Model Development of Smart Transportation using the Performance

Measurement of Smart City Result with It Balance Scorecard and IPA

Matrix: Jakarta Case

Ratna Sari

1,2

, Firman Anindra

2,3

, Raymond Kosala

, Benny Ranti

and Suhono Harso Supangkat

2,4

Information Systems Department, School of Information Systems, Bina Nusantara University, Jakarta 11480, Indonesia

Computer Science Department, BINUS Graduate Program-Doctor of Computer Science, Bina Nusantara University,

Jakarta 11480, Indonesia

Department of Informatics and Communication Universitas Nasional, Jakarta, Indonesia 12520

Sekolah Teknik Elektro dan Informatika, Institut Teknologi Bandung, Bandung, Indonesia

Keywords:

Transportation, Smart Transportation, IPA Matrix, Balance Scorecard, Model, Development

Abstract:

The purpose of this research is to carry out evaluations and measurements related to the application of smart

cities Jakarta which will continue propose a smart transportation model. The methodology used in the evalua-

tion is using the IT Governance approach with the IT Balance Scorecard and IPA Matrix with 200 respondents

from Jakarta residents. The results of this paper show there are three major problems that need to be addressed

immediately: Deliver Value; Manage operational service performance; Deliver successful IT projects, and the

solution purposed is creating smart transportation model, as a form of improvement.

1 INTRODUCTION

The population growth has increased to 29.43% from

2017 to 2050, reaching 9.772 billion people (of Eco-

nomic and Affairs, 2017). The result of this condition

is increasing complex in one of big cities problems,

but transportation also being one problem in large

cities. Good transportation facilities just avoid con-

gestion are always the main points, but beside that the

needs to be considered is provoding safe and comfort-

able transportation being a real thing faced in the city

today. Based on land transportation statistics from In-

donesian Central Bureau of Statistics, it is known that

the level of accidents is still high even the death is

quite high.

The high mobility of people and goods in Jakarta

still not in accordence with safety and comfortable

public transportation. The current trend is designing

smart transportation related to smart alternatives to

control private vehicles and reduce congestion (Pin-

darwati and Wijayanto, 2015) Previously, the simi-

lar development had been carried out, not only us-

ing transportation system management but also using

RFID as part of its development (Wen, 2010).

In Indonesia especially Jakarta City, many imple-

mentation proposals have been made, but for some

people, application of the technology is the main

point, but conversely transportation is not only about

avoiding congestion but also how to create safe trans-

portation. The level of accidents related to transporta-

tion still not good and become a story, we can say, it

happend without stopping.

In this paper will conduct an assessment in ad-

vance related to the implementation of smart city

that has been applied before and propose the design

of transportation system framework speciﬁcally in

Jakarta to provide smart and safe transportation solu-

tions. This study is based on literature and the data

used are land transportation statistics from the Na-

tional Statistics Agency.

2 RELATED RESEARCH

Previously, the research started with reference search

related to smart transportation previous research

which would be used as references:

Sari, R., Anindra, F., Kosala, R., Ranti, B. and Supangkat, S.

Model Development of Smart Transportation using the Performance Measurement of Smart City Result with It Balance Scorecard and IPA Matrix: Jakarta Case.

DOI: 10.5220/0009907901990205

In Proceedings of the International Conferences on Information System and Technology (CONRIST 2019), pages 199-205

ISBN: 978-989-758-453-4

199

Table 1: Previous Research Comparison of Smart Trans-

portation Model

Author Literatur Result

(Pindarwati

and Wi-

jayanto,

2015)

Measuring

performance

level of smart

transportation

system in

big cities of

Indonesia

Presented

a compre-

hensive

framework

for bench-

marking

smart trans-

port cities,

which was

illustrated

using the data

of 5 large city

in Indonesia

(Dirgahayani,

2013)

Environmental

co-beneﬁts

of public

transportation

improvement

initiative:

the case of

Trans-Jogja

bus system in

Yogyakarta,

Indonesia

This paper

examined the

environmen-

tal co-beneﬁt

of public

transport

improvement

programme

by taking the

introducting

of Trans-jogja

bus system

in Greater

Yogyakarta,

Indonesia

as the case

study.

(Wibowo and

Suryanegara,

2016)

On develop-

ing the model

of Smart

Logistic

Transport in

Indonesia

Proposed

the business

model of

smart logistic

transport in

Indonesia

(Effendi et al.,

2016)

Smart city

Nusantara

development

through the

application of

Penta Helix

model

This paper

presented the

framework

for smartcity

nusantara

with penta

helix model

collaboration.

(Purnomo

et al., 2016)

Smart city in-

dicators: A

systematic lit-

erature review

This paper

conducted

research of

generating

the developer

trend in the

Smart City,

especially

on indicators

which are

generally

regarded as

the main

factors in

deciding the

development

of the city

Previous studies focused more on building models

without conducting an assessment process and also

prior evaluation. This research will start with evalua-

tion and also continue with assessment process related

to the application of smart city will be carried out us-

ing the BSC IT approach which the approach is used

not only ttake measurements but also as an assess-

ment of IT management systems and in detail con-

ducting assessments related to 4 perspectives namely:

(1) Corporate Contribution; (2) Stakeholders; (3) Op-

erational Excellence; (4) Future Orientation (Grem-

bergen, 2005)

3 THEORETICAL BACKGROUND

3.1 Smart Transportation Deﬁnition

Smart Transportation can be deﬁned as transportation

model that utilizes wireless technology automatically

so as to facilitate and increase comfort for passen-

gers and drivers in their mobilization activities. In-

creased safety and efﬁciency factors are concern in an

intelligent transportation system (Pindarwati and Wi-

jayanto, 2015).

Previous research has proposed a framework for

intelligent transportation needs. There are 3 layers

needed for intelligent transportation, namely (1)input

layer, (2)storage layer, (3)analysis layer and commu-

nication layer (Shukla et al., 2016).

This is to answer transportation problems that are

common in big cities, such as: congestion, difﬁculties

in parking locations, length of travel due to conges-

tion, inadequate public transportation and disruption

of distribution (Pedersen, 2016).

Previous challenges emerged in the development

of smart transportation such as the availability of good

CONRIST 2019 - International Conferences on Information System and Technology

200

communication / data lines, the provision of accurate

information and adequate electronic devices began to

be overcome with the development of IoT and Cloud

Computing (Yang et al., 2016)

3.2 Smart Transportation Measurement

Smart Transportation Measurement The model of

smart transportation calculation uses an analysis of

the availability of transportation services to provide

solutions to transportation problems. There are 6 cat-

egories with each of the 3 domains analyzed (Pindar-

wati and Wijayanto, 2015) (Debnath et al., 2014) :

a Categories: Process and control, heal, prevent,

Sense, Predict, Communicate

b Domain: Public, Private, Commercial and Emer-

gency

Another concept mentions, the need for the fol-

lowing factors as important things that need to be

measured in assessing smart transportation systems

in a city, namely: (1)mobile services, (2)the creation

of operational efﬁciency of transportation services,

(3)availability of information for users, and diversity

payment model. Other supporting factors that also

need to be addressed are (1)the availability of park-

ing systems, (2)lighting systems, (3)car services and

(4)electricity-based refueling and (5)management of

transportation assets (Pedersen, 2016).

3.3 Jakarta Transportation Systems

Jakarta has an area of 740.28 square kilometers with

a population of around 10.27 million people in early

of 2018, on these working days this number has in-

creased due to the arrival of workers from other cities

such as Bekasi, Tangerang, Bogor and Depok. This

growth has resulted changes in land use often not

in accordance with urban planning and lack of pub-

lic services for urban infrastructure needs (Katadata,

2018) Along with the improvement of road infras-

tructure, economic growth and people’s income, the

number of vehicles has increased (of Economic and

Affairs, 2017). Based on data from the Jakarta Trans-

portation Agency, the ratio number of private vehicles

and public ransport vehicles is 98% and 2%. Various

efforts have been made by the provincial government

of DKI Jakarta to deal with the problems of the cap-

ital city of Indonesia such as: the provision of public

transportation (TransJakarta Bus, LRT, MRT), trafﬁc

engineering (regulation of odd-even vehicle numbers,

restrictions on vehicle types).

3.4 Smart City Deﬁnition

Many deﬁnitions of smart city have emerged, pre-

vious research deﬁnes Smart city as a city with the

ability to monitor, integrate the conditions of in-

frastructure including roads, land transportation, sea

transportation, communications, electricity, water and

buildings including buildings so that resources be-

come more optimal includes monitoring security as-

pects as part of the maximum service to citizens

(Madakam, 2016). As part of implementation and

realization of Smart city, there are 6 characteristics

need to be available in the city, namely: Smart Econ-

omy, Smart Environment, Smart Governance, Smart

Living, Smart Mobility and Smart People (Purnomo

et al., 2016).

3.5 IT Balance Scorecard Deﬁnition

The IT Balance Scorecard governance was developed

to assess how well the organization carries out IT

governance. IT Balance Scorecard governance is not

only done as a measurement system for IT governance

processes, but also can show a cause and effect rela-

tionship between perspectives (Jairak and Praneetpol-

grang, 2013).

The framework from IT Balance Scorecard can

describe in ﬁgure 1 while, ﬁrst the corporate contri-

bution perspective measures the performance of IT

governance processes for ensuring that business can

achieve maximum proﬁt from IT while reducing risk

at a reasonable point. Second, the stakeholder per-

spective are to measure stakeholder satisfaction, man-

agement of stakeholder needs, and legal/ethical com-

pliance. Third, the operation excellence perspective

identiﬁes the maturity of IT governance structures and

processes. Lastly, the future orientation perspective is

designed to measure the foundations of skills, knowl-

edge, and IT/business partnership for IT governance

delivery (Van Grembergen and De Haes, 2005).

Figure 1: IT Balance Scorecard (Van Grembergen and

De Haes, 2005)

4 METHODOLOGY

The method used in this paper is a literature review,

as well as making measurements using the IT Bal-

Model Development of Smart Transportation using the Performance Measurement of Smart City Result with It Balance Scorecard and IPA

Matrix: Jakarta Case

201

ance Scorecard and intensive literature comparison

conducted in Indonesia which is speciﬁcally carried

out in other big cities to see the characteristics of the

region, transportation infrastructure and types of ve-

hicles to suit the conditions of the city of Jakarta .

The initial stage done by providing data and infor-

mation about the area, transportation, accident rates

etc. Then transportation intelligence is valued from

all cities and then compared. Intelligence indicators

were adapted from a journal published by (Debnath

et al., 2014) which is the basis for research prepara-

tion as well as a benchmarking framework and better

development related to a smart and safe transportation

system. Data, transportation and infrastructure areas

were obtained from the Indonesian Central Bureau of

Statistics.

Data collection given to 200 respondents to

Jakarta community which assessment using a Likert

scale and also self-measurement using matrix impor-

tant approaches and performance analysis by (Mar-

tilla and James, 1977) which measurements focus on

how important and the level of achievement related to

implementation.

In ﬁgure 2, Quadrant I is ”concentrate here”, the

valuation of this attribute is an important part but

low performance is identiﬁed, the improvement ef-

fort must be focused on this quadrant. Quadrant II

is ”Keep up the Good Work”, valuation which means

very important and indicates that achievement has

been very good, which has been well implemented

and the organization is able to maintain its perfor-

mance. Quadrant III is labeled ”low priority”, where

the assessment in this quadrant is considered as a low

or not important enough interest. Quadrant IV is la-

beled ”possible overkill”, in this quadrant it is not too

interested but has a relatively high performance.

Figure 2: Important and Performance Analysis (Martilla

and James, 1977)

Likert scale to measure the importance of starting

from a scale of 1 to 3, namely: 1 = not important; 2 =

important; 3 = very important, while measuring per-

formance starts from a scale of 1 to 3, namely: 1 = not

monitoring; 2 = monitoring; 3 = always monitoring.

In this study, the authors used an instrument

adopted in (Abu-Musa, 2007) study where out of the

23 instruments in this study only used 15 instruments

which were divided into 4 BSC IT perspectives where

the authors considered according to the conditions of

smart city implementation in Jakarta as present in ﬁg.

Figure 3: The Instruments of Measurement of IT BSC

(Abu-Musa, 2007)

5 DATA ANALYSIS & RESULTS

The next study continued with evaluating using the IT

BSC on the implementation of smart cities where the

evaluation results are as follows:

Table 2: Cronbrach Reliability Scale

Const

ruct

Item Cronb

ach’s

(Im-

por-

tant

as-

pect)

Cronb

ach’s a

(Per-

for-

mance

aspect)

Corpor

ate

Con-

tribu-

tion

CC1;CC2;CC3;CC4 0.811 0.902

Future

Orien-

tation

FO1;FO2;FO3;FO4 0.765 0.873

Stakeho

lder

Orien-

tation

SO1;SO2;SO3 0.881 0.852

Operati

onal

Excel-

lence

OE1;OE2;OE3;OE4 0.745 0.872

The T test done by check the reliability of each

item of each construct. In table 2 the results of the test

show that Cronbach test results show a value above

0.70 where it can be concluded that the items tested

have a reliable value.

The next test result is to identify signiﬁcantly be-

tween importance and performance. In ﬁgure 3, the

test results show that the respondent’s results show

the distribution in each quadrant. The Importance and

CONRIST 2019 - International Conferences on Information System and Technology

202

Performance Matrix shows that: quadrant I shows at-

tributes that need to be improved; quadrant II informs

the point that the organization has worked well and

needs to maintain its quality; quadrant III shows low

priority priorities and organizations usually limit re-

sources in quadrant III; quadrant IV focuses on opti-

mal use of resources including the re-establishment of

policies in an organization.

The next test result is identify signiﬁcantly be-

tween importance and performance. In ﬁgure 4, the

test results show that the respondent’s results show

the distribution in each quadrant. The Importance and

Performance Matrix shows that: quadrant I shows at-

tributes that need to be improved; quadrant II informs

the point that the organization has worked well and

needs to maintain its quality; quadrant III shows low

priority priorities and organizations usually limit re-

sources in quadrant III; quadrant IV focuses on opti-

mal use of resources including the reestablishment of

policies in an organization.

Figure 4: Result of IT BSC based on Important and Perfor-

mance Analysis Matrix

In Quadrant I, there are 3 instruments namely

Deliver Value; Manage operational service perfor-

mance; Deliver successful IT projects. This indicates

that performance measurement has not yet been car-

ried out properly, especially in the 3 instruments. In

Quadrant II there are 4 instruments, namely Man-

age Costs; Manage Risks; Attract and retain peo-

ple with key competencies; Achieve interorganization

synergies are in good performance but quality must

be maintained to be even better. In Quadrant III

there are 6 instruments, namely Manage operational

service performance; Build a climate empowerment

and responsibility; Propose and validate enabling so-

lutions; Capture knowledge to improve performance;

Delivery good service; Stakeholder satisfaction which

is included in the ”low priority” category where the

organization will usually limit resources to maintain

the quadrant, further a brief interview with respon-

dents is informed that if this is possible if the instru-

ments in quadrant I are carried out in good condi-

tion then all quadrant III instruments will be automati-

cally has good results even with minimal supervision.

In Quadrant IV there are 2 instruments, namely de-

velop good service and focus on professional learning

& development where in this quadrant it is indicated

that there is a need for further policies or improve-

ment of policies, as well as more optimal use of hu-

man resources.

6 PROPOSED MODEL OF SMART

TRANSPORTATION

Based on analytical data from the Central Statistics

Agency, it is known that in Jakarta speciﬁcally there

is a transport ﬂeet with a total of around 45,902 whose

growth decreased 2.08% over the previous year. And

based on the results of the Balance Scorecard IT test

related to the implementation of smart city where the

main focus on the instrument in quadrant I is: Deliver

Value; Manage operational service performance; De-

liver successful IT projects proposed the smart trans-

portation model as follows:

Figure 5: Smart Transportation Purposed Model

Sukhla’s (2016) research has proposed a Smart

Transportation system architecture, which consists of

four layers, namely the input layer, storage layer,

analysis layer and the last communication layer. The

model, we propose is a further development of the

architecture. As illustrated in Figure 5, the process

starts from the ﬁrst layer which is the basic part that

collects all the information entered through several in-

put devices such as: CCTV, RFID, and GPS Vehicle

Tracking System. All of the following input data will

be collected into storage media and processed through

database. The results obtained from this analysis pro-

cess is information according to the needs of the pas-

sengers and the bus managers including the drivers

inside. Intensive communication in the form of infor-

mation requests from buses that operate into data stor-

age, makes this model continue to grow in informa-

Model Development of Smart Transportation using the Performance Measurement of Smart City Result with It Balance Scorecard and IPA

Matrix: Jakarta Case

203

tion and data processing. And in the end the system

can be smart to answer each question or request infor-

mation with accurate data in real time. The commu-

nication media used utilize the network provided by

the network providers in the area, so as to reduce the

investment costs of the implementation of this model.

The use of cloud computing technology and big ana-

lytic data is needed especially in the data storage and

processing.

In detail, to clarify the model proposed, ﬁgure 6

illustrates the use case of the main functions that ex-

ist in the proposed smart transportation model. The

ﬁrst activity is carried out by the system admin who

ﬁlls out the initial data in the form of: Daily route

transportation, route tracking, position tracking, sta-

tus checking. Notiﬁcations will be sent automatically

when data changes occur. Passenger and driver can

request information such as transportation tracking,

status trip updates, daily route and vehicle condition

information especially for drivers. Every incoming

data is processed by the system, so that if a danger-

ous condition occurs, it can be avoided, because the

system has given a warning / notiﬁcation ﬁrst.

Figure 6: Use Case of Smart Transportation Function

7 CONCLUSIONS

According to the development of the Smart City con-

cept that is increasing, transportation problems are

also become a special concern. Based on the re-

sults of data analysis from 200 respondents, using the

IT Balance Scored Card and Importance and Perfor-

mance Analysis matrix approaches it is known that

there are three major problems that need to be ad-

dressed immediately, namely: Deliver Value; Manage

operational service performance; Deliver successful

IT projects. If this problem can be handled prop-

erly, then the problems in other quadrants can also

increase. The purposed smart transportation model,

as a form of improvement in quadrant one, has been

adjusted to the availability of infrastructure and avail-

ability of facilities in the city of Jakarta. With limited

coverage in this study specializing in public trans-

portation facilities in the city of Jakarta, further re-

search can be done by adding data from private trans-

portation modes and other types of vehicles, as well

as adding data from other cities. Application devel-

opment from this model can also be used as further

research, so that it can be directly useful to reduce the

number of accidents.

REFERENCES

Abu-Musa, A. A. (2007). Exploring information technol-

ogy governance (itg) in developing countries: an em-

pirical study. The International Journal of Digital Ac-

counting Research, 7(13):71–117.

Debnath, A. K., Chin, H. C., Haque, M. M., and Yuen, B.

(2014). A methodological framework for benchmark-

ing smart transport cities. Cities, 37:47–56.

Dirgahayani, P. (2013). Environmental co-beneﬁts of pub-

lic transportation improvement initiative: the case of

trans-jogja bus system in yogyakarta, indonesia. Jour-

nal of cleaner production, 58:74–81.

Effendi, D., Syukri, F., Subiyanto, A. F., and Utdityasan,

R. N. (2016). Smart city nusantara development

through the application of penta helix model (a prac-

tical study to develop smart city based on local wis-

dom). In 2016 international conference on ICT for

Smart Society (ICISS), pages 80–85. IEEE.

Jairak, K. and Praneetpolgrang, P. (2013). Applying

it governance balanced scorecard and importance-

performance analysis for providing it governance

strategy in university. Information Management &

Computer Security.

Katadata (2018). Jumlah penduduk di jakarta.

Madakam, S. (2016). Smart cities ( egovernance* smart

governance).

Martilla, J. A. and James, J. C. (1977). Importance-

performance analysis. Journal of marketing,

41(1):77–79.

of Economic, U. N. D. and Affairs, S. (2017). World popu-

lation prospects: The 2017 revision, key ﬁndings and

advance table.

Pedersen, N. (2016). Smart transportation in a smart city.

Pindarwati, A. and Wijayanto, A. W. (2015). Measuring

performance level of smart transportation system in

big cities of indonesia comparative study: Jakarta,

bandung, medan, surabaya, and makassar. In 2015

International Conference on Information Technology

Systems and Innovation (ICITSI), pages 1–6. IEEE.

Purnomo, F., Prabowo, H., et al. (2016). Smart city in-

dicators: A systematic literature review. Journal of

Telecommunication, Electronic and Computer Engi-

neering (JTEC), 8(3):161–164.

Shukla, S., Balachandran, K., and Sumitha, V. (2016). A

framework for smart transportation using big data. In

CONRIST 2019 - International Conferences on Information System and Technology

204

2016 International Conference on ICT in Business In-

dustry & Government (ICTBIG), pages 1–3. IEEE.

Van Grembergen, W. and De Haes, S. (2005). Measur-

ing and improving it governance through the bal-

anced scorecard. Information systems control Journal,

2(1):35–42.

Wen, W. (2010). An intelligent trafﬁc management expert

system with rﬁd technology. Expert Systems with Ap-

plications, 37(4):3024–3035.

Wibowo, A. A. and Suryanegara, M. (2016). On developing

the model of smart logistic transport in indonesia. In

2016 IEEE International Conference on Management

of Innovation and Technology (ICMIT), pages 99–104.

IEEE.

Yang, J., Xu, R., Cui, J., and Ding, Z. (2016). Robust vi-

sual tracking using adaptive local appearance model

for smart transportation. Multimedia Tools and Appli-

cations, 75(24):17487–17500.

Model Development of Smart Transportation using the Performance Measurement of Smart City Result with It Balance Scorecard and IPA

Matrix: Jakarta Case

205