Analisis Keadaan Stunting pada Kelompok Balita di Kecamatan Tukdana dengan Pendekatan Decision Trees

Asep Budiyanto; Dodi Solihudin; Ryan Hamonangan; Cep Lukman Rohmat; Ade Rizki Rinaldi

doi:10.36499/jinrpl.v6i1.10230

Analisis Keadaan Stunting pada Kelompok Balita di Kecamatan Tukdana dengan Pendekatan Decision Trees

Asep Budiyanto, Dodi Solihudin, Ryan Hamonangan, Cep Lukman Rohmat, Ade Rizki Rinaldi

Abstract

The impact of stunting on babies is an important parameter for assessing the health and welfare of children in an area. Stunting, often triggered by demographic and health factors, has serious implications for children's physical and cognitive growth. This research aims to understand the impact of demographic and health factors on stunting in children in Tukdana District, Indramayu Regency. Through data analysis, factors such as maternal age, access to clean water, sanitation facilities, and baby weight and length status were identified as significant contributors to stunting. The Decision Trees method was used to identify factors that play a role in stunting in babies, with an accuracy rate of 95.43%. The implications of this research include planning more effective interventions to deal with stunting, both in Tukdana District and in similar areas in Indonesia. Even though the majority of babies in Tukdana District have good nutritional status, further monitoring and prevention efforts are still needed to ensure optimal nutritional well-being for them. In conclusion, this research highlights the importance of identifying factors that cause stunting in infants in Tukdana District, as a basis for planning more effective interventions.

Keywords

Stunting, Decision Trees, Health Data Analysis, Determinant Factors, Nutrition, Tukdana District, Indonesia

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References

Abidin, Z., Nurhana, E., Permata, & Ulum, F. (2023). Analisis Perbandingan Algoritma Decision Tree C4.5 Dan C5.0 Pada Data Karyawan Berpotensi Promosi Jabatan. ZA. Pagar Alam, 17(2), 567–582. https://doi.org/https://doi.org/10.33365/jti.v17i2.2702

Ainurrohmah, A., & Wiyanti, D. T. (2023). Analisis Performa Algoritma Decision Tree, Naive Bayes, K-Nearest Neighbor untuk Klasifikasi Zona Daerah Risiko Covid-19 di Indonesia. Jurnal Teknologi Informasi Dan Ilmu Komputer, 10(1), 115–122. https://doi.org/10.25126/jtiik.20231015935

Alawiah, R. H., Saifullah, & Damanik, I. S. (2021). Analisis Kepuasan Konsumen Terhadap Pelayanan Bengkel Menggunakan Metode Algoritma C4.5. Januari, 2(1), 31–38. https://doi.org/https://doi.org/10.30645/kesatria.v2i1.55

Hana, F. M. (2020). Klasifikasi Penderita Penyakit Diabetes Menggunakan Algoritma Decision Tree C4.5. Jurnal SISKOM-KB (Sistem Komputer Dan Kecerdasan Buatan), 4(1), 32–39. https://doi.org/10.47970/siskom-kb.v4i1.173

Lestari, S., & Amalia, R. A. (2023). Penerapan Algoritma C. 45 Pada Klasifikasi Status Gizi Balita di Posyandu Desa Sukalilah Cibatu Kabupaten Garut Jawa Barat. Jurnal Sains Dan Teknologi, 5(1), 177–182. https://doi.org/https://doi.org/10.55338/saintek.v5i1.1375

Muttaqin, A. R., Wibawa, A., & Nabila, K. (2021). Inovasi Digital untuk Masyarakat yang Lebih Cerdas 5.0: Analisis Tren Teknologi Informasi dan Prospek Masa Depan. Jurnal Inovasi Teknologi Dan Edukasi Teknik, 1(12), 880–886. https://doi.org/10.17977/um068v1i122021p880-886

Solehuddin, M., Syafei, W. A., & Gernowo, R. (2022). Metode Decision Tree untuk Meningkatkan Kualitas Rencana Pelaksanaan Pembelajaran dengan Algoritma C4.5. Jurnal Penelitian Dan Pengembangan Pendidikan, 6(3), 510–519. https://doi.org/10.23887/jppp.v6i3.52840

Suhanda, Y., Kurniati, I., & Norma, S. (2020). Penerapan Metode Crisp-DM Dengan Algoritma K-Means Clustering Untuk Segmentasi Mahasiswa Berdasarkan Kualitas Akademik. Jurnal Teknologi Informatika Dan Komputer, 6(2), 12–20. https://doi.org/10.37012/jtik.v6i2.299

Surya, K., Kusuma, H., Atros, A., Padri, A. R., Nurdiawan, O., Faqih, A., & Anwar, S. (2021). Model Klasifikasi Analisis Kepuasan Pengguna Perpustakaan Online Menggunakan K-Means dan Decission Tree. JURIKOM (Jurnal Riset Komputer), 8(6), 323–329. https://doi.org/10.30865/jurikom.v8i6.3680

Wahono, H., & Riana, D. (2020). Prediksi Calon Pendonor Darah Potensial Dengan Algoritma Nc4.aïve Bayes, K-Nearest Neighbors dan Decision Tree C4.5. JURIKOM (Jurnal Riset Komputer), 7(1), 7–14. https://doi.org/10.30865/jurikom.v7i1.1953

Wayan Wardani, N., Gede Surya Cipta Nugraha, P., Hartono, E., Wayan Dharma Suryawan, I., Manik Dirgayusari, A., Wayan Darmadi, I., & Surya Mahendra, G. (2022). Penerapan Data Mining Untuk Klasifikasi Penjualan Barang Terlaris Menggunakan Metode Decision Tree C4.5. Jurnal Teknologi Informasi Dan Komputer, 8(3), 268–279. https://jurnal.undhirabali.ac.id/index.php/jutik/article/view/2081

DOI: http://dx.doi.org/10.36499/jinrpl.v6i1.10230