Finding - LIST OF APPENDICES - THE USE OF PICTURE WORD INDUCTIVE MODEL (PWIM) IN TEACHING WRIT

LIST OF APPENDICES

4.1 Finding

32 CHAPTER IV

FINDING AND DISCUSSION

This chapter consists of two section, namely the finding of the research and the discussion of the research. It provides information about description of the findings including the data collected, data process and the result as well.

Table 4.1 the students score in pre-test

No Students Aspects (X₁) (X₂) Classification

C O V L M

1 Student 1 17 13 14 17 3 64 4096 Fair

2 Student 2 17 10 8 10 2 47 2209 Poor

3 Student 3 17 13 14 11 3 58 3364 Fair

4 Student 4 17 10 8 10 2 47 2209 Poor

5 Student 5 22 17 17 21 3 80 6400 Very good

6 Student 6 14 9 7 8 2 40 1600 Poor

7 Student 7 14 9 9 8 2 42 1764 Poor

8 Student 8 14 9 9 8 2 14 196 Very poor

9 Student 9 14 9 9 8 2 42 1764 Poor

10 Student 10 17 10 10 9 2 48 2304 Poor

11 Student 11 14 9 9 8 2 42 1764 Poor

12 Student 12 14 9 9 8 2 42 1764 Poor

13 Student 13 17 10 11 10 3 51 2601 Poor

14 Student 14 21 10 11 11 3 56 3136 Fair

15 Student 15 14 9 9 8 2 42 1764 Poor

16 Student 16 14 9 9 8 2 42 1764 Poor

17 Student 17 14 9 8 7 2 40 1600 Poor

18 Student 18 14 9 8 7 2 40 1600 Poor

19 Student 19 14 9 8 7 2 40 1600 Poor

20 Student 20 15 9 8 7 2 41 1681 Poor

21 Student 21 13 7 7 6 2 35 1225 Very poor

No Students Aspects

(X₁) (X2) Classification C O V L M

22 Student 22 13 7 7 6 2 35 1225 Very poor

23 Student 23 13 7 7 6 2 35 1225 Very poor

24 Student 24 13 7 7 6 2 35 1225 Very poor

25 Student 25 13 7 8 7 2 37 1369 Very poor

26 Student 26 13 7 7 7 2 36 1296 Very poor

27 Student 27 13 7 7 7 2 36 1296 Very poor

28 Student 28 15 9 9 9 2 44 1936 Poor

29 Student 29 13 7 7 7 2 36 1296 Very poor

30 Student 30 16 10 13 17 3 59 3481 Fair

31 Student 31 13 7 7 7 2 36 1296 Very poor

32 Student 32 13 7 7 7 2 36 1296 Very poor

33 Student 33 13 7 7 7 2 36 1296 Very poor

34 Student 34 13 7 7 7 2 36 1296 Very poor

35 Student 35 13 7 7 7 2 36 1296 Very poor

36 Student 35 13 7 7 7 2 36 1296 Very poor

37 Student 37 13 7 7 7 2 36 1296 Very poor

38 Student 38 13 7 7 7 2 36 1296 Very poor

39 Student 39 13 7 7 7 2 36 1296 Very poor

40 Student 40 13 7 7 7 2 36 1296 Very poor

Total 1697 73714

( J. B Heaton, Educational Research: Competencies for analysis and aplication.)

Where : C = Content O = Organization V = Vocabulary L = Language use M = Mechanics

The table above show the students score of pre-test. The lowest score of the pre-test was 35 and the higher score in pre-test was 80. The students score of pre-test was classified into some criteria and percentage and it showed the classification of

the students’ score which has been through the process of the giving value based on the criteria of writing.

Based on classification, it showed the result of writing score before implementation of treatment by using PWIM activities. 18 students in very poor classification, 17 students in poor classification, 4 students in fair classification and 1 student I very good classification. Total score in pre-test was 1666. It could be seen that most of the students of Administrasi Perkantoran has very low writing descriptive text, because most of the students gained very poor.

Firstly, the researcher calculated the mean score of pre-test : 𝑋 = ∑ 𝑥₁

𝑛 𝑋 = 1666

40 = 41,65

That is the result of the pre-test that has been done by the students before implementing the PWIM. It showed that students writing descriptive text still low. It can be seen by the percentage that most of students got very poor. There are 45%

students got score under 39, where the lower score is 35. Whether just 2,5% student god very good classification, but the value showed that most of students in low classification.

Secondly, the researcher calculatedthe standard deviation of the students pre- test :

1. standard deviation of pre-test

SD = √∑ X¹−^{(∑ X)}²

N − 1

SD = √73714 −⁽¹⁶⁶⁶⁾²

40 − 1

SD = √73714 −^2775556

SD = √73714 − 69388,9 39

SD = √4325,1 39 SD = √110,9 SD = 𝟏𝟎, 𝟓𝟑

After determining the mean score of pre-test (41,65) and standard deviation of pre-test (10,53) it could be seen that the students writing descriptive text is still low because most of the in a very poor category.

4.1.2 The students’ csore in post-test

Table 4.2 The sdutents’ post-test result No Students

Aspects

(X1) (X2) Classification

C O V L M

1 Student 1 27 18 18 22 4 89 7921 Very good

2 Student 2

23 17 17 20 4 81 6561 Very good

3 Student 3

21 14 14 17 3 69 4761 Good

4 Student 4

22 17 17 20 3 81 6561 Very good

5 Student 5

27 18 18 21 4 87 7569 Very good

6 Student 6

15 9 9 8 3 43 1849 Poor

7 Student 7

23 16 17 18 3 77 5929 Good

8 Student 8

22 16 16 18 3 75 5625 Good

9 Student 9

25 17 17 21 4 84 7056 Very good

10 Student 10

26 17 17 21 4 85 7225 Very good

11 Student 11

15 10 10 8 2 45 2025 Poor

12 Student 12

21 13 14 15 3 66 4356 Good

13 Student 13

17 10 13 11 3 54 2916 Fair

14 Student 14

21 13 13 17 3 67 4489 Good

15 Student 15

21 13 14 17 2 67 4489 Good

16 Student 16

17 13 10 11 2 53 2809 Poor

17 Student 17

17 10 10 11 2 50 2500 Poor

18 Student 18

18 10 14 11 3 56 3136 Fair

19 Student 19

26 17 18 21 3 85 7225 Very good

20 Student 20

25 15 15 18 3 76 5776 Good

No Students Aspects

(X1) (X₂) classification C O V L M

21 Student 21

26 17 18 21 3 85 7225 Very good

22 Student 22

26 17 18 20 3 84 7056 Very good

23 Student 23

26 17 18 21 3 85 7225 Very good

24 Student 24

25 17 17 20 3 82 6724 Very good

25 Student 25

13 7 8 9 3 40 1600 Poor

26 Student 26

14 10 10 11 2 47 2209 Poor

27 Student 27

15 9 8 8 2 42 1764 Poor

28 Student 28

22 14 18 18 2 74 5476 Good

29 Student 29

17 7 10 10 2 46 2116 Fair

30 Student 30

17 10 13 10 2 52 2704 Fair

31 Student 31

13 7 8 7 2 37 1369 Very poor

32 Student 32

20 14 14 18 2 68 4624 Good

33 Student 33

10 10 10 10 2 42 1764 Poor

34 Student 34

17 10 10 11 2 50 2500 Poor

35 Student 35

22 14 14 18 2 70 4900 Good

36 Student 35

20 13 13 17 2 65 4225 Fair

37 Student 37

20 13 13 17 2 65 4225 Fair

38 Student 38

20 13 13 17 2 65 4225 Fair

39 Student 39

19 12 13 16 2 62 3844 Fair

40 Student 40

20 14 14 17 3 68 4624 Good

Total 2619 181177

( J. B Heaton, Educational Research: Competencies for analysis and aplication.) Where :

C = Content O = Organization V = Vocabulary L = Language use M = Mechanics

The table above show the students score of post-test. The lowest score of the post-test was 37 and the higher score in post-test was 89. The students score of post- test was classified into some criteria and percentage. It showed the classification of the students’ score which has been through the process of the giving value based on the criteria of writing. There was an improvement of students’ score after giving treatment by using PWIM activities. Although, there was a student still got low score in very poor classification because he didn’t focus to did his test. There were 11 students in very good classification, 11 students in good classification, 5 students in fair classification, 12 students in poor classification and one student in very poor classification. It means that students had improvement in writing descriptive text by using PWIM.

Firstly, to get the mean score of the post-test, used formula:

𝑋 = ∑ 𝑥₁ 𝑛

𝑋 = 2619

40 = 65,475

The score of mean in post0test is 65,475. It shows that the mean score obtained by the students was very different. The result of post-test was higher than the pre-test. It means that after giving treatment by using Picture Word Inductive Model the students’ score obtained improved.

Secondly, to get the standard deviation of post-test, used formula:

SD = √∑ X¹−^{(∑ X)}²

N − 1

SD = √73714 −⁽¹⁶⁶⁶⁾²

40 − 1

SD = √73714 −^2775556

SD = √73714 − 69388,9 39

SD = √4325,1 39 SD = √110,9 SD = 𝟏𝟎, 𝟓𝟑

After determining the mean score and the standard deviation of post-test it could be seen that in post-test, there are an improvement of students writing descriptive text compered of the result of post-test.

4.1.3 The result of pre-test and post-test

Table 4.3 the mean score and standard deviation of pre-test and post-test

Test Mean Standard Deviation

Pre-test 42,425 10,53

Post-test 66,775 15,76

In the table above indicates that there was an improvement while doing pre- test up to post-test. In the pre-test had mean score 42,425 and standard deviation score 10,53 Compared the post-test had mean score increased become 66,775 And standard deviation was 15,76.

As the result at this item was the mean score of the post-test was greater than the mean score in pre-test. It means that the students’ writing descriptive text had improve after doing the learning process that used in the class.

4.1.4 the rate percentage of frequency of the pre-test and post-test

Table 4.4 The rate percentage of the frequency of the pre-test and post-test

No Classification Score

Frequency Percentage Pre-test Post-test Pre-test Post-test

1 Excelent 100-86 1 11 2,5% 27,5%

2 Good 85-71 - 11 - 27,5%

3 Fair 70-56 4 5 10% 12,5%

4 Poor 55-41 17 12 42,5% 30%

5 Very Poor ≤40 18 1 45% 2,5%

Total 40 40 100% 100%

Based on the table above, showed that the rate percentage of the students score of the students writing descriptive text before treatment was very poor because most of them in the very poor classification. In the post-test, it showed that the percentage of students’ score achievement writing descriptive text after treatment was varied. According to the data in pre-test, it can be seen that before giving the treatment most of students in pre-test were very poor classification while in post-test most of students get very good and good classification. It means that the students writing descriptive text was improved.

4.2 Data Analysis of Pre-test and Post-test

This part discusses the result of data analysis writing by using Picture Word Inductive Model is able to improve students’ writing descriptive text at the second grade of Administrasi Perkantoran of SMKN 4 Pinrang.

4.2.1 T-test Value

The folowwing is a table to find out the difference of the means score between pre-test and post-test.

Table 4.5 The worksheet of the calculation of the score on pre-test and post- test on the students’ writing ability in descriptive text

No X₁ X₂ (X₁)² (X₂)² D(X₂-X₁) D(X₂-X₁)²

1 64 89 4096 7921 25 625

2 47 81 2209 6561 34 1156

3 58 69 3364 4761 11 121

4 47 81 2209 6561 34 1156

5 80 87 6400 7569 7 49

6 40 43 1600 1849 3 9

No X1 X2 (X1)² (X2)² D(X2-X1) D(X2-X1)²

7 42 77 1764 5929 35 1225

8 14 75 196 5625 61 3721

9 42 84 1764 7056 42 1764

10 48 85 2304 7225 37 1369

11 42 45 1764 2025 3 9

12 42 66 1764 4356 24 576

13 51 54 2601 2916 3 9

14 56 67 3136 4489 11 121

15 42 67 1764 4489 25 625

16 42 53 1764 2809 11 121

17 40 50 1600 2500 10 100

18 40 56 1600 3136 16 256

19 40 85 1600 7225 45 2025

20 41 76 1681 5776 35 1225

21 35 85 1225 7225 50 2500

22 35 84 1225 7056 49 2401

23 35 85 1225 7225 50 2500

24 35 82 1225 6724 47 2209

25 37 40 1369 1600 3 9

26 36 47 1296 2209 11 121

27 36 42 1296 1764 6 36

28 44 74 1936 5476 30 900

29 36 46 1296 2116 10 100

No X₁ X₂ (X₁)² (X₂)² D(X₂-X₁) D(X₂-X₁)²

30 59 52 3481 2704 -7 49

31 36 37 1296 1369 1 1

32 36 68 1296 4624 32 1024

33 36 42 1296 1764 6 36

34 36 50 1296 2500 14 196

35 36 70 1296 4900 34 1156

36 36 65 1296 4225 29 841

37 36 65 1296 4225 29 841

38 36 65 1296 4225 29 841

39 36 62 1296 3844 26 676

40 36 68 1296 4624 32 1024

Total 1666 2619 73714 181177 953 33723

In the other to see the students’ score, the following is t-test was statistically applied:

To find out the mean score difference (d) used formula as follow:

d = ^∑𝐷

𝑁

d = ⁹⁵³

d = 23,825 so, th mean score of difference (d) = 23,825 The calculatin the t-test value:

t = D

√

^{∑ D}²⁻^{(∑ D)2}^N

N(N−1)

t = 23,835

√³³⁷²³⁻⁹⁵³²⁴⁰

40(40−1)

t = 23,835

√³³⁷²³⁻

908209 40 40(39)

t = 23,835

√33723−22705,225 1560

t = 23,835

√^11017,775

1560

t =23,835

√7,062 t =23,835 2,657 t = 𝟖, 𝟗𝟕𝟎

So, the t-test value 8,970

Table 4.6 The test of significance

Variable T-test T table value

Pre-tes and Post-test 8,970 2.023

Based on the data above the T-test was greater than the T-table value. It indicated that there was a significant different between the result students’ pre-test and post-test. From the data analysis in pre-test and post-test, we can conclude that

Picture Word Inductive Model is effective to increase the students’ writing descriptive text.

4.2.2 Hypotesis Testing Df = N-1

= 40-1 = 39

For the level, significant (p) and df = 39, and the value of T-table is 2,023, while the value T-test is 8,870. It means that the t-test value is greater that T-table (8,970 ≥2,023). Thus, it can be concluded that the students writing descriptive text is significant better after getting treatment. So, the null hypothesis (H0) is refused and the alternative hypothesis (H1) is accepted. It has been found that there is a improvement of Picture Word Inductive Model in students writing descriptive text.

Dalam dokumen THE USE OF PICTURE WORD INDUCTIVE MODEL (PWIM) IN TEACHING WRITING DESCRIPTIVE TEXT AT THE SECOND GRADE OF ADMINISTRASI PERKANTORAN OF (Halaman 47-63)