Article Per Year (5 Year)
p-Index From 2020 - 2025
0.23
P-Index
This Author published in this journals
All Journal Journal of International Relations Studies International Journal of Disaster Management
Rizka Puspitasari, Rizka
Program Studi Hubungan Internasional, Fakultas Ilmu Sosial dan Ilmu Politik, Universitas Diponegoro
Humanities Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Environmental Science Social Sciences

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

Found 2 Documents
Search

 1 
22. ANALISIS PENYEBAB PRAKTIK KERJA PAKSA DI ARAB SAUDI: FENOMENA KERJA PAKSA TERHADAP TKI INFORMAL (2011-2014) Puspitasari, Rizka; Windiani, Reni; Hanura, Marten
Journal of International Relations Volume 2, Nomor 3, Tahun 2016
Publisher : International Relations, Faculty of Social and Political Sciences, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (269.602 KB)

Abstract

TKI is foreign exchange hero for Indonesia. TKI send remittances to Indonesia about tens oftrillions rupiah. However, the contribution of remittances is not balanced with the guaranteeof TKI human rights protection. Informal workers sector or domestic workers was the mostvulnerable sector to human rights violations, including forced labour, such as unpaid wages,the job does not match with the contract of employment, confinement, and physical andpsychological violence. One of the major destination countries of TKI placement is SaudiArabia, where about 40% of the total number of TKI torture and deaths occur there. SaudiArabia is a member state that has ratified the International Labour Organization (ILO)concerning forced labor, which are the Forced Labour Convention (No. 29) and the Abolitionof Forced Labour Convention (No. 105). However, the practice of forced labor is still foundunresolved, including against informal sector workers. This study aims to find out why forcedlabour Saudi Arabia could have. In answering these objectives, this study uses constructivismparadigm and compliance theory. The method used in this research is qualitative method withdescriptive-analytical type through interviews and literature technics. Results from this studyis that there are three factors that cause forced labour in Saudi Arabia, they are Saudi Arabiahas not yet understand the provisions of the convention because of the applied domesticnorms there, then Saudi Arabia has limited capacity to conduct labour inspection, and theabolition of forced labor is not a priority interest of Saudi Arabia.
Integration of machine learning and climate data for enhanced dengue hemorrhagic fever (DHF) prediction: A case study in Banda Aceh Puspitasari, Rizka; Gan, Connie Cai Ru; Yani, Muhammad; Zahrina, Zahrina; Abidin, Taufik Fuadi
International Journal of Disaster Management Vol 8, No 2 (2025)
Publisher : TDMRC, Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24815/ijdm.v8i2.47069

Abstract

The increasing impact of climate change, including rising average temperatures and altered precipitation patterns, has led to changes in the prevalence and distribution of climate-sensitive diseases (CSDs), such as Dengue Hemorrhagic Fever (DHF). DHF remains a significant public health concern in Indonesia, particularly in Banda Aceh, due to its high incidence. The burden on healthcare systems is substantial, contributing to increased morbidity and mortality, especially among vulnerable populations. This study aimed to integrate climate data with machine learning methods to develop predictive models for DHF incidence. Data from 2010 to 2023 included DHF case counts and monthly climate variables such as humidity, rainfall, temperature, and wind speed. The predictive models employed Gradient Boosting, Support Vector Regression (SVR), Random Forest, and Linear Regression algorithms. Model performance was evaluated by comparing prediction accuracy using Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) metrics. The results demonstrated that the Linear Regression model predicted monthly DHF incidence with greater accuracy than the other models, as indicated by lower MAE and RMSE values. These findings suggest that integrating climate data with machine learning provides an effective tool for early warning systems for DHF, supporting public health planning and interventions in Banda Aceh City, particularly in anticipation of an increase in DHF cases.
Co-Authors Gan, Connie Cai Ru Marten Hanura, Marten Muhammad Yani, Muhammad Reni Windiani Taufik Fuadi Abidin Zahrina, Zahrina