Penerapan Aproksimasi Fejer dalam Membuktikan Teorema Weierstrass - Aris, Naimah; Massalesse, Jusmawati; Abbas, Islamiyah (Jurnal Matematika, Statistika dan Komputasi, 2018)
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" 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