Pengaruh Data Preprocessing terhadap Imbalanced Dataset pada Klasifikasi Citra Sampah menggunakan Algoritma Convolutional Neural Network
- Muhammad Resa Arif Yudianto Universitas Muhammadiyah Magelang, Magelang, Indonesia
- Pristi Sukmasetya Universitas Muhammadiyah Magelang, Magelang, Indonesia
- Rofi Abul Hasani Universitas Muhammadiyah Magelang, Magelang, Indonesia
-
Dimas Sasongko
*
Universitas Muhammadiyah Magelang, Magelang,
Indonesia
(*) Corresponding Author
Abstract
Garbage is one of Indonesia's most significant problems with an increase in waste each year reaching 187.2 million tonnes/year. Various efforts to reduce the amount of waste such as Garbage Banks have been encouraged. However, this program has not run well, because some people have difficulty distinguishing the type of waste. One solution to overcome this problem is that need a system that can classify the type of waste. The deep learning approach with the CNN algorithm is currently widely used to solve classification problems. This method requires a large number of datasets to increase the level of accuracy. Getting a garbage dataset is a particular problem in the training process because the dataset is unbalanced. The dataset used amounted to 2527 data consisting of 6 classes. Several treatments such as undersampling and image augmentation are applied to overcome imbalanced datasets. Other treatments such as the type of input image channel and the use of filters are combined into 24 experimental scenarios to achieve the highest accuracy. The results of the experiment get the best scenario, namely, the dataset is undersampling and then augmented with 5 geometric transformation parameters with the input image being RGB and applying a sharpening filter to get an accuracy value of 0.9919 with 20 epochs.
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References
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Y. Chen et al., “Classification of lungs infected COVID-19 images based on inception-ResNet,” Comput Methods Programs Biomed, vol. 225, p. 107053, 2022, doi: 10.1016/j.cmpb.2022.107053.
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P. M. Blok, F. K. van Evert, A. P. M. Tielen, E. J. van Henten, and G. Kootstra, “The effect of data augmentation and network simplification on the image-based detection of broccoli heads with Mask R-CNN,” J Field Robot, vol. 38, no. 1, pp. 85–104, 2021, doi: 10.1002/rob.21975.
S. N. Gowda and C. Yuan, ColorNet: Investigating the Importance of Color Spaces for Image Classification, vol. 11364 LNCS. Springer International Publishing, 2019. doi: 10.1007/978-3-030-20870-7_36.
F. M. Hana and I. D. Maulida, “Analysis of contrast limited adaptive histogram equalization (CLAHE) parameters on finger knuckle print identification,” J Phys Conf Ser, vol. 1764, no. 1, 2021, doi: 10.1088/1742-6596/1764/1/012049.
F. Thabtah, S. Hammoud, F. Kamalov, and A. Gonsalves, “Data imbalance in classification: Experimental evaluation,” Inf Sci (N Y), vol. 513, 2020, doi: 10.1016/j.ins.2019.11.004.
T. A. Soomro et al., “Impact of Image Enhancement Technique on CNN Model for Retinal Blood Vessels Segmentation,” IEEE Access, vol. 7, pp. 158183–158197, 2019, doi: 10.1109/ACCESS.2019.2950228.
I. Astuti, W. W. Ariestya, and B. Solehudin, “Deteksi Objek Daun Semanggi Secara Real Time Menggunakan CNN-Single Shot Multibox Detector (SSD),” Jurnal Ilmiah FIFO, vol. 14, no. 1, 2022.
M. Grandini, E. Bagli, and G. Visani, “Metrics for multi-class classification: an overview,” arXiv preprint arXiv:2008.05756, 2020.
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