Optimization of Machine Learning Classification Analysis of Malnutrition Cases in Children
Abstract
Malnutrition is one of the problems that occurs in children due to a lack of nutritional intake. Indonesia contributed 36%, making it the fifth country with the largest cases of malnutrition in the world. On this basis, a solution is needed to reduce the growth rate of malnutrition cases. This research aims to carry out classification analysis to determine nutritional status by optimizing machine learning (ML) performance. The ML classification analysis process will later utilize the performance of the artificial neural network (ANN) method with the Multilayer Perceptron (MLP) algorithm. ML performance can be optimized using the Pearson’s correlation (PC) method to produce optimal classification analysis patterns. This research data set uses child nutrition case data from 576 patients sourced from the M. Djamil Padang Province Regional General Hospital (RSUP). The data set is divided into 417 training data and 159 test data. On the basis of the tests that have been carried out, the performance of the PC method can provide precise and accurate analysis patterns. This analysis pattern has also been able to provide a fairly good level of accuracy, namely 95%. Not only that, this research is also able to present analysis patterns with the best ANN architectural model in classifying nutritional status. Based on the overall results, this research can be used as an alternative solution to the treatment of nutritional problems in children.
Downloads
References
S. A. Norris et al., “Nutrition in adolescent growth and development,” Lancet, vol. 399, no. 10320, pp. 172–184, 2022.
B. S. Zemel, “Body composition during growth and development,” Hum. growth Dev., pp. 517–545, 2022.
J. Julizal, L. Lukman, and I. Sunoto, “Rancang Bangun Aplikasi Sistem Monitoring Pertumbuhan Anak sebagai Alat Deteksi Pertumbuhan,” STRING (Satuan Tulisan Ris. dan Inov. Teknol., vol. 4, no. 1, pp. 18–24, 2019.
M. Shakhnoza, “IMPROVING THE PEDAGOGICAL AND PSYCHOLOGICAL COMPETENCE OF FIRST-YEAR CHILDREN IN PRESCHOOL EDUCATION,” Eur. Int. J. Multidiscip. Res. Manag. Stud., vol. 2, no. 05, pp. 121–124, 2022.
R. A. Mahumud, S. Uprety, N. Wali, A. M. N. Renzaho, and S. Chitekwe, “The effectiveness of interventions on nutrition social behaviour change communication in improving child nutritional status within the first 1000 days: Evidence from a systematic review and meta‐analysis,” Matern. Child Nutr., vol. 18, no. 1, p. e13286, 2022.
F. Mayar and Y. Astuti, “Peran Gizi Terhadap Pertumbuhan dan Perkembangan Anak Usia Dini,” J. Pendidik. Tambusai, vol. 5, no. 3, pp. 9695–9704, 2021.
P. Rahmi, “Peran nutrisi bagi tumbuh dan kembang anak usia dini,” J. Pendidik. Anak Bunayya, vol. 5, no. 1, pp. 1–13, 2019.
A. Amirullah, A. T. A. Putra, and A. A. D. Al Kahar, “Deskripsi status gizi anak usia 3 sampai 5 tahun pada masa Covid-19,” Murhum J. Pendidik. anak usia dini, vol. 1, no. 1, pp. 16–27, 2020.
C. Setyorini and A. D. Lieskusumastuti, “Gambaran Status Gizi Bayi Dan Balita Pada Masa Covid-19 Di Kalurahan Jetis,” Avicenna J. Heal. Res., vol. 4, no. 1, 2021.
S. Thurstans et al., “The relationship between wasting and stunting in young children: A systematic review,” Matern. Child Nutr., vol. 18, no. 1, p. e13246, 2022.
E. N. Hutabarat, “Permasalahan Stunting dan Pencegahannya,” J. Heal. Med. Sci., pp. 158–163, 2022.
A. M. Arsyati, “Pengaruh penyuluhan media audiovisual dalam pengetahuan pencegahan stunting pada ibu hamil di Desa Cibatok 2 Cibungbulang,” Promotor, vol. 2, no. 3, pp. 182–190, 2019.
C. Lawaceng and A. Y. S. Rahayu, “Tantangan pencegahan stunting pada era adaptasi baru ‘new normal’ melalui pemberdayaan masyarakat di kabupaten pandeglang,” J. Kebijak. Kesehat. Indones. JKKI, vol. 9, no. 3, pp. 136–146, 2020.
M. H. Esfe, M. H. Kamyab, and D. Toghraie, “Statistical review of studies on the estimation of thermophysical properties of nanofluids using artificial neural network (ANN),” Powder Technol., p. 117210, 2022.
Y. I. Alatoom and T. I. Al-Suleiman, “Development of pavement roughness models using Artificial Neural Network (ANN),” Int. J. Pavement Eng., vol. 23, no. 13, pp. 4622–4637, 2022.
A. N. Bhatt and N. Shrivastava, “Application of artificial neural network for internal combustion engines: a state of the art review,” Arch. Comput. Methods Eng., vol. 29, no. 2, pp. 897–919, 2022.
V. Gholami and H. Sahour, “Simulation of rainfall-runoff process using an artificial neural network (ANN) and field plots data,” Theor. Appl. Climatol., pp. 1–12, 2022.
M. Ahmad, M. A. Al Mehedi, M. M. S. Yazdan, and R. Kumar, “Development of Machine Learning Flood Model Using Artificial Neural Network (ANN) at Var River,” Liquids, vol. 2, no. 3, pp. 147–160, 2022.
M. H. Alshayeji, H. Ellethy, and R. Gupta, “Computer-aided detection of breast cancer on the Wisconsin dataset: An artificial neural networks approach,” Biomed. Signal Process. Control, vol. 71, p. 103141, 2022.
M. Khan, Z. Ullah, O. Mašek, S. R. Naqvi, and M. N. A. Khan, “Artificial neural networks for the prediction of biochar yield: A comparative study of metaheuristic algorithms,” Bioresour. Technol., vol. 355, p. 127215, 2022.
E. M. Rentería-Vargas et al., “Neural network-based identification of a PSA process for production and purification of bioethanol,” IEEE Access, vol. 10, pp. 27771–27782, 2022.
T. Si, J. Bagchi, and P. B. C. Miranda, “Artificial neural network training using metaheuristics for medical data classification: an experimental study,” Expert Syst. Appl., vol. 193, p. 116423, 2022.
T. Hidayat, “MODEL KLASIFIKASI JARINGAN SARAF TIRUAN UNTUK MENENTUKAN STATUS GIZI PADA BALITA DENGAN METODE LVQ (LEARNING VECTOR QUANTIZATION) STUDI KASUS PUSKESMAS DI KABUPATEN TANGERANG,” 2020.
M. Yanto, “Jaringan Syaraf Tiruan Analisa Pengaruh Gizi Buruk Terhadap Perkembangan Balita dengan Algoritma Perceptron,” J. Ilm. Media Sisfo, vol. 12, no. 1, pp. 1003–1011, 2018.
D. A. Simbolon, D. Hartama, and F. Anggraini, “Penerapan Jaringan Saraf Tiruan Dalam Memprediksi Gizi Balita Pada Puskesmas Siantar Utara Kota Pematangsiantar,” Brahmana J. Penerapan Kecerdasan Buatan, vol. 1, no. 1, pp. 48–54, 2019.
G. Pratama, A. Yusuf, S. Kasau, and A. Jumantio, “Analisis Faktor Risiko Kejadian Stunting Pada Balita Usia 2–5 Tahun Di Wilayah Kerja Puskesmas Sanoba Kabupaten Nabire,” An-Nadaa J. Kesehat. Masy., vol. 9, no. 1, pp. 1–7, 2022.
X.-J. Tang, X. Liu, P.-F. Yan, B.-X. Li, H.-Y. Qi, and F. Huang, “An MLP network based on residual learning for Rice hyperspectral data classification,” IEEE Geosci. Remote Sens. Lett., vol. 19, pp. 1–5, 2022.
C.-F. Tsai and Y.-H. Hu, “Empirical comparison of supervised learning techniques for missing value imputation,” Knowl. Inf. Syst., vol. 64, no. 4, pp. 1047–1075, 2022.
J. KUSUMA, B. H. HAYADI, W. WANAYUMINI, and R. ROSNELLY, “Komparasi Metode Multi Layer Perceptron (MLP) dan Support Vector Machine (SVM) untuk Klasifikasi Kanker Payudara,” MIND (Multimedia Artif. Intell. Netw. Database) J., vol. 7, no. 1, pp. 51–60, 2022.
J. F. Azzahra, H. Sumarti, and H. H. Kusuma, “Klasifikasi Kasus COVID-19 dan SARS Berbasis Ciri Tekstur Menggunakan Metode Multi-Layer Perceptron,” J. Fis., vol. 12, no. 1, pp. 16–27, 2022.
S. S. S., J. Surendiran, N. Yuvaraj, M. Ramkumar, C. N. Ravi, and R. G. Vidhya, “Classification of Diabetes using Multilayer Perceptron,” in 2022 IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE), 2022, pp. 1–5, doi: 10.1109/ICDCECE53908.2022.9793085.
D. Dam Nguyen et al., “Bagging and Multilayer Perceptron Hybrid Intelligence Models Predicting the Swelling Potential of Soil,” Transp. Geotech., vol. 36, p. 100797, 2022, doi: https://doi.org/10.1016/j.trgeo.2022.100797.
M. M. Islam et al., “Application of machine learning based algorithm for prediction of malnutrition among women in Bangladesh,” Int. J. Cogn. Comput. Eng., vol. 3, pp. 46–57, 2022, doi: https://doi.org/10.1016/j.ijcce.2022.02.002.
Z. Šverko, M. Vrankić, S. Vlahinić, and P. Rogelj, “Complex Pearson correlation coefficient for EEG connectivity analysis,” Sensors, vol. 22, no. 4, p. 1477, 2022.
X. Du, D. Niu, Y. Chen, X. Wang, and Z. Bi, “City classification for municipal solid waste prediction in mainland China based on K-means clustering,” Waste Manag., vol. 144, pp. 445–453, 2022.
T. A. Putra, P. A. Widya, R. Afira, and Y. Elva, “Optimization Analysis Model Determining PNMP Mandiri Loan Status Based on Pearson Correlation,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 6, no. 6, pp. 966–973, 2022.
P. Biswas and T. Samanta, “A method for fault detection in wireless sensor network based on pearson’s correlation coefficient and support vector machine classification,” Wirel. Pers. Commun., vol. 123, no. 3, pp. 2649–2664, 2022.
F. Beiranvand, V. Mehrdad, and M. B. Dowlatshahi, “Unsupervised feature selection for image classification: A bipartite matching-based principal component analysis approach,” Knowledge-Based Syst., vol. 250, p. 109085, 2022.
Y.-J. Mao, H.-J. Lim, M. Ni, W.-H. Yan, D. W.-C. Wong, and J. C.-W. Cheung, “Breast tumour classification using ultrasound elastography with machine learning: A systematic scoping review,” Cancers (Basel)., vol. 14, no. 2, p. 367, 2022.
J. J. Tanimu, M. Hamada, M. Hassan, H. Kakudi, and J. O. Abiodun, “A machine learning method for classification of cervical cancer,” Electronics, vol. 11, no. 3, p. 463, 2022.
R. Okunev, “Pearson correlation and using the excel linear trend equation and excel regression output,” in Analytics for Retail: A Step-by-Step Guide to the Statistics Behind a Successful Retail Business, Springer, 2022, pp. 83–106.
C. Pancotti et al., “Predicting protein stability changes upon single-point mutation: a thorough comparison of the available tools on a new dataset,” Brief. Bioinform., vol. 23, no. 2, p. bbab555, 2022.
J. Sawatzki, T. Schlippe, and M. Benner-Wickner, “Deep learning techniques for automatic short answer grading: Predicting scores for English and German answers,” in Artificial Intelligence in Education: Emerging Technologies, Models and Applications: Proceedings of 2021 2nd International Conference on Artificial Intelligence in Education Technology, 2022, pp. 65–75.
C. Zhang et al., “A hybrid MLP-CNN classifier for very fine resolution remotely sensed image classification,” ISPRS J. Photogramm. Remote Sens., vol. 140, pp. 133–144, 2018.
I. N. Anwar, K. Daud, A. A. Abd Samat, Z. H. C. Soh, A. M. S. Omar, and F. Ahmad, “Implementation of Levenberg-Marquardt Based Multilayer Perceptron (MLP) for Detection and Classification of Power Quality Disturbances,” in 2022 IEEE 12th International Conference on Control System, Computing and Engineering (ICCSCE), 2022, pp. 63–68.
U. Riyanto, “PENERAPAN ALGORITMA MULTILAYER PERCEPTRON (MLP) DALAM MENENTUKAN KELAYAKAN KENAIKAN JABATAN: STUDI KASUS PT. ABC-JAKARTA,” JIKA (Jurnal Inform., vol. 2, no. 1, 2018.
W. I. Sabilla, C. B. Vista, and D. S. Hormansyah, “Implementasi Multilayer Perceptron Untuk Memprediksi Harapan Hidup Pada Pasien Penyakit Kardiovaskular,” J-SAKTI (Jurnal Sains Komput. dan Inform., vol. 6, no. 1, pp. 57–68, 2022.
Copyright (c) 2023 Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)
This work is licensed under a Creative Commons Attribution 4.0 International License.
Copyright in each article belongs to the author
- The author acknowledges that the RESTI Journal (System Engineering and Information Technology) is the first publisher to publish with a license Creative Commons Attribution 4.0 International License.
- Authors can enter writing separately, arrange the non-exclusive distribution of manuscripts that have been published in this journal into other versions (eg sent to the author's institutional repository, publication in a book, etc.), by acknowledging that the manuscript has been published for the first time in the RESTI (Rekayasa Sistem dan Teknologi Informasi) journal ;
