Peer-reviewed article
IMPLEMENTATION OF DATA MINING MODELS WITH ALGORITHMS K-NEAREST NEIGHBOR IN MONITORING THE NUTRITIONAL STATUS OF CHILDREN AND STUNTING
Mutammimul, Sayed Fachrurrazi, Reyhan Achmad Rizal, Mauliza Mauliza, Syarkawi ·
Summary
Written by AI, not yet AICS's view · claude-haiku-4-5-20251001This study describes the development of an information system using K-Nearest Neighbor data mining algorithms to monitor children's nutritional status and stunting at a health center in North Aceh, Indonesia. The system integrates weight and height measurements to detect malnutrition early and help staff manage nutritional data more efficiently. The K-Nearest Neighbor model achieved classification accuracy results ranging from 0.56 to 0.69 across different test runs. The system aims to streamline data recording and enable quick assessment of children's nutritional status at the primary health center.
Key points
- The system monitors children's weight and height to detect early malnutrition and stunting
- The information system is web-based and integrated for easier data management by health center staff
- K-Nearest Neighbor model classification accuracy ranged from 0.555 to 0.6944 across test iterations
- The system is designed to reduce data management problems at the health center
- Implementation focused on Paloh Punti Village in North Aceh District
Limitations
The document does not describe the study methodology, sample size, or comparison with other classification methods. It lacks information on whether the system was actually implemented and evaluated for real-world effectiveness in improving child health outcomes.
Abstract
Information systems are needed in the development of children in the developmental period and especially in the world of health. Monitoring of children's nutritional status and stunting is necessary to determine children's weight and meet the criteria for children's nutritional status. Pukesmas Muara Satu, North Aceh District, is an implementing element or assistant to the duties of Poskesdes and Midwives in the Health of children's nutritional status and stunting in Paloh Punti Village, which is one of the agencies under the Ministry of Health. This study aims to monitor the growth and development of children such as measuring weight, height, measured to detect early if unwanted things occur such as malnutrition. The problem in this study is designing and monitoring an Information system for child nutritional status and stunting that is integrated with a web application. The purpose of this study is to find out staff and employees in managing, monitoring and accessing data. So that the data at the puskesmas is recorded in the system, and can quickly determine data on the nutritional status of children and stunting. The results of this study are to be able to find out an information system that is able to reduce problems that occur in managing data on the nutritional status of children and stunting at the Muara Satu Health Center. This system is very important because it can make it easier for staff to record the nutritional status of children and stunting at the Health Center. then the results of the KNN (K-Nearest Neighbor) model classification with the recapitulation of the value of new cases with old cases in the first test section is 0.6944, the second test is 0.6388, the third test is 0.555, the fifth test is 0.6388.
Findings the Hub has extracted
Each claim below is labelled by its type of design and links back to this report.
- Intervention
- Web-based information system using K-Nearest Neighbor data mining algorithms to classify and monitor children's nutritional status
- Population
- Children and health center staff at Pukesmas Muara Satu, North Aceh District
Key finding
K-Nearest Neighbor model achieved classification accuracy of 0.6944 in first test