SINERGI Vol.
No.
October 2023: 383-392 http://publikasi.
id/index.
php/sinergi http://doi.
org/10.
22441/sinergi.
Proposed model and strategy for Indonesian higher education facing technological disruption and Industrial Revolution 4.
using Newton's Laws analogy Erkata Yandri1*.
Sparisoma Viridi2.
Rizal F.
Hariadi3 Graduate School of Renewable Energy.
Darma Persada University.
Indonesia Department of Physics.
Faculty of Mathematics and Natural Sciences.
Institut Teknologi Bandung.
Indonesia Department of Physics.
Arizona State University.
US Abstract All economic sectors in the world cannot be separated from the challenges of facing an era of technological disruption and the Industrial Revolution 4.
0 (TD4IR).
As centers of higher education, campuses also cannot escape from this difficult situation.
Many studies have discussed the impact of TD4IR on higher education, unfortunately, without a detailed strategy for dealing with it.
The global issues were analyzed using a physical approach, such as Newton's laws.
Our study's purpose is to evaluate a strategic principle that Indonesian colleges can use to prepare for TD4IR.
applied Newton's analogy system to strategic management, then modeled with the derivation of the formula, followed by modeling with the simulation, to determine the changes that higher education would need to make in response to Industrial Revolution 4.
Acceleration and deceleration scenarios are implemented with a square or cube increase or reduction.
From the modeling, the parameters of Newton's laws, such as mass, friction .
arriers to chang.
, force .
nternal assets as a driving forc.
, etc.
, must be properly matched to the idea of strategic management of higher education to give a clear picture of the problems.
From the simulation, higher education needs to know the minimum value of its organizational system so it can figure out what needs to be done right away.
The organization doesn't suddenly slow down or stop.
higher education needs to speed up as much as possible.
Keywords:
Driving forces.
Education management.
Higher education.
Scientific collaborations.
Strategic management.
Article History:
Received: May 2, 2019 Revised: May 29, 2019 Accepted: June 2, 2019 Published: June 2, 2019 Corresponding Author:
Erkata Yandri Graduate School of Renewable Energy.
Darma Persada University.
Indonesia Email:
erkata@pasca.
This is an open access article under the CC BY-SA license INTRODUCTION All sectors in the world are currently experiencing great shocks and challenges as they enter the era of technological disruption and industrial revolution 4.
0 (TD4IR) .
To better understand this situation, let's discuss what TD4IR means.
Technological disruption is defined as a fast change in technology as a result of the Internet of Things (IoT), autonomous devices, artificial intelligence (AI), rich media .
irtual and augmented realit.
, producing smart environments, and revolutionizing industry structures, and processes .
While, the industrial revolution 4.
0 is defined by the creation of new products, procedures, and services, as well as the widespread use of intelligent networked systems.
The scale and intensity of the problems faced by each organization in each sector are certainly relatively different.
However, in general, they have the same goal, to survive and get through this difficult situation.
The right strategy is required to win against TD4IR.
Higher education institution, cannot be separated from facing this situation .
The higher education sector in the ASEAN region has anticipated the situation, but questions remain concerning the institutions' management and financial preparedness .
The competition Yandri et al.
Proposed model and strategy for Indonesian higher education facing A SINERGI Vol.
No.
October 2023: 383-392 among campuses in getting and preparing the younger generation, especially in Indonesia, will certainly be tighter .
Researchers from all over the world have been drawn to the issue of higher education in dealing with TD4IR.
Publications on this issue are increasing from year to year.
In general, three topics are discussed.
First, give an overview with an impact analysis .
Ae.
Bonfiel et al.
examined the digital revolution's effects on higher education and the future of curriculum delivery.
Nugraha et al.
put forward higher education students that study management, including empathy, compassion, and other humane Abad-Segura et al.
looked at how digital transformation affects the education sector and how sustainable management can adapt to new technologies.
Second, analyzing readiness and modeling .
Ae.
According to Teng et al.
, the university curriculum enhances student soft skills and supports the relationship between soft skills and student job readiness.
Maria et al.
showed that the Malaysian government is aware of and preparing for TD4IR and education 4.
The lecturer competencies that must be achieved right away were highlighted by Sitepu et al.
including educational competence, research, technology commercialization, future strategy, counseling, globalization, and collaboration.
Lack of knowledge utilization, information, quality of knowledge sharing, and network distribution are four new criteria discovered by Sardjono and Firdaus .
that influence the readiness for the deployment of the knowledge management Hartati et al.
offered a formulation to examine the university's preparedness for the TD4IR era using the Cobit 5 Framework, which combines a descriptive and qualitative approach.
Nurhasan et al.
assessed a university's readiness for TD4IR and advised integrating higher education strategy to industry demand.
Finally how the strategy should handle the TD4IR .
, 19, 20Ae27, 28, .
Lee et al.
suggested expanding the open innovation culture and the feedback loop of an open platform business model.
Based on the facts of Indonesia's education system.
Lukita et al.
proposed the competencies required in the integration of education management and the TD4IR to be presented in a comprehensive Jackson .
examined the historical processes in higher education, found three reoccurring faults, and related them to absorptive Li et al.
created a digital learning requirement for a digitally resilient higher education system and produce graduates who can handle upheaval and uncertainty.
Miranda et .
offered new educational paradigms, teaching tools, learning processes, and infrastructures to shape Education 4.
Supriyanto et al.
suggested Kaizen as a continuous improvement approach to improve the quality and productivity of education.
To improve the standard of education.
Prestiadi et al.
envisioned the crucial role played by visionary leadership in Total Quality Management 4.
create a road map for the modernization of higher institutions.
Alzahrani et al.
utilized the Quality 4.
0 tools and methodologies along several different axes.
To address the 4IR.
Ramirez-Montoya et al.
characterized a professor as a specialist with skills in creativity, problem-solving, teamwork, entrepreneurship, an international viewpoint, leadership, and societal Kipper et al.
proposed the key education, such as knowledge of current fields, leadership, strategic vision, self-organization, giving and receiving feedback, proactivity, creativity, problem-solving, interdisciplinarity, .
nformation automation, software development, and security, data analysis, general systems, and sustainable According to Mulyani and Koencoro .
Indonesia's education policy framework focused on access to education, quality of education, synergies between government, industry, and higher education, industrial linkages, and incentives.
Mian et al.
stated that good financial planning, qualified sophisticated infrastructure, redesigned curricula, and insightful workshops are important prerequisites for higher education in TD4IR.
There have been many studies on various topics discussing the impact of TD4IR, which has an impact on the world's higher education sector, including Indonesia.
However, how they are caused or detailed strategies for dealing with them have not been discussed.
This is an important thing for higher education to master to successfully face TD4IR.
The global issues, including TD4IR, can be analyzed using a physical approach such as Newton's laws .
Based on what has been reviewed, this paper aims to explain strategic concepts in higher education in Indonesia facing technological disruption and the industrial revolution 4.
0 using NewtonAos laws as an analogy.
Newton's laws analogy could give a clearer picture and strategy for higher education in dealing with TD4IR.
Yandri et al.
Proposed model and strategy for Indonesian higher education facing A p-ISSN: 1410-2331 e-ISSN: 2460-1217
METHOD
Concept Analogy Figure 1 illustrates the challenges of higher education in the TD4IR era using the analogy of NewtonAos laws.
As shown, a vehicle considered a higher education or university is moving uphill with a certain driving force from the point of departure towards a challenging height on the Yaxis with a certain achievement on the X-axis.
object moving on an inclination plane enforces all the parameters in NewtonAos laws.
It applies all the forces in NewtonAos laws, such as.
forward force, frictional force, normal force, and gravity.
Here, the Y-axis is called the level of disruption, and the X-axis is called the level of achievement.
The inclination angle is formed by the Y-axis as the challenging height and the X-axis as the level of The level of disruption is a change required by higher education in facing the TD4IR era, as shown in Table 1.
The points of change in Table 1 are not hierarchical, although they are seen as such in Figure 1.
They can be overlapping, sequential, or semi-parallel.
To show differences, they become hierarchical.
For simplification of calculations and further analysis, the level of disruption is given a scale of 0-10, as is the level of achievement.
Table 1 summarizes the key points of higher educationAos immediate response to the TD4IR era .
, 10, 11, 12-18, 19-28, 29, 31.
As shown, there are 10 points of change, h1 to h10, which consist of 2 inputs, 6 processes, and 2 outputs.
All items of change, as shown in Table 1, were developed from the literature with placement and adjustment according to the context of the discussion.
Change occurs in a system.
Thus, comprehensive changes must be made starting from the input, process, and output (IPO).
Input is something entered into the system.
The process is an activity in a system.
While the output is the result of a system.
Figure 1.
NewtonAos laws model for TD4IR Table 1.
Changes required due to TD4IR Changes Required Section Weight Changes lectures, and management.
h1, .
, 26, .
Changes in visionary and sustain the management and operation, h2, .
, 18, 22, 26, 27, .
Changes in roles of students, lecturers, management, and principals in terms of quality, creativity, etc.
, 16 18, 26, .
Changes in the method of teaching, the competence of lecturers, learning organizations in technology, h4, .
, 4, 17, 20, 23, 24, 26, 27, .
Changes in the content of the curriculum to drive disruption and the industrial 0, h5, .
, 19, 24-24, 32, .
Changes in the way to control the quality of operations, research, management, etc.
h6 , .
, 21-23, .
Changes in the collaboration activity nationally and internationally, etc.
h7, .
, 26, 27, .
Changes in the support and involvement of stakeholders, etc.
Changes in the output of research, h9, .
, 24, .
Changes graduatesAo qualification & competence, h10 .
, 24-24, 32, .
Input Input Process Process Process Process Process Process Output Output Total The reason for more processes than input or output is to show the importance of a process in a system with proper inputs and optimal results.
For simplification, each item of change is given a weighted value of 1.
In this case, changes do not have to occur sequentially but can occur simultaneously, even irregularly.
This is due to the opportunity, readiness, and interest of an To apply NewtonAos laws, several other related parameters must be involved.
Table 2 explains the analogy of NewtonAos laws with the management description to describe the challenges of higher education in the TD4IR.
The level of disruption Yn is determined by the number of the i -th disruptive criteria with hi, relate .
to Table 1.
Yandri et al.
Proposed model and strategy for Indonesian higher education facing A SINERGI Vol.
No.
October 2023: 383-392 Table 2.
Analogy system of Newton laws with strategic management for higher education No.
Newton Laws Mass, m Acceleration, a Time, t Velocity.
Gravity, g Displacement.
Inclination angle.
An Driving force.
Static & kinetic friction force, fs , fk Static & kinetic friction coef.
As .
Ak Work.
W Energy.
Ek Power.
P Positive force, the internal driving force of the system/institution .
oney, asset, intellectual property, donor/sponsor, etc.
Negative force, system / institutional barriers to change .
indset, vision, self-learning.
Factors such as barriers to change .
evel of teamwork, level of vision, level of willingness to learn, level of maturity organization, level of technology.
Energy is converted to or from an object along a displacement to make changes The quantitative property that is transferred to a system to make The amount of energy converted per unit of time to make changes yn = Eu hi i =1 The level of difficulty of each criterion does not have to be the same.
hi C hj, then.
The inclination angle of the plane is considered as a ratio .
arget Ae current stat.
to the distance reached st.
there are n criteria and m items have been With, 1 O i O m , then the angle on the A trigonometrically, as.
sin A = n = ( xn ) 2 ( yn ) 2 cos A = n = ( xn ) ( yn ) 2 Eu hi Oe Eu hi tan A n = i =1 Management description Overall organizational burden .
rofessors, lecturers, researchers, students, staff, lecture programs, etc.
Acceleration of the system to move forward Time of work to make certain Speed of work to make certain Natural factors against system/institution to change Change achieved with work and Challenges to making changes .
i =1 sin A is the ratio of the level of disruption y to the displacement s .
cos A is the ratio of the level of achievement to the displacement s .
tan A is the ratio of the level of disruption y to the displacement s .
System Modelling The 2nd Newton law is expressed as.
Eu F = ma F is the forces acting on the object in a plane [N], m is the mass .
, and a is the acceleration .
/s.
For no displacement, the projection of all forces onto the Y axis is given by a y = 0 , then.
N Oe Wy = 0 N = Wy = mg cos A n .
is gravity .
/s.
and A n is the inclination angle of the plane from normal to industrial revolution 4.
0 and disruptive.
For displacement, the projection of the forces to the X axis is given by ax = a , then, where, g Fd Oe Wx Oe f = ma Fd is the driving force on the object [N].
Wx is the weight of the object on the X axis, and f is the frictional force on the object [N], consisting of static f s and kinetic f k friction forces as expressed.
f s = Ak N f k = Ak N .
A x and A k are the static and kinetic friction To remain stable in place.
Eu F = 0 , .
Oe mg sin A n A s N = 0 Oemg sin A n A s mg cos A n = 0 A s = sin A n cos A n .
A s = tan A n If there is no Fd , then A s A tan A n so that the system is stable.
If there is Fd for the object to move up.
Eu F = ma A 0 , then, .
Fd Oe Wx Oe Ak N = ma A 0 Fd Oe mg sin A n Oe Ak mg cos A n A ma Yandri et al.
Proposed model and strategy for Indonesian higher education facing A p-ISSN: 1410-2331 e-ISSN: 2460-1217 where.
Fd Oe mg sin A n A A k mg cos A n for initial After moving, the friction will be smaller with A k A A s .
To move up, the object must resist the static friction force f s .
Fd Oe mg sin A n A A s mg cos A n Fd Oe mg .
in A n A s cos A n ) A 0 Fd A mg .
in A n Oe A s cos A n ) .
Substitute .
, the energy required to carry an object along st is.
Ek = Fd st Oe mgst .
in A Ak cos A ) Fd Oe mg .
in A Ak cos A ) .
Substitute .
, then.
After these conditions are met, the acceleration is expressed by using .
F Oe mg sin A Oe A mg cos A = ma t = 1 a 2 Ek m x = st cos A = .
cos A Fd Oe mg .
in A Oe A k cos A ) y = st sin A = .
sin A Fd Oe .
in A Oe Ak cos A ) Fd Oe mg .
in A n Oe Ak c os A n ) = ma F Oe mg .
in A n Oe Ak cos A n ) a= d a = Oe g ( sin A n Ak cos A n ) Fd m From .
, the time t required to move along st is.
a = Oe g ( sin A f k mg ) Fd m a = Oe g sin A ( Fd Oe f k ) m Substituting .
to A k .
From .
can be understood that: the system will move forward if the acceleration a A 0, means ( Fd Oe f k ) m A g sin A n , or vice versa move a A 0, means ( Fd Oe f k ) m A g sin A n and Ak = 0.
The system is not stationary, but moves straight line and changes uniformly with the velocity vt .
vt = vo at .
For initial conditions, vo = 0 , .
vt = at Then, the relation among vo , vt , a dan st and t are.
2 Ek Fd Oe mg .
in A A k cos A ) m 2 Ek Fd Oe mg .
in A A k cos A ) m Then.
Power P is expressed as.
P = Ek st t and work Wn Wn = Eu Fst Wn = ( Fd Oe f k ) st With minimum a A 0 from .
, then.
Wn / m = g sin A n st To summarize, st , .
ao , vt , t , vt2 = vo2 2ast vt2 = 2ast st = vt2 2a st = vot 1 2 at 2 2st = at 2 standard parameters to determine the initial state, while Power intensity and Work intensity are KPIs to measure the performance of an ongoing system.
To adjust to the actual situation, acceleration and deceleration scenarios are applied with an increase or decrease in squares or cubes as shown in .
a = a .
C x .
) p a 2t 2 = 2ast t = 2st t .
acceleration from .
the xn is the The kinetic energy system Ek is expressed as.
Ek = 1 2 mvt2 mvt2 = mast level of achievement.
the c is constant of 0.
the p is the power with 2 .
and 3 .
Then, operator (A) means ( ) for acceleration and (-) for deceleration.
Yandri et al.
Proposed model and strategy for Indonesian higher education facing A SINERGI Vol.
No.
October 2023: 383-392
RESULTS AND DISCUSSION
Figure 2 shows the relationship between the minimum distance-to-move st to the level of achievement x.
Using polynomial 2 variables, the relationship between t and x is strong with almost perfect R2 = 0.
That is, if the level of achievement x increase, then the minimum time to move required by the system will also increase The regression equation illustrates that for the lower x = 0, the system still required the minimum distance to move st = 9.
At x C 4 , the distance to move is relatively smaller when compared to the displacement distance at x C 5.
Figure 3 shows the relationship between forward acceleration a to the level of achievement x.
Using polynomial 2 variables, the relationship between a and x is strong with the perfect R2 = 1.
That is, if the level of achievement x will be greater, then the acceleration required by the system will increase gradually.
The regression equation illustrates that if the minimum a0 is not reached, then the system will move backward.
The effect of squared and cubed acceleration will make the acceleration graph farther away from the minimum forward acceleration graph, and vice versa for As shown, squared and cubed acceleration gives a significant difference, while deceleration gives almost the same results.
Figure 4 shows the relationship between the minimum time to move t to the level of Using polynomial 2 variables, the relationship between t and x is strong with R2 = If the level of achievement x will be greater, then the minimum time to move required by the system will be smaller.
The regression equation illustrates that for the lower x C 4 , the system still required time to After x C 5 , t gets smaller and tends to be constant.
The effect of squared or cubed acceleration makes the time to move further away from the minimum time to move, and vice versa for deceleration.
During acceleration, the time needed to make changes becomes longer.
As shown, the effect of squared and cubed acceleration gives a significant difference in time to move, while deceleration gives almost the same time to move.
Figure 5 shows the relationship between minimum velocity-to-move v to the level of Using polynomial 2 variables, the relationship between v and x is strong with R2 = 0.
That is, if the level of achievement x will be greater, then the minimum velocity-to-move required by the system will increase gradually.
The regression equation illustrates that the minimum v = 3.
04 should be maintained for the system to move forwards.
The effect of squared or cubed acceleration will make the velocity-tomove graph move further away from the minimum velocity-to-move, and vice versa for This means, during acceleration, the time needed to make changes becomes As shown, the effect of squared and cubed acceleration gives a significant difference in velocity-to-move, while deceleration gives almost the same velocity-to-move.
Figure 2.
Distance to move to the level of Figure 4.
Time to move vs level of achievement Figure 3.
Forward acceleration to move vs level of achievement Yandri et al.
Proposed model and strategy for Indonesian higher education facing A p-ISSN: 1410-2331 e-ISSN: 2460-1217 Figure 5.
Velocity to move vs level of Figure 6 shows the relationship between minimum power-to-move P / m to the level of achievement x .
Using polynomial 2 variables, the relationship between P / m and x is strong with R2 = 0.
If the level of achievement x increase, then the minimum power-to-move required by the system P / m also increases The regression equation illustrates that for the lower x = 0 , the system still losses power-to-move P / m = Oe20.
08 , and increases even more for x A 0 .
The effect of squared or cubed acceleration makes the power-to-move graph move further away from the graph of minimum power-to-move, and vice versa for During acceleration, the power-tomove needed to make changes becomes bigger.
As shown, the effect of squared and cubed acceleration gives a significant difference in power-to-move, while deceleration gives almost the same power-to-move.
Figure 7 shows the relationship between minimum work intensity to move W / m to the level of achievement x .
Using polynomial 2 variables, the relationship between W / m and x is strong with R 2 = 1 .
Figure 6.
Power to move Figure 7.
Work to move vs level of achievement That is, if the level of achievement x increase, then the minimum power-to-move required by the system W / m will also increase The regression equation illustrates that for the lower x = 0 , the system still losses workto-move W / m = Oe66.
49 , and will increase even more for x A 0 .
The effect of squared or cubed acceleration will make the work-to-move graph move further away from the graph of minimum power to move, and vice versa for It means, during acceleration, the work-to-move needed to make changes becomes As shown, the effect of squared and cubed acceleration gives a significant difference in work-to-move, while deceleration gives almost the same power to move.
Based on the results described above, there are three things to discuss.
First.
basic parameters: such as.
forward acceleration a , minimum time to move t , minimum velocity to move v , and minimum distance to move st , as shown in Figure 2-5.
Based on individual graph simulations, it turns out that these four parameters can provide an overview of a system in the TD4IR era.
general, the simulation of these parameters can provide an understanding of higher education management and what should be done when preparing a strategic plan in dealing with TD4IR.
n this strategic planning, higher educationAos management must focus on several things, such what to prepare in the early stages, what actions to take when just starting and reaching some milestones, and how to secure achievements when at the peak of the challenge.
Second, regarding power intensity P / m and work intensity W / m as shown in Figure 67.
These two parameters illustrate that certain power and energy reserves must be owned by higher education when running the program.
Losing a certain amount of power and energy will cause the campus to move backward or collapse.
Yandri et al.
Proposed model and strategy for Indonesian higher education facing A SINERGI Vol.
No.
October 2023: 383-392 For this reason, campus management has to find sources providing power and energy to move forward in facing the challenges of TD4IR.
Third, higher education in Indonesia have to understand its current position in the TD4IR This is an important point in accelerating understanding of what should be done.
Or, act organizational system in a state of deceleration.
With applied engineering in management and information systems, the proposed model can be developed through a mobile application integrated with a centralized data processing The system receives information manually and automatically, then processes and displays the results as the campus movement status in the TD4IR era .
Thus, all stakeholders can monitor the progress of their campus at anytime from anywhere.
Based on what has been discussed.
Newton's laws analogy can provide practical application to higher education in making a management decision related to TD4IR.
The results of Newton's law analogy study further complement the results of previous research, which has provided an overview of impact analysis .
, 10, .
, analyzed readiness and modeling .
, 13, 14, 15, 16, .
, and how the strategy should handle the TD4IR .
, 19, 28, .
, thereby providing a clearer picture of how higher education in Indonesia should succeed in dealing with technological disruption and Industrial Revolution 4.
Apart from being a tool for decision-making, this study also contributes to the development of advanced strategic management science.
For future research, this study needs to be developed further and in detail related to management science, and information CONCLUSION We have discussed the proposed model and strategy of Indonesian higher education in dealing with TD4IR using Newton's Law analogy.
Some important results can be concluded as A A simple Newton's laws analogy can be used to explain the challenges of higher education in the TD4IR era.
A Higher education in Indonesia must immediately understand the 10 points required of change in facing the TD4IR era.
A The .
rganizational burde.
, friction .
arriers to chang.
, force .
nternal assets as a driving forc.
, etc, must be appropriately analogized to the concept of strategic management of higher education to provide a clear picture of the problems faced in the TD4IR era.
A From the simulation, higher education must understand the minimum value of its organizational system to understand what action must be taken immediately.
A Higher education must accelerate as much as possible so that the organization does not slow down or stop suddenly in facing the challenges of TD4IR.
Newton's Law analogy already provides an overview of practical applications in strategic management, but needs to be further developed a real-time monitoring system concerning management science and information systems.
REFERENCES