Article Per Year (5 Year)
p-Index From 2021 - 2026
0.562
P-Index
This Author published in this journals
All Journal CAUCHY: Jurnal Matematika Murni dan Aplikasi BAREKENG: Jurnal Ilmu Matematika dan Terapan Jurnal Ilmiah Ilmu Terapan Universitas Jambi
Zhafira, Azizah Atsariyyah
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Energy Engineering Industrial & Manufacturing Engineering Mathematics Mechanical Engineering Physics Transportation

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
SPATIAL EXTRAPOLATION OF MALARIA CASES IN CENTRAL PAPUA USING CO-KRIGING BASED ON RAINFALL AND OBSERVATIONAL DATA FROM PAPUA PROVINCE Saifudin, Toha; Chamidah, Nur; Zhafira, Azizah Atsariyyah; Budijono, Gabriella Agnes; Sihite, Rivaldi; Baihaqi, Mochamad; Januarta, R. Arya
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 2 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss2pp1485-1500

Abstract

Malaria is an infectious disease that remains a significant health burden in Indonesia, particularly in Papua Province. This province has the highest malaria incidence rate nationally, influenced by various environmental factors such as rainfall. This study aims to estimate the number of malaria cases in districts/cities of Central Papua Province that do not have direct observation data, by utilizing the Co-Kriging method based on rainfall as a secondary variable and malaria cases as a primary variable from Papua Province. The secondary data used in this study were obtained from the official website of the Badan Pusat Statistik (BPS) of Papua Province, which includes the number of malaria cases in districts/cities as well as rainfall data from meteorological stations in the same region, collected in 2023. Three types of semivariogram models-spherical, exponential, and gaussian-were used to select the best model through statistical evaluation using Mean Squared Error (MSE) and Mean Absolute Percentage Error (MAPE). The results showed that the Gaussian semivariogram model provided the most optimal prediction results with an MSE of 10.895 and an MAPE of 4.67%. The estimates show that malaria cases in Central Papua are relatively uniform, with the highest incidence in Puncak Jaya district (219/1000 population) and the lowest in Mimika district (211/1,000 population). This approach is expected to be an important tool in spatially based disease planning and control and support the achievement of Sustainable Development Goals (SDGs), especially goals 3 (Good Health and Well-Being) and 13 (Climate Action).
MODELING INDONESIA'S FOOD SECURITY INDEX USING SPATIAL ECONOMETRIC PANEL APPROACH Amelia, Dita; Suliyanto, Suliyanto; Aldawiyah, Najwa Khoir; Zhafira, Azizah Atsariyyah
Jurnal Ilmiah Ilmu Terapan Universitas Jambi Vol. 10 No. 2 (2026): Volume 10, Nomor 2, April 2026
Publisher : LPPM Universitas Jambi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22437/jiituj.v10i2.53267

Abstract

Food security is a pressing issue for economic stability and public well-being in Indonesia, where the 2020–2022 Food Security Index (FSI) remained at Priority 4 which indicate low provincial food security. This study examines the determinants of FSI across 34 provinces from 2020 to 2023 using a spatial panel approach to capture both temporal and spatial spillovers. Secondary data from the National Food Agency and the Central Statistics Agency (BPS) include poverty rate, electricity and clean water access, Desirable Dietary Pattern (DDP) score, per capita caloric intake, and mean years of schooling. Analysis employed panel regression and spatial-panel models particularly corporating the spatial fixed effects model. The SAR-FE model provided the best fit, with a significant positive spatiall ag coefficient. This study emphasizes that FSI in one province is influenced by neighboring provinces. These spillover effects highlight the interconnectedness of provincial food security and emphasizing that interventions in one region can affect adjacent areas. The combination of spatial and panel methodologies with individual fixed effects defines the novelty of this research. Such an approach, which is rarely utilized in the context of Indonesian food security, uncovers provincial-level spillover dynamics that previous studies relying solely on either spatial or panel frameworks tended to ignore. Findings provide actionable insights for coordinated regional policies and contribute to Sustainable Development Goal 2 (Zero Hunger) by promoting more equitable food availability and accessibility.
Modelling Factors Affecting the Middle Income Trap in Indonesia Using Generalized Additive Models (GAM) Amelia, Dita; Suliyanto, Suliyanto; Zhafira, Azizah Atsariyyah; Ramadhanti, Aulia; Suyono, Billy Christandy; Hizbullah, Firqa Aqila
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 11, No 1 (2026): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI
Publisher : Mathematics Department, Universitas Islam Negeri Maulana Malik Ibrahim Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/cauchy.v11i1.35119

Abstract

Indonesia is currently facing the risk of the Middle Income Trap (MIT), a condition in which economic growth stagnates after reaching middle-income status. This study aims to identify and model socio--economic factors affecting MIT at the provincial level in Indonesia during 2020--2023. The Generalized Additive Model (GAM) is employed to capture nonlinear and heterogeneous relationships between predictors and GRDP per capita with complex patterns that conventional linear or parametric models often fail to detect. The use of GAM in this context represents a methodological contribution, as studies applying GAM for MIT analysis in Indonesia remain very limited. This research therefore introduces a novel analytical approach by demonstrating how GAM can reveal flexible functional relationships and uncover nonlinear effects that are overlooked by traditional panel regression. GRDP per capita is modeled using six predictors: life expectancy, poverty rate, informal employment share, upper secondary education completion, food insecurity prevalence, and population density. The best model is obtained using the Gaussian family with an identity link, with five predictors showing nonlinear effects and food insecurity exhibiting a negative linear influence. The selected model demonstrates strong performance, indicated by an AIC value of 2743.279 and a R^2 of 98.6%, suggesting a very high explanatory power. In addition, the model achieves good predictive accuracy, with a MAPE of 8.04%. The findings support evidence-based policies aligned with Sustainable Development Goal (SDG) 8, promoting inclusive and sustainable economic growth.
Co-Authors Aldawiyah, Najwa Khoir Baihaqi, Mochamad Budijono, Gabriella Agnes Dita Amelia, Dita Hizbullah, Firqa Aqila Januarta, R. Arya Nur Chamidah Ramadhanti, Aulia Sihite, Rivaldi Suliyanto Suliyanto Suyono, Billy Christandy Toha Saifudin