Article Per Year (5 Year)
p-Index From 2020 - 2025
0.23
P-Index
This Author published in this journals
All Journal Bilangan: Jurnal Ilmiah Matematika, Kebumian dan Angkasa
Melita Handayani
Unknown Affiliation
Astronomy Mathematics

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Analisis Diskriminan pada Indikator yang Memengaruhi Indeks Kerentanan Pangan Menurut Provinsi di Indonesia Tahun 2023 Melita Handayani; Natasya Liana Putri; Sri Pingit Wulandari
Bilangan : Jurnal Ilmiah Matematika, Kebumian dan Angkasa Vol. 2 No. 6 (2024): Bilangan : Jurnal Ilmiah Matematika, Kebumian dan Angkasa
Publisher : Asosiasi Riset Ilmu Matematika dan Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62383/bilangan.v2i6.319

Abstract

Indonesia is committed to achieving zero hunger as one of the goals of fulfilling the Sustainable Development Goals (SDGs) where this commitment focuses on addressing the problem of food availability but also ensuring that every individual has access to sufficient, nutritious, and safe food throughout the year for everyone. However, reviewing the current conditions in Indonesia, there is still an imbalance in food availability that will cause food vulnerability. Therefore, a prediction of food vulnerability in the future is needed where discriminant analysis is one of the appropriate statistical methods to analyze qualitative dependent and quantitative independent variables. This study uses secondary data from the official website of the food agency and the central statistics agency. The results of the study show that the characteristics of the data have small variations, asymmetric distribution, and there are outliers in several categories. The assumptions of multivariate normality, the suitability of the dependent variables, and the identity of the variance-covariance matrix have been met. Through discriminant analysis, the variables of the percentage of poverty and the percentage of households with access to clean drinking water are proven to significantly affect the IKP category. The discriminant model produces one significant function that is able to group the IKP category with a model accuracy rate of 86.8% and a classification accuracy of 64.7%.
Co-Authors Natasya Liana Putri Sri Pingit Wulandari