IMPLEMENTASI DATA MINING ALGORITMA DECISION TREE UNTUK KLASIFIKASI STATUS GIZI BALITA DI KECAMATAN CILEDUG

Siti Bulkisah Bulkisah; Rini Astuti; Agus Bahtiar

doi:10.35760/ik.2024.v29i1.10346

IMPLEMENTASI DATA MINING ALGORITMA DECISION TREE UNTUK KLASIFIKASI STATUS GIZI BALITA DI KECAMATAN CILEDUG

Siti Bulkisah Bulkisah
STMIK IKMI Cirebon
Indonesia

Rini Astuti
STMIK IKMI Cirebon
Indonesia

Agus Bahtiar
STMIK IKMI Cirebon
Indonesia

DOI: http://dx.doi.org/10.35760/ik.2024.v29i1.10346

Abstract

The nutritional intake plays a crucial role in supporting the physical development of toddlers; however, not all toddlers in Ciledug District receive adequate nutrition. The number of toddlers experiencing nutritional status disorders or nutritional problems fluctuates annually, influenced by the fluctuation in the total number of toddlers. Currently, 2.9% of toddlers in Ciledug District are experiencing nutritional status disorders. This study aims to implement a classification process to determine the nutritional status of toddlers in Ciledug District using the decision tree algorithm. The achieved accuracy of the results is 99.18%, with detailed predictive outcomes as follows: 2298 instances correctly predicted as normal nutrition, 23 instances correctly predicted as abnormal nutrition, 2290 instances correctly predicted as abnormal nutrition, and 15 instances correctly predicted as normal nutrition. The classification results based on age indicate that infants aged 2 weeks have normal nutrition, toddlers aged 1 to 11 months exhibit both normal and abnormal nutrition, toddlers aged 12 months have normal nutrition, toddlers aged 13 to 58 months show both normal and abnormal nutrition, and toddlers aged 59 to 61 months have normal nutrition.

Keywords

Toddlers; Nutritional Status; Data Mining; Classification; Decision Tree

References

A. Z. Zami, O. Nurdiawan, and G. Dwiletari, “Klasifikasi Kondisi Gizi Bayi Bawah Lima Tahun Pada Posyandu Melati Dengan Menggunakan Algoritma Decision Tree,” J. Sist. Komputer dan Informatika., 2022, [Online]. Available: http://www.ejurnal.stmik-budidarma.ac.id/index.php/JSON/article/view/3892

Firdausia Ismi Nurhayati, B. Nugroho, and I. Yuniar Purbasari, “Implementasi Metode Decision Tree Pada Identifikasi Status Gizi Balita,” J. Inform. dan Sist. Inf., vol. 2, no. 2, pp. 204–213, 2021, doi: 10.33005/jifosi.v2i2.326.

N. L. Ratniasih, N. K. S. Julyantari, N. W. N. Jayanti, and N. L. M. Elma Yuniawati, “PENENTUAN STATUS GIZI BALITA PADA POSYANDU MENGGUNAKAN METODE K-NEAREST NEIGHBOR,” J. Inf. dan Komputer., 2023, [Online]. Available: http://dcckotabumi.ac.id/ojs/index.php/jik/article/view/398

N. Mutiara Shandhini Maylita, H. Zulfia Zahro’, and N. Vendyansyah, “Penerapan Metode K-Nearest Neighbor (Knn) Untuk Menentukan Status Gizi Balita,” JATI (Jurnal Mhs. Tek. Inform., vol. 6, no. 2, pp. 953–956, 2023, doi: 10.36040/jati.v6i2.5406.

E. Darnila, M. Maryana, and M. Azmi, “Aplikasi Klasifikasi Status Gizi Balita Menggunakan Metode Naïve Bayes Berbasis Android,” J. Manaj. Inform. Kmputerisasi Akutansi., 2021, [Online]. Available: https://ejurnal.methodist.ac.id/index.php/methomika/article/view/478

S. K. P. Loka and A. Marsal, “Perbandingan Algoritma K-Nearest Neighbor dan Naïve Bayes Classifier untuk Klasifikasi Status Gizi Pada Balita di Kota Solok: Comparison Algorithm of K-Nearest Neighbor and Naive Bayes Classifier For Classifying Nutritional Status in Toddlers,” MALCOM Indones. J. Mach. Learn. and Computer Science., vol. 3, no. April, pp. 8–14, 2023, [Online]. Available: https://journal.irpi.or.id/index.php/malcom/article/view/474

N. Nurainun, E. Haerani, F. Syafria, and L. Oktavia, “Penerapan Algoritma Naïve Bayes Classifier Dalam Klasifikasi Status Gizi Balita dengan Pengujian K-Fold Cross Validation,” J. Comput. System and Informatics., 2023, [Online]. Available: http://ejurnal.seminar-id.com/index.php/josyc/article/view/3414

M. Iqbal, M. Angga Wijaya, T. Herdiawan Apandi, and L. Nurlani, “Sistem Pakar Diagnosa Status Gizi Balita Dengan Metode Naïve Bayes Classifier Di Desa Xyz,” JIKO (Jurnal Inform. dan Komputer), vol. 5, no. 3, pp. 201–208, 2022, doi: 10.33387/jiko.v5i3.5258.

M. N. F. Hidayat, “PENENTUAN GIZI ANAK MENGGUNAKAN KOMPARASI METODE C4. 5 DAN K-NEAREST NEIGHBOR (KNN),” NJCA (Nusantara J. Comput. Its Applications., 2020, [Online]. Available: http://journal.csnu.or.id/index.php/njca/article/view/202

M. Y. Titimeidara and W. Hadikurniawati, “Implementasi Metode Naïve Bayes Classifier Untuk Klasifikasi Status Gizi Stunting Pada Balita,” J. Ilm. Inform., vol. 9, no. 01, pp. 54–59, 2021, doi: 10.33884/jif.v9i01.3741.

S. Lestari and R. A. Amalia, “Penerapan Algoritma C. 45 Pada Klasifikasi Status Gizi Balita di Posyandu Desa Sukalilah Cibatu Kabupaten Garut Jawa Barat,” J. Sains dan Teknol., 2023, [Online]. Available: http://ejournal.sisfokomtek.org/index.php/saintek/article/view/1375

M. Mahpuz, A. Muliawan Nur, and L. M. Samsu, “Penerapan Algoritma C4.5 Dalam Mengklasifikasi Status Gizi Balita Pada Posyandu Desa Dames Damai Kabupaten Lombok Timur,” Infotek J. Inform. dan Teknol., vol. 5, no. 1, pp. 72–81, 2022, doi: 10.29408/jit.v5i1.4414.

H. I. Islam, M. Khandava Mulyadien, U. Enri, U. Singaperbangsa, and K. Abstract, “Penerapan Algoritma C4.5 dalam Klasifikasi Status Gizi Balita,” J. Ilm. Wahana Pendidik., vol. 8, no. 10, pp. 116–125, 2022, [Online]. Available: https://doi.org/10.5281/zenodo.6791722

M. Ula, A. F. Ulva, M. Mauliza, M. A. Ali, and Y. R. Said., “Application Of Machine Learning In Determining The Classification Of Children’s Nutrition With Decision Tree,” J. Tek. Inform., 2022, [Online]. Available: http://jutif.if.unsoed.ac.id/index.php/jurnal/article/view/599

R. R. R. Arisandi, B. Warsito, and A. R. Hakim, “Aplikasi Naïve Bayes Classifier (Nbc) Pada Klasifikasi Status Gizi Balita Stunting Dengan Pengujian K-Fold Cross Validation,” J. Gaussian, vol. 11, no. 1, pp. 130–139, 2022, doi: 10.14710/j.gauss.v11i1.33991.