Implementasi Metode Bagging dan Teknik Discretization pada Algoritma Machine Learning untuk Memprediksi Status Stunting pada Anak Balita
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Jan 10, 2025
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Stunting is one of the problems of toddler growth, making toddlers susceptible to disease. Efforts to prevent stunting with routine checks every month. Posyandu Sukasejati is a facility for routinely checking the growth of toddlers, but data collection requires early stunting analysis to help health workers reduce the number of stunting statuses in toddlers. Previous research on stunting prediction using the Naive Bayes Machine Learning algorithm has been carried out, but the level of accuracy is still low, so accuracy improvement techniques are needed to provide accurate information. The purpose of the study was to implement the Bagging method to improve accuracy and the discretization technique to change continuous attributes to categorical in the Naïve Bayes Machine Learning algorithm in predicting stunting in toddlers, the results of the study showed an increase in accuracy, recall, and precision using a combination of the Bagging method and the Naïve Bayes algorithm, namely accuracy of 100% increased by 5.83% compared to using the Naïve Bayes algorithm alone, which was 94.17% and an increase in recall and precision results of 28.33%.
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Majid, A. M., & Nawangsih, I. (2025). Implementasi Metode Bagging dan Teknik Discretization pada Algoritma Machine Learning untuk Memprediksi Status Stunting pada Anak Balita. Jurnal Indonesia : Manajemen Informatika Dan Komunikasi, 6(1), 663-676. https://doi.org/10.35870/jimik.v6i1.1279
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Cui, G., Zhang, Y., Tao, H., Man, S., & Chen, H. (2024). Optimised prediction of tunnel fire heat release rate using the ResNet18_2CLSTM model with bagging for multimodal data. Case Studies in Thermal Engineering, 63, 105268.
Dessiaming, T. Z., Anraeni, S., & Pomalingo, S. (2022). College Academic Data Analysis Using Data Visualization. Jurnal Teknik Informatika (Jutif), 3(5), 1203-1212. https://doi.org/10.20884/1.jutif.2022.3.5.310.
Fatmawati, D., Trisnawati, W., Jumaryadi, Y., & Triyono, G. (2023). Klasifikasi Tingkat Kepuasan Penggunaan Layanan Teknologi Informasi Menggunakan Decision Tree. KLIK: Kajian Ilmiah Informatika dan Komputer, 3(6), 1056-1062. https://doi.org/10.30865/klik.v3i6.803.
Gu, Y. (2023). Exploring the application of teaching evaluation models incorporating association rules and weighted naive Bayesian algorithms. Intelligent Systems with Applications, 20, 200297.
Hartono, H., Hajjah, A., & Marlim, Y. N. (2023). PENERAPAN METODE NAÏVE BAYES CLASSIFIER UNTUK KLASIFIKASI JUDUL BERITA. Jurnal Simantec, 12(1), 37-46.
Hastuti, N. T., & Budiman, F. (2024). Optimasi Klasifikasi Stunting Balita dengan Teknik Boosting pada Decision Tree. Edumatic: Jurnal Pendidikan Informatika, 8(2), 655-664. https://doi.org/10.47709/digitech.v4i1.4481.
Islam, M. M., Rahman, M. J., Islam, M. M., Roy, D. C., Ahmed, N. F., Hussain, S., ... & Maniruzzaman, M. (2022). Application of machine learning based algorithm for prediction of malnutrition among women in Bangladesh. International Journal of Cognitive Computing in Engineering, 3, 46-57.
Jafari, S., Kim, J., & Byun, Y. C. (2024). Integrating ensemble learning and meta bagging techniques for temperature-specific State of Health prediction in Lithium-ion Batteries. Energy Reports, 12, 2388-2407.
Malakouti, S. M., Menhaj, M. B., & Suratgar, A. A. (2023). The usage of 10-fold cross-validation and grid search to enhance ML methods performance in solar farm power generation prediction. Cleaner Engineering and Technology, 15, 100664.
NURFA’IZAH, M. E. L. A. (2022). KLASIFIKASI PENENTUAN GIZI STUNTING PADA BALITA MENGGUNAKAN METODE NAÏVE BAYES (Doctoral dissertation, Universitas Islam Sultan Agung).
Paraijun, F., Aziza, R. N., & Kuswardani, D. (2022). Implementasi Algoritma Convolutional Neural Network Dalam Mengklasifikasi Kesegaran Buah Berdasarkan Citra Buah. Kilat, 11(1), 1-9. https://doi.org/10.33322/kilat.v10i2.1458.
Peretz, O., Koren, M., & Koren, O. (2024). Naive Bayes classifier – An ensemble procedure for recall and precision enrichment. Engineering Applications of Artificial Intelligence, 136, 108972. https://doi.org/10.1016/j.engappai.2024.108972.
Pertiwi, D. A. A., Setyorini, P. R., Muslim, M. A., & Sugiharti, E. (2023). Implementation of discretisation and correlation-based feature selection to optimize support vector machine in diagnosis of chronic kidney disease. Buletin Ilmiah Sarjana Teknik Elektro, 5(2), 201–209. https://doi.org/10.12928/biste.v5i2.7548.
Putri, P. A. R., Prasetiyowati, S. S., & Sibaroni, Y. (2023). The performance of the equal-width and equal-frequency discretization methods on data features in classification process. Sinkron, 8(4), 2082–2098. https://doi.org/10.33395/sinkron.v8i4.12730.
Ridwansyah, T. (2022). Implementasi text mining terhadap analisis sentimen masyarakat dunia di Twitter terhadap kota Medan menggunakan K-fold cross-validation dan Naïve Bayes classifier. KLIK: Kajian Ilmiah Informatika dan Komputer, 2(5), 178–185. https://doi.org/10.30865/klik.v2i5.362.
Sabili, N. L., Umbara, F. R., & Melina, M. (2024). KLASIFIKASI PENYAKIT DIABETES MENGGUNAKAN ALGORITMA CATEGORICAL BOOSTING DENGAN FAKTOR RISIKO DIABETES. JATI (Jurnal Mahasiswa Teknik Informatika), 8(6), 11391-11398. https://doi.org/10.36040/jati.v8i6.11447.
Sahu, M., Shrivastava, A., Jhariya, D. C., Diwan, S., & Subhadarsini, J. (2024). Evaluation of correlation of physicochemical parameters and major ions present in groundwater of Raipur using discretization. Measurement: Sensors, 34, 101278. https://doi.org/10.1016/j.measen.2024.101278.
Shaban, W. M., Rabie, A. H., Saleh, A. I., & Abo-Elsoud, M. A. (2021). Accurate detection of COVID-19 patients based on distance biased Naïve Bayes (DBNB) classification strategy. Pattern Recognition, 119, 108110. https://doi.org/10.1016/j.patcog.2021.108110.
Situmorang, Z., Mandasari, S., Franciska, Y., Andriyani, K., & Ramadhan, P. S. (2022). Algoritma C45 dalam memprediksi minat calon mahasiswa. Journal of Science and Social Research, 5(1), 125. https://doi.org/10.54314/jssr.v5i1.809.
Sridhar, V., Annamani, T., Renuka, M., Kumar, V. V., & Madupu, A. (2024). Bagging ensemble mean-shift Gaussian kernelized clustering-based D2D connectivity-enabled communication for 5G networks. E-Prime - Advances in Electrical Engineering, Electronics and Energy, 7, 100400. https://doi.org/10.1016/j.prime.2023.100400.
Steven Joses, Yulvida, D., & Rochimah, S. (2024). Pendekatan metode ensemble learning untuk prakiraan cuaca menggunakan soft voting classifier. Journal of Applied Computer Science and Technology, 5(1), 72–80. https://doi.org/10.52158/jacost.v5i1.741.
Viandari, N. P. V., Suarjaya, I. M. A. D., & Piarsa, I. N. (2022). Pemetaan Pelanggan dengan LRFM dan Two Stage Clustering untuk Memenuhi Strategi Pengelolaan. Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), 6(1), 130-139. https://doi.org/10.29207/resti.v6i1.3778.
Wahyudin, W. C. (2020). Klasifikasi Stunting Balita Menggunakan Naive Bayes Dengan Seleksi Fitur Forward Selection. JURNAL ILMU KOMPUTER DAN MATEMATIKA, 1(1), 71-74.
Yanarateş, C., Zhou, Z., & Altan, A. (2024). Investigating the impact of discretization techniques on real-time digital control of DC-DC boost converters: A comprehensive analysis. Heliyon, 10(20). https://doi.org/10.1016/j.heliyon.2024.e39591.