Djuraidah, A., Astuti, E. T., Efendi, A., Abdurakhman, A., Widodo, E., Mangku, I. W., Ginanjar, I., Tarno, T., Ruslan, S., Sadik, K., & others. (2021). Pedoman Statistika Ria dan Festival Sains Data (SATRIA DATA) tahun 2021.

Novkaniza, F., Notodiputro, K. A., Mangku, I. W., & Sadik, K. (2021). Density Estimation of Neonatal Mortality Rate Using Empirical Bayes Deconvolution in Central Java Province, Indonesia. Procedia Computer Science, 179, 361–367.

Khotimah, K., Sadik, K., & Rizki, A. (2020). KAJIAN REGRESI KEKAR MENGGUNAKAN METODE PENDUGA-MM DAN KUADRAT MEDIAN TERKECIL. Indonesian Journal of Statistics and Its Applications, 4(1), 97–115.

Sadik, K., Anisa, R., & Aqmaliyah, E. (2020). Small Area Estimation on Zero-Inflated Data Using Frequentist and Bayesian Approach. Journal of Modern Applied Statistical Methods, 18(1), 8.

Bodro, D., Sartono, B., & Sadik, K. (2019). A simulation study with log, Box-Cox, and dual-power transformation on handling curvilinear relationship in small area estimation. IOP Conference Series: Earth and Environmental Science, 299, 012029.

Santi, V., Kurnia, A., & Sadik, K. (2019). Modelling of the number of malarias suffers in Indonesia using Bayesian generalized linear models. Journal of Physics: Conference Series, 1402, 077091.

Rumahorbo, K. R., Susetyo, B., & Sadik, K. (2019). PEMODELAN DATA TERSENSOR KANAN MENGGUNAKAN ZERO INFLATED NEGATIVE BINOMIAL DAN HURDLE NEGATIVE BINOMIAL. Indonesian Journal of Statistics and Its Applications, 3(2), 184–201.

Kusuma, L. P., Sadik, K., & others. (2019). Regresi Terboboti Geografis dengan Fungsi Pembobot Kernel Gaussian pada Kekuatan Sinyal Seluler. Xplore: Journal of Statistics, 8(1).

Eminita, V., Kurnia, A., & Sadik, K. (2019). PENANGANAN OVERDISPERSI PADA PEMODELAN DATA CACAH DENGAN RESPON NOL BERLEBIH (ZERO-INFLATED). FIBONACCI: Jurnal Pendidikan Matematika Dan Matematika, 5(1), 71–80.

Putri, Y. E., Sadik, K., & Suhaeni, C. (2018). Perbandingan Metode Dalil Limit Pusat Transformasi dan Resampling Bootstrap dalam Pembentukan Selang Kepercayaan. Xplore: Journal of Statistics, 2(2), 73–73.

Raudlah, S., Masjkur, M., Sadik, K., & others. (2018). Perbandingan Metode Koreksi Pencaran pada Data Hasil Alat Pemantau Kadar Glukosa Darah Non-Invasif. Xplore: Journal of Statistics, 7(3).

Hayati, M., Sadik, K., & Kurnia, A. (2018). Conwey-Maxwell Poisson Distribution: Approach for Over-and-Under-Dispersed Count Data Modelling. IOP Conference Series: Earth and Environmental Science, 187, 012039.

Kuraysia, F., Sadik, K., & Kurnia, A. (2018). Estimating Poverty Indicator with Small Area Estimation in Simulation Study of Different Population and Sample Size. Small, 96(10), 89.

Muslim, A., Kurnia, A., & Sadik, K. (2018). Hierarchical Generalized Linear Model Approach For Estimating Of Working Population In Kepulauan Riau Province. IOP Conference Series: Earth and Environmental Science, 187, 012042.

Yulita, T., Notodiputro, K. A., & Sadik, K. (2018). M-Estimation Use Bisquare, Hampel, Huber, and Welsch Weight Functions in Robust Regression.

Permatasari, A., Notodiputro, K. A., & Sadik, K. (2018). Mengukur Indeks Kebahagiaan Mahasiswa IPB Menggunakan Analisis Faktor. Xplore: Journal of Statistics, 2(1), 1–8.

Hariyanto, S., Notodiputro, K., Kurnia, A., & Sadik, K. (2018). Measurement error in small area estimation: a literature review. IOP Conference Series: Earth and Environmental Science, 187, 012034.

Susanti, A. N., Sadik, K., & Kurnia, A. (2018). A Comparison of Cluster Method and Nearest Neighbor Method for Non-sample Area in the Small Area Estimation.

Haji, H. A., Sadik, K., & Soleh, A. M. (2018). A Comparative Simulation Study of ARIMA and Fuzzy Time Series Model for Forecasting Time Series Data. International Journal of Scientific Research in Science, Engineering and Technology, 4(11), 49–56. https://doi.org/10.32628/IJSRSET1184112

Sumarni, C., Sadik, K., Notodiputro, K., & Sartono, B. (2017). Robustness of location estimators under t-distributions: a literature review. IOP Conference Series: Earth and Environmental Science, 58, 012015.

Salma, A., Sadik, K., & Notodiputro, K. A. (2017). Small area estimation of per capita expenditures using robust empirical best linear unbiased prediction (REBLUP). AIP Conference Proceedings, 1827, 020027.

Arisanti, R., Notodiputro, K., Sadik, K., & Lim, A. (2017). Bias Reduction in Estimating Variance Components of Phytoplankton Existence at Na Thap River Based on Logistics Linear Mixed Models. IOP Conference Series: Earth and Environmental Science, 58, 012014.

Sundara, V., Kurnia, A., & Sadik, K. (2017). Clustering Information of Non-Sampled Area in Small Area Estimation of Poverty Indicators. IOP Conference Series: Earth and Environmental Science, 58, 012020.

Sundara, V. Y., Sadik, K., & Kurnia, A. (2017). Cluster information of non-sampled area in small area estimation of poverty indicators using Empirical Bayes. AIP Conference Proceedings, 1827, 020026.

Ubaidillah, A., Kurnia, A., & Sadik, K. (2017). Generalized Multilevel Linear Model dengan Pendekatan Bayesian untuk Pemodelan Data Pengeluaran Perkapita Rumah Tangga. Jurnal Aplikasi Statistika & Komputasi Statistik, 9(1), 12–12.

Girinoto, Sadik, K., & Indahwati. (2017). Robust small area estimation of poverty indicators using M-quantile approach (Case study: Sub-district level in Bogor district). AIP Conference Proceedings, 1827, 020032.

Handayani, D., Notodiputro, K. A., Sadik, K., & Kurnia, A. (2017). A comparative study of approximation methods for maximum likelihood estimation in generalized linear mixed models (GLMM). AIP Conference Proceedings, 1827, 020033.

Angraini, Y., Notodiputro, K. A., Sadik, K., & Chesoh, S. (2017). Linear Mixed Models for Analyzing Total Weights of Fish in Na Thap River, Southern Thailand. ISI Regional Statistics Conference.

Arisona, D. C., Kurnia, A., & Sadik, K. (2017). Study of Small Area Estimation on Overdispersion Data with the Zero-Inflated Poisson Regression. International Journal of Engineering and Management Research (IJEMR), 7(6), 121–123.

Maulida, A., Afendi, F. M., & Sadik, K. (2017). The Estimation of The Total Number of Agricultural Families in Ogan Komering Ilir Regency of South Sumatra Province Under Incomplete Sampling Frame. Repositories-Dept. of Statistics, IPB University, 792–799.

Hanike, Y., Sadik, K., & Kurnia, A. (2016). Post-stratification sampling in small area estimation (SAE) model for unemployment rate estimation by Bayes approach. AIP Conference Proceedings, 1707, 080019.

Suhartini, T., Sadik, K., & Indahwati, I. (2016). Proporsi Kemiskinan di Kabupaten Bogor. Sosio Informa, 1(2).

Asfar, K. A., & Sadik, K. (2016). Optimum spatial weighted in small area estimation. Glob J Pure Appl Math, 12(5), 3977–3989.

Rahayu, L. P., Sadik, K., & others. (2016). Overdispersion study of poisson and zero-inflated poisson regression for some characteristics of the data on lamda, n, p. International Journal of Advances in Intelligent Informatics, 2(3), 140–148.

Suhartini, T., Sadik, K., & Indahwati. (2016). Small area estimation (SAE) model: Case study of poverty in West Java Province. AIP Conference Proceedings, 1707, 080016.

Angraini, Y., Kurnia, A., & Sadik, K. (2016). Kajian Kriteria Pemilihan Matriks Structur Korelasi pada Generalized Estimating Equation (GEE). In SEMASTAT 2016 (pp. 1014–1024).

Zainuddin, H. A., Notodiputro, K. A., & Sadik, K. (2015). A SIMULATION STUDY OF LOGARITHMIC TRANSFORMATION MODEL IN SPATIAL E MPIRICAL BEST LINEAR UNBIASED PREDICTION (SEBLUP) METHOD OF SMALL AREA ESTIMATION. Forum Statistika Dan Komputasi, 20.

Supriatin, F. E., Susetyo, B., & Sadik, K. (2015). EBLUP METHOD OF TIME SERIES AND CROSS-SECTION DATA FOR ESTIMATING EDUCATION INDEX IN DISTRICT PURWAKARTA. Forum Statistika Dan Komputasi, 20.

Sadik, K., Si, S., & Si, M. (2015). Statistika Terapan.

Hasanah, S. H., Sadik, K., & Afendi, F. M. (2015). COMPARISON OF METHOD CLASSIFICATION ARTIFICIAL NEURAL NETWORK BACK PROPAGATION, LOGISTIC REGRESSION, AND MULTIVARIATE ADAPTIVE REGRESSION SPLINES (MARS)(CASE STUDY DATA OF UNSECURED LOAN). ICCS-13, 477.

Utami, I. T., Sartono, B., & Sadik, K. (2014). Comparison of single and ensemble classifiers of support vector machine and classification tree. Journal of Mathematical Sciences and Applications, 2(2), 17–20.

Iqbal, T. A., Meranti, K. I., Sadik, K., Sumertajaya, I., & others. (2014). Pemodelan pengukuran luas panen padi nasional menggunakan Generalized Autoregressive Conditional Heteroscedastic model (GARCH).

Hajarisman, N., Khairi, A., & Sadik, K. (2013). Pendugaan angka kematian bayi dengan menggunakan Model Poisson Bayes berhirarki dua-level. MIMBAR: Jurnal Sosial Dan Pembangunan, 29(1), 49–56.

Rumiati, A. T., Notodiputro, K. A., Sadik, K., & Mangku, I. W. (2012). Empirical Bayesian Method for the Estimation of Literacy Rate at Sub-district Level Case Study: Sumenep District of East Java Province. IPTEK The Journal for Technology and Science, 23(1).

Diputra, T. F., Sadik, K., & Angraini, Y. (2012). PEMODELAN DATA PANEL SPASIAL DENGAN DIMENSI RUANG DAN WAKTU (Spatial Panel Data Modeling with Space and Time Dimensions). FORUM STATISTIKA DAN KOMPUTASI, 17.

Sadik, K., & Notodiputro, K. A. (2009). Hierarchical Bayes Estimation Using Time Series and Cross-sectional Data: A Case of Per-capita Expenditure in Indonesia. Conference of Small Area Estimation, 29.

Sadik, K. (2009). Metode Prediksi Tak-Bias Linear Terbaik Dan BayesBerhirarki Untuk Pendugaan Area Kecil Berdasarkan Model State Space.

Sadik, K. (2009). There have been two main topics developed by statisticians in a survey, ie sampling techniques and estimation methods. The current issues in estimation methods related to estimation of a particular domain having small size of samples or, in more extreme cases, there is no sample available for direct estimation. Sample survey data provide effective reliable estimators of totals and means for large area and domains. But it is recognized that the usual direct survey estimator performing statistics for a small area, have unacceptably large standard errors, due to the circumstance of small sample size in the area. The most commonly used models for this case, usually in small area estimation, are based on generalized linear mixed models. Some time happened that some surveys are carried out periodically so that the estimation could be improved by incorporating both the area and time random effects. In this paper we propose a state space model which accounts for the two random effects and is based on two equation, namely transition equation and measurement equation. Based on a evaluation criterion, the proposed hierarchical Bayes estimator turns out to be superior to both estimated best linear unbiased prediction (BLUP) and the direct survey estimator. The posterior variances which measure accuracy of the hierarchical Bayes estimates are always smaller than the corresponding variances of the BLUP and the direct survey estimates. FORUM STATISTIKA DAN KOMPUTASI, 14.

Saefuddin, A., Notodiputro, K. A., Alamudi, A., & Sadik, K. (2009). Statistika Dasar. Jakarta. Grasindo.

Sadik, K., & Noviyanita, W. (2008). Pendeteksian Pencilan Aditif Dan Inovatif Dalam Data Deret Waktu Melalui Metode Iteratif.