Stunting Disease Classification Using Multi-Layer Perceptron Algorithm with GridSearchCV
DOI:
10.33395/sinkron.v9i1.13245Keywords:
Classification, Deep Learning, Disease, GridSearchCV, Multi-Layer Perceptron, StuntingAbstract
Stunting is a growth and development disorder caused by malnutrition characterized by a child's height less than the standard deviation set by WHO. In 2022, stunting cases in Indonesia are considered a high prevalence rate, reaching 21.6%. There are several factors that can cause stunting in children, namely maternal and antenatal care factors, home environment factors, breastfeeding practices, and feeding factors during toddlerhood. There are several impacts that occur when children are stunted, namely increased risk of child mortality, susceptibility to illness, impaired brain development, physical disorders and metabolic disorders. Currently, deep learning has been widely used for disease classification and prediction, one of the deep learning methods is Multi-Layer Perceptron (MLP). The purpose of this research is to classify stunting disease using a deep learning method, namely MLP. The dataset used consists of 8 attributes, namely gender, age, birth weight, birth length, body weight, body length, breastfeeding and stunting with a total of 10,000 records. The encoding process is carried out to convert categorical data into numeric attributes of gender, breastfeeding, and stunting. This research produces a higher accuracy value than previous research which used the C4.5 algorithm with an accuracy of 61.82%, whereas in this study using MLP which was integrated with the GridSearchCV hyperparameter it obtained an accuracy of 82.37%. This proves that the MLP method is successful in classifying stunting compared to previous research algorithms.
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Anggreani, D., Herman, & Astuti, W. (2018). Kinerja Metode Naïve Bayes dalam Prediksi Lama Studi Mahasiswa Fakultas Ilmu Komputer. Seminar Nasional Ilmu Komputer Dan Teknologi Informasi, 3(2), 107–111. http://e-journals.unmul.ac.id/index.php/SAKTI/article/view/1843
Astarani, K., Idris, D. N. T., & Oktavia, A. R. (2020). Prevention of Stunting Through Health Education in Parents of Pre-School Children. STRADA Jurnal Ilmiah Kesehatan, 9(1), 70–77. https://doi.org/10.30994/sjik.v9i1.270
Azmat, U., Ghadi, Y. Y., Al Shloul, T., Alsuhibany, S. A., Jalal, A., & Park, J. (2022). Smartphone Sensor-Based Human Locomotion Surveillance System Using Multilayer Perceptron. Applied Sciences (Switzerland), 12(5). https://doi.org/10.3390/app12052550
Desai, M., & Shah, M. (2021). An anatomization on breast cancer detection and diagnosis employing multi-layer perceptron neural network (MLP) and Convolutional neural network (CNN). Clinical EHealth, 4(2021), 1–11. https://doi.org/10.1016/j.ceh.2020.11.002
Firmansyah, I., & Hayadi, B. H. (2022). Komparasi Fungsi Aktivasi Relu Dan Tanh Pada Multilayer Perceptron. JIKO (Jurnal Informatika Dan Komputer), 6(2), 200. https://doi.org/10.26798/jiko.v6i2.600
Jiang, X., & Xu, C. (2022). Deep Learning and Machine Learning with Grid Search to Predict Later Occurrence of Breast Cancer Metastasis Using Clinical Data. Journal of Clinical Medicine, 11(19). https://doi.org/10.3390/jcm11195772
Jyotiyana, M., & Kesswani, N. (2020). Classification and prediction of Alzheimer’s disease using multi-layer perceptron. International Journal of Reasoning-Based Intelligent Systems, 12(4), 238–247. https://doi.org/10.1504/IJRIS.2020.111785
Kemenkes RI. (2023). Prevalensi Stunting di Indonesia Turun ke 21,6% dari 24,4%. Kemenkes. https://www.kemkes.go.id/id/rilis-kesehatan/prevalensi-stunting-di-indonesia-turun-ke-216-dari-244
Kwon, K., Kim, D., & Park, H. (2017). A parallel MR imaging method using multilayer perceptron. Medical physics, 44(12), 6209-6224.
Markoulidakis, I., Rallis, I., Georgoulas, I., Kopsiaftis, G., Doulamis, A., & Doulamis, N. (2021). Multiclass Confusion Matrix Reduction Method and Its Application on Net Promoter Score Classification Problem. Technologies, 9(4). https://doi.org/10.3390/technologies9040081
Mediani, H. S., Setyawati, A., Hendrawati, S., Nurhidayah, I., & Firdianty, N. F. (2023). Pengaruh Faktor Maternal terhadap Insidensi Stunting pada Anak Balita di Negara Berkembang: Narrative Review. Jurnal Obsesi : Jurnal Pendidikan Anak Usia Dini, 7(2), 1868–1886. https://doi.org/10.31004/obsesi.v7i2.4160
Ni Kadek Ary Indah Suryani, Oka Sudana, & Ayu Wirdiani. (2022). Forecasting Pneumonia Toddler Mortality Using Comparative Model ARIMA and Multilayer Perceptron. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 6(4), 528–537. https://doi.org/10.29207/resti.v6i4.4106
Nurrokhman, M. Z. (2023). Perbandingan Algoritma Support Vector Machine dan Neural Network untuk Klasifikasi Penyakit Hati. Indonesian Journal of Computer Science, 12(4). https://doi.org/10.33022/ijcs.v12i4.3274
Panca Saputra, E., & Panca, E. (2020). Classification Using Artifical Neural Network Method in Protecting Credit Fitness. Indonesian Journal of Artificial Intelligence and Data Mining (IJAIDM), 3(1), 50–56.
Purbolaksono, M. D., Irvan Tantowi, M., Imam Hidayat, A., & Adiwijaya, A. (2021). Perbandingan Support Vector Machine dan Modified Balanced Random Forest dalam Deteksi Pasien Penyakit Diabetes. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 5(2), 393–399. https://doi.org/10.29207/resti.v5i2.3008
Rahmi, I., Susanti, M., Yozza, H., & Wulandari, F. (2022). Classification of Stunting in Children Under Five Years in Padang City Using Support Vector Machine. BAREKENG: Jurnal Ilmu Matematika Dan Terapan, 16(3), 771–778. https://doi.org/10.30598/barekengvol16iss3pp771-778
Riyanto, U. (2018). Penerapan Algoritma Multilayer Perceptron (Mlp) Dalam Menentukan Kelayakan Kenaikan Jabatan: Studi Kasus Pt. Abc - Jakarta. JIKA (Jurnal Informatika), 2(1), 58–65. http://jurnal.umt.ac.id/index.php/jika/article/view/5481
Selwal, A., & Raoof, I. (2020). A Multi-layer perceptron based intelligent thyroid disease prediction system. Indonesian Journal of Electrical Engineering and Computer Science, 17(1), 524–532. https://doi.org/10.11591/ijeecs.v17.i1.pp524-532
Sharma, R., Kim, M., & Gupta, A. (2022). Motor imagery classification in brain-machine interface with machine learning algorithms: Classical approach to multi-layer perceptron model. Biomedical Signal Processing and Control, 71(PA), 103101. https://doi.org/10.1016/j.bspc.2021.103101
Vashisth, S., Dhall, I., & Saraswat, S. (2020). Chronic kidney disease (CKD) diagnosis using multi-layer perceptron classifier. Proceedings of the Confluence 2020 - 10th International Conference on Cloud Computing, Data Science and Engineering, January 2020, 346–350. https://doi.org/10.1109/Confluence47617.2020.9058178
Venkateswara Reddy, L., & Damodaram, A. K. (2022). REMOVAL OF DUPLICATES IN DATABASE RELATIONS AND THE ASSOCIATED PROPAGATION MANAGEMENT. Article in International Journal of Advanced Research in Computer Science, 9(2). https://doi.org/10.26483/ijarcs.v9i2
Wahyudin, W. C., Hana, F. M., Prihandono, A., Kudus, U. M., No, J. G., Email, I., Semarang, P. K., Classifier, N. B., Naive, A., Classifier, B., Classifier, N. B., & Classifier, N. B. (2023). P Rediksi S Tunting P Ada B Alita D I R Umah S Akit K Ota. 2019, 32–36.
Wang, J., Xu, Z., & Che, Y. (2019). Power quality disturbance classification based on DWT and multilayer perceptron extreme learning machine. Applied Sciences (Switzerland), 9(11). https://doi.org/10.3390/app9112315
WHO. (2023). Joint Child Malnutrition Estimates. https://www.who.int/data/gho/data/themes/topics/joint-child-malnutrition-estimates-unicef-who-wb#:~:text=In 2022%2C 148.1 million children,for their height (overweight)
Wibawa, M. S., & Maysanjaya, I. M. D. (2018). Multi Layer Perceptron Dan Principal Component Analysis Untuk Diagnosa Kanker Payudara. Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI), 7(1), 90. https://doi.org/10.23887/janapati.v7i1.12909
Yellamma, P., & Journal, I. (2020). Breast Cancer Diagnosis Using MLP Back Propagation. International Journal of Emerging Trends in Engineering Research, 8(9), 5539–5544. https://doi.org/10.30534/ijeter/2020/102892020
Yunus, M., Biddinika, M. K., & Fadlil, A. (2023). Classification of Stunting in Children Using the C4.5 Algorithm. Jurnal Online Informatika, 8(1), 99–106. https://doi.org/10.15575/join.v8i1.1062
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