indra, N. H., Rahman, A. H. A., Sapiee, N. A., & Ahmad, I. (2019). A study on Image Classification based on Deep Learning and Tensorflow. International Journal of Engineering Research and Technology, 12(4), 563–569.
Balaji, K., & Lavanya, K. (2019). Medical Image Analysis With Deep Neural Networks. In Deep Learning and Parallel Computing Environment for Bioengineering Systems (Issue 1, pp. 75–97). Elsevier Inc. https://doi.org/10.1016/b978-0-12-816718-2.00012-9
Cherry, K. (2021). What a Messy Room Says About You. Dotdash Media, Inc. https://www.verywellmind.com/psychology-of-a-messy-room-4171244
Dharmali, M. J., Lioner, T., & Susilo, V. V. (2021). Sistem Klasifikasi Kerapihan Kamar Hotel Menggunakan Convolutional Neural Network (CNN). Computatio: Journal of Computer Science and Information Systems, 5(2), 61–72.
Giovany, S., Putra, A., Hariawan, A. S., Wulandhari, L. A., & Irwansyah, E. (2019). Indonesian Food Image Recognition Using Convolutional Neural Network. In Springer (Issue May). Springer International Publishing. https://doi.org/10.1007/978-3-030-19810-7_21
Hidayat, R., Agustiani, S., Wildah, S. K., Mustopa, A., & Safitri, R. A. (2021). Penerapan Metode Pembelajaran Menggunakan Ekstraksi Fitur dan Algoritma Klasifikasi untuk Identifikasi Pengenalan Iris. Jurnal Teknik Komputer AMIK BSI, 7(2), 151–157. https://doi.org/10.31294/jtk.v4i2
Islam, M. T., Rahman, S., Siddique, B. M. N. K., & Jabid, T. (2018). Food Image Classification with Convolutional Neural Network. ICIIBMS 2018, Track 2: Artificial Intelligent, Robotics, and Human-Computer Interaction, Bangkok, Thailand Food, 257–262. https://doi.org/10.1007/978-3-030-70542-8_18
Kamavisdar, P., Saluja, S., & Agrawal, S. (2013). A Survey on Image Classification Approaches and Techniques. International Journal of Advanced Research in Computer and Communication Engineering, 2(1), 1005–1009. www.ijarcce.com
Muhathir, M., Santoso, M. H., & Larasati, D. A. (2021). Wayang Image Classification Using SVM Method and GLCM Feature Extraction. Journal of Informatics and Telecommunication Engineering, 4(2), 373–382. https://doi.org/10.31289/jite.v4i2.4524
Othman, K. M., & Rad, A. B. (2019). An indoor room classification system for social robots via integration of CNN and ECOC. Applied Sciences (Switzerland), 9(3). https://doi.org/10.3390/app9030470
Pratama, R. R. (2020). Analisis Model Machine Learning Terhadap Pengenalan Aktifitas Manusia. MATRIK : Jurnal Manajemen, Teknik Informatika Dan Rekayasa Komputer, 19(2), 302–311. https://doi.org/10.30812/matrik.v19i2.688
Santos, G. M., Malmiran, M., & Sorbo, P. (2020). Dirty Room Notifier Final Report.
Shakya, D. S. (2020). Analysis of Artificial Intelligence based Image Classification Techniques. Journal of Innovative Image Processing, 2(1), 44–54. https://doi.org/10.36548/jiip.2020.1.005
Wibawa, A. P., Guntur, M., Purnama, A., Akbar, M. F., & Dwiyanto, F. A. (2018). Metode-metode Klasifikasi. Prosiding Seminar Ilmu Komputer Dan Teknologi Informasi, 3(1), 134–138.
Wildah, S. K., Agustiani, S., Mustopa, A., Wuryani, N., Nawawi, H. M., & Safitri, R. A. (2021). Pengenalan Wajah Menggunakan Pembelajaran Mesin Berdasarkan Ekstraksi Fitur pada Gambar Wajah Berkualitas Rendah Face Recognition Using Machine Learning Based on Feature Extraction on Low Quality Face Images. INFOTECH: Jurnal Informatika & Teknologi, 2(2), 95–103.
Wiley, V., & Lucas, T. (2018). Computer Vision and Image Processing: A Paper Review. International Journal of Artificial Intelligence Research, 2(1), 22. https://doi.org/10.29099/ijair.v2i1.42
Winarna, Bawole, P., & Hadilinatih, B. (2021). Redefinisi Ruang Publik di Masa Pandemi Covid-19. Vitruvian: Jurnal Arsitektur, Bangunan, & Lingkungan, 10(3), 237–256.
Downloads
|
How to Cite
Issue
Section
License
Copyright (c) 2022 Suparni suparni, Hilda Rachmi, Ahmad Al Kaafi
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Muhammad Khoiruddin harahap
Google Scholar 
Moraref
PKP Index
Garuda Kemdikbud
Indonesia OneSearch
OCLC Worldcat
Dimensions
Index Copernicus
Scilit
