Perbandingan Metode Classification and Regression Trees (CART) dengan Naïve Bayes Classification (NBC) dalam Klasifikasi Status Gizi Balita di Kelurahan Pagesangan Barat

Nurul - Insan, Mustika Hadijati, Irwansyah Irwansyah

Abstract


This study aims to compare the Classification and Regression Trees (CART) and Naïve Bayes Classification (NBC) methods in classifying the nutritional status of toddlers in West Pagesangan by looking at their accuracy and also knowing the variables that influence the classification of toddler nutritional status. The data used in this study were toddlers who come to the posyandu in May 2019, with predictor variables used namely gender, ages, weight, mother’s employment status, mother’s education level, number of children and parents income. The result showed that Naïve Bayes Classification (NBC) is better in classifying the nutritional status of toddlers in West Pagesangan than Classification and Regression Trees (CART). This can be seen from the accuracy values obtained with three comparisons of training data and testing data. In the comparison of 90% of training data: 10% of testing data, obtained an accuracy value of 90% for NBC and 85% for CART, in the comparison of 80% of training data: 20% of testing data, obtained an accuracy value 0f 82.5% for NBC and 80% for CART, while in comparison 70% traing data : 30% testing data, obtained an accuracy value 72% for NBC and 70%for CART. This study also showed that significant variables the classification of nutritional status of toddlers in West Pagesangan village are age, gender, weight and parents income.

Keywords


Accuracy; Classification; Classification and Regression Trees (CART); Naïve Bayes Classification (NBC); Toddler Nutrition Status.

References


Berry, M. W. dan Browne, M. (2006). Lecture Notes In Data Mining. USA: Word Scientific.

Bustami. (2014). Penerapan Algoritma Naïve Bayes Untuk Mengklasifikasikan Para Nasabah, Jurnal Informatika, Vol. 8, No. 1, pp. 885-887.

Breiman, L., dkk. (1993). Classification and Regression Trees (CART). New York: Chapman And Hall.

Han, J. dan Kamber, M. (2001). Data Mining: Concepts and Techniques Second Edition. San Fransisco: Morgan Kaufmann Publishers.

Hartati, A., Zain, I., dan Ulama, B. S. S. (2012). Analisis Cart (Classification And Regression Trees) pada Faktor-Faktor yang Mempengaruhi Kepala Rumah Tangga di Jawa Timur Melakukan Urbanisasi. Jurnal Sains dan Seni ITS, Vol. 1, No. 1, pp. 100-105.

Johnson, R. A. dan Wichern, D. W. (2007). Applied Multivariate Statistical Analysis, Sixth Edition. USA: Pearson Education.

Kementerian Kesehatan. (2013). Situasi Keluarga Berencana di Indonesia. Jakarta: Kementerian Kesehatan RI.

Kementerian Kesehatan. (2019). Data dan Informasi Profil Kesehatan Indoensia 2018. Jakarta: Kementerian Kesehatan RI.

Kusumadewi, S. (2009). Klasifikasi Status Gizi Menggunakan Naïve Bayes Classification. Comm IT, Vol. 3, No. 1, pp. 6-11.

Lewis, R. J. (2000). An Introduction T Classification and Regression Trees (CART) Analysis, Present at The 2000 Annual Meeting of Society for Academic Emergency Medicine of Sans Fransisco, California.

Pratiwi, F. E. dan Zain, I. (2014). Klasifikasi Pengangguran Menggunakan CART (Classification and Regression Tree) di Provinsi Sulawesi Utara. Jurnal Sains dan Seni Pomits. Vol. 3, No. 1, pp. 55.

Rahmi, I., Yozza, H., dan Rahmy, H. A. (2017). Telaah Faktor-Faktor yang Mempengaruhi Status Gizi Balita di Kota Padang Berdasarkan Berat Badan Per Tinggi Badan Menggunakan Metode CART. Jurnal Eksakta, Vol. 18, No. 2, pp. 87.

Ratnaningrum, D., Mukid, M. A., dan Wuryandari, T. (2016). Analisis Klasifikasi Nasabah Kredit Menggunakan Bootstrap Aggregating Classification and Regression Trees (Bagging CART). Jurnal Gaussian, Vol. 5, No. 1, pp. 82-83.

Tanjung, R. H. dan Kartiko, K. (2017). Penerapan Metode CART (Classification And Regression Trees) untuk Menentukan Faktor-Faktor yang Mempengaruhi Pembayaran Kredit oleh Nasabah (Studi Kasus Bank BRI Unit Aek Tarum- Sumatera Utara), Jurnal Statistika dan Komputasi, Vol. 2, No. 2, pp. 78-83.




DOI: https://doi.org/10.29303/emj.v3i1.68

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