Implementasi Metode Decision Tree pada Sistem Prediksi Status Gizi Balita

Dasilva Nike Aria Kurniawan(1*), M Maryam(2),

(1) Universitas Muhammadiyah Surakarta, Jawa Tengah, Indonesia
(2) Universitas Muhammadiyah Surakarta, Jawa Tengah, Indonesia
(*) Corresponding Author

Abstract


Examination of the nutritional status of toddlers is one way to monitor and identify toddlers who are at risk of experiencing nutritional problems. Implementation of the method in predicting nutritional status using relevant parameters. The Decision Tree method is used as a predictive model, using a dataset with parameters such as age, sex, height, weight, and nutritional status as labels. At the mining stage, data processing starts from preprocessing, namely the cleansing process to clean up incorrect data and data transformation to change the data type so that it is easy to process during the classification process. Furthermore, the Decision Tree model will be trained, tested and measured based on accuracy. The model is described in the form of a decision tree so that it can be used as a rule in system implementation. The implementation results provide accurate predictions with an accuracy value of 92.73%. The prediction system is designed to assist health workers in supporting decisions on predicting the nutritional status of children under five, as well as facilitating the community to carry out independent checks. This prediction can help identify toddlers at risk of nutritional disorders so that early intervention steps can be taken appropriately..

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References


N. R. Fauzia, N. M. A. Sukmandari, and K. Y. Triana, “Hubungan Status Pekerjaan Ibu Dengan Status Gizi Balita,” J. Cent. Res. Publ. Midwifery Nurs., vol. 3, no. 1, pp. 28–32, 2019, doi: 10.36474/caring.v3i1.101.

L. Suryani, S. Payung, and N. Pekanbaru, “Faktor Yang Mempengaruhi Status Gizi Balita Di Wilayah Kerja Puskesmas Payung Sekaki,” JOMIS (Journal Midwifery Sci., vol. 1, no. 2, pp. 47–53, 2017,

P. Studi et al., “Status Gizi Balita Di Dinas Kesehatan”.

A. Prasetio, “Simulasi Penerapan Metode Decision Tree (C4.5) Pada Penentuan Status Gizi Balita,” J. Nas. Komputasi dan Teknol. Inf., vol. 4, no. 3, pp. 209–214, 2021, doi: 10.32672/jnkti.v4i3.2983.

R. Setiawan and A. Triayudi, “Klasifikasi Status Gizi Balita Menggunakan Naïve Bayes dan K-Nearest Neighbor Berbasis Web,” J. Media Inform. Budidarma, vol. 6, no. 2, p. 777, 2022, doi: 10.30865/mib.v6i2.3566.

E. T. Lestari and J. Adhiva, “Implementation Naive Bayes Classifier Algorithm and K-Nearest Neighbor For Obesity Nutritional Status of Children with Disabilities Implementasi Algoritma Naive Bayes Classifier dan K-Nearest Neighbor Untuk Klasifiasi Status Gizi Obesitas Anak Disabilitas,” SENTIMAS Semin. Masional Penelit. dan Penabdian Masy., pp. 1–11, 2022.

K. Riskerdas, “Hasil Utama Riset Kesehatan Dasar (RISKEDAS),” J. Phys. A Math. Theor., vol. 8, no. 44, pp. 1–200, 2018.

F. Gorunescu, Data Mining: Concepts, models, and techniques (Softcover). 2011.

D. Setianingsih and R. F. Hakim, “Penerapan Data Mining dalam Analisis Kejadian Tanah Longsor di Indonesia dengan Menggunakan Association Rule Algoritma Apriori,” Pros. Semin. Nas. Mat. dan Pendidik. Mat. UMS 2015, pp. 731–741, 2015.

Lindawati, “Data Mining Dengan Teknik Clustering Dalam Pengklasifikasian Data Mahasiswa Studi Kasus Prediksi Lama Studi Mahasiswa Universitas Bina Nusantara,” Univ. Stuttgart, vol. 2008, no. semnasIF, pp. 174–180, 2008.

R. Kurniasari and A. Fatmawati, “Penerapan Algoritma C4.5 Untuk Penjurusan Siswa Sekolah Menengah Atas,” Komputa J. Ilm. Komput. dan Inform., vol. 8, no. 1, pp. 19–27, 2019, doi: 10.34010/komputa.v8i1.3045.

V. M. Patro and M. Ranjan Patra, “Augmenting Weighted Average with Confusion Matrix to Enhance Classification Accuracy,” Trans. Mach. Learn. Artif. Intell., vol. 2, no. 4, 2014, doi: 10.14738/tmlai.24.328.

E. Sudarmilah and P. A. Wibowo, “Aplikasi Augmented Reality Game Edukasi Untuk Pengenalan Organ Tubuh Manusia,” Khazanah Inform. J. Ilmu Komput. dan Inform., vol. 2, no. 1, pp. 20–25, 2016, doi: 10.23917/khif.v2i1.1945.

A. A. Wahid, “Analisis Metode Waterfall Untuk Pengembangan Sistem Informasi,” J. Ilmu-ilmu Inform. dan Manaj. STMIK, no. November, pp. 1–5, 2020.

M. Susilo, “Rancang Bangun Website Toko Online Menggunakan Metode Waterfall,” InfoTekJar (Jurnal Nas. Inform. dan Teknol. Jaringan), vol. 2, no. 2, pp. 98–105, 2018, doi: 10.30743/infotekjar.v2i2.171.

M. N. Arifin and D. Siahaan, “Structural and Semantic Similarity Measurement of UML Use Case Diagram,” Lontar Komput. J. Ilm. Teknol. Inf., vol. 11, no. 2, p. 88, 2020, doi: 10.24843/lkjiti.2020.v11.i02.p03.

B. Khafid and D. A. P. Putri, “Pesma Apps as Android-based Integrated Applications for Mahasantri Pesma KH Mas Mansur UMS,” Khazanah Inform. J. Ilmu Komput. dan Inform., vol. 6, no. 2, pp. 95–102, 2020, doi: 10.23917/khif.v6i2.10494.

R. B. Susanto, P. S. Informatika, F. Komunikasi, D. A. N. Informatika, and U. M. Surakarta, “Gudang ( Studi Kasus Di Yulian Aquatic ),” 2022.

T. Hidayat and M. Muttaqin, “Pengujian sistem informasi pendaftaran dan pembayaran wisuda online menggunakan black box testing dengan metode equivalence partitioning dan boundary value analysis,” J. Tek. Inform. UNIS, vol. 6, no. 1, pp. 2252–5351, 2018, doi: 10.33005/jifosi.v2i2.326.

M. Maryam and H. W. Ariono, “Sistem Pakar Pengklasifikasi Stadium Kanker Serviks Berbasis Mobile Menggunakan Metode Decision Tree,” J. Kaji. Ilm., vol. 22, no. 3, pp. 267–278, 2022, doi: 10.31599/jki.v22i3.1368.

M. V. Ruma, Yohanes, “Klasifikasi Status Gizi Balita Menggunakan Metode Naive Bayes Classification,” p. 133, 2020.




DOI: http://dx.doi.org/10.30645/j-sakti.v7i2.681

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