Estimasi Produktivitas Padi Level Kecamatan di Kabupaten Tulungagung Menggunakan Geoadditive SAE


Garinca Firgiana Santoso Siti Muchlisoh


Paddy productivity data is one of the benchmarks for the government to audit the success of local food self-sufficiency program. Paddy productivity data is used to calculate paddy production in a region. Local government needs paddy production data at sub-district level to identify local food supply for the population. However, the estimation of paddy production data at sub-district level is constrained by the absence of paddy productivity data at sub-district level. BPS presents the data at regency level only. This research aims to estimate paddy productivity at sub-district level in Tulungagung Regency in 2019 using geoadditive small area estimation, evaluate the accuracy of the estimation using Root Mean Square Error (RMSE) and Relative Standard Error (RSE), and identify the rice surplus-deficit at sub-district level. Analysis method being used was inferential analysis using indirect estimation by geoadditive SAE. The estimation showed that the highest paddy productivity was in Pucanglaban Sub-district (8,8648 ton/ha), while the lowest paddy productivity in Pagerwojo Sub-district (3,6576 ton/ha). The use of geoadditive SAE gave more precision to the estimation because it produced smaller RMSE and RSE than direct estimation method. The estimation also showed that major sub-districts of Tulungagung Regency experienced surplus in rice during 2019, but there were also six sub-districts which suffered deficit in rice.


How to Cite
SANTOSO, Garinca Firgiana; MUCHLISOH, Siti. Estimasi Produktivitas Padi Level Kecamatan di Kabupaten Tulungagung Menggunakan Geoadditive SAE. Jurnal Aplikasi Statistika & Komputasi Statistik, [S.l.], v. 14, n. 1, p. 23-36, mar. 2022. ISSN 2615-1367. Available at: <>. Date accessed: 26 june 2022. doi:


Apriani, F. (2017). Pemodelan Pengeluaran per Kapita Menggunakan Small Area Estimation dengan Pendekatan Semiparametrik Penalized Spline [Tesis]. Surabaya: Institut Teknologi Sepuluh Nopember.
Ardiansyah, M. (2018). Pendugaan Produktivitas Padi di Tingkat Kecamatan Menggunakan Geoadditive Small Area Model [Tesis]. Bogor: Institut Pertanian Bogor.
Biemer, P. P., & Lyberg, L. E. (2003). Introduction to Survey Quality. Hoboken: John Wiley & Sons, Inc.
Bocci, C. (2009). Geoadditive Models for Data with Spatial Information. Florence: University of Florence.
BPS Jawa Timur. (2020). Provinsi Jawa Timur Dalam Angka 2020. Surabaya: Badan Pusat Statistik Provinsi Jawa Timur.
BPS RI. (2018). Kajian Konsumsi Bahan Pokok 2017. Jakarta: Badan Pusat Statistik.
BPS RI. (2018). Konversi Gabah ke Beras (SKGB) Tahun 2018. Jakarta: Badan Pusat Statistik.
BPS Tulungagung. (2021). Kabupaten Tulungagung Dalam Angka 2021. Tulungagung: Badan Pusat Statistik Kabupaten Tulungagung.
Chand, N., & Alexander, C. H. (1995). Using Administrative Records for Small Area Estimation in the American Community Survey. 1999 FCSM Research Conference (hal. 1-9). Maryland, United States: U.S. Bureau of the Cencus.
Ghosh, M., & Rao, J. (1994). Small Area Estimation: An Appraisal. Statistical Science, 9(1), 55-76.
Kammann, E. E., & Wand, M. P. (2003). Geoadditive Models. Journal of the Royal Statistical Society Applied Statistics, 52(1), 1-18. doi:10.1111/1467-9876.00385
Kurnia, A. (2009). Prediksi Terbaik Empirik untuk Model Transformasi Logaritma di dalam Pendugaan Area Kecil dengan Penerapan pada Data Susenas [Tesis]. Bogor: Institut Pertanian Bogor.
Petrucci, A., & Pratesi, M. (2014). Spatial Models in Small Area Estimation in the Context of Official Statistics. Statistica Applicata - Italian Journal of Applied Statistics, 24(1), 9-27.
Pusponegoro, N. H., Djuraidah, A., Fitrianto, A., & Sumertajaya, I. M. (2019). Geo-additive Models in Small Area Estimation of Poverty. Journal of Data Science and Its Applications, 2(1), 11-18. doi:10.21108/JDSA.2019.2.15
Rao, J., & Molina, I. (2015). Small Area Estimation Second Edition. New Jersey: John Wiley & Sons, Inc.
Ruppert, D., Wand, M. P., & Carroll, R. J. (2003). Semiparametric Regression. New York: Cambridge University Press.