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All Journal Jurnal Varian Prosiding Seminar Nasional Official Statistics
Shafira, Hervira Nur
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Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Mathematics Social Sciences Other

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Perbandingan Agglomerative Nesting dan K-Means untuk Klasterisasi Ketimpangan Gender berdasarkan Dimensi Kesehatan Reproduksi Raihannabil, Syfriza Davies; Ilyas, Hilmi Malika Atim; Shafira, Hervira Nur; Riani, May Alya; Hastin, Nadya Noor; Siregar, Tiara Khorijah Hamid
Seminar Nasional Official Statistics Vol 2024 No 1 (2024): Seminar Nasional Official Statistics 2024
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/semnasoffstat.v2024i1.1977

Abstract

Gender inequality in Indonesia is ranked 4th out of 11 ASEAN countries with key problems such as high maternal mortality rates and teenage births. Indonesia ranks 3rd with the highest maternal mortality rate in Southeast Asia. Besides that, around 61% of provinces still have adolescent birth rates above the national average. This research uses clustering techniques to group provinces based on reproductive health dimensions to provide insight for policymakers. The two clustering methods used are Agglomerative Nesting (AGNES) and K-Means. The analysis found that the K-Means method was more effective in producing three clusters: 15 provinces in the medium category, 10 provinces in the high category, and 9 provinces in the low category. It is hoped that the results of this research can help the government make appropriate policies regarding improvements in the reproductive health dimensions to achieve gender equality in Indonesia, especially in provinces with high categories.
Prediction of CO2 Emissions Using ANN, ARIMAX, and Hybrid ARIMAX-ANN Models Syaharani, Afifah Dayan; Shafira, Hervira Nur; Irianto, Hikmal Mardian; Kartiasih, Fitri
Jurnal Varian Vol. 8 No. 3 (2025)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/varian.v8i3.5045

Abstract

The escalation of carbon dioxide (CO2) emissions has emerged as a critical environmental concern, particularly in the context of Indonesia’s pursuit of sustainable development. This study aims to forecast CO2 emissions in Indonesia using annual time-series data spanning 1967–2023. Three methodological approaches are employed: an artificial neural network (ANN), an autoregressive model with exogenous variables (ARIMAX), and a hybrid ARIMAX-ANN model. The dataset comprises Gross Domestic Product obtained from the World Bank, along with per capita CO2 emissions, per capita natural gas consumption, and per capita hydropower consumption sourced from Our World in Data. The findings of this research demonstrate that the hybrid ARIMAX-ANN model provides the best forecasting performance, as evidenced by the lowest RMSE, MAPE, and MAE values among the other two models. These results suggest that the hybrid model is currently the most reliable for predicting CO2 emissions in the Indonesian context. The study enriches the expanding literature on emission forecasting by providing empirical evidence to support data-driven policymaking for climate change mitigation and sustainable energy development in Indonesia. 
Co-Authors Fitri Kartiasih Hastin, Nadya Noor Ilyas, Hilmi Malika Atim Irianto, Hikmal Mardian Raihannabil, Syfriza Davies Riani, May Alya Siregar, Tiara Khorijah Hamid Syaharani, Afifah Dayan