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All Journal International Journal of Applied Sciences and Smart Technologies
Kartono Pinaryanto
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Computer Science & IT Energy Engineering Industrial & Manufacturing Engineering

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Classification of Toddler Nutrition Using C4.5 Decision Tree Method Kartono Pinaryanto; Robertus Adi Nugroho; Yanuarius Basilius
International Journal of Applied Sciences and Smart Technologies Volume 03, Issue 01, June 2021
Publisher : Universitas Sanata Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24071/ijasst.v3i1.3366

Abstract

Nutrition is very much needed in the growth of toddlers. It is very important to give babies a balanced nutritional intake at the right stage so that the baby grows healthy and is accustomed to a healthy lifestyle in the future. Children under five years of age are a group that is vulnerable to health and nutrition problems. In determining the nutritional status, it can be done in a system manner using the C4.5 decision tree classification method and entering several variables or attributes. The dataset tested was 853 toddlers. Classification is carried out to determine the nutritional status based on the weight/age (BB/U), height/age (TB/U) and weight/height (BB/TB) categories. The attributes used for the classification of BB/U are gender, weight and age. The attributes used for TB/U are gender, body length or height, and age. The attributes used for BB/TB are gender, weight, body length or height, and age. The average accuracy of the BB/U category is 90.16%, the average accuracy of the TB/U category is 76.64%, and the average accuracy of the BB/TB category is 83.83%.
The Improvement of Watershed Algorithm Accuracy for Image Segmentation Handwritten Numbered Musical Notation Kartono Pinaryanto
International Journal of Applied Sciences and Smart Technologies Volume 01, Issue 01, June 2019
Publisher : Universitas Sanata Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24071/ijasst.v1i1.1875

Abstract

In the Implementation of image processing to translate the image of the numbered musical notation into a numerical character requires some initial process that must be passed like image segmentation process. The advantage of successful segmentation process is that it can reduce the failure rate in the object recognition process. Segmentation process determines the success of object recognition process, it takes segmentation algorithm that can perform accurate object separation. The combination segmentation process developed in this research used projection profile algorithm, watershed and non object filtering. Profile projection algorithm is used to crop the image of the musical horizontally and vertically. The watershed algorithm is used to segment the numerical object of numerical notation generated from the projection profile process. Non object filtering is a continuation of the watershed algorithm that includes the non-object reduction process and the process of combining objects so that the original object segment will be generated. The based on the results of the research, the accuracy of the segment on watershed segmentation is 99.74% higher than watershed segmentation without combination of 94.82%.
Co-Authors Robertus Adi Nugroho Yanuarius Basilius