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Aurellia, Nur Aisya
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Humanities Computer Science & IT Economics, Econometrics & Finance Social Sciences

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Determinan Kejadian Wasting pada Balita Aurellia, Nur Aisya; Ramadhani, Anindita Ayu; Pamungkas, Khrisna Aji; Kartiasih, Fitri
Seminar Nasional Official Statistics Vol 2023 No 1 (2023): Seminar Nasional Official Statistics 2023
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/semnasoffstat.v2023i1.1901

Abstract

Wasting is a serious problem because it has a significant impact on various sectors of life, especially hindering the progress of individuals and human resources in a country. The highest waste incident in Indonesia is in the Province of East Nusa Tenggara. Therefore, this study aims to analyze the factors that influence the incidence of under-five wasting in NTT Province in 2021. The research method used is multiple linear regression. The research findings show that the percentage of extremely poor people, the percentage of women who give birth to babies with low birth weight, and the percentage of two-year-old babies who have ever been breastfed have a significant effect on the percentage of toddlers wasting. As a solution, it requires a collaborative effort from governments, health agencies, civil society organizations, and the community as a whole to tackle the problem of wasting and improve the health and well-being of children.
Analisis Kualitas Modal Manusia Tingkat Provinsi di Indonesia Menggunakan K-Means Clustering dan Regresi Logistik Biner Aurellia, Nur Aisya; Sari, Riska Meyliana; Muzakki, Naufal Fadli
Seminar Nasional Official Statistics Vol 2025 No 1 (2025): Seminar Nasional Official Statistics 2025
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/semnasoffstat.v2025i1.2577

Abstract

The Human Capital Index (HCI) is one of the indicators used in human development evaluations, with the aim of improving the welfare and advancement of human resources in various sectors of life. Limitations in provincial-level HCI data, as well as limitations in the data of HCI components, hinder the HCI calculation process. Therefore, an alternative approach was applied to assess human capital quality by examining components such as life expectancy, average years of schooling, and stunting prevalence using K-Means cluster analysis. The results indicate that provinces in Indonesia form two clusters: the low HCI group and the high HCI group. This study aims to examine the influence of several variables on HCI categories using binary logistic regression analysis. The results show that per capita GDP, internet penetration rates, and rice productivity have a significant positive impact on human capital quality in Indonesia.
Co-Authors Fitri Kartiasih Muzakki, Naufal Fadli Pamungkas, Khrisna Aji Ramadhani, Anindita Ayu Sari, Riska Meyliana