Optimization of Machine Learning Classification Analysis of Malnutrition Cases in Children
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 resea...
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| Format: | Article |
| Language: | English |
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Ikatan Ahli Informatika Indonesia
2023-12-01
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| Series: | Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) |
| Subjects: | |
| Online Access: | https://jurnal.iaii.or.id/index.php/RESTI/article/view/5278 |
| _version_ | 1797326436713889792 |
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| author | Musli Yanto Febri Hadi Syafri Arlis |
| author_facet | Musli Yanto Febri Hadi Syafri Arlis |
| author_sort | Musli Yanto |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-08T06:24:11Z |
| format | Article |
| id | doaj.art-4de1114f2a9342b3b53b32d81cf3ac1c |
| institution | Directory Open Access Journal |
| issn | 2580-0760 |
| language | English |
| last_indexed | 2024-03-08T06:24:11Z |
| publishDate | 2023-12-01 |
| publisher | Ikatan Ahli Informatika Indonesia |
| record_format | Article |
| series | Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) |
| spelling | doaj.art-4de1114f2a9342b3b53b32d81cf3ac1c2024-02-03T15:09:07ZengIkatan Ahli Informatika IndonesiaJurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)2580-07602023-12-01761378138610.29207/resti.v7i6.52785278Optimization of Machine Learning Classification Analysis of Malnutrition Cases in ChildrenMusli Yanto0Febri Hadi1Syafri Arlis2Universitas Putra Indonesia YPTK PadangUniversitas Putra Indonesia YPTK PadangUniversitas Putra Indonesia YPTK PadangMalnutrition 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.https://jurnal.iaii.or.id/index.php/RESTI/article/view/5278analysis of classificationmalnutritionartificial neural network (ann)multilayer perceptron (mlp)west sumatra province |
| spellingShingle | Musli Yanto Febri Hadi Syafri Arlis Optimization of Machine Learning Classification Analysis of Malnutrition Cases in Children Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) analysis of classification malnutrition artificial neural network (ann) multilayer perceptron (mlp) west sumatra province |
| title | Optimization of Machine Learning Classification Analysis of Malnutrition Cases in Children |
| title_full | Optimization of Machine Learning Classification Analysis of Malnutrition Cases in Children |
| title_fullStr | Optimization of Machine Learning Classification Analysis of Malnutrition Cases in Children |
| title_full_unstemmed | Optimization of Machine Learning Classification Analysis of Malnutrition Cases in Children |
| title_short | Optimization of Machine Learning Classification Analysis of Malnutrition Cases in Children |
| title_sort | optimization of machine learning classification analysis of malnutrition cases in children |
| topic | analysis of classification malnutrition artificial neural network (ann) multilayer perceptron (mlp) west sumatra province |
| url | https://jurnal.iaii.or.id/index.php/RESTI/article/view/5278 |
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