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All Journal Journal of Environmental Science and Sustainable Development Indonesian Journal of Statistics and Its Applications
Thamrin, Nurafiza
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Humanities Computer Science & IT Energy Environmental Science Mathematics 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

Show Abstract | Download Original | Original Source | Check in Google Scholar

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.
Determinants of Antenatal Care Visits in Indonesia with Synthetic Minority Over-Sampling Techniques for Imbalance Data: Determinan Kunjungan Antenatal Care di Indonesia dengan Teknik Synthetic Minority Over-Sampling untuk Imbalanced Data Thamrin, Nurafiza; Baktiar, Aditya Firman; Addawiyah, Firda Aini; Husna, Miftahul; Irwati, Tati
Indonesian Journal of Statistics and Applications Vol 7 No 2 (2023)
Publisher : Statistics and Data Science Program Study, SSMI, 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.v7i2p86-104

Abstract

Maternal mortality and infant mortality are two indicators that describe the degree of public health as well as indicators of sustainable development in Indonesia. The acceleration of reduction these two indicators must be supported by antenatal care services since pregnancy for the safety of mothers and babies. Based on the results of the IDHS 2017, antenatal care coverage in Indonesia (77.4%) is still far from the target in 2024 (95%.) This study used logistic regression analysis with Synthetic Minority Oversampling Technique (SMOTE) resampling method because of imbalance data to explore the determinants of complete antenatal care visits in Indonesia and descriptive analysis to find out an overview of complete antenatal care associated with factors that are considered influencing it. Data that was used in this study is the Indonesia Demographic and Health Survey (IDHS) 2017 with unit of analysis for women of childbearing age who are married or live together and gave birth to their last child in the period 2012-2017. The logistic regression results of the SMOTE method show that the variables of mother's education, husband's work status, knowledge of pregnancy danger signs, distance to health facilities, timing first antenatal check, mother's age, economic status, birth order, and number of problems during pregnancy significantly affect the completeness of antenatal care visits. The policy recommendations in this study are expected to be adopted by government to increase antenatal care visits in Indonesia as an effort to reduce maternal and infant mortality.
Comparison of Soft and Hard Clustering: A Case Study on Welfare Level in Cities on Java Island: Analisis cluster dengan menggunakan hard clustering dan soft clustering untuk pengelompokkan tingkat kesejahteraan kabupaten/kota di pulau Jawa Thamrin, Nurafiza; Wijayanto, Arie Wahyu
Indonesian Journal of Statistics and Applications Vol 5 No 1 (2021)
Publisher : Statistics and Data Science Program Study, SSMI, 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.v5i1p141-160

Abstract

The National Medium Term Development Plan 2020-2024 states that one of the visions of national development is to accelerate the distribution of welfare and justice. Cluster analysis is analysis that grouping of objects into several smaller groups where the objects in one group have similar characteristics. This study was conducted to find the best clustering method and to classify cities based on the level of welfare in Java. In this study, the cluster analysis that used was hard clustering such as K-Means, K-Medoids (PAM and CLARA), and Hierarchical Agglomerative as well as soft clustering such as Fuzzy C Means. This study use elbow method, silhouette method, and gap statistics to determine the optimal number of clusters. From the evaluation results of the silhouette coefficient, dunn index, connectivity coefficient, and Sw/Sb ratio, it was found that the best cluster analysis was Agglomerative Ward Linkage which produced three clusters. The first cluster consists of 27 cities with moderate welfare, the second cluster consists of 16 cities with high welfare, the third cluster consists of 76 cities with low welfare. With the best clustering results, the government of cities in Java shall be able to make a better policies of welfare based on the dominant indicators found in each cluster.
Co-Authors Addawiyah, Firda Aini Baktiar, Aditya Firman Husna, Miftahul Irawan, Puguh Bodro Irwati, Tati Wulansari, Ika Yuni