Prediksi Penambahan Piutang Iuran Jaminan Sosial Ketenagakerjaan menggunakan Algoritma K-Nearest Neighbor

Devi Efriadi, Rahmaddeni Rahmaddeni, Agustin Agustin, Junadhi Junadhi
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Abstract

There are several issues with Social Security Organizing Agency (BPJS) employment at the moment, one of which is contribution receivable. To reduce the BPJS contribution receivables, BPJS has done various ways. However, the resulting effort is not maximal enough to reduce the number of receivables in BPJS. This study aims to provide input by predicting the addition of receivables from social security contributions made by several companies or organizations. This study used the K-Nearest Neighbor (KNN) Algorithm with a cross-validation technique. KNN is a very simple classification method in classifying an image based on the closest distance to its neighbors. This study conducted data processing from BPJS use, which amounted to 1193 data. The data is then preprocessed so that the processed data is clean from missing and noise, this data uses 70:30 data splitting. After the preprocessing and splitting of data were carried out, the next step was to do modeling using KNN, so the cross-validation to improve the accuracy of results obtained from the KNN algorithm. The results obtained from this research get the highest accuracy of 92% with the Optimal K value being 6, then the ROC curve gets 94% accuracy. From these results, it can be said that the use of cross-validation can increase the accuracy of this study.
目前,社会保障组织机构(BPJS)的就业存在几个问题,其中一个问题是应收款。为了减少BPJS的应收款贡献,BPJS采取了多种方式。然而,由此产生的努力不足以最大限度地减少BPJS中的应收账款数量。本研究旨在通过预测几家公司或组织的社会保障缴款中应收款项的增加来提供输入。本研究使用了k -最近邻(KNN)算法和交叉验证技术。KNN是一种非常简单的分类方法,它基于与相邻图像的最近距离对图像进行分类。本研究对使用BPJS的数据进行了处理,共计1193个数据。然后对数据进行预处理,使处理后的数据没有丢失和噪声,该数据使用70:30的数据分割。在对数据进行预处理和分割之后,下一步就是使用KNN进行建模,因此通过交叉验证来提高KNN算法得到的结果的准确性。本研究得到的结果最高准确率为92%,最优K值为6,则ROC曲线的准确率为94%。从这些结果来看,可以说交叉验证的使用可以提高本研究的准确性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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