

































 

 

 

CONTROL OF THE LEARNING PROCESS AT DISTRICT 5 JULY 
ELEMENTARY SCHOOL, BIREUEN 

Rahmi Hayati1, Asrul Karim2, Facrurazi3, Marzuki4, Sumarlin 
Mangandar Marianus5, Hasratuddin6 

 
1, 2 ,34Primary Teacher Education, Almuslim University 

5  Primary Teacher Education, Katolik Santo Thomas University 
1e-mail:hayatirahmi@yahoo.com 

6Email:siregarhasratuddin@yahoo.com 
 
 
Abstrak.  The focus of this article is on a process and outcome analysis of teaching at the 
District 5 JuliElementary School in the city of Bireuen. The point is to figure out if the end 
result of a process matches the initial expectations, or if the process is still progressing 
toward meeting those standards or performing as expected. The educational process is a 
procedure whose implementation and results must be monitored in order to provide 
outcomes that are in keeping with the desired goals and standards of quality. For this 
reason, it's intriguing to ponder how debating the legitimacy of an event based on 
ostensibly objective data, how to make crucial data easily digestible for those who need 
to see it, and how to analyze data accurately with appropriate statistics will give us a 
schematic of a process's workings that's both detailed and accessible. However, the data 
used are authentic data, such as the results of the National Exam taken by students at 5 
JuliElementary School, bireuen district from 2013/2014 to 2017/2018 in four subject 
areas (mathematics, Indonesian, English, and Natural Science). 
 
 
KataKunci:control analysi, learning process, state elementary school 
 
Introduction 
 
 Multivariate analysis is a common research tool in studies with several independent 
variables. Choosing the appropriate multivariate analysis technique requires 
consideration of the research's aims, the assumptions behind the technique(s) under 
consideration, and the measurement scale(s) to be used during data collection in order to 
yield accurate and reliable results. Multivariate statistical analysis is a branch of statistical 
science that measures the strength of associations between groups of variables to draw 
conclusions about those groups as a whole. Scholars benefit greatly from multivariate 
analysis in their quest to find answers to broad, complex, or deterministic problems. 
(Wustqa et al., 2018). Multivariate analysis is a statistical method for analyzing data with 
more than one independent variable. Because multivariate data analysis requires more 
complicated calculations than single-variate analysis, using a statistical software package 
will simplify the process.The main purpose of multivariate analysis is to discover and 
comprehend the underlying structures of the data. (Djauhari, MA., Sagadapan,R., and Lee, 
S.L (2016). Newly discovered variables through multivariate analysis may be small in 
number, but the insights gained into the nature of variation gained from these variables 
for the first time are invaluable.(Aulele et al., 2017)Multivariate regression analysis is a 

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mailto:hayatirahmi@yahoo.com


 

 

 

statistical method for explaining the existence of a correlation or relationship between 
two or more independent variables. 
 The focus of this article is a process and outcome analysis of teaching at the at the 
District 5 Juli Elementary School in the city of Bireuen  regency of Indonesian. This 
analysis's purpose is to determine whether the final product of a given process matches 
initial expectations, whether the process is currently being carried out in accordance with 
expected standards, and whether those expectations have been met or not. original 
artwork for the manufactured goods. To ensure that the educational process is effective 
and produces results that meet expectations, it is necessary to monitor both the process 
and the outcomes. In this case, it seems obvious that discussing real-world events in 
relation to data that can be differentiated, keeping in mind the importance of specific 
pieces of information, and then summarizing the results of the analysis using appropriate 
statistical methods will aid in providing a complete picture of the procedure being 
described. It will make use of authentic Bireuen data from the years 2013/2014 to 
2017/2018 across four disciplinary domains: mathematics, Indonesian, English, and natural 
sciences. 
 The main question this article seeks to answer is how to use multivariate statistical 
methods to establish connections between different types of pedagogical content areas. 
How do we regulate the ongoing learning process? The purpose of this report is to analyze 
the connections and correlations between the many subject areas tested on the National 
Assessment of Educational Progress.Inform relevant parties about the outcomes of a 
multivariate statistical analysis of the teaching and learning processes at the National Basic 
Education School on July 5. 
 

Discussion 
 The authentic data nilai Ujian Nasional students from elementary school Negeri 5 
Juli from the academic years 2013/2014 - 2017/2018 with the subjects of mathematics, 
Indonesian, English, and natural science, and a total student body of 110, were used as a 
sample for this article's analysis. This section analyzes national standardized test scores at 
the elementary school level in the subjects of Indonesian, English, mathematics, and 
environmental studies. The following are derived from the individual mean calculation 
results. 
 

Table 1: Corelation and Covarians Matrix 

Subject Indonesian Engglish Mathemathics science 

Jlh 1733 1747 1752 1713 
Mean 82.524 83.190 83.429 81.571 

Var 14.362 9.762 7.457 8.957 
Range 12.000 11.000 10.000 11.000 

 

This analysis assumes normally distributed data and that all instruction is carried out by 
teachers of equal ability. The highest mean scores can be found on the mathematics 
curriculum, at 83,429, and the lowest on the Science curriculum, at 81,571. This indicates 

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that students perform best on mathematicsbased learning assessments and worst on 
science-based assessments. Here we provide the results of a cross-cultural comparison of 
mathematics textbooks in order to better understand the connections between different 
disciplines. 

Table 2: Korelasian and Kosovarians Matrix 

Indonesian 1 0.801923 0.788907 0.8187 

Engglish 0.801922788 1 0.962756 0.955602 
Mathematics 0.788907241 0.962756 1 0.941275 

Science 0.818699537 0.955602 0.941275 1 
 Indonesian Engglish Mathematics Science 

 

The highest correlation is found between the engglish and mathematics learning 
outcomes, with a value of 0.962756, and the lowest between the indonesian and 
Mathematics learning outcomes, with a value of 0.788907241. These results suggest that 
the strongest pedagogical connection exists between English  and Mathematics, whereas 
the weakest link is seen between indonesian and Mathematics. However, when looking at 
the correlation coefficient as a whole, the numbers point to a positive correlation. This 
demonstrates that students generally exhibit desirable behaviors in all spheres of 
education.This is in accordance with the research results of Hasratuddin's (2018) which 
shows that high mathematical abilities will affect students' English skills.Below are 
displayed results of a matrix correlation analysis to help you determine which subjects 
best predict others. 

Table 3: Korealisation Inversions 

Indonesian 3.0761571 -0.5605776 -0.18229 -1.81118 

Engglish -0.5605776 19.4414702 -10.7002 -8.04751 

Mathematics -0.1822869 -10.7002239 14.73981 -3.49982 

Science -1.8111769 -8.0475144 -3.49982 13.46733 

 Indonesian Engglish Mathematics Science 

Each diagonal element in the inverse correlation matrix is proportionally related to 
the corespondence variable explained by regression. This is made clear by the fact that 

each diagonal is equal to 
�

����
where R is the multivariate correlation coefficient between 

other variables. The highest percentage, as calculated above, is for the subject of 

mathematics 94.85% (
��.�������

��.�����
)) while the lowest is for the subject of Indonesian 

language study (67.49%)(
�.���������

�.�������
). This suggests that Math is the subject most reliably 

predicted by other disciplines, while Indonesian language studyis the least reliable 
predictor of any academic field. 

Analytical results for predicting test scores using regressive analysis�� = �� +
���(�, �). ���(�)��(� − ��). 

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 Largest predicted value from each Indonesian language value to each 

Mathematica value:����� = 3,652 + 0,568��� 
 The magnitude of the predictive value assigned to each English-language numeric 

by the mathematical values is as follows:����� = 1,343 + 0,841���� 
 Each value of natural science and mathematics is accompanied by a significant 

predictive value.����� = 1,337 + 0,859�������� 
 Each value of Indonesian language, English, and the Science of Natural Knowledge 

confers a larger predictive value upon the value of mathematics. ����� = 1,224 +
0,009��� + 0,634���� + 0,217�������� 
 

Study of Instructional Process Control Analysis 
This discussion makes use of data collected from the National Assessment of Educational 
Progress  at the national elementary school level between the academic years 2013–2014 
and 2017–2018 for the subjects of Indonesian Languange, English, mathematics, and 
science. The first step in any process-control analysis is to determine the value of the 
covarians' matrix of determinates, using the information obtained in the previous step. 

Table 4: Kovarians and UCL Height Determinants 

Tahun MDK0 UCL2 UCL3 

2013/2014 17.94513 73.75357 92.24166 
2014/2015 28.09671 73.75357 92.24166 
2015/2016 10.72374 73.75357 92.24166 

2016/2017 1.222907 73.75357 92.24166 
2017/2018 38.93975 73.75357 92.24166 

 

 

Slot 1. Learning Process Variability Control 

Table 5: T2 Means and UCL 95% Confidence Intervals for Upcoming 
Subsamples 

Tahun T^2 UCL2 UCL3 

2013/2014 16.809 368.7679 461.2083 
2014/2015 63.226 368.7679 461.2083 
2015/2016 14.823 368.7679 461.2083 
2016/2017 28.522 368.7679 461.2083 

0

50

100

2013/2014 2014/2015 2015/2016 2016/2017 2017/2018

Learning Process Variability Control

MDK0 UCL2 UCL3

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2017/2018 77.653 368.7679 461.2083 

 

 

Slot  2: Capacity-Based Learning and Teaching Process Control 

Both the variance control limits (MDcov) and the probability balance limit (T^2) lie below 
their respective confidence intervals (UCL(2) and UCL(3). as seen in the accompanying 
table and graph. From the graph above, it can be concluded that the learning process that 
took place at the District 5 Juli Elementary School in the city of Bireuen for the 2013/2014 
school year to 2017/2018 went smoothly as expected. 

Conclusion 
The results of analysis of data on national exam scores for 5 years, namely from 

2013/2014 – 2017/2018 show that the learning process is carried out in accordance with 
what has been planned. This is shown by the variability graph where the determinant 
value of the covariance matrix does not exceed the Uper Control Limit. This can be seen 
as evidence that the educational process over the past five years has been going 
swimmingly, in line with the educational abilities that have been developed. 
 
Bibliography 

Aulele, S. N., Wattimena, A. Z., & Tahya, C. (2017). Analisis Regresi Multivariat 
Berdasarkan Faktor-Faktor Yang Mempengaruhi Derajat Kesehatan Di Provinsi 
Maluku. BAREKENG: Jurnal Ilmu Matematika Dan Terapan, 11(1), 39–
48.https://doi.org/10.30598/barekengvol11iss1pp39-48 

Djauhari, MA., Sagadapan,R., and Lee, S.L 2016. Monitoring Multivariat Process Variability 
Monitoring. Communication in Statistic. p.1742-1754. 

Djauhari, M.A., dan Dyah E. Herwindiati. 2022. Kontrol Kualitas Proses Kompleks. ITB 
Press: Bandung. 

Hasratuddin. 2019. Weakness Analysis Learning Mathematics Junior High School in 
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Johnson,RichardA.2002. 

AppliedMultivariateStatisticalAnalysis(5th).NewJersey:PersonEducationInternasiona
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Whittaker,Joe.1996. GraphicalModelsinAppliedMultivariateStatistics.NewYork:John 

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Control of the Learning and Teaching 
Process

T^2 UCL2 UCL3

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Wiley&Sons 
Wustqa, D. U., Listyani, E., Subekti, R., Kusumawati, R., Susanti, M., & Kismiantini, K. 

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