Evaluasi Implementasi Algoritma Machine Learning K-Nearest Neighbors (kNN) pada Data Spektroskopi Gamma Resolusi Rendah
Abstract
Abstrak
Pada artikel ini kami mengevaluasi bagaimana implementasi algoritma machine learning k-Nearest Neighbors (kNN) pada data spektroskopi gamma beresolusi rendah. Penelitian ini bertujuan untuk mengetahui bagaimana performa kNN dalam mempelajari data tersebut. Kami melakukan berbagai variasi, yaitu: jumlah data training, jumlah data tes, jenis metric, dan nilai k untuk memperoleh performa terbaik dari algoritma ini. Data spektroskopi gamma diambil menggunakan sintilator NaI(Tl) Leybold Didactic dengan resolusi energi sebesar 10.9 keV per channel. Hasil variasi menunjukkan bahwa algoritma kNN memberikan hasil prediksi klasifikasi radioisotop yang sangat fluktuatif.
Abstract
In this paper we evaluate the implementation of a machine learning algorithm namely k-Nearest Neighbors (kNN) on low resolution gamma spectroscopy data. The aim is to provide the information of how well the algorithm performs on learning the data. We did the variation of number of training and test data, type of metric used, and values of k in order to see the best performance of the algorithm. The gamma spectroscopy data were taken using NaI(Tl) scintillator made by Leybold Didactic with resolution of 10.9 keV per channel. The variations show that the kNN algorithm produce significantly fluctuating accuracy to the prediction of radioisotope class.
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DOI: https://doi.org/10.15408/fiziya.v3i1.16180 Abstract - 0 PDF - 0
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