IDENTIFICATION OF X-RAY RADIATION EXPOSURE FOR RADIATION SAFETY USING RANDOM FOREST CLASSIFICATION

Teknika
Sekolah Tinggi Teknologi "Warga" Surakarta

📄 Abstract

The safety level of X-ray exposure is very important because it has short-term and long-term effects that significantly affect the health of radiation workers and the surrounding environment. Measurements of the safety level of X-Ray radiation are generally carried out using conventional methods, namely manually identifying the radiation exposure value data for workers and the surrounding environment from a survey meter, then adding up periodically. However, this can potentially cause errors in the addition so that the method produces less accurate data. This study aims to X-Ray radiation exposure dose using Random Forest Classification. The radiation data processed is the dose value of X-Ray exposure measures using a digital survey meter (in µSv/h) unit of as many as 160 datasets and consists of 87 safe and 73 unsafe doses. Data are classified according to the International Atomic Energy Agency (IAEA) dosage limit value rule. The performance of Random Forest Classification is evaluated with Naïve Bayes dan K-Nearest Neighbor (KNN). The result shows that the Random Forest Classification accuracy value is 90%, the Naïve Bayes accuracy value is 85%, and the KNN accuracy value is 86%. Therefore, the performance value from the Random Forest Classification of 97% is taken as the best result. As a summary of this study, Random Forest Classification performed better than other Naïve Bayes and K-Nearest Neighbor (KNN) for identifying the safety level of X-Ray radiation exposure as proven with the optimum given the parameters applied.  

🔖 Keywords

#dosis radiasi; proteksi radiasi; detektor isian gas; identifikasi; akurasi

ℹ️ Informasi Publikasi

Tanggal Publikasi
28 October 2023
Volume / Nomor / Tahun
Volume 8, Nomor 2, Tahun 2023

📝 HOW TO CITE

Ningtias, Diah Rahayu; Rofi’i, Muhammad; Wahyudi, Bayu; Simanjuntak, Josepa ND; Muttaqin, Rodhotul, "IDENTIFICATION OF X-RAY RADIATION EXPOSURE FOR RADIATION SAFETY USING RANDOM FOREST CLASSIFICATION," Teknika, vol. 8, no. 2, Oct. 2023.

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