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All Journal Jurnal Statistika Universitas Muhammadiyah Semarang Jurnal Sistem Cerdas Jurnal Teknologi Informasi Cyberku Jurnal Fokus Elektroda (Energi Listrik, Telekomunikasi, Komputer, Elektronika dan Kendali)
Ahmad Ilham
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Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science

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Journal : Jurnal Statistika Universitas Muhammadiyah Semarang

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HYBRID METODE BOOSTRAP DAN TEKNIK IMPUTASI PADA METODE C4-5 UNTUK PREDIKSI PENYAKIT GINJAL KRONIS Ahmad Ilham
Jurnal Statistika Universitas Muhammadiyah Semarang Vol 8, No 1 (2020): Jurnal Statistika Universitas Muhammadiyah Semarang
Publisher : Department Statistics, Faculty Mathematics and Natural Science, UNIMUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (566.06 KB) | DOI: 10.26714/jsunimus.8.1.2020.%p

Abstract

Missing values is a serious problem that most often found in real data today. The C4.5 method is a popular classification predictive modeling used because of its ease of implementation. However, C4.5 is still weak when testing data that contains large missing. In this study we used a hybrid approach the bootstrap method and k-NN imputation to overcome missing values. The proposed method tested using Chronic Kidney Disease (CKD) data, and evaluated using accuracy and AUC. The results showed that the proposed method was superior in overcoming missing values in CKD. It can be concluded that the proposed method is able to overcome missing values for chronic kidney disease prediction.
Co-Authors Catur Supriyanto Didit Hermawan Farida Yusuf Heru Pranoto Muhammad Idaham Romi Satria Wahono