Research Finding RESEARCH FINDING AND INTERPRETATION

17 Student 17 60 18 Student 18 80 19 Student 19 78 20 Student 20 80 21 Student 21 88 22 Student 22 70 23 Student 23 85 24 Student 24 76 25 Student 25 50 26 Student 26 68 27 Student 27 60 28 Student 28 80 29 Student 29 75 30 Student 30 76 31 Student 31 85 32 Student 32 80 33 Student 33 60 34 Student 34 72 35 Student 35 82 36 Student 36 85 37 Student 37 85 38 Student 38 82 39 Student 39 85 40 Student 40 82 2. The Analysis of Data The next steps after scoring each variable, the write calculate the data using t-test formula. To gain the formula, the writer divided the data into two according to their attendance in English language course. Table 4.3 List of Students’ score classification in English language course attendance No 70 Y1X1 70 Y2X2 Y1 2 X1 2 Y2 2 X2 2 1 88 82 7744 6724 2 70 76 4900 5776 3 85 80 7225 6400 4 76 66 5776 4356 5 50 58 2500 3364 6 68 56 4624 3136 7 60 62 3600 3844 8 80 55 6400 3025 9 75 60 5625 3600 10 76 62 5776 3844 11 85 75 7225 5625 12 80 65 6400 4225 13 60 52 3600 2704 14 72 68 5184 4624 15 82 60 6724 3600 16 85 65 7225 4225 17 85 60 7225 3600 18 82 80 6724 6400 19 85 78 7225 6084 20 82 80 6724 6400 Total 1526 1340 118426 91556 From the table above we can get some number to gain the formula of t-est. t ̅ ̅ √ Before we calculated the t formula, we have to find the SS score, to find the score of SS, we used the formula as follow; SS 1 ∑ ∑ or SS 2 ∑ ∑ Firstly we find the SS 1 according to formula as follow. SS 1 ∑ ∑ = 118426 – 86433,8 = 1992.2 After that we find the SS 2 according to formula as follow. SS 2 ∑ ∑ = 915566 – 89780 = 1776 And now we have everything to fill out on the formula to find the t-score. After that we substitute the number for each symbol of the formula. t √ √ √ √ From the result of the calculation, indicates that the result of t-score is 2.952 and after that we calculate the degree of freedom, the formula as follow: df = N – 2 = 40 – 2 = 38 After the calculation of the t-formula and knowing the degrees of freedom then we compared it with t-table with the degrees of freedom 38, the significance level that used is in the level 1 and 5 . And the score of the significance level in 1 and 5 is as follow: - Significant level 1 = 2.423 - Significant level 5 = 1.684 So that, the result of t-table in 1 2.423, can be seen that t-result t-table = 2.952 2.423 and in 5 scale is 1.684, then in this scale t-result t-table = 2.952 1.684. We know that t is higher than t-table in both level 1 and 5. After we know the difference between the students who always attend the English language course and the students who rarely attend the course then the writer continue to know is there any correlation between English language course attendance and students’ achievement in English language learning in the classroom. And the correlation of them is presented on the table as follow: Table 4.4 Students’ Correlation Score No Kehadiran Nilai XY X 2 Y 2 X S.2 Y 1 63 82 5166 3969 6724 2 13 76 988 169 5776 3 38 80 3040 1444 6400 4 38 66 2508 1444 4356 5 44 58 2552 1936 3364 6 44 56 2464 1936 3136 7 50 62 3100 2500 3844 8 50 55 2750 2500 3025 9 56 60 3360 3136 3600 10 56 62 3472 3136 3844 11 56 75 4200 3136 5625 12 56 65 3640 3136 4225 13 63 52 3276 3969 2704 14 63 68 4284 3969 4624 15 63 60 3780 3969 3600 16 63 65 4095 3969 4225 17 63 60 3780 3969 3600 18 69 80 5520 4761 6400 19 69 78 5382 4761 6084 20 69 80 5520 4761 6400 21 75 88 6600 5625 7744 22 75 70 5250 5625 4900 23 75 85 6375 5625 7225 24 75 76 5700 5625 5776 25 81 50 4050 6561 2500 26 81 68 5508 6561 4624 27 81 60 4860 6561 3600 28 81 80 6480 6561 6400 29 81 75 6075 6561 5625 30 88 76 6688 7744 5776 31 88 85 7480 7744 7225 32 88 80 7040 7744 6400 33 88 60 5280 7744 3600 34 88 72 6336 7744 5184 35 88 82 7216 7744 6724 36 88 85 7480 7744 7225 37 88 85 7480 7744 7225 38 88 82 7216 7744 6724 39 88 85 7480 7744 7225 40 88 82 7216 7744 6724 2759 2866 200687 203059 209982 r xy= ∑ ∑ ∑ √ ∑ ∑ ∑ ∑ √ ∑ √ √ √ r xy = 0,40 The next step is to know that the correlation between them is significant or not , therefore the result of calculation “r” is compared with the “r” table. Before we compared it, we should know “df” or “db” according to the rule df=N-nr. The respondents of this research are 40 and there are two variable. So, N=40, and nr=2. df = N-nr df =40-2 = 38 According to the calculation be fore, “df” of this research is 38. In “r-table” of significant level 1 and 5 of 38 are:  Significant level 1 = 0.413  Significant level 5 = 0.320 As we know from the calculation above, in the level of significance 5 the score of “r xy” or “r” is significant because “r xy r table ”, but in the level significance 1 is insignificant “r xy r table ”. The score of the r xy is 0.40 and r table in significance level 1 is 0.413, and the result is as 0.40 0.413, the r xy is lower than r table. And in the significance level 5 r table is 0.320, and the result is 0.40 0.320, mean the r xy is higher than r table. After doing the hypothesis testing, then we should know the coefficient of determination with r = 0,40, because in th e simple implementation before in r” product moment between 0,40 – 0,700, r xy = 0,40, means that between X variable and Y variable has enough correlation . To know how much the influences of X to Y, we used coefficient of determination formula with r = 0,40. KD = r xy 2 X 100 = 0,40 2 X 100 = 0,16 X 100 = 16 From the calculation of coefficient of determination abo ve, “KD” of this research is 16 , means that the attendance of the students in English language course only has the influence 16 to the students’ achievement in English language course, and there are 84 from the other aspect that can influence their achievement in English Language Learning.

B. The Test of Hypothesis

As it has been discussed in the chapter one, this research was conducted to know the difference between students who always attend English language course and the students who rarely attend the course, and also to know the correlation between students’ attendance in English course on the students’ achievement in English language learning. After calculating the data, the hypothesis was tested bas on the statistical hypothesis. The statistical hypothesis states: a. If t t-table it means the null hypothesis Ho is rejected and the alternative hypothesis Ha is accepted. Hence, there is significant difference between the students who always attend English language course and the students who rarely attend the course. b. If t t-table it means the null hypothesis Ho is accepted and the alternative hypothesis Ha is rejected. Hence, there is no significant difference between the students who always attend English language course and the students who rarely attend the course. And d in correlational student, the statistical hypothesis states: a. If r xy r table it means the null hypothesis Ho is rejected and the alternative Ha is accepted. Hence, there is a correlation between the students’ attendance in English language course and the students’ achievement in English language learning. b. If r xy r table it means the null hypothesis Ho is accepted and the alternative Ha is rejected. Hence, there is no correlation between the students’ attendance in English language course and the students’ achievement in English language learning.

C. The Interpretation of Data

From the result of the statistical calculation, it is obtained the t-result is 2.952; meanwhile the table of df 38 in significance 5 is 1.684 and in significance 1 is 2.423. It means t-result is higher than t-table, so the alternative hypothesis Ha is accepted and null hypothesis Ho is rejected. It shows that there is a significant difference between the students who always attend the English language course and the students who rarely attend the course. And in the next result of the correlation or the r xy result is 0.40. In the level of significance 1 we know that “r xy r table ” 0.40 0.413 means that “r xy ” is lower than “r table ”, thus means alternative hypothesis Ha is rejected and null hypothesis Ho is accepted. But in the level of significance 5 shows that “r xy r table ” 0.40 0.320, can be seen that “r” is higher than “r table ”, thus means alternative hypothesis Ha is accepted and null hypothesis Ho is rejected. So, it can be concluded that English Language Course is influenced the students’ achievement in English language learning. From the result of the calculation above shows that there is a correlation between students’ attendance in English language course and the students’ achievement in English language learning. And the score of that calculation is 0.40 , if we see to the interpretation to the “r” product moment, it’s between 0.40 – 0.70, mean that both variable has a positive correlation in medium correlation, and it’s in the level enough correlation. Based on the explanation about the analysis of the result about the analysis of the result above, it can be concluded that there is the significant different between the students who always come to English language course and the students who rarely co to English language course. it can be seen from the result above t-result = 2.952 t-table 1 = 2.423, 5 = 1.684. And the correlation between the students’ attendance in English language course and students’ achievement in English language learning is low, it can be seen from the result above r xy = 0.40 r table 1 = 0.413, r xy = 0.40 r table 5 = 0.320. 43

CHAPTER V CONCLUSION AND SUGGESTION

This chapter is about conclusion and suggestions. It is a core review of previous concern in this paper and some suggestions that might be useful for the students, teachers, language education principles and readers in general.

A. Conclusion

Based on the results of the research, it can be concluded thatthe calculation of “t-result” is higher than t-table 2.952 2.4231.684. Mean that between the two groups of students have significantly differences on their achievement. In this part the alternative hypothesis Ha is accepted and the null hypothesis Ho is rejected. In other hand, the result of “r” on the calculation in the significant level 1, “r xy ” is 0.40 and it is lower than the “r table ” 0.413. So that in this level, alternative hypothesis Ha is rejecteed and null hypothesis Ho is accepted. But in the significant level 5, showed that “r xy ” 0.40 is higher than “r table ” 0.320 and it has significant correlation. Thus, alternative hypothesis Ha is accepted and null hypothesis Ho is rejected. English language course is a good activity to improve the students’ achievement in English language learning in the classroom; it has a good impact for students in learning English. Even the correlation between two variables is in the level enough or medium; actually there is a significant difference achievement between students who always attend the course and the student who rarely attend the course. So that, both of the hypothesis of this research are accepted, because the result of the calculation showed the score is significantly higher in the level 5 .

B. Suggestions

Based on the calculation above, it could be deliver the suggestions go to: 1. Tutors in English language course should also cooperate with the teacher in the classroom. They should integrate the material between English language course material and classroom material. Also have to aware of the student who rarely attend the course. 2. English language course committee should make the independent flow chart material of English language, have the certain goal and indicators and also make the syllabus for teaching learning activities. 3. Students should improve their attendance in English language course and improve their achievement in English language learning.