CLASSIFICATION OF NUTRITIONAL STATUS OF TODDLERS USING THE NAÏVE BAYES ALGORITHM BASED ON ANTHROPOMETRIC MEASUREMENTS (Case Study: Jogorogo Public Health Centre, Ngawi, East Java

Taufiq, Safwa (2025) CLASSIFICATION OF NUTRITIONAL STATUS OF TODDLERS USING THE NAÏVE BAYES ALGORITHM BASED ON ANTHROPOMETRIC MEASUREMENTS (Case Study: Jogorogo Public Health Centre, Ngawi, East Java. S1 Undergraduate thesis, Universitas Darussalam Gontor.

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Abstract

The nutritional status of toddlers refers to the condition of their bodies as a consequence of food consumption and nutrient utilisation. Nutrition plays a vital role in supporting the growth and development of toddlers. If nutritional needs are not met, health complications may arise. Currently, the processing of toddler nutritional status data at Puskesmas Jogorogo is conducted manually and classified using the Sigizi Terpadu or E-PPBGM application. However, these applications do not feature a specific classification function for height-for-age (TB/U) that can be directly utilised by healthcare professionals at the health centre. As a result, medical personnel must perform manual calculations before inputting the data into the Ministry of Health’s system. This study aims to classify the nutritional status of toddlers at Puskesmas Jogorogo using anthropometric measurements, employing the Naïve Bayes algorithm. The research follows the CRISP-DM (Cross-Industry Standard Process for Data Mining) methodology for data analysis, incorporating the Naïve Bayes algorithm for classification. The data used in this study consists of toddler health records collected at Puskesmas Jogorogo over two periods: August 2023, comprising 1,784 records, and February 2024, comprising 2,147 records. The model evaluation is conducted using a confusion matrix to assess classification performance. The results indicate that the Naïve Bayes algorithm provides consistent outcomes across both datasets, with an accuracy of 91% for the 2023 dataset and 91% for the 2024 dataset.

Item Type:	Thesis ( S1 Undergraduate )
Additional Information:	Skripsi : Safwa Alifiyah Taufiq Nim : 422021618056
Uncontrolled Keywords:	Toddler nutrition, Classification, Naive Bayes, CRISP-DM.
Subjects:	Dewey Decimal Classification > 000 - Komputer, Informasi dan Referensi Umum > 000 - Ilmu komputer, informasi dan pekerjaan umum > 000 - Ilmu komputer, informasi dan pekerjaan umum 23rd Dewey Decimal Classification > 000 - Komputer, Informasi dan Referensi Umum > 000 - Ilmu komputer, informasi dan pekerjaan umum > 000 - Ilmu komputer, informasi dan pekerjaan umum Dewey Decimal Classification > 000 - Komputer, Informasi dan Referensi Umum > 000 - Ilmu komputer, informasi dan pekerjaan umum > 004 - Pemrosesan data dan ilmu komputer 23rd Dewey Decimal Classification > 000 - Komputer, Informasi dan Referensi Umum > 000 - Ilmu komputer, informasi dan pekerjaan umum > 004 - Pemrosesan data dan ilmu komputer Dewey Decimal Classification > 000 - Komputer, Informasi dan Referensi Umum > 000 - Ilmu komputer, informasi dan pekerjaan umum > 005 - Pemrograman komputer, program dan data 23rd Dewey Decimal Classification > 000 - Komputer, Informasi dan Referensi Umum > 000 - Ilmu komputer, informasi dan pekerjaan umum > 005 - Pemrograman komputer, program dan data
Divisions:	Fakultas Sains dan Teknologi UNIDA Gontor > Teknik Informatika
Depositing User:	Safwa Alifiyah Taufiq
Date Deposited:	14 Feb 2025 10:51
Last Modified:	14 Feb 2025 10:51
URI:	http://repo.unida.gontor.ac.id/id/eprint/5418

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