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Using Agent Based Modeling to Determine High School Student Selection System in

Bandung, Indonesia

Utomo, Dhanan Sarwo

Bandung Institute of Technology

Putro, Utomo SarjonoBandung Institute of Technology

Novani, SantiBandung Institute of Technology

Siallagan, ManahanBandung Institute of Technology

Dhanan Sarwo Utomo, Sch. of Business & Management, Bandung Institute of Technology, Jalan Ganesha 10,Bandung, Indonesia, krigjsman@gmail.com

ABSTRACTAccording to Indonesian Republic Law No.32 2004,each local government has an obligation to design a

high school student selection system that is suitable tobe implemented in their region. Unfortunately,student selection systems that are currently

implemented still cannot produce the expected result.We construct an agent-based model that mimic the

student selection process in Bandung. By conductingexperiments using this model, the limitations ofcurrent student selection system are identified andmodification is suggested. By observing agents

success rate in this model, we also able to identify thebest strategy for agent in order to be qualified in theschool of their choice under different kind of system.

INTRODUCTION

As the impact of the implementation of regional

autonomy, each local government has an obligation todesign and implement high school students selectionand acceptance program (known as the PSB program).

Currently, local governments in Indonesia areimplementing their own student selection system.These systems vary in terms of technology used, thenumber of schools that can be selected by student

candidates, and how schools are categorized orclustered.

Although there are various systems, a number of

education experts and practitioners in Indonesiaagreed that a good student selection (PSB) systemshould be able to distribute the number of incoming

students evenly to all schools that participate in thePSB program. However, the systems that are applied

at this time can not guarantee that the incomingstudents are distributed evenly. This failure hascaused a number of school students suffering from

student deficiency. Student deficiency will not onlyreduce schools income but also, create opportunityfor corruption and nepotism practices.

Every year, local governments seek to improve studentselection system that they conducted based on theevaluation of systems performance in the previous

year. This practice is a form of experiments that isconducted on real systems, which not only wastemoney, but also often considered to be detrimental for

the parents and student candidates. Computersimulation can help in designing a better studentselection system. Through computer simulation,

experiments can be conducted with various systemsand populations configurations. Impacts caused by

the implementation of a system can be anticipatedwithout causing any risk on the real system [1].

The objective of this study is to build an agent-based

simulation that is able to mimic the high schoolstudent selection system in Bandung city. Throughthis simulation, the performance of the current studentselection system is tested under various populationconditions. Several system configurations are also

tested and their performances are compared to the

performance of the current system. System's ability toreduce student deficiency becomes the main parameterto determine whether the system is good or bad.

SIMULATION MODEL

There are four main assumptions used in buildingagent-based simulation for student selection system in

Bandung. The first is that, the condition of applicants,the condition of schools, and the selection systemshould be appropriate for the case of Bandung.According to the PSB data in 2008, there are

approximately 36,000 applicants and 26 schools thatparticipate in the PSB program. The schools areclustered into five, based on their previous

achievement (the most prestigious schools are placedin the first cluster). Each applicant may apply to two

schools from different cluster. Each school then selectN (the school capacity) applicants with the highestnational examination score.

The second, each applicant select the schools to whichthey will apply based on the schools aggregate benefit(utility). There are three criteria in calculating the

aggregate benefit value.a) First is the distance from the applicants

home to each school [5]. The distance

mailto:krigjsman@gmail.commailto:krigjsman@gmail.com

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travelled to school is calculated usingEuclidean distance. The x and y position ofschools and applicants homes is obtained

by dividing the Bandung city into 6 x 4 grid.The position of each school is assignedbased on the coordinate in which the schoolis really located. Applicants home x and y

position are initiated by using Monte Carlomethod. Theprobability that the applicantshome is located at the point of x = i iscalculated based on the total population in

each district along the line of x = i dividedby the total population in Bandung. Thesame procedure is applied in assigning the yposition.

b) Second is the applicants expectation aboutthe school quality [3] that is determined by

the schools previous achievement.c) The number of competitor that will be faced

by the applicant. This number is representedby the number of applications that have beensent to the school and will vary during the

simulation process.

The third assumption is that, the applicants nationalexamination score will become a constraint inchoosing the schools to which they will sent their

application. In the case of Bandung, the schools have

no exact minimum standard of applicants nationalexamination score to be accepted. The minimumnational examination score that was accepted in the

given school (known as passing grade) usuallybecome an anchor for the applicants. Therefore, theapplicants may have different perception toward thepassing grade. First, an applicant may act neutrally

(apply to a school only if their score is higher than thepassing grade of that school), second, an applicant

may act pessimistically (make some adjustment toanticipate in case the minimum score needed to be

qualified increase), third, an applicant may actoptimistically (dare to make speculation and apply tothe top school of their choice).

The last assumption used in this simulation is that theapplicants only have limited information about the

schools attributes. They relied upon social network

[2] that is modeled using Watts-Strogatz small worldnetworks [6]. This kind of network is chosen because

schools in Bandung have no certain catchment area.Therefore, the network an applicant may have is notlimited to their neighborhood and the nearest neighbormodel seems inappropriate. At the beginning of the

simulation, an applicant will gather information fromrandom school. In every iteration, each applicant willtransmit school information they have to a randomfriend in their social network.

FIGURE 1: The general simulation algorithm

SIMULATION RESULTThe simulation is conducted using Spot OrientedAgent Role Simulator (SOARS) that developed byProf. Hiroshi Deguchi at Tokyo Institute of

Technology, Japan. In the simulation process threeexperiments are conducted. The aim of the first

experiment is to test the performance of the currentpolicy (five clusters with two choices) under variouspopulation variations. The second and third

experiment aimed to test the performance of theselection policy using different number of cluster. Ineach experiment, four kinds of scenarios are carried

out. The first scenario involve only neutral agents, thesecond involve only pessimistic agents, the third

involve only optimistic agents and in the last scenarioall types of agents are involved with equal proportion.

The results of our early simulation are shown in thefollowing figures.

A)

B)

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C)

FIGURE 2: A) The number of student deficiency under

current policy; B) the number of student deficiency if the

cluster is reduced into three clusters; C) the number ofstudent deficiency if the cluster is increased into six clusters

In the first experiment observed that in the currenthigh school student selection system, the student

deficiency is always occurring in every scenario. Thenumber of student deficiency increases drastically inthe second scenario, when all agents are pessimistic.In the second experiment, the number of cluster is

reduced into three clusters. This system performsbetter in eliminating the number of student deficiency

in the second and fourth scenario. But, this systemperforms worse than the current high school studentselection system in the first and third scenario. In the

third experiment, the number of cluster is increasedinto six clusters. This system performs better inminimizing student deficiency in all scenarios. Thestudent deficiency only occurred in the third scenario,

with less number compare to the two previoussystems.

In order to decide the best agents strategy, we

calculate the proportion of each type of agent to thetotal number agent who qualified in all experiment.

From all simulation result the number of neutral agentwho qualified is 41.67%, pessimistic agent is 32.92%

and optimistic agent is 25.42%.

CONCLUSION

From the simulation result it can be concluded that the

current student selection is very sensitive to thevariation of population proportion. The currentstudent selection system is vulnerable in resulting highnumber of student deficiency especially, when the

number of pessimistic agents is high. In order toimprove student selection system in Bandung, wesuggest increasing the number of cluster that is usedfrom five clusters to six clusters. This adjustment will

minimize the number of student deficiency that occursin the selection process. By comparing the proportion

of each type of agent to the total number of qualifiedagents the best agents strategy can be obtained. Fromthe simulation result, it can be observed that agentswith neutral type perform better than pessimistic andoptimistic agents. Neutral agent is tending to berealistic. They do not make any speculation but also

will not avoid competition.

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