Publikasi Ilmiah

Riset dan Kolaborasi

Daftar Publikasi di Scopus

2016

1.Astuti, A. B., Iriawan, N., Irhamah, Kuswanto, H., & Sasiarini, L. (2016). Blood sugar levels of diabetes mellitus patients modeling with bayesian mixture model averaging. Global Journal of Pure and Applied Mathematics, 12(4), 3143-3158.

2.Lefrandt, L., Sulistio, H., Wicaksono, A., Djakfar, L., & Otok, B. W. (2016). The combination of importance performance analysis and structural equation model for modeling pedestrian satisfaction in manado. Journal of Theoretical and Applied Information Technology, 90(2), 158-166.

3.Prawiro Kusumo, R., & Iriawan, N. (2016). On the modeling of tourists visit to tourist attraction in surabaya using neural network. Journal of Theoretical and Applied Information Technology, 93(2), 433-440.

4.Rahman, N. H. A., Lee, M. H., Suhartono, & Latif, M. T. (2016). Evaluation performance of time series approach for forecasting air pollution index in johor, malaysia. Sains Malaysiana, 45(11), 1625-1633.

5.Rezeki, S., Suyadi, Suhartono, Ramli, Z., & Razman, M. R. (2016). Implementation of multiple functions intervention model to evaluate the impact of the crisis and gambling prohibition policy on tourism in batam. Information (Japan), 19(7A), 2447-2458.

6.Ruliana, Budiantara, I. N., Otok, B. W., & Wibowo, W. (2016). Simultaneous hypothesis testing of spline truncated model in nonlinear structural equation modeling (SEM). Journal of Theoretical and Applied Information Technology, 89(2), 372-380.

7.Ruliana, Nyoman Budiantara, I., Otok, B. W., & Wibowo, W. (2016). Partially statistical test of parameter spline truncated in nonlinear structural equation modeling (SEM). Global Journal of Pure and Applied Mathematics, 12(4), 3775-3786.

8. Santoso, R., Subanar, Rosadi, D., & Suhartono. (2016). The development of an optimization procedure in WRBNN for time series forecasting. Electronic Journal of Applied Statistical Analysis, 9(1), 198-212. doi:10.1285/ i20705948v9n1-p198

9.Soehardjoepri, Iriawan, N., Su, B. S., & Irhamah. (2016). On the identification of the structural pattern of terms occurrence in a document using bayesian network. Journal of Theoretical and Applied Information Technology, 92(2), 253-264.

10.Sugiarti, H., Purhadi, Sutikno, & Purnami, S. W. (2016). Parameter estimation of geographically weighted multivariate t regression model. Journal of Theoretical and Applied Information Technology, 92(1), 45-51.

11.Suyitno, Purhadi, Sutikno, & Irhamah. (2016). Parameter estimation of geographically weighted trivariate weibull regression model. Applied Mathematical Sciences, 10(17-20), 861-878. doi:10.12988/ams.2016.6129

12.Triyanto, Purhadi, Otok, B. W., & Purnami, S. W. (2016). Hypothesis testing of geographically weighted multivariate poisson regression. Far East Journal of Mathematical Sciences, 100(5), 747-762. doi:10.17654/MS100050747

13.Alkindi, Mashuri, M., & Prastyo, D. D. (2016). T2 hotelling fuzzy and W2 control chart with application to wheat flour production process. The AIP Conference Proceedings, 1746, doi:10.1063/1. 4953977

14.Ampulembang, A. P., Otok, B. W., Rumiati, A. T., & Budiasih. (2016). Simulation study for biresponses nonparametric regression model using MARS. the AIP Conference Proceedings, 1707.

15.Apriliadara, M., Suhartono, & Prastyo, D. D. (2016). VARI-X model for currency inflow and outflow forecasting with eid fitr effect in indonesia. The AIP Conference Proceedings, 1746, doi: 10.1063/1.4953966

16.Choir, A. S., Prasetyo, R. B., Ulama, B. S. S., Iriawan, N., Fitriasari, K., & Dokhi, M. (2016). Parameter estimation of general regression neural network using bayesian approach. Paper presented at the AIP Conference Proceedings, 1707, doi:10.1063/1.4940858

17.Laili, M., Otok, B. W., & Ratnasari, V. (2016). Hierarchical linear models on abdominal circumference research data using generalized least square. Paper presented at the AIP Conference Proceedings, 1746.

18.Pane, R., Otok, B. W., Zain, I., & Budiantara, I. N. (2016). Bootstrap inference longitudinal semiparametric regression model. Paper presented at the AIP Conference Proceedings, 1707, doi: 10.1063/1.4940867

19.Purnami, S. W., Inayati, K. D., Sari, N. W. W., Chosuvivatwong, V., & Sriplung, H. (2016). Survival analysis of cervical cancer using stratified cox regression. Paper presented at the AIP Conference Proceedings, 1723, doi:10.1063/1.4945076

20.Purnami, S. W., Khasanah, P. M., Sumartini, S. H., Chosuvivatwong, V., & Sriplung, H. (2016). Cervical cancer survival prediction using hybrid of SMOTE, CART and smooth support vector machine. Paper presented at the AIP Conference Proceedings, 1723, doi:10.1063/1.4945075

21.Rismal, Budiantara, I. N., & Prastyo, D. D. (2016). Mixture model of spline truncated and kernel in multivariable nonparametric regression. Paper presented at the AIP Conference Proceedings, 1739, doi:10.1063/1. 4952565

22.Rodliyah, M., Otok, B. W., & Wibowo, W. (2016). Path, centroid, and factor scheme for modeling the remuneration of educational staff in ITS with partial least square (PLS). Paper presented at the AIP Conference Proceedings, 1746, doi:10.1063/1.4953967

23.Santoso, R., Subanar, Rosadi, D., & Suhartono. (2016). Multiresolution radial basis model for nonlinear time series prediction. Paper presented at the AIP Conference Proceedings, 1755 doi:10.1063/1.4958542

24.Wibawati, Mashuri, M., Purhadi, & Irhamah. (2016). Fuzzy multinomial control chart and its application. Paper presented at the AIP Conference Proceedings, 1718, doi:10.1063/1.4943351

25.Zahrati, Z., Fithriasari, K., & Irhamah. (2016). Multi-output neural network for the temperature forecasting in semarang. Paper presented at the AIP Conference Proceedings, 1746, doi:10.1063/1.4953965

2015

2015

1.Ampulembang, A. P., Otok, B. W., Rumiati, A. T., & Budiasih. (2015). Bi-responses nonparametric regression model using MARS and its properties. Applied Mathematical Sciences, 9(29-32), 1417-1427.

2.Astuti, A. B., Iriawan, N., Irhamah, & Kuswanto, H. (2015). An algorithm for determining the number of mixture components on the bayesian mixture model averaging for microarray data. Journal of Mathematics and Statistics, 11(2), 45-51. doi:10.3844/jmssp.2015.45.51

3.Budiantara, I. N., Ratnasari, V., Ratna, M., & Zain, I. (2015). The combination of spline and kernel estimator for nonparametric regression and its properties. Applied Mathematical Sciences, 9(122), 6083-6094. doi:10.12988/ams. 2015.58517

4.Diana, R., Nyoman Budiantara, I., Purhadi, & Darmesto, S. (2015). Smoothing spline in semiparametric additive regression model with bayesian approach. Journal of Mathematics and Statistics, 9(3), 161-168. doi:10.3844/ jmssp.2013.161.168

5.Fernandes, A. A. R., Budiantara, I. N., Otok, B. W., & Suhartono. (2015). Spline estimator for bi-responses and multi-predictors nonparametric regression model in case of longitudinal data. Journal of Mathematics and Statistics, 11(2), 61-69. doi:10.3844/jmssp.2015.61.69

6.Marty, R., Fortin, V., Kuswanto, H., Favre, A. -., & Parent, E. (2015). Combining the bayesian processor of output with bayesian model averaging for reliable ensemble forecasting. Journal of the Royal Statistical Society. Series C: Applied Statistics, 64(1), 75-92. doi:10.1111/rssc.12062

7.Purhadi, Dewi, Y. S., & Amaliana, L. (2015). Zero inflated poisson and geographically weighted zero-inflated poisson regression model: Application to elephantiasis (filariasis) counts data. Journal of Mathematics and Statistics, 11(2), 52-60. doi:10.3844/jmssp.2015.52.60

8.Ruliana, Nyoman Budiantara, I., Otok, B. W., & Wibowo, W. (2015). Parameter estimation of nonlinear structural model SEM using spline approach. Applied Mathematical Sciences, 9(149-150), 7439-7451.

9.Sa’Adah, U., Subanar, Guritno, S., & Suhartono. (2015). Wavelet neural network model selection for nonlinear-seasonal time series forecasting. Global Journal of Pure & Applied Mathematics, 11(1), 25-36.

10.Soehardjoepri, Iriawan, N., Su, B. S., & Irhamah. (2015). Identifying text document pattern for two terms appearances via latent semantic analysis (LSA) method and term distance between two documents. Journal of Theoretical and Applied Information Technology, 79(2), 322-329.

11.Triyanto, Purhadi, Otok, B. W., & Purnami, S. W. (2015). Parameter estimation of geographically weigthed multivariate poisson regression. Applied Mathematical Sciences, 9(81-84), 4081-4093. doi:10.12988/ams. 2015.54329

12.Wayan Sudiarsa, I., Nyoman Budiantara, I., Suhartono, S., & Purnami, S. W. (2015). Combined estimator fourier series and spline truncated in multivariable nonparametric regression. Applied Mathematical Sciences, 9(97-100), 4997-5010. doi:10.12988/ams.2015.55394

13.Ahmad, I. S., Setiawan, Suhartono, & Masun, N. H. (2015). Forecasting of monthly inflow and outflow currency using time series regression and ARIMAX: The idul fitri effect. Paper presented at the AIP Conference Proceedings, 1691, doi:10.1063/1.4937084

14.Kuswanto, H., Andari, S., & Permatasari, E. O. (2015). Identification of extreme events in climate data from multiple sites. Paper presented at the Procedia Engineering, 125 304-310. doi:10.1016/j.proeng.2015.11.067

15.Kuswanto, H., Asfihani, A., Sarumaha, Y., & Ohwada, H. (2015). Logistic regression ensemble for predicting customer defection with very large sample size. Paper presented at the Procedia Computer Science, 72 86-93. doi:10.1016/j.procs.2015.12.108

16.Purnami, S. W., Andari, S., & Pertiwi, Y. D. (2015). High-dimensional data classification based on smooth support vector machines. The Procedia Computer Science, 72 477-484. doi:10.1016/j.procs.2015.12.129.

17.Sain, H., & Purnami, S. W. (2015). Combine sampling support vector machine for imbalanced data classification. Paper presented at the Procedia Computer Science, 72, 59-66. doi:10.1016/j.procs.2015.12.105

18.Setiawan, Suhartono, Ahmad, I. S., & Rahmawati, N. I. (2015). Configuring calendar variation based on time series regression method for forecasting of monthly currency inflow and outflow in central java. Paper presented at the AIP Conference Proceedings, 1691 doi:10.1063/1.4937106

19.Suharsono, A., Suhartono, Masyitha, A., & Anuravega, A. (2015). Time series regression and ARIMAX for forecasting currency flow at bank indonesia in sulawesi region. Paper presented at the AIP Conference Proceedings, 1691, doi:10.1063/1.4937107

20.Suhartono, Lee, M. H., & Prastyo, D. D. (2015). Two levels ARIMAX and regression models for forecasting time series data with calendar variation effects. Paper presented at the AIP Conference Proceedings, 1691, doi:10.1063/1.4937108

21.Wibowo, W., Haryatmi, S., & Budiantara, I. N. (2015). Simulation study for model performance of multiresponse semiparametric regression. Paper presented at the AIP Conference Proceedings, 1691 doi:10.1063/1. 4937111

22.Winahju, W. S., Mukarromah, A., & Putri, S. (2015). Modeling both of the number of pausibacillary and multibacillary leprosy patients by using bivariate poisson regression. Paper presented at the AIP Conference Proceedings, 1651 147-152. doi:10.1063/1.4914446.