Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal JAKPP : Jurnal Analisis Kebijakan & Pelayanan Publik Jurnal Statistika dan Aplikasinya
Qorinul Huda
Unknown Affiliation
Agriculture, Biological Sciences & Forestry Computer Science & IT Economics, Econometrics & Finance Environmental Science Social Sciences Other

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
COMPARISON OF MACHINE LEARNING CLASSIFICATION ALGORITHMS IN GROUPING INCOME DISTRIBUTION INEQUALITIES IN JAVA AND BALI Qorinul Huda; Puput Budi Aji
Jurnal Statistika dan Aplikasinya Vol. 8 No. 2 (2024): Jurnal Statistika dan Aplikasinya
Publisher : LPPM Universitas Negeri Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21009/JSA.08208

Abstract

Inequality is a growing issue in several countries, both developed and developing countries. The level of state income reflected in Gross Domestic Product (GDP) cannot yet describe whether income allocation is equitable or not. High GDP is the goal of a country, but welfare is much more important. Community welfare in a country can be interpreted as how much state income is enjoyed by the community. One benchmark for whether a country's income is equally enjoyed by its people or not is through the Gini index. As industry 4.0 progresses, economic growth continues to increase. The largest share of Indonesia's GDP is on the islands of Java and Bali. Behind the rapid economic growth on the two islands, there is also inequality in income distribution. This research aims to classify districts and cities on the islands of Java and Bali based on factors that influence inequality using a data mining classification algorithm. This research uses four algorithms, namely Decision Tree, Logistic Classification, Random Forest, and Support Vector Machine (SVM). These four methods will be compared (compared) based on model evaluation, so that they are able to predict testing data for the next period in order to produce the correct regional classification. This research also accommodates handling of imbalanced data, data imputation, and forecasting using Generalized Regression Neural Network (GRNN).
Inter Provincial Youth Human Capital Mapping and Its Implications for Economic Growth Policy in Indonesia Qorinul Huda
JAKPP (Jurnal Analisis Kebijakan & Pelayanan Publik) Volume 12 No. 1, Maret 2026
Publisher : Departemen Ilmu Administrasi FISIP UNHAS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31947/jakpp.v12i1.48408

Abstract

Youth are a strategic national asset because they are of productive age, determining the direction of future economic development. This study is based on the hypothesis that the quality of youth human capital influences economic growth across provinces in Indonesia. Data are sourced from the Central Statistics Agency (BPS) in 2024, with 38 provinces as analysis units. The methods used include biplot analysis to map variations in youth human capital indicators (education, health, and employment) and multiple linear regression to examine their influence on per capita economic growth. The results show disparities in youth human capital across provinces, with Java and parts of Sumatra being relatively superior compared to eastern Indonesia. Health and employment factors have been shown to influence economic growth, while education shows a contradictory relationship due to the phenomena of skill mismatch and time lag. These findings confirm that human development, especially youth development, is a crucial foundation for achieving the 2025–2045 RPJPN targets towards an Advanced Indonesia. Mapping human capital potential allows for targeted local-scale policy mapping.
Co-Authors Puput Budi Aji