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Dampak Faktor Ekonomi dan Non Ekonomi Terhadap Jumlah Anak di Indonesia: Analisis Data Demographic Health Survey 2017 Munthe, MaySarah Qonita; Salsabila, Nasywa Nayifa; Kautsar, Achmad
Jurnal Ekonomi Kesehatan Indonesia Vol. 9, No. 2
Publisher : UI Scholars Hub

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Abstract

Indonesia has a large population, with a declining annual growth trend. Economic and non-economic factors contribute to the desired number of children. This study uses the latest data from the 2017 Demographic and Health Survey (DHS) to analyze the relationship between these factors and the desired number of children. A logit model is employed to evaluate the probability of having more than two children.The findings indicate that higher economic status is associated with a lower likelihood of having more than two children. Women in the middle economic group are 7.1% less likely to have more than two children compared to women in the low-income group. Additionally, non-economic factors, such as education level, show significant associations. Women with higher education are 21.1% less likely to have more than two children compared to women with lower education levels. This highlights the importance of women's education as a key non-economic factor in managing population growth. The government could consider expanding access to education, such as providing scholarships specifically for women, to help them achieve higher education levels. This, in turn, could indirectly contribute to population control in Indonesia.
Peran Kepemimpinan Wirausaha dan Orientasi Wirausaha Terhadap Kinerja Keuangan IKM Makanan Di Kota Kediri Kautsar, Achmad; Kusumaningrum, Trias Madanika; Chusnaini, Azmil
Bisei : Jurnal Bisnis dan Ekonomi Islam Vol 5 No 01 (2020): Juni
Publisher : Fakultas Ekonomi, Universitas Hasyim Asy'ari Tebuireng Jombang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33752/bisei.v5i01.719

Abstract

This study aims to analyze and determine the relationship of leadership roles and entrepreneurial orientation to the performance of SMIs in the City of Kediri, where good management of SMEs can encourage SMEs to compete with other SMIs. The population of this study was all food SMIs in Kediri City, from that population, it was agreed that the judge sampling method would examine 30 SMIs in Kediri City. Data analysis techniques in this study used path analysis. The results showed that the management of SMIs is highly dependent on the role of entrepreneurial leadership, entrepreneurial orientation, and business strategy. The results of this study indicate that entrepreneurial leadership factors and leadership orientation have a significant positive effect on business performance, while business strategies have not yet influenced business strategies and cannot be a mediating factor in this study.
Kelompok Menengah yang Hilang: Pekerja Gig dan Ketimpangan Akses terhadap Perlindungan Sosial di Indonesia Kautsar, Achmad; Noviani, Annisa Dwi; Salsabila, Nasywa Nayifa; Putri, Latifa Azzahra
Jurnal Ketenagakerjaan Vol 20 No 3 (2025): Gig Workers
Publisher : Pusat Pengembangan Kebijakan Ketenagakerjaan Kementerian Ketenagakerjaan Republik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47198/jnaker.v20i3.625

Abstract

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Model Ensemble untuk Prediksi Risiko Diabetes dengan Pertimbangan Efisiensi Biaya Qosimah, Rofiatul; Dhenabayu, Riska; Kautsar, Achmad; Safitri, Anita
JOM Vol 6 No 4 (2025): Indonesian Journal of Humanities and Social Sciences , December
Publisher : Universitas Islam Tribakti Lirboyo Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33367/ijhass.v6i4.8505

Abstract

This study addresses the growing global burden of diabetes by evaluating whether ensemble-based machine learning models can support reliable and cost-efficient early risk prediction. Moving beyond accuracy-centered evaluation, the study integrates cost-sensitive threshold optimization and probability calibration to enhance clinical relevance. Random Forest and XGBoost are evaluated using two datasets with contrasting population characteristics. Model performance is examined in terms of discriminative ability, calibration quality, and total misclassification cost. The results indicate that while XGBoost remains competitive on small-scale datasets, Random Forest provides more stable calibration and more consistent cost efficiency. These findings suggest that cost-sensitive and calibrated ensemble approaches have the potential to support more rational and economically efficient diabetes screening policies.