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b,c,d KESIMPULAN DAN SARAN

Omnibus Tests of Model Coefficients Chi-square df Sig. Step 1 Step 30.657 6 .000 Block 30.657 6 .000 Model 30.657 6 .000 Model Summary Step -2 Log likelihood Cox Snell R Square Nagelkerke R Square 1 9.046 a .264 .806 a. Estimation terminated at iteration number 20 because maximum iterations has been reached. Final solution cannot be found. Hosmer and Lemeshow Test Step Chi-square df Sig. 1 .534 6 .997 Contingency Table for Hosmer and Lemeshow Test Rekurensi = Kambuh Rekurensi = Tidak Kambuh Total Observed Expected Observed Expected Step 1 1 5 4.584 5 5.416 10 2 .416 4 3.584 4 3 .000 13 13.000 13 4 .000 10 10.000 10 5 .000 10 10.000 10 6 .000 10 10.000 10 7 .000 2 2.000 2 8 .000 41 41.000 41 Classification Table a Observed Predicted Rekurensi Percentage Correct Kambuh Tidak Kambuh Step 1 Rekurensi Kambuh 3 2 60.0 Tidak Kambuh 1 94 98.9 Overall Percentage 97.0 a. The cut value is ,500 commit to user Variables in the Equation B S.E. Wald df Sig. ExpB Step 1 a Umur 1.802 3.095E3 .000 1 .992 6.478 Severity -.104 1.384 .006 1 .940 .901 Kepatuahan 5.032 1.565 10.341 1 .001 153.311 Kemudahan 2.597 2.527 1.056 1 .304 13.419 Pendidikan -1.367 1.735 .621 1 .431 .255 Ekonomi 2.607 3.030 .740 1 .390 13.560 Constant -2.834 2.515 1.270 1 .260 .059 a. Variables entered on step 1: Umur, Severity, Kepatuahan, Kemudahan, Pendidikan, Ekonomi. Block 0: Beginning Block Iteration History

a,b,c

Iteration -2 Log likelihood Coefficients Constant Step 0 1 48.596 1.800 2 40.516 2.555 3 39.720 2.885 4 39.703 2.943 5 39.703 2.944 6 39.703 2.944 a. Constant is included in the model. b. Initial -2 Log Likelihood: 39,703 c. Estimation terminated at iteration number 6 because parameter estimates changed by less than ,001. Classification Table

a,b

Observed Predicted Rekurensi Percentage Correct Kambuh Tidak Kambuh Step 0 Rekurensi Kambuh 5 .0 Tidak Kambuh 95 100.0 Overall Percentage 95.0 a. Constant is included in the model. b. The cut value is ,500 Variables in the Equation B S.E. Wald df Sig. ExpB commit to user Logistic Regression Case Processing Summary Unweighted Cases a N Percent Selected Cases Included in Analysis 100 100.0 Missing Cases .0 Total 100 100.0 Unselected Cases .0 Total 100 100.0 a. If weight is in effect, see classification table for the total number of cases. Dependent Variable Encoding Original Value Internal Value Tidak Patuh Patuh 1 Block 1: Method = Enter Iteration History

a,b,c,d

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