Optimasi Klasifikasi Stunting Balita dengan Teknik Boosting pada Decision Tree
DOI:
https://doi.org/10.29408/edumatic.v8i2.27913Keywords:
adaboost, decision tree, gradient boosting, stunting, xgboostAbstract
Malnutrition in the growth of small children is known as stunting. Currently, nutrition is still a serious problem that needs to be addressed, especially the nutrition of children under five. Considering the target prevalence rate (14%) in 2024 and how dangerous stunting is in Indonesia, this stunting problem needs to be addressed. The purpose of this research is to optimize the decision tree algorithm in stunting classification using boosting technique optimization. The boosting techniques used are AdaBoost, XGBoost, and Gradient Boosting methods. The boosting technique was chosen because it can improve classifier performance by combining multiple models that are learned sequentially, resulting in more effective predictions. This research uses infant data from Kaggle, which has a total of 10,000 data points, 8 attributes, and 2 classes. Based on the results of this study, decision tree optimization using the XGBoost method achieved the best results with accuracy of 83.8%, precision of 82%, recall of 83.8%, and F1-score of 81.2%, which shows great potential in improving the classification of stunted infants. The boosting technique is the best choice compared to other techniques. Based on the results of this study, the boosting technique can accurately predict and demonstrate a high level of precision in handling stunting classification.
References
Amirudin, M., & Wowor, A. D. (2023). Analisis Perbandingan Klasifikasi Balita Beresiko Stunting Menggunakan Metode Support Vector Machine dan Decission Tree. Centive, 3(1), 581–591.
Anku, E. K., & Duah, H. O. (2024). Predicting and identifying factors associated with undernutrition among children under five years in Ghana using machine learning algorithms. PLoS ONE, 19(2), 1–16. https://doi.org/10.1371/journal.pone.0296625
Anugrah, M. I., Zeniarja, J., & Setiawan, D. S. (2024). Peningkatan Performa Model Hard Voting Classifier dengan Teknik Oversampling ADASYN pada Penyakit Diabetes. Edumatic: Jurnal Pendidikan Informatika, 8(1), 290–299. https://doi.org/10.29408/edumatic.v8i1.25838
Bitew, F. H., Sparks, C. S., & Nyarko, S. H. (2022). Machine learning algorithms for predicting undernutrition among under-five children in Ethiopia. Public Health Nutrition, 25(2), 269–280. https://doi.org/10.1017/S1368980021004262
Damayanti, D. K. D., & Jakfar, M. (2023). Klasifikasi Status Stunting Balita Menggunakan Algoritma Fuzzy C-Means (Studi Kasus Posyandu Rw 01 Kelurahan Jepara Surabaya). MATHunesa: Jurnal Ilmiah Matematika, 11(3), 533–542. https://doi.org/10.26740/mathunesa.v11n03.p524-533
Finda, S. M., & Utomo, D. W. (2024). Klasifikasi Stunting Balita menggunakan Metode Ensemble Learning dan Random Forest. Infotekmesin, 15(2), 287-295.
Hanif, K. H., & Muntiari, N. R. (2024). Penerapan Algoritma Decision Tree, Svm, Naive Bayes Dalam Deteksi Stunting Pada Balita. METHOMIKA Jurnal Manajemen Informatika Dan Komputerisasi Akuntansi, 8(1), 105–109. https://doi.org/10.46880/jmika.vol8no1.pp105-109
Hardiani, T., & Putri, R. N. (2024). Implementasi Metode Naïve Bayes Classifier Untuk Klasifikasi Stunting Pada Balita. Digital Transformation Technology, 4(1), 621–627. https://doi.org/10.47709/digitech.v4i1.4481
Harelvi, D. A., Salma, A., Kurniawati, Y., & Fitri, F. (2024). The Comparison of C4 . 5 and C5 . 0 Algorithms in Classifying the Nutritional Status of Stunted Toddlers. 2, 213–218.
Herliansyah, V., Latuconsina, R., & Dinimaharawati, A. (2021). Prediksi Stunting Pada Balita Dengan Menggunakan Algoritma Klasifikasi NaÏve-bayes. eProceedings of Engineering, 8(5), 6642–6649.
Masacgi, G. N., & Rohman, M. S. (2023). Optimasi Model Algoritma Klasifikasi menggunakan Metode Bagging pada Stunting Balita. Edumatic: Jurnal Pendidikan Informatika, 7(2), 455–464. https://doi.org/10.29408/edumatic.v7i2.23812
Matdoan, M. Y., Matdoan, U. A., & Saleh Far-Far, M. (2022). Algoritma K-Means Untuk Klasifikasi Provinsi di Indonesia Berdasarkan Paket Pelayanan Stunting. PANRITA Journal of Science, Technology, and Arts, 1(2), 41–46.
Muche, A., Gezie, L. D., Baraki, A. G. egzabher, & Amsalu, E. T. (2021). Predictors of stunting among children age 6–59 months in Ethiopia using Bayesian multi-level analysis. Scientific Reports, 11(1), 1–12. https://doi.org/10.1038/s41598-021-82755-7
Mulyanto, Y., Idifitriani, F., & Wati, A. (2024). Klasifikasi Data Mining Untuk Penentuan Stunting Pada Balita Menggunakan Metoden Naïve Bayes. Jurnal Mnemonic, 7(2), 129-135. https://doi.org/10.36040/mnemonic.v7i2.8849
Nabila, Z., Rahman Isnain, A., & Abidin, Z. (2021). Analisis Data Mining Untuk Clustering Kasus Covid-19 Di Provinsi Lampung Dengan Algoritma K-Means. Jurnal Teknologi Dan Sistem Informasi (JTSI), 2(2), 100.
Pramadhana, D. (2021). Klasifikasi Penyakit Diabetes Menggunakan Metode CFS dan ROS dengan Algoritma J48 Berbasis Adaboost. Edumatic: Jurnal Pendidikan Informatika, 5(1), 89–98. https://doi.org/10.29408/edumatic.v5i1.3336
Pratama, Y. A., Budiman, F., Winarno, S., & Kurniawan, D. (2023). Analisis Optimasi Algoritma Decision Tree, Logistic Regression dan SVM Menggunakan Soft Voting. Jurnal Media Informatika Budidarma, 7(4), 1908-1919. https://doi.org/10.36040/mnemonic.v7i2.8849
Rahayu, S., Yumarlin, M. Z., Bororing, J. E., & Hadiyat, R. (2022). Implementasi Metode K-Nearest Neighbor (K-NN) untuk Analisis Sentimen Kepuasan Pengguna Aplikasi Teknologi Finansial FLIP. Edumatic: Jurnal Pendidikan Informatika, 6(1), 98-106. https://doi.org/10.29408/edumatic.v6i1.5433
Setiawan, A., & Suryono, R. R. (2024). Analisis Sentimen Ibu Kota Nusantara menggunakan Algoritma Support Vector Machine dan Naïve Bayes. Edumatic: Jurnal Pendidikan Informatika, 8(1), 183-192.
Setiyani, L., Wahidin, M., Awaludin, D., & Purwani, S. (2020). Analisis Prediksi Kelulusan Mahasiswa Tepat Waktu Menggunakan Metode Data Mining Naïve Bayes: Systematic Review. Faktor Exacta, 13(1), 35-43. https://doi.org/10.30998/faktorexacta.v13i1.5548
Sholihah, N. N., & Hermawan, A. (2023). Implementation of Random Forest and Smote Methods for Economic Status Classification in Cirebon City. Jurnal Teknik Informatika (Jutif), 4(6), 1387–1397. https://doi.org/10.52436/1.jutif.2023.4.6.1135
Sihombing, L. O., Hannie, H., & Dermawan, B. A. (2021). Sentimen Analisis Customer Review Produk Shopee Indonesia Menggunakan Algortima Naïve Bayes Classifier. Edumatic: Jurnal Pendidikan Informatika, 5(2), 233-242. https://doi.org/10.29408/edumatic.v5i2.4089
Takdirillah, R. (2020). Penerapan Data Mining Menggunakan Algoritma Apriori Terhadap Data Transaksi Penjualan Bisnis Ritel. Edumatic: Jurnal Pendidikan Informatika, 4(1), 37-46. https://doi.org/10.29408/edumatic.v4i1.2081
Titimeidara, M. Y., & Hadikurniawati, W. (2021). Implementasi Metode Naïve Bayes Classifier Untuk Klasifikasi Status Gizi Stunting Pada Balita. Jurnal Ilmiah Informatika, 9(01), 54–59. https://doi.org/10.33884/jif.v9i01.3741
Uska, M. Z., Wirasasmita, R. H., Usuluddin, U., & Arianti, B. D. D. (2020). Evaluation of rapidminer-aplication in data mining learning using persiva model. Edumatic: Jurnal Pendidikan Informatika, 4(2), 164-171. https://doi.org/10.29408/edumatic.v4i2.2688
Wahyuni, S. D., & Kusumodestoni, R. H. (2024). Optimalisasi Algoritma Support Vector Machine (SVM) Dalam Klasifikasi Kejadian Data Stunting. Bulletin of Information Technology (BIT), 5(2), 56–64.
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