Klasifikasi Teks Mining Terhadap Analisa Isu Kegiatan Tenaga Lapangan Menggunakan Algoritma K-Nearest Neighbor (KNN)

Nur Ajijah(1*), Adi Kurniawan(2), S Susilawati(3),

(1) Universitas Singaperbangsa Karawang, Indonesia
(2) Universitas Singaperbangsa Karawang, Indonesia
(3) Universitas Singaperbangsa Karawang, Indonesia
(*) Corresponding Author

Abstract


Information and communication technology at this time is developing very rapidly, including in the easier dissemination of information. With the sophisticated technology in conveying information, it is possible that there is information that is not certain of its truth. The issue that occurs is due to the discrepancy of expectations expected by distributors, kiosks, and farmers.  The amount of issue data obtained greatly affects the efficiency of the results that will be obtained. Therefore, it is necessary to have a text analysis  to find out the issues spread in the field regarding the services of products and services provided by PT XYZ. In this study, it applied the CRISP-DM research stages and the application of the K-Nearest Neighbor (KNN) algorithm  which showed that the resulting accuracy rate was 93.88% with data of 2,500 data. And the highest precission value is obtained by the payment qualification of 98.67%.

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References


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DOI: http://dx.doi.org/10.30645/j-sakti.v7i1.589

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