Integrated Marine Carrying Capacity (MCC) Monitoring for

Managing Marine Resource in the Age of Big Data:

Case Study - Nunukan Regency

Armi Susandi, Aristyo Wijaya, Wahyu Setyo Kuntoro, Irvan Faisal, Ayub Yoga Pratama, Indriyanti

Nurdin, and Faridl Gifari Kertabudi

Bandung Institute of Technology, Bandung, Indonesia

Keywords: Marine Ecological Carrying Capacity (MECC), Nunukan Regency, Big Data, Integration.

Abstract: In supporting the development of Indonesia's frontier islands, a method is needed to plan regional resource-

based development, especially in the marine sector. Therefore, research on integrated marine carrying capacity

(MCC) monitoring for managing marine resources in the age of big data has been conducted. In this study,

Nunukan District, North Kalimantan was chosen as the research location. In managing coastal resources in the

era of big data, a data integration process consists of regional statistical data, field observation data, and

predictive data into one portal. This integration process aims to support the one data policy and provide

convenience to policymakers to develop sustainable coastal potential based on MCC calculations. The results

of this study can then be used as benchmarks in data-driven development plans and policies.

1 INTRODUCTION

In the last six years, Indonesia has tried to return the

economic pedestal to a blue economy because

Indonesia has abundant resources in the sea.

However, the increasing intensity of climate change

on sea and coastal conditions affects efforts to manage

marine natural resources in Indonesia. Currently,

these data are still available partially or separately in

the ministries or agencies that have the authority to

provide them. Therefore, the Coordinating Ministry

for Maritime Affairs and Investment has established

an effective program for marine space management

and marine environmental protection, one of which is

creating integrated thematic and basic geospatial

information data (Silalahi, 2020). When existing data

are not integrated, it can obstruct sustainable

development and uncontrolled development

management.

At present, rapid technological developments

must be utilized to encourage the sustainable

development needed to maximise the region's

potential, including in coastal areas. The use of

technology in managing the processing of marine

resources can be an opportunity and challenge in the

future. The number of institutions and institutions that

are interested in the development of marine and

coastal areas is one of the crucial issues (Rosly, et al.,

2020). Overlapping and sectoral egos of each agency

resulted in inefficiency and underutilization of data in

supporting sustainable development.

Research on integrating data to monitor

development in marine and coastal areas is still

scarce. It is a challenge to see how integrating data

from one institution to another amid the development

of big data. To explain the reasons above, we have a

goal of how to integrate Marine Carrying Capacity

(MCC) monitoring in managing coastal resources in

the data age.

2 RESEARCH METHOD

The MECC calculation to become An MPI consists of

the carrying object component (human activities and

socioeconomic growth) and the Carrier resistance

component (ecological resilience). Human activities

include coastal and marine activities that directly

suppress ecosystems (Carriers). Socioeconomic

growth includes elements that represent coastal

populations, economies and actions to protect coastal

areas. Carrier resilience components consist of

elements that maintain or damage the structure and

function of coastal and marine ecosystems. The

Susandi, A., Wijaya, A., Kuntoro, W., Faisal, I., Pratama, A., Nurdin, I. and Kertabudi, F.

Integrated Marine Carrying Capacity (MCC) Monitoring for Managing Marine Resource in the Age of Big Data: Case Study - Nunukan Regency.

DOI: 10.5220/0010794600003317

In Proceedings of the 2nd International Conference on Science, Technology, and Environment (ICoSTE 2020) - Green Technology and Science to Face a New Century, pages 78-81

ISBN: 978-989-758-545-6

elements calculated for each component can be

adjusted according to local conditions and data

availability. The MPI calculation consists of three

stages, (1) standardizing the value of each indicator

(element); (2) determining the weight of each

indicator (element); and (3) MECC index (MPI).

Table 1. MECC Indicator

3 RESULTS AND DISCUSSION

3.1 Marine Big Data

Big data has four characteristics: volume, variety,

velocity, value (Kaiser, et al., 2013). Therefore, big

marine data can be described as a collection of large

amounts of data obtained from observational data and

prediction data (Huang, et al., 2015). Based on this

description, big marine data has the following

characteristics:

a. Diverse Data Provisions

Marine big data is obtained from satellite

observations, field measurements, statistical data

from related institutions, and predictive data. Each

data has a variety of formats and types following

the characteristics of big marine data.

b. Temporality and Spatiality

Each marine big data has spatial and temporal

information stored and analyzed based on these

two components.

c. High Dimension

Apart from spatially and temporally, each marine

data still has attributes both physically,

chemically, and biology, such as temperature,

salinity, density, etc. It can be said to be a high

dimension.

d. Huge Volume

Any collected marine data will produce an

enormous volume.

e. Data Availability

Techniques are needed to maintain data reliability

in big marine data.

f. Data Security

Marine big data consists of a lot of strategic data,

so it needs to be secured.

3.2 Marine Ecological Carrying

Capacity (MECC)

Carrying Capacity can be defined as an ecological

concept which assumes that there is a limited number

of individuals who can be supported by a given

consumption value provided that the surrounding

environment does not experience degradation; this

concept directly seeks to demonstrate the relationship

between the population as a supported object and the

environment as support (carrier) to ensure

sustainability. Population carrying capacity evolves

into resource and environmental carrying capacity,

then into ecological carrying capacity (ECC)

(Martire, et al., 2015). The ECC assessment focuses

more on a more holistic framework on the conditions

of the ocean-atmosphere environment, living things

and their interactions. Such assessments provide a

comprehensive understanding of sustainable

economic and social development's environmental

impacts and reveal capacity deficits and surpluses of

specific ecosystem components. These parameters are

more accessible for the public and decision-makers to

understand (Wang, et al., 2014). The ECC is an

essential index for the sustainable development of

regional ecological environments and is used in

terrestrial environmental (Wang, et al., 2014). With

the development of the marine economy, human

activities, coastal development and pollution have

altered it.

Compo

nen

Elements Sub-Element Indicato

Carrying

Objects

(OI)

Human

Activi

ties

(HI)

Tourism

Number of

visitors

Fishing

Annual

production

of fishing

Marine

culture

Number of

aquaculture

households

Fishing

Number of

shipping vessel

Socio

Eco

nomic

develop

ment (SI)

Economy

Population

density

PDRB per

capita

Number of

electricity

customers

Protective

Actions

Human

development

index

Carriers

resilienc

e (RI)

Eco

logical

resilience

(RI)

Physical

Environ

mental

Seawater level

Current sea

Sea surface

temperature

The volume of

water in the

soil laye

Biological

Environ

mental

Leaf Area

Index

Ecological

Risk

Chlorophyll-a

Integrated Marine Carrying Capacity (MCC) Monitoring for Managing Marine Resource in the Age of Big Data: Case Study - Nunukan

Regency

Several studies have developed an index system

that evaluates the carrying capacity of the marine in

China. In their research, have developed a conceptual

model in the form of a maritime performance index

(MPI) to evaluate the Marine Ecological Carrying

Capacity (MECC) (Ma, et al., 2017) . By using the

MPI, a fast and easy to understand MECC condition

index will be obtained. Thus, the 3T area development

planning provides a new perspective for exploring the

unique use of coastal and marine resources in the 3T

area.

3.3 Marine Big Data for MECC

The marine significant data development architecture

for MECC monitoring can be seen in Figure 1. The

MECC marine significant data architecture comprises

data provision, data preprocessing, data storage, data

analysis and applications, quality control and data

security. In making the MECC big data, data for each

parameter used comes from regional statistical data,

observation data, and predictive data collected from

ministries and institutions such as the Central

Statistics Agency (BPS), the Meteorology,

Climatology and Geophysics Agency (BMKG), the

National Agency for Statistics. Geospatial

Information (BIG), Ministry of Environment and

Forestry (KLHK), National Space Agency (LAPAN),

and Ministry of Marine Affairs and Fisheries (KKP).

Data that is still separated will go through the data

preprocessing stage, such as extraction,

transformation and integration as the characteristics

of big data. Each data used has a type, format, and size

that results in a large volume of data, so it is necessary

to adjust data storage such as storage platforms, data

queries, data queries, data migration, data partitions,

and data indexes.

Furthermore, data analysis in MECC

calculations collaborates machine learning

techniques, statistics, and data mining based on the

MECC calculation theory described in the previous

section. Besides, quality control and data security also

play a role in this development. Through this stage,

MECC monitoring for each region can be carried out,

especially in Nunukan Regency as one of the 3T

areas. Making big marine data in MECC calculations

can support a one data policy and make it easier for

policymakers to develop sustainable coastal potential.

The results of this study can then be used as

benchmarks in data-based development plans and

policies.

Figure 1. Marine Big Data Architecture for MECC

(modified from Huang et al., 2015)

3.4 Marine Big Data Development

Challenges

Several challenges must be faced before integrating

big marine data. Based on the need for the data used,

the first challenge that can hinder is that data is still

partially scattered across various institutions and

ministries by their respective authorities. The

procedure for integrating data between institutions

requires an understanding between related institutions

to be implemented. Furthermore, if the data

integration process has been realized, big marine data

architecture development can be carried out. The

amount of data that is integrated with various types,

sizes, real-time, and high dimensions requires storage

settings, data availability, processing efficiency.

4 CONCLUSIONS

The development of MECC's marine big data

architecture consists of data provision, preprocessing,

data storage, analysis, applications, and data security

and quality control. Making big marine data in MECC

calculations can support a one-data policy and make

it easy for policymakers to develop sustainable

coastal potential and can be used as benchmarks in

data-based development plans and policies.

Integrating data with various types, sizes, real-time,

and high dimensions is a challenge in developing big

marine data for MECC monitoring

ICoSTE 2020 - the International Conference on Science, Technology, and Environment (ICoSTE)

ACKNOWLEDGEMENTS

Parts of this research were funded by the Ministry of

Research and Technology (Kemenristek) / National

Agency for Research and Innovation with contract

2/AMD/E1/KP.ptnbh/2020. The authors would like to

thank you for the supports of BRIN during this

research.

REFERENCES

Huang D, Zhao D, Wei L, Wang Z, and Du Y 2015 Math.

Probl. Eng.

Kaiser S, Armour F, Espinosa JA, and Money W, 2013, Big

Data : Issues and Challenge Movings Forward, The 46

Hawaii International Conference on System Sciences,

pp. 995-1004.

Martire S, Castellani V, and Sala S, 2015, Carrying

Capacity Assessment of Forest Resources: Enhancing

Environmental Sustainability in Energy Production at

Local State, Resources, Conservation and Recycling,

Vol. 94, pp. 11-20.

Rosly MA, Ahmad A, Tarmidi Z, Khair Z, and Zulkifli N,

2020, Towards a Collaborative Strategyfor Land and

Marine Geospatial Data Integration : A Review, IOP

Conference Series : Earth and Environmental Science,

Vol. 494, 14

International UMT Annual Symposium

23-25 July 2019.

Silalahi NFLP, 2020, Kebijakan Kelautan Indonesia II

untuk Pengelolaan Kawasan Konservasi Perairan

Indonesia, Kementerian Koordinator Bidang

Kemaritiman dan Investasi.

Wang S, Xu L, Yang F, and Wang H 2014, Assessment of

Water Ecological Carrying Capacity Under the Two

Policies in Tieling City on the Basis of the Integrated

System Dynamics Model, Sciences of the Total

Environment, Vol. 472, pp. 1070-1081.

Integrated Marine Carrying Capacity (MCC) Monitoring for Managing Marine Resource in the Age of Big Data: Case Study - Nunukan

Regency