Deep Learning Pada Detektor Jerawat: Model YOLOv5
Main Article Content
Abstract
Jerawat (Acne Vulgaris) merupakan masalah utama yang sulit untuk dihindari pada masyarakat daerah perkotaan, seperti Jakarta dan sekitarnya. Penyebab utama dari jerawat yaitu tingginya polusi udara yang disebabkan oleh hasil pembakaran transportasi dan sektor industri. Sisa pembakaran ini umumnya mengandung PM (Particulate Matter) dengan ukuran yang cukup kecil (PM2.5 dan PM10) yang mampu masuk ke dalam kulit melalui pori-pori dan bereaksi dengan beberapa senyawa diudara sehingga menyebabkan banyak permasalahan kulit lainnya. Penelitian ini berfokus pada pendeteksian jerawat dengan menggunakan model Deep Learning, yaitu YOLOv5. YOLOv5 dilatih dengan menggunakan tiga optimizer berbeda (SGD, Adam, dan AdamW) sebanyak 100 epochs. Setelah dilakukan pelatihan, didapatkan hasil F1-score dengan optimizer SGD sebesar 43%, Adam 39%, dan AdamW sebesar 40%. Pada penelitian ini, optimizer SGD memiliki nilai F1 tertinggi sehingga dijadikan sebagai optimizer teroptimum yang dapat digunakan pada permasalahan di penelitian ini.
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How to Cite
Kusumah, H., Zahran, M., Rifqi, K., Putri, D., & Wakti Hapsari, E. (2023). Deep Learning Pada Detektor Jerawat: Model YOLOv5. Journal Sensi: Strategic of Education in Information System, 9(1), 24-35. https://doi.org/https://doi.org/10.33050/sensi.v9i1.2620
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Articles
References
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[36] Y. Arouri and M. Sayyafzadeh, “An adaptive moment estimation framework for well placement optimization,” Computational Geosciences 2022 26:4, vol. 26, no. 4, pp. 957–973, Feb. 2022, doi: 10.1007/S10596-022-10135-9.
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[38] R. Llugsi, S. el Yacoubi, A. Fontaine, and P. Lupera, “Comparison between Adam, AdaMax and Adam W optimizers to implement a Weather Forecast based on Neural Networks for the Andean city of Quito,” ETCM 2021 - 5th Ecuador Technical Chapters Meeting, Oct. 2021, doi: 10.1109/ETCM53643.2021.9590681.
[39] I. Markoulidakis, I. Rallis, I. Georgoulas, G. Kopsiaftis, A. Doulamis, and N. Doulamis, “Multiclass Confusion Matrix Reduction Method and Its Application on Net Promoter Score Classification Problem,” Technologies 2021, Vol. 9, Page 81, vol. 9, no. 4, p. 81, Nov. 2021, doi: 10.3390/TECHNOLOGIES9040081.
[40] Z. C. Lipton, C. Elkan, and B. Naryanaswamy, “Optimal thresholding of classifiers to maximize F1 measure,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 8725 LNAI, no. PART 2, pp. 225–239, 2014, doi: 10.1007/978-3-662-44851-9_15.
[41] D. Fourure, M. U. Javaid, N. Posocco, and S. Tihon, “Anomaly Detection: How to Artificially Increase Your F1-Score with a Biased Evaluation Protocol,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12978 LNAI, pp. 3–18, 2021, doi: 10.1007/978-3-030-86514-6_1/COVER.
[42] J. Miao and W. Zhu, “Precision–recall curve (PRC) classification trees,” Evolutionary Intelligence 2021 15:3, vol. 15, no. 3, pp. 1545–1569, Apr. 2021, doi: 10.1007/S12065-02100565-2.