p–ISSN: 2723 – 6609 e-ISSN: 2745-5254
Vol. 5, No. 11, November 2024 http://jist.publikasiindonesia.id/
Indonesian Journal of Social Technology, Vol. 5, No. 11, November 2024 5306
Efficiency Analysis of PT Dayamitra Telekomunikasi Tbk in
the Telecommunication Infrastructure Industry Using the
Data Envelopment Analysis (DEA) Method
Kaila Zahra Nandika
Universitas Telkom, Indonesia
Email: [email protected]
*Correspondence
ABSTRACT
Keywords: data
envelopment analysis
(DEA), efficiency,
telecommunications
infrastructure.
The projected increase in the number of telecommunication
towers in Indonesia by 100% by 2026 reflects the rapid
growth in the telecommunications infrastructure sector,
driven by increasing data demand and the adoption of 5G
technology (WCA, 2021). In the Asia-Pacific region, similar
demand indicates a huge opportunity for the expansion of
this industry. PT Dayamitra Telekomunikasi Tbk (Mitratel)
is one of the companies that excels in the
Telecommunication infrastructure industry in Indonesia.
This study uses the Data Envelopment Analysis (DEA)
method, which is effective in measuring the efficiency of
organizational units in various fields. The results show that
in five years of observation, Mitratel managed to achieve full
efficiency in almost all years of observation, with high-
efficiency level results comparable to PT Sarana Menara
Nusantara Tbk. Meanwhile, the annual efficiency level for
Indus Towers and China Tower is quite volatile, although it
remains at a high value. Trend analysis and Pearson
Correlation coefficient between average efficiency and
input/output variables show that the current assets, non-
current liabilities, and CAPEX variables have a significant
influence on Mitratel's efficiency with a negative correlation,
while the current liabilities variable has a significant
influence on a positive correlation.
Introduction
The number of towers in Indonesia is projected to increase by 100% from 100,000
to 200,000 by 2026 (WCA, 2021). This growth is driven by increasing data demand and
the adoption of 5G technology. This is in line with the trend in the Asia-Pacific region,
where there were approximately 5.4 million towers at the end of 2020, with an estimated
compound annual growth rate (CAGR) of 3.74% from 2021 to 2031 (S&P Global, 2021).
This projection shows that both in Indonesia and in the Asia-Pacific, there are still
significant needs and opportunities for the development of the telecommunications
infrastructure industry. PT Dayamitra Telekomunikasi Tbk (Mitratel), a subsidiary of PT
Vol. 5, No. 11, November 2024 http://jist.publikasiindonesia.id/
Indonesian Journal of Social Technology, Vol. 5, No. 11, November 2024 5306
Efficiency Analysis of PT Dayamitra Telekomunikasi Tbk in
the Telecommunication Infrastructure Industry Using the
Data Envelopment Analysis (DEA) Method
Kaila Zahra Nandika
Universitas Telkom, Indonesia
Email: [email protected]
*Correspondence
ABSTRACT
Keywords: data
envelopment analysis
(DEA), efficiency,
telecommunications
infrastructure.
The projected increase in the number of telecommunication
towers in Indonesia by 100% by 2026 reflects the rapid
growth in the telecommunications infrastructure sector,
driven by increasing data demand and the adoption of 5G
technology (WCA, 2021). In the Asia-Pacific region, similar
demand indicates a huge opportunity for the expansion of
this industry. PT Dayamitra Telekomunikasi Tbk (Mitratel)
is one of the companies that excels in the
Telecommunication infrastructure industry in Indonesia.
This study uses the Data Envelopment Analysis (DEA)
method, which is effective in measuring the efficiency of
organizational units in various fields. The results show that
in five years of observation, Mitratel managed to achieve full
efficiency in almost all years of observation, with high-
efficiency level results comparable to PT Sarana Menara
Nusantara Tbk. Meanwhile, the annual efficiency level for
Indus Towers and China Tower is quite volatile, although it
remains at a high value. Trend analysis and Pearson
Correlation coefficient between average efficiency and
input/output variables show that the current assets, non-
current liabilities, and CAPEX variables have a significant
influence on Mitratel's efficiency with a negative correlation,
while the current liabilities variable has a significant
influence on a positive correlation.
Introduction
The number of towers in Indonesia is projected to increase by 100% from 100,000
to 200,000 by 2026 (WCA, 2021). This growth is driven by increasing data demand and
the adoption of 5G technology. This is in line with the trend in the Asia-Pacific region,
where there were approximately 5.4 million towers at the end of 2020, with an estimated
compound annual growth rate (CAGR) of 3.74% from 2021 to 2031 (S&P Global, 2021).
This projection shows that both in Indonesia and in the Asia-Pacific, there are still
significant needs and opportunities for the development of the telecommunications
infrastructure industry. PT Dayamitra Telekomunikasi Tbk (Mitratel), a subsidiary of PT
Efficiency Analysis of PT Dayamitra Telekomunikasi Tbk in the Telecommunication
Infrastructure Industry Using the Data Envelopment Analysis (DEA) Method
Indonesian Journal of Social Technology, Vol. 5, No. 11, November 2024 5307
Telekomunikasi Indonesia (Persero) Tbk, is one of the leading companies in the
telecommunications infrastructure sector in Indonesia (CNBC Indonesia, 2023). Through
its vision of "Becoming InfraCo's #1 digital in the growing APAC (Asia-Pacific) market
by offering sustainable best-in-class services," Mitratel demonstrates its excellence in
profitability, corporate health, and connectivity performance (CNBC Indonesia, 2023). In
December 2022, Mitatel was recorded to have 35,418 towers, this number is higher than
its competitors, namely PT Sarana Nusantara Tbk which has 29,794 towers, and PT
Tower Bersama Infrastructure Tbk with 21,758 towers. Followed by the following year,
Mitratel still maintains its position advantage. Figure I. 1 below shows a comparison of
the number of towers mentioned earlier.
Figure 1 Comparison of the Number of Towers "The Big Three" of Indonesian
Telecommunication Infrastructure Companies
However, according to a 5G Magazine report by TeckNexus (November 2023),
Mitratel is not yet in the ranks of the best telecommunications infrastructure companies
in Asia-Pacific (APAC), led by China Tower, Indus Towers, and GTL Infrastructure.
In realizing Mitratel's vision to become the number one digital InfraCo in the APAC
market, efforts to maintain its main position in national competition are crucial. In
addition, companies need to make improvements to compete with the best
telecommunications infrastructure companies in the APAC region. (Setiajatnika &
Hasyim, 2019). To achieve this, it can be done by benchmarking methods to compare
with similar companies in the telecommunications infrastructure sector, especially
focusing on tower infrastructure, both on a national scale and at the Asia-Pacific regional
level. This method will assist Mitratel in identifying areas that need improvement and
setting higher standards in this fierce industry competition. (Lee, Lee, Kho, & Kim, 2019).
According to Patterson (1996), benchmarking is the process of comparing the
measurement of efficiency of a company with other companies to obtain the benefit of
information that will be used for continuous improvement. Several previous studies have
Infrastructure Industry Using the Data Envelopment Analysis (DEA) Method
Indonesian Journal of Social Technology, Vol. 5, No. 11, November 2024 5307
Telekomunikasi Indonesia (Persero) Tbk, is one of the leading companies in the
telecommunications infrastructure sector in Indonesia (CNBC Indonesia, 2023). Through
its vision of "Becoming InfraCo's #1 digital in the growing APAC (Asia-Pacific) market
by offering sustainable best-in-class services," Mitratel demonstrates its excellence in
profitability, corporate health, and connectivity performance (CNBC Indonesia, 2023). In
December 2022, Mitatel was recorded to have 35,418 towers, this number is higher than
its competitors, namely PT Sarana Nusantara Tbk which has 29,794 towers, and PT
Tower Bersama Infrastructure Tbk with 21,758 towers. Followed by the following year,
Mitratel still maintains its position advantage. Figure I. 1 below shows a comparison of
the number of towers mentioned earlier.
Figure 1 Comparison of the Number of Towers "The Big Three" of Indonesian
Telecommunication Infrastructure Companies
However, according to a 5G Magazine report by TeckNexus (November 2023),
Mitratel is not yet in the ranks of the best telecommunications infrastructure companies
in Asia-Pacific (APAC), led by China Tower, Indus Towers, and GTL Infrastructure.
In realizing Mitratel's vision to become the number one digital InfraCo in the APAC
market, efforts to maintain its main position in national competition are crucial. In
addition, companies need to make improvements to compete with the best
telecommunications infrastructure companies in the APAC region. (Setiajatnika &
Hasyim, 2019). To achieve this, it can be done by benchmarking methods to compare
with similar companies in the telecommunications infrastructure sector, especially
focusing on tower infrastructure, both on a national scale and at the Asia-Pacific regional
level. This method will assist Mitratel in identifying areas that need improvement and
setting higher standards in this fierce industry competition. (Lee, Lee, Kho, & Kim, 2019).
According to Patterson (1996), benchmarking is the process of comparing the
measurement of efficiency of a company with other companies to obtain the benefit of
information that will be used for continuous improvement. Several previous studies have
Kaila Zahra Nandika
Indonesian Journal of Social Technology, Vol. 5, No. 11, November 2024 5308
conducted benchmarking, including research by (Abdel-Halim, Al Khars, & Alnasser,
2023), (Amin & Hendrawan, 2023), (Hendrawan & Nugroho, 2018), Iamratanakul
(2018), (Masson, Jain, Ganesh, & George, 2016), etc. Based on a literature study from
previous research, this shows that the benchmarking method can be used in efficiency
comparative analysis to compare the efficiency level of companies in a predetermined
period. Efficiency comparison is aimed at finding out how a company can maximize its
resources but with limited data through the company's annual report data. So that parties
from outside the company can analyze the comparison of the efficiency of a company
compared to other companies (Nishiuchi, Todoroki, & Kishi, 2015).
The measurement of efficiency level can be carried out through three approaches,
such as ratio, regression, and frontier approaches. Of the three, the frontier approach is
considered more scientific and precise compared to other approaches. This is due to the
limitations of the ratio approach which makes it difficult to manage many variables, as
well as the regression approach which focuses only on many inputs and one output.
Comparing the level of efficiency with the frontier approach can be grouped into
parametric and nonparametric categories. According to Berger (1997), methods that fall
into the parametric category include the Stochastic Frontier Approach (SFA), the
Distribution Free Approach (DFA), and the Think Frontier Approach (TFA). The
methods included in the nonparametric category include Free Disposal Hull (FDH) and
Data Envelopment Analysis (DEA).
Based on previous research, efficiency level analysis can and is widely carried out
using the nonparametric frontier Data Envelopment Analysis method. The advantage of
this method is that it does not require certain assumptions regarding the distribution of
the analyzed population. In addition, this method is suitable for use in research data that
has many input and output variables. According to Avkiran (1999), DEA is a technique
for measuring the relative efficiency of various organizational units. This technique can
reveal the exact relationship between diverse inputs and outputs that were previously
difficult to accommodate through traditional ratio analysis. Data Envelopment Analysis
(DEA) is one of the nonparametric statistical methods that is often applied in operations
and economic research to measure the level of production efficiency. This method is used
when the production process involves a variety of complex input and output factors.
Most of the comparative analysis studies of efficiency levels use the DEA method
in measuring efficiency in various areas of management and engineering, such as CEO
performance evaluation, transportation service performance, telecommunications, energy
efficiency banking, manager and team effectiveness, operational efficiency in hotel
management, and other industries. (Allen-Zhu & Hazan, 2016).
The update brought in this study is the analysis of efficiency in the telecommunications
infrastructure industry, using the DEA method.
Based on the formulation of the problem, the objectives of this research are:
1. Conduct a comparative analysis of Mitratel's efficiency level with competitor
companies in the domestic telecommunications infrastructure industry based on data
from 2019 to 2023.
Indonesian Journal of Social Technology, Vol. 5, No. 11, November 2024 5308
conducted benchmarking, including research by (Abdel-Halim, Al Khars, & Alnasser,
2023), (Amin & Hendrawan, 2023), (Hendrawan & Nugroho, 2018), Iamratanakul
(2018), (Masson, Jain, Ganesh, & George, 2016), etc. Based on a literature study from
previous research, this shows that the benchmarking method can be used in efficiency
comparative analysis to compare the efficiency level of companies in a predetermined
period. Efficiency comparison is aimed at finding out how a company can maximize its
resources but with limited data through the company's annual report data. So that parties
from outside the company can analyze the comparison of the efficiency of a company
compared to other companies (Nishiuchi, Todoroki, & Kishi, 2015).
The measurement of efficiency level can be carried out through three approaches,
such as ratio, regression, and frontier approaches. Of the three, the frontier approach is
considered more scientific and precise compared to other approaches. This is due to the
limitations of the ratio approach which makes it difficult to manage many variables, as
well as the regression approach which focuses only on many inputs and one output.
Comparing the level of efficiency with the frontier approach can be grouped into
parametric and nonparametric categories. According to Berger (1997), methods that fall
into the parametric category include the Stochastic Frontier Approach (SFA), the
Distribution Free Approach (DFA), and the Think Frontier Approach (TFA). The
methods included in the nonparametric category include Free Disposal Hull (FDH) and
Data Envelopment Analysis (DEA).
Based on previous research, efficiency level analysis can and is widely carried out
using the nonparametric frontier Data Envelopment Analysis method. The advantage of
this method is that it does not require certain assumptions regarding the distribution of
the analyzed population. In addition, this method is suitable for use in research data that
has many input and output variables. According to Avkiran (1999), DEA is a technique
for measuring the relative efficiency of various organizational units. This technique can
reveal the exact relationship between diverse inputs and outputs that were previously
difficult to accommodate through traditional ratio analysis. Data Envelopment Analysis
(DEA) is one of the nonparametric statistical methods that is often applied in operations
and economic research to measure the level of production efficiency. This method is used
when the production process involves a variety of complex input and output factors.
Most of the comparative analysis studies of efficiency levels use the DEA method
in measuring efficiency in various areas of management and engineering, such as CEO
performance evaluation, transportation service performance, telecommunications, energy
efficiency banking, manager and team effectiveness, operational efficiency in hotel
management, and other industries. (Allen-Zhu & Hazan, 2016).
The update brought in this study is the analysis of efficiency in the telecommunications
infrastructure industry, using the DEA method.
Based on the formulation of the problem, the objectives of this research are:
1. Conduct a comparative analysis of Mitratel's efficiency level with competitor
companies in the domestic telecommunications infrastructure industry based on data
from 2019 to 2023.
Efficiency Analysis of PT Dayamitra Telekomunikasi Tbk in the Telecommunication
Infrastructure Industry Using the Data Envelopment Analysis (DEA) Method
Indonesian Journal of Social Technology, Vol. 5, No. 11, November 2024 5309
2. Conducting a comparative analysis of Mitratel's efficiency level with competitors in
the telecommunications infrastructure industry in the Asia-Pacific (APAC) region
based on data from 2019 to 2023.
3. Analyzing input and output variables that have a significant influence on Mitratel's
efficiency level.
4. Determine what variables/factors need to be improved to improve Mitratel's efficiency.
Method
Problem-solving systematics is an explanation and description of the stages carried
out in research to solve existing problems. The systematics in this study are compiled to
achieve the final result in the form of strategy recommendations that can help companies
improve their efficiency based on feedback that has been obtained previously from
efficiency comparisons with its competitors. The systematic stages of this study consist
of introduction, data collection, data processing, data analysis, as well as conclusions and
suggestions.
Data Collection
The data collection stage is a stage to study literature materials related to the
research formulation. The data collected in this study is secondary data sourced from the
annual report (annual and financial report) for 2019-2023 through the official website of
the related company. In addition, researchers also accessed the company's financial
information from several sources such as Yahoo Finance, Ticker, and Finbox. Regarding
the selection of competing companies, the information is taken from the ranking data of
leading telecommunications infrastructure companies, both at the national and Asia-
Pacific (APAC) levels. The companies selected as Mitratel's competitors for analysis
include one company at the national level, namely PT Sarana Menara Nusantara, as well
as two APAC regional companies, namely China Tower and Indus Towers.
Data Processing
In the data processing stage, the data that has been collected in the previous stage
will be processed to produce answers to the formulation of problems that have been
determined in the preliminary stage.
Specifying Input and Output Variables
Research variables are defined as all forms determined by the researcher to be
investigated to obtain information and conclusions about them. (Yayuk & Sugiyono,
2019). In dividing variables, variable operationalization is carried out.
The operationalization of variables in research is a step to break down the variables
in the problem statement into the smallest components so that they can be understood
more deeply. This process consists of two stages, namely defining the variables to be
measured and determining the measurement indicators. (Amin & Hendrawan, 2023).
According to Sekaran & Bougie in Amin et al. (2023).
Data Processing Engineering
In processing research data, the collected data will be obtained according to the
following stages:
Infrastructure Industry Using the Data Envelopment Analysis (DEA) Method
Indonesian Journal of Social Technology, Vol. 5, No. 11, November 2024 5309
2. Conducting a comparative analysis of Mitratel's efficiency level with competitors in
the telecommunications infrastructure industry in the Asia-Pacific (APAC) region
based on data from 2019 to 2023.
3. Analyzing input and output variables that have a significant influence on Mitratel's
efficiency level.
4. Determine what variables/factors need to be improved to improve Mitratel's efficiency.
Method
Problem-solving systematics is an explanation and description of the stages carried
out in research to solve existing problems. The systematics in this study are compiled to
achieve the final result in the form of strategy recommendations that can help companies
improve their efficiency based on feedback that has been obtained previously from
efficiency comparisons with its competitors. The systematic stages of this study consist
of introduction, data collection, data processing, data analysis, as well as conclusions and
suggestions.
Data Collection
The data collection stage is a stage to study literature materials related to the
research formulation. The data collected in this study is secondary data sourced from the
annual report (annual and financial report) for 2019-2023 through the official website of
the related company. In addition, researchers also accessed the company's financial
information from several sources such as Yahoo Finance, Ticker, and Finbox. Regarding
the selection of competing companies, the information is taken from the ranking data of
leading telecommunications infrastructure companies, both at the national and Asia-
Pacific (APAC) levels. The companies selected as Mitratel's competitors for analysis
include one company at the national level, namely PT Sarana Menara Nusantara, as well
as two APAC regional companies, namely China Tower and Indus Towers.
Data Processing
In the data processing stage, the data that has been collected in the previous stage
will be processed to produce answers to the formulation of problems that have been
determined in the preliminary stage.
Specifying Input and Output Variables
Research variables are defined as all forms determined by the researcher to be
investigated to obtain information and conclusions about them. (Yayuk & Sugiyono,
2019). In dividing variables, variable operationalization is carried out.
The operationalization of variables in research is a step to break down the variables
in the problem statement into the smallest components so that they can be understood
more deeply. This process consists of two stages, namely defining the variables to be
measured and determining the measurement indicators. (Amin & Hendrawan, 2023).
According to Sekaran & Bougie in Amin et al. (2023).
Data Processing Engineering
In processing research data, the collected data will be obtained according to the
following stages:
Kaila Zahra Nandika
Indonesian Journal of Social Technology, Vol. 5, No. 11, November 2024 5310
1. Compile categories of input and output variables based on predetermined indicators
2. Create a graph visualization of the progression of predefined input and output variables
3. Conducting simulations using the nonparametric DEA method with data collected
based on predetermined input and output variables. This process will compare the
determinants among the existing Data Measurement Units (DMUs). DEA uses relative
comparisons between DMUs rather than generating absolute numbers for efficiency.
The equations used for the simulation in finding the DMU efficiency value are as
follows:
𝑬𝒃 = ∑ 𝒖𝒓𝒃𝒚𝒓𝒃
𝑹
𝒓=𝟏
∑ 𝒗𝒊𝒃𝒙𝒊𝒃
𝑰
𝒊=𝟏
Constraint function:
𝑬𝒃 = ∑ 𝒖𝒓𝒃𝒚𝒓𝒋
𝑹
𝒓=𝟏
∑ 𝒗𝒊𝒃𝒙𝒊𝒋
𝑰
𝒊=𝟏
≤ 𝟏, ∀𝒋, 𝒋 = 𝟏, 𝟐, 𝟑, … , 𝑵
and
𝒖𝒓𝒃, 𝒗𝒊𝒃 ≥ for each (where and 𝒓, 𝒊𝒓 = 𝟏, 𝟐, 𝟑, … , 𝑹𝒊 = 𝟏, 𝟐, 𝟑, … , 𝑰)
𝑬𝒃Is the efficiency in unit B
𝒚𝒓𝒋is the quantity of the output produced by units j=1, 2, 3..., N
𝒙𝒊𝒋Is the quantity of the input produced by the unit j=1, 2, 3..., N
𝒖𝒓𝒃is the weight given to the output based on unit b
𝒗𝒊𝒃is the weight given to the input based on the unit b
𝒆A a very small positive number
4. The simulation results with the DEA method will produce the efficiency value of each
DMU. The efficiency values of each company are then compared for further analysis
5. The Pearson Correlation coefficient is then used to measure the strength of the
relationship between input and output variables to the Mitratel efficiency value. The
correlation coefficient value ranges from -1 to 1, indicating the degree of strength and
direction of the relationship between two random variables. The results of the
correlation coefficient are then interpreted using criteria that have been predetermined
by (Astini, Sri Budhi, Suyana Utama, & Ramantha, 2024), as follows in Table III. 1.
Table 1
Interpretation of Pearson Correlation Coefficients
Coefficient Interval Relationship Level
0 There is no correlation between the two
variables.
0 – 0,199 Correlation is very weak.
0,20 – 0,399 Weak correlation
0,40 – 0,599 Medium correlation
0,60 – 0,799 Strong correlation
Indonesian Journal of Social Technology, Vol. 5, No. 11, November 2024 5310
1. Compile categories of input and output variables based on predetermined indicators
2. Create a graph visualization of the progression of predefined input and output variables
3. Conducting simulations using the nonparametric DEA method with data collected
based on predetermined input and output variables. This process will compare the
determinants among the existing Data Measurement Units (DMUs). DEA uses relative
comparisons between DMUs rather than generating absolute numbers for efficiency.
The equations used for the simulation in finding the DMU efficiency value are as
follows:
𝑬𝒃 = ∑ 𝒖𝒓𝒃𝒚𝒓𝒃
𝑹
𝒓=𝟏
∑ 𝒗𝒊𝒃𝒙𝒊𝒃
𝑰
𝒊=𝟏
Constraint function:
𝑬𝒃 = ∑ 𝒖𝒓𝒃𝒚𝒓𝒋
𝑹
𝒓=𝟏
∑ 𝒗𝒊𝒃𝒙𝒊𝒋
𝑰
𝒊=𝟏
≤ 𝟏, ∀𝒋, 𝒋 = 𝟏, 𝟐, 𝟑, … , 𝑵
and
𝒖𝒓𝒃, 𝒗𝒊𝒃 ≥ for each (where and 𝒓, 𝒊𝒓 = 𝟏, 𝟐, 𝟑, … , 𝑹𝒊 = 𝟏, 𝟐, 𝟑, … , 𝑰)
𝑬𝒃Is the efficiency in unit B
𝒚𝒓𝒋is the quantity of the output produced by units j=1, 2, 3..., N
𝒙𝒊𝒋Is the quantity of the input produced by the unit j=1, 2, 3..., N
𝒖𝒓𝒃is the weight given to the output based on unit b
𝒗𝒊𝒃is the weight given to the input based on the unit b
𝒆A a very small positive number
4. The simulation results with the DEA method will produce the efficiency value of each
DMU. The efficiency values of each company are then compared for further analysis
5. The Pearson Correlation coefficient is then used to measure the strength of the
relationship between input and output variables to the Mitratel efficiency value. The
correlation coefficient value ranges from -1 to 1, indicating the degree of strength and
direction of the relationship between two random variables. The results of the
correlation coefficient are then interpreted using criteria that have been predetermined
by (Astini, Sri Budhi, Suyana Utama, & Ramantha, 2024), as follows in Table III. 1.
Table 1
Interpretation of Pearson Correlation Coefficients
Coefficient Interval Relationship Level
0 There is no correlation between the two
variables.
0 – 0,199 Correlation is very weak.
0,20 – 0,399 Weak correlation
0,40 – 0,599 Medium correlation
0,60 – 0,799 Strong correlation
Efficiency Analysis of PT Dayamitra Telekomunikasi Tbk in the Telecommunication
Infrastructure Industry Using the Data Envelopment Analysis (DEA) Method
Indonesian Journal of Social Technology, Vol. 5, No. 11, November 2024 5311
0,80 – 0,99 Correlation is very strong.
1 Perfect correlation
Results and Discussion
Determination of Competitor Companies
The determination of competitors in this study considers Mitratel's vision to become
the number one company in the telecommunications infrastructure industry in Asia-
Pacific (APAC) while maintaining its superiority at the national level. Based on this
vision, the researcher looked at the ranking of companies in the telecommunications
infrastructure sector on a national and regional scale in Asia-Pacific and then selected the
top two companies from each scale.
Figure 1 The Three Largest Telecommunications Infrastructure Companies at the APAC
Regional Level (TeckNexus, 2023)
Figure 2 The Three Largest Telecommunications Infrastructure Companies at the
National Level (CNBC Indonesia, 2023)
Infrastructure Industry Using the Data Envelopment Analysis (DEA) Method
Indonesian Journal of Social Technology, Vol. 5, No. 11, November 2024 5311
0,80 – 0,99 Correlation is very strong.
1 Perfect correlation
Results and Discussion
Determination of Competitor Companies
The determination of competitors in this study considers Mitratel's vision to become
the number one company in the telecommunications infrastructure industry in Asia-
Pacific (APAC) while maintaining its superiority at the national level. Based on this
vision, the researcher looked at the ranking of companies in the telecommunications
infrastructure sector on a national and regional scale in Asia-Pacific and then selected the
top two companies from each scale.
Figure 1 The Three Largest Telecommunications Infrastructure Companies at the APAC
Regional Level (TeckNexus, 2023)
Figure 2 The Three Largest Telecommunications Infrastructure Companies at the
National Level (CNBC Indonesia, 2023)
Kaila Zahra Nandika
Indonesian Journal of Social Technology, Vol. 5, No. 11, November 2024 5312
Based on information obtained from CNBC Indonesia and TeckNexus (2023), PT
Sarana Menara Nusantara Tbk has been determined as Mitratel's competitors on a national
scale, as well as China Tower and Indus Towers as the main competitors on the APAC
regional scale. The determination of the number of competitors is also adjusted to the
minimum number of Decision-Making Units (DMUs) needed for accurate data
processing (Golany & Roll, in Masson et al., 2016).
Development of Input Variables
The input variables that have been selected from the previous process consist of
current assets, non-current assets, current liabilities, non-current liabilities, CAPEX
(capital expenditure), number of towers, and operating expenses. Table IV.2 provides
more information regarding the mean, minimum, maximum, slope, N, and CAGR values
of the input variables.
The mean shows the average value of each input variable and provides an overview
of the magnitude of the values that often appear. The minimum and maximum indicate
the highest and lowest values of the observation, as well as identify the value range of
each input variable. Skewness indicates a picture of the data distribution, and a slope
value close to 0 describes a symmetrical distribution of data and vice versa. N shows the
number of DMUs used in this study. CAGR (Compound Annual Growth Rate) measures
the compound annual average growth rate of each input variable over the observation
period.
Development of Non-Current Assets Inputs
Based on data from the following non-current assets variables, the development of
non-current assets of each company is depicted through the graph in Figure 2 below.
Figure 3 Average Growth Non-Current Assets per Company Chart
The following are the results of the CAGR of variable non-current assets of each
company (Table 2)
Indonesian Journal of Social Technology, Vol. 5, No. 11, November 2024 5312
Based on information obtained from CNBC Indonesia and TeckNexus (2023), PT
Sarana Menara Nusantara Tbk has been determined as Mitratel's competitors on a national
scale, as well as China Tower and Indus Towers as the main competitors on the APAC
regional scale. The determination of the number of competitors is also adjusted to the
minimum number of Decision-Making Units (DMUs) needed for accurate data
processing (Golany & Roll, in Masson et al., 2016).
Development of Input Variables
The input variables that have been selected from the previous process consist of
current assets, non-current assets, current liabilities, non-current liabilities, CAPEX
(capital expenditure), number of towers, and operating expenses. Table IV.2 provides
more information regarding the mean, minimum, maximum, slope, N, and CAGR values
of the input variables.
The mean shows the average value of each input variable and provides an overview
of the magnitude of the values that often appear. The minimum and maximum indicate
the highest and lowest values of the observation, as well as identify the value range of
each input variable. Skewness indicates a picture of the data distribution, and a slope
value close to 0 describes a symmetrical distribution of data and vice versa. N shows the
number of DMUs used in this study. CAGR (Compound Annual Growth Rate) measures
the compound annual average growth rate of each input variable over the observation
period.
Development of Non-Current Assets Inputs
Based on data from the following non-current assets variables, the development of
non-current assets of each company is depicted through the graph in Figure 2 below.
Figure 3 Average Growth Non-Current Assets per Company Chart
The following are the results of the CAGR of variable non-current assets of each
company (Table 2)
Efficiency Analysis of PT Dayamitra Telekomunikasi Tbk in the Telecommunication
Infrastructure Industry Using the Data Envelopment Analysis (DEA) Method
Indonesian Journal of Social Technology, Vol. 5, No. 11, November 2024 5313
Table 2
CAGR Non-current Assets
Based on the graph and table above, it can be seen that the average compound
growth of variable assets of the four companies in the 2019-2023 range is 21.42%.
However, the figure does not reflect positive growth (improvement) in all companies.
Growth above average was experienced by Mitratel with an increase of 32.83%.
followed by Indus Towers at 31.02%, and Sarana Menara at 26.25%. On the other hand,
China Tower experienced a negative growth (decline) of non-current assets of 4.42%.
Development of Input Non-Current Liabilities
Based on data from the non-current liabilities variable, the development of non-
current liabilities of each company can be seen through the graph in the following Graph
3.
Figure 4 Average Growth Non-Current Assets per Company Chart
The following are the results of the CAGR of non-current assets variables of each
company (Table 3).
Table 3
CAGR Non-current Assets
Based on the graph and table above, it can be seen that the average compound
growth of variable assets of the four companies in the 2019-2023 range is 21.42%.
However, the figure does not reflect positive growth (improvement) in all companies.
Growth above average was experienced by Mitratel with an increase of 32.83%.
followed by Indus Towers at 31.02%, and Sarana Menara at 26.25%. On the other hand,
China Tower experienced a negative growth (decline) of non-current assets of 4.42%.
These figures show that the company's strategy for managing assets is not current. For
example, according to an article written by HSB Investment, the strategy of increasing
Infrastructure Industry Using the Data Envelopment Analysis (DEA) Method
Indonesian Journal of Social Technology, Vol. 5, No. 11, November 2024 5313
Table 2
CAGR Non-current Assets
Based on the graph and table above, it can be seen that the average compound
growth of variable assets of the four companies in the 2019-2023 range is 21.42%.
However, the figure does not reflect positive growth (improvement) in all companies.
Growth above average was experienced by Mitratel with an increase of 32.83%.
followed by Indus Towers at 31.02%, and Sarana Menara at 26.25%. On the other hand,
China Tower experienced a negative growth (decline) of non-current assets of 4.42%.
Development of Input Non-Current Liabilities
Based on data from the non-current liabilities variable, the development of non-
current liabilities of each company can be seen through the graph in the following Graph
3.
Figure 4 Average Growth Non-Current Assets per Company Chart
The following are the results of the CAGR of non-current assets variables of each
company (Table 3).
Table 3
CAGR Non-current Assets
Based on the graph and table above, it can be seen that the average compound
growth of variable assets of the four companies in the 2019-2023 range is 21.42%.
However, the figure does not reflect positive growth (improvement) in all companies.
Growth above average was experienced by Mitratel with an increase of 32.83%.
followed by Indus Towers at 31.02%, and Sarana Menara at 26.25%. On the other hand,
China Tower experienced a negative growth (decline) of non-current assets of 4.42%.
These figures show that the company's strategy for managing assets is not current. For
example, according to an article written by HSB Investment, the strategy of increasing
Kaila Zahra Nandika
Indonesian Journal of Social Technology, Vol. 5, No. 11, November 2024 5314
non-current assets can be used to support the company's operational activities, long-term
investments, company expansion, or credit financing. Thus, the improvement of non-
current assets is not always through the purchase of new assets, but can also be achieved
through the optimization of existing assets.
Development of Output Variables
The output variable data of China Tower, Indus Towers, Mitratel, and Sarana
Menara was obtained from the company's official website source taken from the annual
report (annual and financial report) from 2019 to 2023. It consists of total revenue,
EBITDA (earnings before interest, taxes, depreciation, and amortization), and number of
tenants. Table IV.10 provides more information regarding the mean, minimum,
maximum, slope, N, and CAGR values of the output variables.
Correlation Calculation
In the correlation calculation stage, the DMU data set and the efficiency values that
have been obtained from the previous stage are grouped based on each company.
Furthermore, the value of Pearson's correlation coefficient between each input and output
variable against the efficiency value of each company is calculated. This method is used
to evaluate the linear relationship between these variables and the company's efficiency
level.
Table 4 below, presents an example of the results of the calculation of Pearson's
correlation coefficient for variables that affect efficiency in Mitratel. The table provides
an overview of how strong and directional the relationship between each input and output
variable is with the efficiency value, which can help in understanding the factors that
contribute to the company's efficiency.
Table 4
Pearson Correlation Coefficient Between Mitratel Efficiency and Input/Output
The results of the Pearson Correlation between input and output variables to the
efficiency value, shown in Table 4, show that some variables have the opposite
relationship with the Mitratel efficiency value. For example, the input variable current
assets have a correlation level result of (0.96) or -0.96, indicating a significant negative
relationship. On the other hand, some variables have a positive (unidirectional)
relationship with Mitratel's efficiency, such as the input variable current liabilities which
correlates with 0.33.
Efficiency Analysis of Mitratel and Its Competitors
Indonesian Journal of Social Technology, Vol. 5, No. 11, November 2024 5314
non-current assets can be used to support the company's operational activities, long-term
investments, company expansion, or credit financing. Thus, the improvement of non-
current assets is not always through the purchase of new assets, but can also be achieved
through the optimization of existing assets.
Development of Output Variables
The output variable data of China Tower, Indus Towers, Mitratel, and Sarana
Menara was obtained from the company's official website source taken from the annual
report (annual and financial report) from 2019 to 2023. It consists of total revenue,
EBITDA (earnings before interest, taxes, depreciation, and amortization), and number of
tenants. Table IV.10 provides more information regarding the mean, minimum,
maximum, slope, N, and CAGR values of the output variables.
Correlation Calculation
In the correlation calculation stage, the DMU data set and the efficiency values that
have been obtained from the previous stage are grouped based on each company.
Furthermore, the value of Pearson's correlation coefficient between each input and output
variable against the efficiency value of each company is calculated. This method is used
to evaluate the linear relationship between these variables and the company's efficiency
level.
Table 4 below, presents an example of the results of the calculation of Pearson's
correlation coefficient for variables that affect efficiency in Mitratel. The table provides
an overview of how strong and directional the relationship between each input and output
variable is with the efficiency value, which can help in understanding the factors that
contribute to the company's efficiency.
Table 4
Pearson Correlation Coefficient Between Mitratel Efficiency and Input/Output
The results of the Pearson Correlation between input and output variables to the
efficiency value, shown in Table 4, show that some variables have the opposite
relationship with the Mitratel efficiency value. For example, the input variable current
assets have a correlation level result of (0.96) or -0.96, indicating a significant negative
relationship. On the other hand, some variables have a positive (unidirectional)
relationship with Mitratel's efficiency, such as the input variable current liabilities which
correlates with 0.33.
Efficiency Analysis of Mitratel and Its Competitors
Efficiency Analysis of PT Dayamitra Telekomunikasi Tbk in the Telecommunication
Infrastructure Industry Using the Data Envelopment Analysis (DEA) Method
Indonesian Journal of Social Technology, Vol. 5, No. 11, November 2024 5315
Referring to the results of the calculation in the previous section, the efficiency
value of each DMU has been grouped by company. Furthermore, the average efficiency
value for each company for 5 years, starting from 2019 to 2023, is calculated.
Efficiency Analysis in Nusantara Tower Facilities
Sarana Menara is one of the companies with the highest average efficiency value
during the observation year period (2019-2023) with an average efficiency of 0.9998 and
the lowest value of 0.9990 (2022).
Figure 5 Graph of Development and Correlation of Efficiency of Tower Facilities
Based on Figure 5, the input and output variables that affect the average value of
the efficiency of the Tower Facilities according to the Pearson Correlation calculation are
as follows:
a. There was a positive correlation (unidirectional) with a very weak correlation level (0
≤ r ≤ 0.199) between the average efficiency value of Tower Facilities and the input
variable current assets (r = +0.16)
b. There was a negative correlation (opposite) with a strong correlation level (0.60 ≤ r ≤
0.799) between the average efficiency value of Tower Facilities and the input variable
non-current liabilities (r = -0.65). There was also a moderate correlation level (0.40 ≤
r ≤ 0.599) with input variables of non-current assets (r = -0.42), number of towers (r =
-0.41), total revenue output variables (r = -0.49), and EBITDA (r = -0.50). In addition,
there was a weak correlation (0.20 ≤ r ≤ 0.399) to several input variables such as the
number of towers, operating expenses, and output variables number of tenants
c. There is no correlation between the average efficiency of Tower Facilities and the
input variable of current liabilities.
The results show that the increase in current asset variables does not have a
significant effect on the increase in efficiency value in Tower Facilities.
Based on the analysis of the trend and Pearson Correlation coefficient of
input/output variables to the average efficiency value of Sarana Menara previously,
Infrastructure Industry Using the Data Envelopment Analysis (DEA) Method
Indonesian Journal of Social Technology, Vol. 5, No. 11, November 2024 5315
Referring to the results of the calculation in the previous section, the efficiency
value of each DMU has been grouped by company. Furthermore, the average efficiency
value for each company for 5 years, starting from 2019 to 2023, is calculated.
Efficiency Analysis in Nusantara Tower Facilities
Sarana Menara is one of the companies with the highest average efficiency value
during the observation year period (2019-2023) with an average efficiency of 0.9998 and
the lowest value of 0.9990 (2022).
Figure 5 Graph of Development and Correlation of Efficiency of Tower Facilities
Based on Figure 5, the input and output variables that affect the average value of
the efficiency of the Tower Facilities according to the Pearson Correlation calculation are
as follows:
a. There was a positive correlation (unidirectional) with a very weak correlation level (0
≤ r ≤ 0.199) between the average efficiency value of Tower Facilities and the input
variable current assets (r = +0.16)
b. There was a negative correlation (opposite) with a strong correlation level (0.60 ≤ r ≤
0.799) between the average efficiency value of Tower Facilities and the input variable
non-current liabilities (r = -0.65). There was also a moderate correlation level (0.40 ≤
r ≤ 0.599) with input variables of non-current assets (r = -0.42), number of towers (r =
-0.41), total revenue output variables (r = -0.49), and EBITDA (r = -0.50). In addition,
there was a weak correlation (0.20 ≤ r ≤ 0.399) to several input variables such as the
number of towers, operating expenses, and output variables number of tenants
c. There is no correlation between the average efficiency of Tower Facilities and the
input variable of current liabilities.
The results show that the increase in current asset variables does not have a
significant effect on the increase in efficiency value in Tower Facilities.
Based on the analysis of the trend and Pearson Correlation coefficient of
input/output variables to the average efficiency value of Sarana Menara previously,
Kaila Zahra Nandika
Indonesian Journal of Social Technology, Vol. 5, No. 11, November 2024 5316
several conclusions and recommendations can be drawn for Mitratel to maintain
efficiency and improve the management of its company:
1. Tower Facilities Efficiency Trend: Tower Facilities show a relatively stable efficiency
trend with high values every year during the observation period (2019-2023). This
reflects the excellent management of input variables in producing maximum output.
2. Pearson Correlation Analysis: Based on the results of Pearson Correlation, Sarana
Menara shows that the control of variables non-current assets, non-current liabilities,
CAPEX, number of towers, total revenue, EBITDA, and number of tenants contributes
to increased efficiency.
3. Tower Facilities Situation: Based on the results of Pearson Correlation, there are
indications that Tower Facilities have not fully optimized the management of the
number of towers. The construction of telecommunication infrastructure towers does
not seem to have optimally considered economic factors, the purchasing power of
tenants, and market potential in the region (Siregar & Nurlaila, 2023).
Efficiency Analysis on Indus Towers
Indus Towers had a high average efficiency value during the observation year
period (2019-2023) with an average efficiency of 0.9832 with the lowest efficiency value
of 0.9522 (2023).
Figure 6 Development Graph and Efficiency Correlation of Indus Towers
Based on Figure 6, the input and output variables that affect the average value of
Indus Towers efficiency according to the Pearson Correlation calculation are as follows:
a. There was a positive correlation (unidirectional) with a weak correlation level (0.20 ≤
r ≤ 0.399) between the average efficiency value of Indus Towers and the EBITDA
output variable (r = +0.36)
b. There was a negative correlation (opposite) with a very strong correlation level (0.80
≤ r ≤ 0.99) between the average efficiency value of Indus Towers and the CAPEX
input variable (r = -0.96). There was also a strong correlation level (0.60 ≤ r ≤ 0.799)
with input variables of non-current assets (r = -0.69), current liabilities (r = -0.63), non-
Indonesian Journal of Social Technology, Vol. 5, No. 11, November 2024 5316
several conclusions and recommendations can be drawn for Mitratel to maintain
efficiency and improve the management of its company:
1. Tower Facilities Efficiency Trend: Tower Facilities show a relatively stable efficiency
trend with high values every year during the observation period (2019-2023). This
reflects the excellent management of input variables in producing maximum output.
2. Pearson Correlation Analysis: Based on the results of Pearson Correlation, Sarana
Menara shows that the control of variables non-current assets, non-current liabilities,
CAPEX, number of towers, total revenue, EBITDA, and number of tenants contributes
to increased efficiency.
3. Tower Facilities Situation: Based on the results of Pearson Correlation, there are
indications that Tower Facilities have not fully optimized the management of the
number of towers. The construction of telecommunication infrastructure towers does
not seem to have optimally considered economic factors, the purchasing power of
tenants, and market potential in the region (Siregar & Nurlaila, 2023).
Efficiency Analysis on Indus Towers
Indus Towers had a high average efficiency value during the observation year
period (2019-2023) with an average efficiency of 0.9832 with the lowest efficiency value
of 0.9522 (2023).
Figure 6 Development Graph and Efficiency Correlation of Indus Towers
Based on Figure 6, the input and output variables that affect the average value of
Indus Towers efficiency according to the Pearson Correlation calculation are as follows:
a. There was a positive correlation (unidirectional) with a weak correlation level (0.20 ≤
r ≤ 0.399) between the average efficiency value of Indus Towers and the EBITDA
output variable (r = +0.36)
b. There was a negative correlation (opposite) with a very strong correlation level (0.80
≤ r ≤ 0.99) between the average efficiency value of Indus Towers and the CAPEX
input variable (r = -0.96). There was also a strong correlation level (0.60 ≤ r ≤ 0.799)
with input variables of non-current assets (r = -0.69), current liabilities (r = -0.63), non-
Efficiency Analysis of PT Dayamitra Telekomunikasi Tbk in the Telecommunication
Infrastructure Industry Using the Data Envelopment Analysis (DEA) Method
Indonesian Journal of Social Technology, Vol. 5, No. 11, November 2024 5317
current liabilities (r = -0.64), number of towers (r = -0.68), and number of tenants (r =
-0.60). The correlation was moderate (0.40 ≤ r ≤ 0.599) with the input variable of
operating expenses and the output variable of total revenue. In addition, there was a
weak correlation (0.20 ≤ r ≤ 0.399) to the input variable of current assets.
Efficiency Analysis on Mitratel
Mitratel is one of the companies with the highest average efficiency value during
the observation year period (2019-2023) with an average efficiency of 0.9998 and a low
value of 0.9990 (2021).
Picture 1 Graph of Mitratel's Development and Efficiency Correlation
Based on Figure 7, the input and output variables that affect the average value of
Mitratel's efficiency according to the Pearson Correlation calculation are as follows:
a. There was a positive correlation (unidirectional) with a weak correlation (0.20 ≤ r ≤
0.399) between the average efficiency value of Mitratel and the input variable current
liabilities (r = +0.33). In addition, there was a very weak correlation (0 ≤ r ≤ 0.199)
with the total revenue output variable (r = +0.03)
b. There was a negative correlation (opposite) with a very strong correlation level (0.80
≤ r ≤ 0.99) between the average efficiency value of Mitratel and the input variables of
current assets (r = -0.96) and non-current liabilities (r = -0.87). There was also a strong
correlation level (0.60 ≤ r ≤ 0.79) with the CAPEX input variable (r = -0.75). In
addition, there is a very weak correlation (0 ≤ r ≤ 0.199) to several input variables such
as non-current assets, number of towers, operating expenses, and output variables
EBITDA and number of tenants.
The results show that the increase in current liabilities variables, total revenue, or
reduction of non-current asset variables, number of towers, operating costs, EBITDA, and
number of tenants have no significant effect on increasing the efficiency value of Mitratel.
Based on the analysis of trends and Pearson Correlation coefficients of input/output
variables to the average efficiency value of Mitratel previously, several conclusions can
be drawn that can be recommendations for Mitratel to maintain efficiency and improve
the management of its company:
Infrastructure Industry Using the Data Envelopment Analysis (DEA) Method
Indonesian Journal of Social Technology, Vol. 5, No. 11, November 2024 5317
current liabilities (r = -0.64), number of towers (r = -0.68), and number of tenants (r =
-0.60). The correlation was moderate (0.40 ≤ r ≤ 0.599) with the input variable of
operating expenses and the output variable of total revenue. In addition, there was a
weak correlation (0.20 ≤ r ≤ 0.399) to the input variable of current assets.
Efficiency Analysis on Mitratel
Mitratel is one of the companies with the highest average efficiency value during
the observation year period (2019-2023) with an average efficiency of 0.9998 and a low
value of 0.9990 (2021).
Picture 1 Graph of Mitratel's Development and Efficiency Correlation
Based on Figure 7, the input and output variables that affect the average value of
Mitratel's efficiency according to the Pearson Correlation calculation are as follows:
a. There was a positive correlation (unidirectional) with a weak correlation (0.20 ≤ r ≤
0.399) between the average efficiency value of Mitratel and the input variable current
liabilities (r = +0.33). In addition, there was a very weak correlation (0 ≤ r ≤ 0.199)
with the total revenue output variable (r = +0.03)
b. There was a negative correlation (opposite) with a very strong correlation level (0.80
≤ r ≤ 0.99) between the average efficiency value of Mitratel and the input variables of
current assets (r = -0.96) and non-current liabilities (r = -0.87). There was also a strong
correlation level (0.60 ≤ r ≤ 0.79) with the CAPEX input variable (r = -0.75). In
addition, there is a very weak correlation (0 ≤ r ≤ 0.199) to several input variables such
as non-current assets, number of towers, operating expenses, and output variables
EBITDA and number of tenants.
The results show that the increase in current liabilities variables, total revenue, or
reduction of non-current asset variables, number of towers, operating costs, EBITDA, and
number of tenants have no significant effect on increasing the efficiency value of Mitratel.
Based on the analysis of trends and Pearson Correlation coefficients of input/output
variables to the average efficiency value of Mitratel previously, several conclusions can
be drawn that can be recommendations for Mitratel to maintain efficiency and improve
the management of its company:
Kaila Zahra Nandika
Indonesian Journal of Social Technology, Vol. 5, No. 11, November 2024 5318
1. Mitratel Efficiency Trends: Mitratel shows a relatively stable efficiency trend with
high values every year during the observation period (2019-2023). This reflects the
excellent management of input variables in producing maximum output
2. Pearson Correlation Analysis: Based on the results of Pearson Correlation, Mitratel
shows that the control of current assets, non-current liabilities, and CAPEX variables
contributes significantly to increasing efficiency. The increase in the variable of
current liabilities can also be considered for increased efficiency.
3. Recommendation for Mitratel: Based on the results of the Pearson Correlation analysis
in the previous point, current asset control can be carried out by focusing on increasing
the tenancy ratio, liquidity management, short-term investment, inventory
optimization, and receivables management. This can be achieved through an
operational excellence strategy, where companies can focus on optimizing their assets
and implementing consistent and efficient processes in asset management.
The control of non-current liabilities factors can be done by controlling the capital
needed, long-term investments, capital structure, and risk management. For CAPEX
control, Mitratel can manage technology and innovation, company growth and expansion,
as well as changes in market demand. Cost leadership strategies can be used to ensure
that costs are spent selectively so that Mitratel can offer products or services at lower
prices without sacrificing quality. To consider increasing current liabilities, Mitratel can
strengthen cash management and debt payments.
Based on benchmarks with Sarana Menara, Indus Tower, and China Towers,
Mitratel can use these references to optimize the number of towers by considering
economic factors, the purchasing power of tenants, and market potential in the region.
With this, Mitratel can increase efficiency in tower management.
Conclusion
Based on data analysis for 2019-2023, PT Dayamitra Telekomunikasi Tbk
(Mitratel) shows a good trend and efficiency level, with an average efficiency of 0.9998.
This value is comparable to its competitor at the national level, PT Sarana Menara
Nusantara Tbk, which also has an average efficiency of 0.9998. However, the results of
Pearson Correlation indicate that Sarana Menara has weaknesses in managing the number
of towers, so Mitratel needs to make more efforts to maintain its superiority. At the Asia-
Pacific regional level, Mitratel's efficiency of 0.9998 is superior to Indus Towers (0.9832)
and China Tower (0.9594). However, the average number of Mitratel towers during the
period is still far below the Indus Towers (178,097) and China Tower (2,029,800). China
has good market potential with a large population and a moderate Gini Index, while India
has a large social inequality with a high Gini Index, which affects the efficiency and
management of towers.
The correlation results show that the variables of current assets, non-current
liabilities, and CAPEX have a significant negative influence on Mitratel's efficiency,
while current liabilities have a significant positive influence. Therefore, to improve
Indonesian Journal of Social Technology, Vol. 5, No. 11, November 2024 5318
1. Mitratel Efficiency Trends: Mitratel shows a relatively stable efficiency trend with
high values every year during the observation period (2019-2023). This reflects the
excellent management of input variables in producing maximum output
2. Pearson Correlation Analysis: Based on the results of Pearson Correlation, Mitratel
shows that the control of current assets, non-current liabilities, and CAPEX variables
contributes significantly to increasing efficiency. The increase in the variable of
current liabilities can also be considered for increased efficiency.
3. Recommendation for Mitratel: Based on the results of the Pearson Correlation analysis
in the previous point, current asset control can be carried out by focusing on increasing
the tenancy ratio, liquidity management, short-term investment, inventory
optimization, and receivables management. This can be achieved through an
operational excellence strategy, where companies can focus on optimizing their assets
and implementing consistent and efficient processes in asset management.
The control of non-current liabilities factors can be done by controlling the capital
needed, long-term investments, capital structure, and risk management. For CAPEX
control, Mitratel can manage technology and innovation, company growth and expansion,
as well as changes in market demand. Cost leadership strategies can be used to ensure
that costs are spent selectively so that Mitratel can offer products or services at lower
prices without sacrificing quality. To consider increasing current liabilities, Mitratel can
strengthen cash management and debt payments.
Based on benchmarks with Sarana Menara, Indus Tower, and China Towers,
Mitratel can use these references to optimize the number of towers by considering
economic factors, the purchasing power of tenants, and market potential in the region.
With this, Mitratel can increase efficiency in tower management.
Conclusion
Based on data analysis for 2019-2023, PT Dayamitra Telekomunikasi Tbk
(Mitratel) shows a good trend and efficiency level, with an average efficiency of 0.9998.
This value is comparable to its competitor at the national level, PT Sarana Menara
Nusantara Tbk, which also has an average efficiency of 0.9998. However, the results of
Pearson Correlation indicate that Sarana Menara has weaknesses in managing the number
of towers, so Mitratel needs to make more efforts to maintain its superiority. At the Asia-
Pacific regional level, Mitratel's efficiency of 0.9998 is superior to Indus Towers (0.9832)
and China Tower (0.9594). However, the average number of Mitratel towers during the
period is still far below the Indus Towers (178,097) and China Tower (2,029,800). China
has good market potential with a large population and a moderate Gini Index, while India
has a large social inequality with a high Gini Index, which affects the efficiency and
management of towers.
The correlation results show that the variables of current assets, non-current
liabilities, and CAPEX have a significant negative influence on Mitratel's efficiency,
while current liabilities have a significant positive influence. Therefore, to improve
Efficiency Analysis of PT Dayamitra Telekomunikasi Tbk in the Telecommunication
Infrastructure Industry Using the Data Envelopment Analysis (DEA) Method
Indonesian Journal of Social Technology, Vol. 5, No. 11, November 2024 5319
efficiency, Mitratel needs to control current assets, non-current liabilities, and CAPEX,
as well as consider increasing current liabilities. Current asset control can be carried out
through an operational excellence strategy with a focus on increasing the tenancy ratio,
liquidity management, short-term investment, inventory optimization, and receivables
management. Non-current liabilities control is carried out by capital management, long-
term investment, capital structure, and risk. For CAPEX, Mitratel can manage technology
and innovation, growth, expansion, and changes in market demand with a cost leadership
strategy.
Infrastructure Industry Using the Data Envelopment Analysis (DEA) Method
Indonesian Journal of Social Technology, Vol. 5, No. 11, November 2024 5319
efficiency, Mitratel needs to control current assets, non-current liabilities, and CAPEX,
as well as consider increasing current liabilities. Current asset control can be carried out
through an operational excellence strategy with a focus on increasing the tenancy ratio,
liquidity management, short-term investment, inventory optimization, and receivables
management. Non-current liabilities control is carried out by capital management, long-
term investment, capital structure, and risk. For CAPEX, Mitratel can manage technology
and innovation, growth, expansion, and changes in market demand with a cost leadership
strategy.
Kaila Zahra Nandika
Indonesian Journal of Social Technology, Vol. 5, No. 11, November 2024 5320
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kompetensi lulusan SMK di kabupaten Gunungkidul. Jurnal Akuntabilitas
Manajemen Pendidikan, 7(1), 84–96.
Indonesian Journal of Social Technology, Vol. 5, No. 11, November 2024 5320
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