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All Journal Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer
Yogi Pinanda
Fakultas Ilmu Komputer, Universitas Brawijaya
Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering

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Klasifikasi Risiko Penyakit pada Ibu Hamil menggunakan Metode Modified K-Nearest Neighbor (MKNN) Yogi Pinanda; Wayan Firdaus Mahmudy; Edy Santoso
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 6 No 5 (2022): Mei 2022
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Pregnant women need to increase their knowledge to find out how big the level of risk of getting a disease, especially because of the vulnerability of pregnant women. Classification of the level of disease risk in pregnant women is expected to assist users in finding the right solution to overcome it. The classification method used to determine the level of disease risk for pregnant women uses Modified K-Nearest Neighbor (MKNN). Classification of disease risk levels in pregnant women using the Modified K-Nearest Neighbor (MKNN) method can make it easier to detect disease based on existing factors. The Modified K-Nearest Neighbor (MKNN) method is implemented on the expert system inference engine so that conclusions can be drawn based on existing knowledge. The results of the accuracy of the system obtained after testing is 85% which indicates that the Modified K¬-Nearest Neighbor (MKNN) method is suitable for studying the level of disease risk in pregnant women.
Co-Authors Edy Santoso Wayan Firdaus Mahmudy