Prediction of Stunting in Toddlers Combining the Naive Bayes Method and the C4.5 Algorithm

Authors

  • Sri Melyani Universitas Labuhanbatu
  • Syaiful Zuhri Harahap Universitas Labuhanbatu
  • Irmayanti Universitas Labuhanbatu

DOI:

10.33395/sinkron.v8i2.13651

Keywords:

C4.5 Algorithm; Classification; Data Mining; Metode Decision Tree; Metode Naïve Bayes

Abstract

Research conducted to predict the incidence of stunting in toddlers, using data mining methods such as Naive Bayes and the C4.5 algorithm has been applied to analyze health data. The main aim of this research is to develop a predictive model that can identify toddlers who are at high risk of stunting, based on variables that have been collected from medical records and health surveys. The use of the Naive Bayes and C4.5 methods in this research aims to compare the effectiveness of the two methods in dealing with complex and unbalanced classification problems. This research involves a series of crucial stages starting from data selection, data pre-processing, data mining model design, data mining model testing, to method evaluation. In this study, the sample used consisted of 200 toddlers, of which 159 were diagnosed as having stunting and 41 others were not. The classification results show significant effectiveness in both methods used. The accuracy results of both methods are very encouraging, with both methods showing success rates of more than 90%. This shows that both Naive Bayes and C4.5 are very effective in identifying patterns related to the risk of stunting among toddlers. These highly accurate results not only demonstrate the power of data mining techniques in the field of public health but also provide insights that health practitioners can use to intervene earlier in at-risk populations.

GS Cited Analysis

Downloads

Download data is not yet available.

References

Abas, M. I., Ibrahim, I., Syahrial, S., Lamusu, R., Baderan, U. S., & Kango, R. (2023). Analysis of Covid-19 Growth Trends Through Data Mining Approach As Decision Support. Sinkron, 8(1), 101–108. https://doi.org/10.33395/sinkron.v8i1.11861

Aji, G. W., & Devi, P. A. R. (2023). Data Mining Implementation For Product Transaction Patterns Using Apriori Method. Sinkron, 8(1), 421–432. https://doi.org/10.33395/sinkron.v8i1.12071

Alam, A., Alana, D. A. F., & Juliane, C. (2023). Comparison Of The C.45 And Naive Bayes Algorithms To Predict Diabetes. Sinkron, 8(4), 2641–2650. https://doi.org/10.33395/sinkron.v8i4.12998

Anam, M. K., Rahmiati, R., Paradila, D., Mardainis, M., & Machdalena, M. (2023). Application of Naïve Bayes Algorithm for Non-Cash Food Assistance Recipients in Kampar Regency. Sinkron, 8(1), 433–441.

https://doi.org/10.33395/sinkron.v8i1.12032

Apriyani, M. E., Maskuri, R. A., Ratsanjani, M. H., Pramudhita, A. N., & Rawansyah, R. (2023). Digital Forensic Investigates Sexual Harassment on Telegram using Naïve Bayes. Sinkron, 8(3), 1409–1417. https://doi.org/10.33395/sinkron.v8i3.12514

Bustomi, Y., Nugraha, A., Juliane, C., & Rahayu, S. (2023). Data Mining Selection of Prospective Government Employees with Employment Agreements using Naive Bayes Classifier. Sinkron, 8(1), 1–8. https://doi.org/10.33395/sinkron.v8i1.11968

Hakim, R. X., Putrawansyah, F., & Syahri, R. (n.d.). Penerapan Algoritma C4 . 5 Untuk Prediksi Anak Stunting Di Kota Pagar Alam. 18(2), 269–279.

Harjanto, T. D., Vatresia, A., & Faurina, R. (2021). STUNTING MENGGUNAKAN METODE. 9(1), 30–42.

Hasibuan, F. F., Dar, M. H., & Yanris, G. J. (2023). Implementation of the Naïve Bayes Method to determine the Level of Consumer Satisfaction. SinkrOn, 8(2), 1000–1011. https://doi.org/10.33395/sinkron.v8i2.12349

Hasibuan, S. A., Sihombing, V., & Nasution, F. A. (2023). Analysis of Community Satisfaction Levels using the Neural Network Method in Data Mining. Sinkron, 8(3), 1724–1735. https://doi.org/10.33395/sinkron.v8i3.12634

Kaputama, S., Data, A., Mendapatkan, T., Ekslusifdan, A. S. I., Kurang, J., Makanandan, A., … Stunting, A. (2021). Data Mining Pengelompokan Anak Stunting Berdasarkan Usia , Penyebab dan Pekerjaan Orang Tua Dengan Menggunakan Metode Clustering ( Studi Kasus : Dinas Kesehatan Kabupaten Langkat ).

Lubis, A. I., & Chandra, R. (2023). Forward Selection Attribute Reduction Technique for Optimizing Naïve Bayes Performance in Sperm Fertility Prediction. Sinkron, 8(1), 275–285. https://doi.org/10.33395/sinkron.v8i1.11967

Madjid, F. M., Ratnawati, D. E., & Rahayudi, B. (2023). Sentiment Analysis on App Reviews Using Support Vector Machine and Naïve Bayes Classification. Jurnal Dan Penelitian Teknik Informatika, 8(1), 556–562. Retrieved from https://doi.org/10.33395/sinkron.v8i1.12161

Maizura, S., Sihombing, V., & Dar, M. H. (2023). Analysis of the Decision Tree Method for Determining Interest in Prospective Student College. SinkrOn, 8(2), 956–979. https://doi.org/10.33395/sinkron.v8i2.12258

Mulyanto, Y., Idifitriani, F., Wati, A., Sumbawa, U. T., Mining, D., & Tano, K. P. (2024). Vol 7 No 2 , September 2024 KLASIFIKASI DATA MINING UNTUK PENENTUAN STUNTING. 7(2), 129–135.

Nasution, R. F., Dar, M. H., & Nasution, F. A. (2023). Implementation of the Naïve Bayes Method to Determine Student Interest in Gaming Laptops. Sinkron, 8(3), 1709–1723. https://doi.org/10.33395/sinkron.v8i3.12562

Pratama, H. A., Yanris, G. J., Nirmala, M., & Hasibuan, S. (2023). Implementation of Data Mining for Data Classification of Visitor Satisfaction Levels. 8(3), 1832–1851.

Pratistha, R. N., & Kristianto, B. (2024). Implementasi Algoritma K-Means dalam Klasterisasi Kasus Stunting pada Balita di Desa Randudongkal Abstrak. 5(2), 1193–1205.

Rahman, R., & Fauzi Abdulloh, F. (2023). Performance of Various Naïve Bayes Using GridSearch Approach In Phishing Email Dataset. Sinkron, 8(4), 2336–2344. https://doi.org/10.33395/sinkron.v8i4.12958

Saleh, A., Dharshinni, N., Perangin-Angin, D., Azmi, F., & Sarif, M. I. (2023). Implementation of Recommendation Systems in Determining Learning Strategies Using the Naïve Bayes Classifier Algorithm. Sinkron, 8(1), 256–267. https://doi.org/10.33395/sinkron.v8i1.11954

Saputra, A. D. S., Hindarto, D., & Haryono, H. (2023). Supervised Learning from Data Mining on Process Data Loggers on Micro-Controllers. Sinkron, 8(1), 157–165. https://doi.org/10.33395/sinkron.v8i1.11942

Sari, M., Yanris, G. J., & Hasibuan, M. N. S. (2023). Analysis of the Neural Network Method to Determine Interest in Buying Pertamax Fuel. SinkrOn, 8(2), 1031–1039. https://doi.org/10.33395/sinkron.v8i2.12292

Sinaga, B., Marpaung, M., Tarigan, I. R. B., & Tania, K. (2023). Implementation of Stock Goods Data Mining Using the Apriori Algorithm. Sinkron, 8(3), 1280–1292. https://doi.org/10.33395/sinkron.v8i3.12852

Siregar, A. P., Irmayani, D., & Sari, M. N. (2023). Analysis of the Naïve Bayes Method for Determining Social Assistance Eligibility Public. SinkrOn, 8(2), 805–817. https://doi.org/10.33395/sinkron.v8i2.12259

Supendar, H., Rusdiansyah, R., Suharyanti, N., & Tuslaela, T. (2023). Application of the Naïve Bayes Algorithm in Determining Sales Of The Month. SinkrOn, 8(2), 873–879. https://doi.org/10.33395/sinkron.v8i2.12293

Tanjung, J. P., Tampubolon, F. C., Panggabean, A. W., & Nandrawan, M. A. A. (2023). Customer Classification Using Naive Bayes Classifier With Genetic Algorithm Feature Selection. Sinkron, 8(1), 584–589. https://doi.org/10.33395/sinkron.v8i1.12182

Downloads


Crossmark Updates

How to Cite

Melyani, S. ., Harahap, S. Z. ., & Irmayanti, I. (2024). Prediction of Stunting in Toddlers Combining the Naive Bayes Method and the C4.5 Algorithm. Sinkron : Jurnal Dan Penelitian Teknik Informatika, 8(2), 1160-1168. https://doi.org/10.33395/sinkron.v8i2.13651

Most read articles by the same author(s)