Minimizing the number of stunting prevalence using the euclid algorithm clustering approach
Superior and quality human resources are based on healthy human resources with indicators of adequate nutritional intake according to age development. However, the world still faces the problem of hunger and malnutrition today. According to a UNICEF report, the number of people suffering from malnut...
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| Format: | Conference or Workshop Item |
| Language: | English English |
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Institute of Electrical and Electronics Engineers Inc.
2023
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| Online Access: | http://umpir.ump.edu.my/id/eprint/41914/1/Minimizing%20the%20number%20of%20stunting%20prevalence.pdf http://umpir.ump.edu.my/id/eprint/41914/2/Minimizing%20the%20number%20of%20stunting%20prevalence%20using%20the%20euclid%20algorithm%20clustering%20approach_ABS.pdf |
| _version_ | 1811138515562397696 |
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| author | Zarlis, Muhammad Oktavia, Tanty Buaton, Relita Ernawan, Ferda Andrian, Kevin |
| author_facet | Zarlis, Muhammad Oktavia, Tanty Buaton, Relita Ernawan, Ferda Andrian, Kevin |
| author_sort | Zarlis, Muhammad |
| collection | UMP |
| description | Superior and quality human resources are based on healthy human resources with indicators of adequate nutritional intake according to age development. However, the world still faces the problem of hunger and malnutrition today. According to a UNICEF report, the number of people suffering from malnutrition in the world will reach 767.9 million people in 2021. The World Health Organization (WHO) said that malnutrition is a dangerous threat to the health of the world's population. Stunting also has an impact in Indonesia, the prevalence of toddlers experiencing stunting in Indonesia is 24.4% in 2021. The solution created is to classify and cluster the prevalence of stunting to produce a pattern that can be used as best practice to be transmitted to other affected areas. The algorithm used is Euclid. The Euclid algorithm can cluster stunting prevalence data into 4 clusters with the very little category at 79%, the little category at 67%, the many categories at 51%, and the very much category at 21%. The results of the classification and clustering of the best stunting prevalence in cluster one with a very small number, can be used as a source of accurate and updated information that can be used by the government in its efforts to optimize stunting handling in each district/city based on artificial intelligence which can provide handling and optimization patterns. stunting in every district/city. |
| first_indexed | 2024-09-25T03:51:25Z |
| format | Conference or Workshop Item |
| id | UMPir41914 |
| institution | Universiti Malaysia Pahang |
| language | English English |
| last_indexed | 2024-09-25T03:51:25Z |
| publishDate | 2023 |
| publisher | Institute of Electrical and Electronics Engineers Inc. |
| record_format | dspace |
| spelling | UMPir419142024-08-30T00:16:47Z http://umpir.ump.edu.my/id/eprint/41914/ Minimizing the number of stunting prevalence using the euclid algorithm clustering approach Zarlis, Muhammad Oktavia, Tanty Buaton, Relita Ernawan, Ferda Andrian, Kevin QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) TA Engineering (General). Civil engineering (General) Superior and quality human resources are based on healthy human resources with indicators of adequate nutritional intake according to age development. However, the world still faces the problem of hunger and malnutrition today. According to a UNICEF report, the number of people suffering from malnutrition in the world will reach 767.9 million people in 2021. The World Health Organization (WHO) said that malnutrition is a dangerous threat to the health of the world's population. Stunting also has an impact in Indonesia, the prevalence of toddlers experiencing stunting in Indonesia is 24.4% in 2021. The solution created is to classify and cluster the prevalence of stunting to produce a pattern that can be used as best practice to be transmitted to other affected areas. The algorithm used is Euclid. The Euclid algorithm can cluster stunting prevalence data into 4 clusters with the very little category at 79%, the little category at 67%, the many categories at 51%, and the very much category at 21%. The results of the classification and clustering of the best stunting prevalence in cluster one with a very small number, can be used as a source of accurate and updated information that can be used by the government in its efforts to optimize stunting handling in each district/city based on artificial intelligence which can provide handling and optimization patterns. stunting in every district/city. Institute of Electrical and Electronics Engineers Inc. 2023 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/41914/1/Minimizing%20the%20number%20of%20stunting%20prevalence.pdf pdf en http://umpir.ump.edu.my/id/eprint/41914/2/Minimizing%20the%20number%20of%20stunting%20prevalence%20using%20the%20euclid%20algorithm%20clustering%20approach_ABS.pdf Zarlis, Muhammad and Oktavia, Tanty and Buaton, Relita and Ernawan, Ferda and Andrian, Kevin (2023) Minimizing the number of stunting prevalence using the euclid algorithm clustering approach. In: 2023 IEEE International Conference of Computer Science and Information Technology: The Role of Artificial Intelligence Technology in Human and Computer Interactions in the Industrial Era 5.0, ICOSNIKOM 2023. 7th IEEE International Conference of Computer Science and Information Technology, ICOSNIKOM 2023 , 10 - 11 November 2023 , Hybrid, Binjia. pp. 1-7. (195866). ISBN 979-835036075-2 (Published) https://doi.org/10.1109/ICoSNIKOM60230.2023.10364489 |
| spellingShingle | QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) TA Engineering (General). Civil engineering (General) Zarlis, Muhammad Oktavia, Tanty Buaton, Relita Ernawan, Ferda Andrian, Kevin Minimizing the number of stunting prevalence using the euclid algorithm clustering approach |
| title | Minimizing the number of stunting prevalence using the euclid algorithm clustering approach |
| title_full | Minimizing the number of stunting prevalence using the euclid algorithm clustering approach |
| title_fullStr | Minimizing the number of stunting prevalence using the euclid algorithm clustering approach |
| title_full_unstemmed | Minimizing the number of stunting prevalence using the euclid algorithm clustering approach |
| title_short | Minimizing the number of stunting prevalence using the euclid algorithm clustering approach |
| title_sort | minimizing the number of stunting prevalence using the euclid algorithm clustering approach |
| topic | QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) TA Engineering (General). Civil engineering (General) |
| url | http://umpir.ump.edu.my/id/eprint/41914/1/Minimizing%20the%20number%20of%20stunting%20prevalence.pdf http://umpir.ump.edu.my/id/eprint/41914/2/Minimizing%20the%20number%20of%20stunting%20prevalence%20using%20the%20euclid%20algorithm%20clustering%20approach_ABS.pdf |
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