Claim Missing Document
Check
Articles

Found 2 Documents
Search
Journal : IJASDS: Indonesian Journal of Applied Statistics and Data Science

Perbandingan Regresi Nonparametrik Kernel dan Spline pada Pemodelan Hubungan antara Rata-Rata Lama Sekolah dan Pengeluaran per Kapita di Indonesia Zulhan Widya Baskara; Syahrul, Muhammad; Amanda, Humami Syifa; Fahrani, Indi Rizqy; Yasmin, Yasmin; Purnamasari, Nur Asmita; Baskara, Zulhan Widya
Indonesian Journal of Applied Statistics and Data Science Vol. 1 No. 1 (2024): November
Publisher : Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/ijasds.v1i1.5725

Abstract

Poverty remains a major issue in developing countries, including Indonesia. In 2021, Indonesia’s poverty rate reached 10.14%, or approximately 27.5 million people (BPS). Poverty alleviation is a primary goal within the Sustainable Development Goals (SDGs). Two important indicators for measuring poverty are per capita expenditure and average years of schooling, which can aid in formulating policies to reduce poverty. This study analyzes the relationship between average years of schooling and per capita expenditure in 2023 using nonparametric regression methods, specifically kernel and spline regression. The kernel regression analysis yielded an optimal bandwidth of 0.860 and a minimum GCV of 0.574. However, the truncated spline method, with one optimal knot, a minimum GCV of 0.5263514 at the 3rd order, and the smallest MSE of 0.4097892, proved to be more accurate in describing the relationship between the two variables. The study concludes that the truncated spline method is superior in modeling the relationship between per capita expenditure and average years of schooling, providing valuable insights for policy formulation aimed at poverty alleviation in Indonesia.
Peramalan Jumlah Kasus Demam Berdarah Dengue di Pulau Lombok Menggunakan Model Space Time Autoregressive (STAR) Haryati, Haryati; Bahri, Syamsul; Purnamasari, Nur Asmita; Jurniati, Jurniati
Indonesian Journal of Applied Statistics and Data Science Vol. 2 No. 2 (2025): November
Publisher : Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/ijasds.v2i2.8170

Abstract

Dengue Hemorrhagic Fever (DHF) is an endemic disease with potential to cause outbreaks. It progresses and often proves fatal, with a high mortality rate frequently attributed to delayed treatment. According to data from the West Nusa Tenggara (NTB) Provincial Health Office, the incidence of DHF in the region has shown a consistent upward trend year over year, necessitating increased vigilance and preventative measures. This study aims to develop an accurate forecasting model to predict the number of DHF cases. The resulting model is intended to serve as tool for the community and policymakers to anticipate the spread of the disease, particularly on Lombok Island. The analytical method employed is the Space-Time Autoregressive (STAR) model, a time-series technique that incorporates interdependencies across both location (space) and time. The data analyzed consists of monthly DHF case counts on Lombok Island from January 2018 to December 2-22. The research results indicate that the best-perfoming model is STAR (3, 1). The forecasting accuracy of this optimal model, measured by the Mean Absolute Scaled Error (MASE), for Central Lombok and North Lombok Regencies was 0.87 and 0.59, respectively. These MASE values, being less than 1, indicate that the forecasting performance of the STAR model is superior to that of a simple naïve baseline model.