KLASIFIKASI STATUS GIZI BALITA MENGGUNAKAN ALGORITMA C4.5 (STUDI KASUS : PUSKESMAS KALIRUNGKUT)

Kumara Dewi, Putu (2023) KLASIFIKASI STATUS GIZI BALITA MENGGUNAKAN ALGORITMA C4.5 (STUDI KASUS : PUSKESMAS KALIRUNGKUT). Undergraduate thesis, UPN Veteran Jawa Timur.

[img]
Preview
Text (Cover)
19082010118-cover.pdf

Download (1MB) | Preview
[img]
Preview
Text (BAB 1)
19082010118-bab1.pdf

Download (138kB) | Preview
[img] Text (BAB 2)
19082010118-bab2.pdf
Restricted to Registered users only until 24 July 2025.

Download (467kB)
[img] Text (BAB 3)
19082010118-bab3.pdf
Restricted to Registered users only until 24 July 2025.

Download (203kB)
[img] Text (BAB 4)
19082010118-bab4.pdf
Restricted to Registered users only until 24 July 2025.

Download (1MB)
[img]
Preview
Text (BAB 5)
19082010118-bab5.pdf

Download (12kB) | Preview
[img]
Preview
Text (DAFTAR PUSTAKA)
19082010118-daftarpustaka.pdf

Download (234kB) | Preview
[img] Text (LAMPIRAN)
19082010118-lampiran.pdf
Restricted to Registered users only until 24 July 2025.

Download (106kB)

Abstract

Nutrition is an external factor of growth and development of toddlers. At the Kalirungkut Health Center, nutrition monitoring for toddlers aged 0-5 years is important for their growth and development. However, with the amount of data that continues to grow every month, it is difficult to predict the nutritional status of toddlers quickly. Therefore, the C4.5 algorithm is used in data processing to accelerate the prediction of the nutritional status of toddlers. In a business sense, there are 825 toddler data with 23 columns at the Kalirungkut Health Center. 752 toddlers with good nutritional status, 4 toddlers with poor nutritional status, 11 toddlers with less nutritional status, 7 toddlers with overweight status, 1 toddler with obesity status, and 50 toddlers with excess nutrition risk. Obesity class was omitted, so that in exploration, 824 data were used with 6 columns containing Weight, Height, Lila, Age, Gender, and Nutritional Status. Modeling uses the C4.5 algorithm with 3 scenarios (80:20, 70:30, 60:40) using the Oversampling technique to handle imbalance data, oversampling is done on the training data in each scenario in the modeling. Evaluation of the 70:30 scenario after imbalance data, the resulting F1-Score is 97% good nutrition, 67% poor nutrition, 67% malnutrition, 0% over nutrition, and 60% risk of over nutrition. The accuracy obtained is 93%. The model is implemented on the website. The level of accuracy of the website in predicting the nutritional status of toddlers reaches 92.13% correct predictions and 7.86% wrong predictions. Keywords : Classification algorithm C4.5, prediction of nutritional status of toddlers, flask, python

Item Type: Thesis (Undergraduate)
Contributors:
ContributionContributorsNIDN/NIDKEmail
Thesis advisorWahyuni, Eka Dyar0001128406UNSPECIFIED
Thesis advisorArifiyanti, Amalia Anjani0712089201UNSPECIFIED
Subjects: Q Science > Q Science (General)
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Q Science > QA Mathematics > QA76 Computer software
Q Science > QA Mathematics > QA76.6 Computer Programming
Q Science > QA Mathematics > QA76.625 Internet Programming
T Technology > T Technology (General)
Divisions: Faculty of Computer Science > Departemen of Information Systems
Depositing User: Putu Kumara Dewi
Date Deposited: 24 Jul 2023 07:53
Last Modified: 24 Jul 2023 07:53
URI: http://repository.upnjatim.ac.id/id/eprint/15969

Actions (login required)

View Item View Item