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All Journal Jurnal Sains Matematika dan Statistika Indonesian Journal of Applied Statistics ESTIMASI: Journal of Statistics and Its Application Parameter: Journal of Statistics MATHunesa: Jurnal Ilmiah Matematika Indonesian Journal of Statistics and Its Applications
Kalondeng, Anisa
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Agriculture, Biological Sciences & Forestry Computer Science & IT Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Environmental Science Mathematics Other

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PEMODELAN STATISTICAL DOWNSCALING DENGAN PEUBAH DUMMY BERDASARKAN TEKNIK CLUSTER HIERARKI DAN NON- HIERARKI UNTUK PENDUGAAN CURAH HUJAN Sahriman, Sitti; Kalondeng, Anisa; Koerniawan, Vieri
Indonesian Journal of Statistics and Applications Vol 3 No 3 (2019)
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.v3i3.471

Abstract

Statistical downscaling (SD) is a statistical technique used to predict local scale rainfall based on global atmospheric circulation. The global scale climate variable used is precipitation from GCM (Global Circulation Model). However, the precipitation data of GCM outputs have a large dimension, giving rise to multicollinearity in the data. This problem is handled by the Principal Component Regression (PCR) method. In addition, the SD models have heterogeneous error variances. The dummy variable is added to the PCR models to solve the problem. Hierarchical (k-means) and non-hierarchical cluster techniques (average linkage, median linkage, and ward linkage) are used in modeling to determine rainfall data groups. Furthermore, the group formed is the basis of the formation of dummy variables. This study aims to estimate local rainfall data in Pangkep district as a salt-producing area in South Sulawesi. There are 4 dummy variables based on the 5 groups formed. Dummy variables are able to improve predictions from the PCR models. R2 values of the PCR-dummy models (ranging from 89.89% to 95.58%) are relatively higher than the PCR models (ranging from 55.87% to 57.61%). This result is also consistent with the model validation stage. The PCR-dummy models based on non-hierarchical cluster techniques (k-means) are better than the PCR-dummy models based on cluster hierarchy techniques. In general, the best model is the PCR-dummy model of the non-hierarchical cluster technique (k-means ) and involves 4 main components.
Pemodelan Data Kemiskinan di Pulau Sumatera dengan Regresi Multilevel Spline Linear Truncated Davala, Muhammad Ridzky; Annisa, Nurul Mutiara; Siswanto, Siswanto; Kalondeng, Anisa
Indonesian Journal of Applied Statistics Vol 7, No 1 (2024)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13057/ijas.v7i1.80768

Abstract

Poverty is one of the world's biggest challenges that is still a problem, both in developing and developed countries, including Indonesia. Around 27.5 million people live below the national poverty line in Indonesia. Because it is the largest archipelago, poverty problems in each region also vary, including on the Sumatra Island. One of the efforts to alleviate poverty can be done through identifying factors that affect the percentage of poor population using truncated linear spline multilevel regression model. Multilevel modeling is a statistical approach specifically used to analyze data with a two-level structure. This approach allows an understanding of the contribution of individual and group-level factors to the response variable. The predictor variables considered are per capita expenditure, open unemployment rate, and human development index at the district/city level (level-1), as well as population growth rate and economic growth rate at the provincial level (level-2). The results of this study show that the best multilevel regression model at level-1 uses three knot points, while at level-2 it uses two knot points. The factors that affect PPM in Sumatra Island in 2021 at level-1 are per capita expenditure and at level-2 are population growth rate and economic growth rate. The factors that affect percentage of poor population in Sumatra Island in 2021 are expected to provide a more in-depth view of the socio-economic conditions on the island of Sumatra.
Co-Authors Abdia, Hikma Akhsan, Tiara Annisa Andi Isna Yunita Ania, Samsir Aditya Anna Islamiyati Annisa, Nurul Mutiara Arfan, Muh. Indirwan Arman, Agym Nastiar Baso, Andi M. Alfin Davala, Muhammad Ridzky Fajri, Belia Nurul Fitri Fitri Georgina Maria Tinungki Jelita Zalzabila Koerniawan, Vieri Larasati, Dwi Ina Lemido, Ryo Muhammad Fadil Nabila Miftakhurriza Nailah, Fadia Nirwan Ilyas Nurdin, Afifah Mutiah Nurkamalia, Nurkamalia Palungan, Kevin Ekarinaldo Pannu, Abdullah Rahman, Anugrah Nur Isnaeni S Siswanto Sahriman, Sitti Sirajang, Nasrah Siswanto Syam, Surya Cahyadi Vallarino, Alfian Akbar Wulandari, A. Elisha