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All Journal Jurnal Ilmu Lingkungan Knowledge Engineering and Data Science Journal of Environmental Science and Sustainable Development Jurnal Statistika dan Aplikasinya Jurnal Ekonomi Indonesia PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON DATA SCIENCE AND OFFICIAL STATISTICS Jurnal Ekonomi Kesehatan Indonesia Indonesian Journal of Statistics and Its Applications
Wulansari, Ika Yuni
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Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Energy Engineering Environmental Science Health Professions Mathematics Medicine & Pharmacology Public Health Social Sciences Other

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MEASURING THE PERFORMANCE OF SDGS IN PROVINCIAL LEVEL USING REGIONAL SUSTAINABLE DEVELOPMENT INDEX Thamrin, Nurafiza; Wulansari, Ika Yuni; Irawan, Puguh Bodro
Journal of Environmental Science and Sustainable Development Vol. 6, No. 2
Publisher : UI Scholars Hub

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Abstract

Measuring the national and sub-national progress in achieving such globally adopted development agendas as Sustainable Development Goals (SDGs) is particularly challenging due to data availability and compatibility of indicators to measure SDGs, especially in Indonesia. This paper attempts to measure the performance of sustainable development at the regional level in Indonesia by newly constructing a multidimensional composite index called the Regional Sustainable Development Index (RSDI). RSDI comprises four dimensions, covering comprehensive economic, social, environmental, and governance indicators. By applying factor analysis, the paper assesses the uncertainty of RSDI and the sensitivity of its composing indicators, then further investigates the relationship between RSDI and the Human Development Index (HDI). RSDI is proven to have high precision with low uncertainty. A significantly positive relationship between RSDI and HDI suggests a consistent direction between both progresses (0.7726). RSDI in Indonesia can be categorized as medium-high level, with two provinces (East Nusa Tenggara and Papua) having low RSDI. RSDI helps identify provinces with the latest progress in SDG performance, allowing the government to prioritize interventions for provinces lagging behind.
Analysis Of Risk for Class Shifting And Determinants of BPJS Kesehatan Membership Using Generalized Ordered Logit-Unconstrained Partial Proportional Odds Model Oktora, Siskarossa Ika; Wulansari, Ika Yuni; Ermawan, Geri Yesa
Jurnal Ekonomi Kesehatan Indonesia Vol. 4, No. 2
Publisher : UI Scholars Hub

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Abstract

The main source of funding of BPJS Kesehatan comes from the different premium class in which the participant registered. The medical benefits among classes are equivalent, except inpatient facilities. But when the improvement health degree is not linear with the incurred costs, problem would arise. This study aims to analyze class shifting and determinants of BPJS Kesehatan membership. Around 1.53 percent of participants access higher classes, while 5.62 percent access lower classes. Class III participants with inpatient status severity level 2 and 3, reaching 41% and 43%, respectively. In addition, 60% of non-PBI participants are Class II premium participants; most of them are male, productive age, and workers. This research using Generalized Ordered Log- it Unconstrained Partial Proportional Odds Model concludes that participants who are married tend to choose higher premium class. Whereas productive age participants and a worker is in the lower premium class. The recommendation is the evaluation of membership based on class premium contributions considering potential participants (productive age and workers) who tend should be conducted in a lower class. Although mutual assistance is the principle of National Health Insurance, specific mechanisms should be established to examine the relation of age and health status to each participant regarding the difference in the registered class, besides their economic factors
Optimizing Malaria Control: Granular and Cost-Effective Mosquito Habitat Index in Endemic Areas Through Satellite Imagery Daulay, Nur Ainun; Putri, Salwa Rizqina; Wijayanto, Arie Wahyu; Wulansari, Ika Yuni
Knowledge Engineering and Data Science Vol 7, No 1 (2024)
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17977/um018v7i12024p40-57

Abstract

Malaria, classified as a tropical disease under the Sustainable Development Goals (SDGs) indicator 3.3, remains a significant global health challenge. In this study, by taking advantage of multiple spectral composite indexes of multisource satellite imagery to capture various geospatial features relevant to the suitability of marsh mosquito habitat, we introduced the Mosquito Habitat Suitability Index (MHSI) to assess potential Anopheles mosquito breeding sites in terms of the vegetation density, water bodies, environment temperature, and humidity in any particular areas. The MHSI integrates the publicly accessible granular level of the normalized difference vegetation index, water index, land surface temperature, and moisture index from cost-effective low and medium-resolution optical satellite data. We focus on West Papua Province, Indonesia, known for diverse ecological conditions and varying malaria prevalence, as a case study area. From the built index, the risk zone map is then formed with the K-Means algorithm. One key finding is the elevated risk in Fakfak Regency, demanding particular attention, as its high-risk area represents 45% of its total. This research aids localized decision-making to combat malaria's unique challenges in West Papua Province which are relevant for implementation in other regions, contributing to SDG-aligned interventions for malaria eradication by 2030.
Detection of Factors Affecting Rainfall Intensity in Jakarta Sumargo, Bagus; Handayani, Dian; Lubis, Alvi Pauziah; Firmasyah, Irman; Wulansari, Ika Yuni
Jurnal Ilmu Lingkungan Vol 23, No 1 (2025): January 2025
Publisher : School of Postgraduate Studies, Diponegoro Univer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jil.23.1.133-140

Abstract

The increased intensity of rainfall is becoming one of the most pressing climate-related issues in many parts of the world. Detecting the factors that affect rainfall intensity requires a combination of modern technologies, such as weather satellites, radar systems, and advanced atmospheric models. Extreme conditions (outliers) often occur. This study aims to model data that is not symmetric or contains outliers. This study examines and models quantile regression on daily rainfall intensity in Jakarta which has extreme rainfall events. The results of the study found that the extreme values in the daily rainfall intensity data in Jakarta are outliers and the assumptions on modeling using linear regression are not satisfied so that the characteristics of the parameter estimator based on OLS do not have BLUE characteristic. In modeling with quantile regression using six quantiles 0.25, 0.50, 0.75, 0.95, 0.99, and 0.9999 with consideration of these quantile values representing all parts of the data distribution including extreme values, it was found that the factors affecting rainfall intensity in Jakarta are different in each rainfall intensity condition. The best model is shown by quantile 0.999 with a coefficient of determination of 58.21%. Based on the best model, it is known that the factors affecting extreme rainfall are maximum temperature, dew point temperature, air humidity, wind speed, air pressure, and length of irradiation. This study indicates that quantile regression can provide a more detailed insight into how these variables affect rainfall intensity in various rainfall conditions ranging from low rainfall to extreme rainfall.
Analisis Determinan Status Kemiskinan Berisiko COVID-19 Level Kabupaten/Kota di Indonesia Putri M., Mustika; Wulansari, Ika Yuni
Jurnal Ikatan Sarjana Ekonomi Indonesia Vol 11 No 1 (2022): April
Publisher : Jurnal Ekonomi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52813/jei.v11i1.174

Abstract

Penelitian ini bertujuan untuk menganalisis kaitan antara persentase penduduk miskin berisiko COVID-19 dengan variabel-variabel makro yang memengaruhinya menggunakan analisis Klaster K-Medoids dan metode Regresi Logistik Ordinal PPOM. Hasil penelitian menunjukkan bahwa kelompok wilayah dengan persentase penduduk miskin berisiko COVID-19 sedang dan tinggi dipengaruhi oleh Indeks Pembangunan Manusia (IPM), Indeks Ketahanan Pangan (IKP), kepadatan penduduk, pertumbuhan ekonomi, Transfer ke Daerah dan Dana Desa (TKDD), dan Pendapatan Asli Daerah (PAD), sedangkan untuk kelompok wilayah dengan persentase penduduk miskin berisiko COVID-19 tinggi dipengaruhi oleh IPM, IKP, dan pertumbuhan ekonomi. Pemerintah perlu berfokus pada penanganan penduduk miskin berisiko COVID-19, memaksimalkan peningkatan kualitas sumber daya manusia, mendorong program pemerataan penduduk, serta lebih intensif melakukan pemantauan dan evaluasi ketepatan sasaran TKDD.
Small Area Estimation Using Empirical Bayes Poisson Gamma on Adolescent Fertility Rate in Indonesia: Small Area Estimation Menggunakan Empirical Bayes Poisson Gamma pada Angka Fertilitas Remaja di Indonesia Septianingsih, Putri; Wulansari, Ika Yuni
Indonesian Journal of Statistics and Applications Vol 7 No 2 (2023)
Publisher : Statistics and Data Science Program Study, IPB University, IPB University, in collaboration with the Forum Pendidikan Tinggi Statistika Indonesia (FORSTAT) and the Ikatan Statistisi Indonesia (ISI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v7i2p114-129

Abstract

High population growth is one of the main population problems facing Indonesia. One of the government's efforts to overcome this is by preventing adolescent fertility. The Adolescence Fertility Rate (AFR) produced by the IDHS is designed until provincial level, whereas the availability of AFR at the district/city level is needed as an indicator of regional development measurement. The purpose of this research is to produce an estimation of AFR at the district/city level in Indonesia and find out which auxiliary variables significantly influence it and evaluate the performance of the model in estimating AFR. The analytical method used is descriptive analysis to explain the characteristics of adolescent fertility and auxiliary variables and also direct estimation and the indirect estimation method using Small Area Estimation Empirical Bayes Poisson Gamma. The results showed that the number of villages, school facilities, health facilities, health workers, telephone lines and operators significantly affected the fertility of adolescents and the results of the SAE EB Poisson Gamma estimation were better than the direct estimation method. Suggestions proposed are the government need to increase attention to districts/cities that have AFR that is higher than the average AFR or National AFR and increase the number of school facilities and the number of health workers.
FORECASTING THE PRICE OF CURLY RED CHILI PEPPERS IN EAST JAVA PROVINCE USING ARIMA MODEL WITH ITERATIVE OUTLIER DETECTION PROCEDURE Erdien, Fareka; Rahayu, Widyanti; Sumargo, Bagus; Wulansari, Ika Yuni; Ali, Didiq Rosadi
Jurnal Statistika dan Aplikasinya Vol. 9 No. 2 (2025): Jurnal Statistika dan Aplikasinya
Publisher : LPPM Universitas Negeri Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21009/JSA.09208

Abstract

Curly red chili is one of the vegetables with high economic value because it plays a role in supporting the food industry and meeting domestic needs. Fluctuations in the price of curly red chili peppers can change at any time, requiring forecasting to prevent losses for economic actors. This research aims to get the best model for forecasting and determine the accuracy of forecasting the price of curly red chili. The Autoregressive Integrated Moving Average (ARIMA) model is one method that can be used for forecasting with limitations requiring data that must be stationary. Outliers in the ARIMA model affect the autocorrelation structure of a time series so that the estimated values of the Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF) become biased so that forecasting with the ARIMA model is less accurate and requires handling outliers in the form of outlier detection, one of which is an iterative procedure. From this study, it was found that the ARIMA(0,2,3) model with outlier detection was the best model for forecasting. Forecasting tends to show a downward trend with an accuracy level of MAPE value of 4.612, which means that the model is very good for forecasting.
The Influences of Climate Change and Social Vulnerability on Dengue Fever Incidence Rate in West Java Province 2019–2023 Hanif, Alwan Nabil; Sohibien, Gama Putra Danu; Wulansari, Ika Yuni
Proceedings of The International Conference on Data Science and Official Statistics Vol. 2025 No. 1 (2025): Proceedings of 2025 International Conference on Data Science and Official St
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/icdsos.v2025i1.606

Abstract

In Indonesia, dengue fever is a serious public health problem. The increase in dengue fever cases is influenced by climate change and social vulnerability factors. This study focuses on West Java Province in 2019–2023, aiming to describe the spatial-temporal pattern of dengue fever incidence and analyze the influence of climate factors and social vulnerability using a spatial-temporal model, namely Geographically Temporally Weighted Regression (GTWR). The exploration results show a high concentration of dengue fever incidence rates in 2019, while in 2023, the intensity of dengue fever incidence decreases. The GTWR model produces local parameters across various regions and time periods, indicating that in most regencies/cities, rainfall, population density, access to inadequate sanitation, health facility ratio, and education level have a positive effect on dengue fever incidence rates, while land surface temperature and the percentage of poor people have a negative effect. From the GTWR model results, areas with high levels of dengue fever vulnerability can be identified as priorities for dengue fever management interventions. Therefore, this study contributes to early warning research and dengue fever control program planning by considering the risk of dengue fever vulnerability in each region.
Co-Authors Ali, Didiq Rosadi Arie Wahyu Wijayanto Bagus Sumargo, Bagus Daulay, Nur Ainun Dian Handayani Erdien, Fareka Ermawan, Geri Yesa Firmasyah, Irman Hanif, Alwan Nabil Irawan, Puguh Bodro Lubis, Alvi Pauziah Pratiwi, Kandi Dwi Putri M., Mustika Putri, Salwa Rizqina Septianingsih, Putri Siskarossa Ika Oktora Sohibien, Gama Putra Danu Thamrin, Nurafiza Widyanti Rahayu