PENERAPAN MODEL LOG LINIER TERHADAP PERSEPSI MAHASISWA PADA SINETRON RELIGIUS : APPLICATION OF THE LINEAR LOG METHOD TO STUDENT PERCEPTION ON RELIGIOUS SINETRON

Azwar Habibi

doi:10.33019/fraction.v3i2.46

Authors

Azwar Habibi Institut Agama Islam Negeri Madura

DOI:

https://doi.org/10.33019/fraction.v3i2.46

Keywords:

Model Log Linier, Pernah Mengalami Kejadian, Jenis Kelamin, Memperdalam Ilmu Agama

Abstract

The purpose of this study is to find out which variables in the cell cause the contingency to be dependent in the case of student perceptions in religious soap operas. The data taken from this study are the results of a survey of IAIN Madura students with the variables having experienced events, gender and deepening religious knowledge. The data analysis method used is Linear Log Model. The conclusions in this study are as follows based on the estimated parameter values presented in the table above, it can be seen that male and female IAIN MADURA students believe that religious soap operas can deepen religious knowledge, and also male and female students believe that religious soap operas cannot deepen religious knowledge. Based on the Chi Square values presented in the table above, it can be seen that the opinion variable, deepening religious knowledge and having experienced an incident have no relationship or each variable is independent, so it cannot be continued to a linear logistic test.

Downloads

Download data is not yet available.

References

J. Andreas and D. Klein, “When and why are log-linear models self-normalizing ?,” pp. 244–249, 2015.

D. Budiati, Y. Wilandari, M. Jurusan Statistika FSM UNDIP, and S. Pengajar Jurusan Statistika, “Analisis Hubungan Antara Lama Studi, Jalur Masuk Dan Indeks Prestasi Kumulatif (Ipk) Menggunakan Model Log Linier (Studi Kasus: Lulusan Mahasiswa Fsm Undip Periode Wisuda Tahun 2012/2013),” J. Gaussian, vol. 3, no. 1, pp. 41–50, 2014, [Online]. Available: http://ejournal-s1.undip.ac.id/index.php/gaussian.

C. Chatfield, J. Zidek, B. Columbia, A. Pole, M. West, and J. Harrison, CHAPMAN & HALL / CRC Texts in Statistical Science Series Series Editors. 2002.

G. Fauziah and S. Sunendiari, “Estimasi Pseudo Poisson Maximum Likelihood untuk Mengatasi Masalah dalam Model Log-Linear pada Kasus Kusta di Jawa Barat Tahun 2018,” J. Ris. Stat., vol. 1, no. 1, pp. 57–62, 2021, doi: 10.29313/jrs.v1i1.147.

S. C. Gerritse, P. G. M. van der Heijden, and B. F. M. Bakker, “Sensitivity of population size estimation for violating parametric assumptions in log-linear models,” J. Off. Stat., vol. 31, no. 3, pp. 357–379, 2015, doi: 10.1515/JOS-2015-0022.

Z. Huang, “S e n s o r s & T r a n s d u c e r s An Improved Differential Evolution Algorithm Based on Statistical Log-linear Model,” vol. 159, no. 11, pp. 277–281, 2013.

Z. Huang and Y. Chen, “Log-linear model based behavior selection method for artificial fish swarm algorithm,” Comput. Intell. Neurosci., vol. 2015, 2015, doi: 10.1155/2015/685404.

D. A. N. Indeks and P. Kumulatif, “MODEL LOG LINEAR TIGA DIMENSI UNTUK MENGETAHUI HUBUNGAN ANTARA BEASISWA , JENIS MODEL LOG LINEAR TIGA DIMENSI UNTUK MENGETAHUI HUBUNGAN ANTARA BEASISWA , JENIS,” 2022.

M. Junczys-Dowmunt and R. Grundkiewicz, “Log-linear Combinations of Monolingual and Bilingual Neural Machine Translation Models for Automatic Post-Editing,” Proc. Annu. Meet. Assoc. Comput. Linguist., vol. 2, pp. 751–758, 2016, doi: 10.18653/v1/w16-2378.

R. E. Kewinay, “Aplikasi Analisis Log-Linier pada Pengaruh Sikap dan Motivais Mahasiswa terhadap Kedisiplinan,” J. Difer., vol. 2, no. 2, pp. 163–179, 2020.

Y. Wilandari, S. Sugito, and C. Silvia, “Analisis Kecelakaan Lalu Lintas Di Kota Semarang Menggunakan Model Log Linier,” Media Stat., vol. 9, no. 1, pp. 51–61, 2016, doi: 10.14710/medstat.9.1.51-61.

H. Wang and A. Rodríguez, “Identifying Pediatric Cancer Clusters in Florida Using Log-Linear Models and Generalized Lasso Penalties,” Stat. Public Policy, vol. 1, no. 1, pp. 86–96, 2014, doi: 10.1080/2330443X.2014.960120.

R. Kompresi, J. S. Sari, Y. Wilandari, and A. Hoyyi, “Pembentukan Model Log Linier Empat Dimensi (Studi Kasus : Rata-Rata Pengguna Jenis Bahan Bakar Minyak Berdasarkan Jenis Kendaraan, Rasio Kompresi Dan Kapasitas Mesin),” None, vol. 5, no. 3, pp. 437–446, 2016.

T. H. Nguyen and R. Grishman, “Combining Neural Networks and Log-linear Models to Improve Relation Extraction,” 2015, [Online]. Available: http://arxiv.org/abs/1511.05926.

A. Wulandari and A. Babelia, “Penerapan Metode Regresi Linear Berganda Terhadap Application of Multiple Linear Regression Method on Daily Income and,” Fraction, vol. 1, no. 1, pp. 1–9, 2021.

Y. Yang and J. Eisenstein, “A log-linear model for unsupervised text normalization,” EMNLP 2013 - 2013 Conf. Empir. Methods Nat. Lang. Process. Proc. Conf., no. October, pp. 61–72, 2013.

T. A. Nurman, “Analisis data kategori dengan log linier menggunakan prinsip hirarki (studi kasus jumlah kecelakaan lalu lintas di kota makassar tahun 2011).,” J. Teknosains, vol. 7, no. 1, pp. 99–110, 2013.

F. Petitjean, G. I. Webb, and A. E. Nicholson, “Scaling log-linear analysis to high-dimensional data,” Proc. - IEEE Int. Conf. Data Mining, ICDM, no. March 2014, pp. 597–606, 2013, doi: 10.1109/ICDM.2013.17.

R. Spring and A. Shrivastava, “A New Unbiased and Efficient Class of LSH-Based Samplers and Estimators for Partition Function Computation in Log-Linear Models,” 2017, [Online]. Available: http://arxiv.org/abs/1703.05160.

A. Törner et al., “The underreporting of hepatocellular carcinoma to the cancer register and a log-linear model to estimate a more correct incidence,” Hepatology, vol. 65, no. 3, pp. 885–892, 2017, doi: 10.1002/hep.28775.