THE DEVELOPMENT OF SPATIAL DECISION SUPPORT SYSTEM
FOR INDUSTRIAL WASTE WATER MONITORING
(A CASE STUDY: UPPER CITARUM RIVER BASIN, WEST JAVA)
Budi Susetyo G051040101
GRADUATE SCHOOL
THE DEVELOPMENT OF SPATIAL DECISION SUPPORT SYSTEM
FOR INDUSTRIAL WASTE WATER MONITORING
(A CASE STUDY: UPPER CITARUM RIVER BASIN, WEST JAVA)
Budi Susetyo G051040101
A Thesis submitted to the degree Master of Science of Bogor Agricultural University
MASTER OF SCIENCE IN INFORMATION TECHNOLOGY
FOR NATURAL RESOURCE MANAGEMENT
GRADUATE SCHOOL
STATEMENT
I, Budi Susetyo, here by stated that this thesis entitled:
The Development of Spatial Decision Support System for Industrial Waste Water Monitoring System (A Case Study: Upper Citarum River Basin, West Java)
are results of my own work during the period of April 2006 until July 2007 and that it has not been published before. The content of the thesis has been examined by the advising committee and the external examiner.
Bogor, September 2007
ACKNOWLEDGEMENTS
Alhamdulillahirrabbil ‘alamien, I would like to express my thanks to Allah the Almighty God, who gives me strength, knowledge and inspirations every time. Without His Grace, Help and Guide, this thesis will never come into being.
I would like to express my gratitude to the University of Ibn Khaldun Bogor (UIKA Bogor) for supporting me to continue my study at the Master of Information Technology for Natural Resource Management (MIT), Bogor Agricultural University.
I would like to express my thanks to my supervisor Prof. Dr. Ir. Kudang B. Seminar, MSc., who has mainly supervised my thesis during research work until reporting. His guidance, encouragement, extensive knowledge and creative thinking is very valuable in finishing the thesis.
I would like also to thank Dr. Yuli Suharnoto, MSc. as Co-supervisor, who has encouraged me to generate and improve the idea toward logically research. Moreover, I am indebted to my External Examiner, Dr. Ir. Setyo Pertiwi, MAgr., who has a lot of constructive comments and discussion to review my research process to become a complete thesis especially related to the method, analysis and writing.
I would like to express my sincere gratitude to MIT Program Coordinator, Dr. Tania June, who has facilitated the research and also to all of MIT Students.
the user of the system; and also Mr. Yahya as a professional reader, who is very concern to help me write this thesis.
I am obliged to many persons at MIT secretariat especially to Mr. Bambang Sulistio and Miss Devi, who support all of the MIT students to finish the courses.
I would like to dedicate this thesis to my lovely wife, Reni Handayani and all of my children, Muhammad Taufiqurrahman, Amrina Husna Salimah, Hanif Hidayaturrahman, and Qonita Nailurrahmah, who have shown their patient, psychological support, understanding, encouragement and for their prayers. Without their sincere support, I never could have finished this thesis.
Finally, my appreciation should go to my parents and my sibling, who have given more pay attention and encouraged me to continue my study. Hopefully this thesis would be valuable to me, useful for every one and mankind’s life. Thank you very much for all.
CURRICULUM VITAE
The Author, Budi Susetyo was born on January 20th 1966 in Semarang, Central Java. He is the youngest son of M. Soewignyo and Sriwidati. His educational background is as follows, studied at the elementary school, SD Kanisius Genuk; Junior High School, SMP N 1; and Senior High School, SMA N 1 and passed in 1984, all of the schools are located in Ungaran, Central Java. Then he studied at the Bogor Agricultural University and received his Ir degree from the Agro-meteorology Study Program in 1991. Since 1991 he worked as professional consultant in several consulting companies until 1998. He took a short course program on Environmental Impact Assessment (Amdal Certificate A and B) in 1993. He experienced in many sectors related to his background (environmental science which is supported by information technology). He has done a several system designs and development such as (1) Cooperation Information System (SIMKOP), (2) Human Development Index Information System (SI-IPM), (3) Database System of Development Planning for Bappeda (SIDANOS), (4) Information system of Imbal Swadaya Management Project (SIMPIS), etc.
ABSTRACT
BUDI SUSETYO (2007). The Development of Spatial Decision Support System for Industrial Waste Water Monitoring (A Case Study: Upper Citarum River Basin, West Java). Under supervision of KUDANG B. SEMINAR and YULI SUHARNOTO.
The study aims to make an industrial pollution monitoring application system as a prototype system, called IWMS System (Industrial Waste Water Monitoring Support System). IWMS System should give some spatial information to support the government decision for the industry monitoring. It is designed in accordance with industrial profile, pollution monitoring reports and the Government Regulation in the format of Water Quality Standard.
Upper Citarum River Basin is selected as the study area due to: (1) The river is one of the high priorities river basin in Indonesia, (2) The region covers the Capital City of West Java, which has a lot of important activities, (3) Many industries are located in this river basin with negative impacts that should be managed, (4) The subject of study should not be too wide, and can be implemented to the other river basins especially in Indonesia; and (5) There is a specific management to achieve the sustainable development, without disruption to the economic growth in this area.
The objectives of this study are: (1) To develop the DSS of industrial wastewater monitoring system as an application system to support the Government Decision, and (2) To implement the IWMS as a prototype of the system.
Visualization of Monitoring Site, Comparing the Chart Pattern, Identifying Polluted Industry, Identifying Polluted River, Findings of Industrial Compliance Level, Findings of the Priority Scale of River, Quick Response of Environmental Cases, Findings of the Potential of Polluted Industry, Water Intake and Exploration Control, Reward & Punishment based on Possessing of License, Reward & Punishment based on Possessing of Environmental Document, and Estimation of BOD Potential (sources).
Research Title : The Development of Spatial Decision Support System for Industrial Wastewater Monitoring (A Case Study: Upper
Citarum River Basin, West Java) Name : Budi Susetyo
Student ID : G051040101
Study Program : Master of Science in Information Technology for Natural Resources Management
Approved by, Advisory Board
Prof. Dr. Ir. Kudang Boro Seminar, MSc. Dr. Ir. Yuli Suharnoto, MSc.
Supervisor Co-Supervisor
Endorsed by,
Program Coordinator Dean of the Graduate School
Dr. Ir. Tania June, MSc. Prof. Dr. Ir. Khairil A. Notodiputro, MS
TABLE OF CONTENTS
STATEMENT ... i
ACKNOWLEDGEMENT ... ii
CURRICULUM VITAE ... iv
ABSTRACT ... v
TABLE OF CONTENTS ... vii
LIST OF TABLES ... xii
LIST OF FIGURES ... xiii
LIST OF APPENDIXES ... xvi
I. INTRODUCTION ... 1
1.1 Background ... 1
1.2 Objectives ... 2
1.3 Problem Identification ... 3
1.4 Location ... 3
1.5 Outcome ... 4
1.6 Expected Benefit ... 5
1.7 Scope of Research ... 5
II. LITERATURE PREVIEWS ... 6
2.1 Information System ... 6
2.2 Tools for Analysis ... 8
2.2.1 Decision Support System ... 8
2.2.2 Database Model ... 13
2.2.3 Geographic Information System ... 14
2.3 Industrial Pollutions ... 17
2.4 Water Pollutants ... 19
2.5 Storet Method ... 19
2.6 Global Pollution Policy ... 20
THE DEVELOPMENT OF SPATIAL DECISION SUPPORT SYSTEM
FOR INDUSTRIAL WASTE WATER MONITORING
(A CASE STUDY: UPPER CITARUM RIVER BASIN, WEST JAVA)
Budi Susetyo G051040101
GRADUATE SCHOOL
THE DEVELOPMENT OF SPATIAL DECISION SUPPORT SYSTEM
FOR INDUSTRIAL WASTE WATER MONITORING
(A CASE STUDY: UPPER CITARUM RIVER BASIN, WEST JAVA)
Budi Susetyo G051040101
A Thesis submitted to the degree Master of Science of Bogor Agricultural University
MASTER OF SCIENCE IN INFORMATION TECHNOLOGY
FOR NATURAL RESOURCE MANAGEMENT
GRADUATE SCHOOL
STATEMENT
I, Budi Susetyo, here by stated that this thesis entitled:
The Development of Spatial Decision Support System for Industrial Waste Water Monitoring System (A Case Study: Upper Citarum River Basin, West Java)
are results of my own work during the period of April 2006 until July 2007 and that it has not been published before. The content of the thesis has been examined by the advising committee and the external examiner.
Bogor, September 2007
ACKNOWLEDGEMENTS
Alhamdulillahirrabbil ‘alamien, I would like to express my thanks to Allah the Almighty God, who gives me strength, knowledge and inspirations every time. Without His Grace, Help and Guide, this thesis will never come into being.
I would like to express my gratitude to the University of Ibn Khaldun Bogor (UIKA Bogor) for supporting me to continue my study at the Master of Information Technology for Natural Resource Management (MIT), Bogor Agricultural University.
I would like to express my thanks to my supervisor Prof. Dr. Ir. Kudang B. Seminar, MSc., who has mainly supervised my thesis during research work until reporting. His guidance, encouragement, extensive knowledge and creative thinking is very valuable in finishing the thesis.
I would like also to thank Dr. Yuli Suharnoto, MSc. as Co-supervisor, who has encouraged me to generate and improve the idea toward logically research. Moreover, I am indebted to my External Examiner, Dr. Ir. Setyo Pertiwi, MAgr., who has a lot of constructive comments and discussion to review my research process to become a complete thesis especially related to the method, analysis and writing.
I would like to express my sincere gratitude to MIT Program Coordinator, Dr. Tania June, who has facilitated the research and also to all of MIT Students.
the user of the system; and also Mr. Yahya as a professional reader, who is very concern to help me write this thesis.
I am obliged to many persons at MIT secretariat especially to Mr. Bambang Sulistio and Miss Devi, who support all of the MIT students to finish the courses.
I would like to dedicate this thesis to my lovely wife, Reni Handayani and all of my children, Muhammad Taufiqurrahman, Amrina Husna Salimah, Hanif Hidayaturrahman, and Qonita Nailurrahmah, who have shown their patient, psychological support, understanding, encouragement and for their prayers. Without their sincere support, I never could have finished this thesis.
Finally, my appreciation should go to my parents and my sibling, who have given more pay attention and encouraged me to continue my study. Hopefully this thesis would be valuable to me, useful for every one and mankind’s life. Thank you very much for all.
CURRICULUM VITAE
The Author, Budi Susetyo was born on January 20th 1966 in Semarang, Central Java. He is the youngest son of M. Soewignyo and Sriwidati. His educational background is as follows, studied at the elementary school, SD Kanisius Genuk; Junior High School, SMP N 1; and Senior High School, SMA N 1 and passed in 1984, all of the schools are located in Ungaran, Central Java. Then he studied at the Bogor Agricultural University and received his Ir degree from the Agro-meteorology Study Program in 1991. Since 1991 he worked as professional consultant in several consulting companies until 1998. He took a short course program on Environmental Impact Assessment (Amdal Certificate A and B) in 1993. He experienced in many sectors related to his background (environmental science which is supported by information technology). He has done a several system designs and development such as (1) Cooperation Information System (SIMKOP), (2) Human Development Index Information System (SI-IPM), (3) Database System of Development Planning for Bappeda (SIDANOS), (4) Information system of Imbal Swadaya Management Project (SIMPIS), etc.
ABSTRACT
BUDI SUSETYO (2007). The Development of Spatial Decision Support System for Industrial Waste Water Monitoring (A Case Study: Upper Citarum River Basin, West Java). Under supervision of KUDANG B. SEMINAR and YULI SUHARNOTO.
The study aims to make an industrial pollution monitoring application system as a prototype system, called IWMS System (Industrial Waste Water Monitoring Support System). IWMS System should give some spatial information to support the government decision for the industry monitoring. It is designed in accordance with industrial profile, pollution monitoring reports and the Government Regulation in the format of Water Quality Standard.
Upper Citarum River Basin is selected as the study area due to: (1) The river is one of the high priorities river basin in Indonesia, (2) The region covers the Capital City of West Java, which has a lot of important activities, (3) Many industries are located in this river basin with negative impacts that should be managed, (4) The subject of study should not be too wide, and can be implemented to the other river basins especially in Indonesia; and (5) There is a specific management to achieve the sustainable development, without disruption to the economic growth in this area.
The objectives of this study are: (1) To develop the DSS of industrial wastewater monitoring system as an application system to support the Government Decision, and (2) To implement the IWMS as a prototype of the system.
Visualization of Monitoring Site, Comparing the Chart Pattern, Identifying Polluted Industry, Identifying Polluted River, Findings of Industrial Compliance Level, Findings of the Priority Scale of River, Quick Response of Environmental Cases, Findings of the Potential of Polluted Industry, Water Intake and Exploration Control, Reward & Punishment based on Possessing of License, Reward & Punishment based on Possessing of Environmental Document, and Estimation of BOD Potential (sources).
Research Title : The Development of Spatial Decision Support System for Industrial Wastewater Monitoring (A Case Study: Upper
Citarum River Basin, West Java) Name : Budi Susetyo
Student ID : G051040101
Study Program : Master of Science in Information Technology for Natural Resources Management
Approved by, Advisory Board
Prof. Dr. Ir. Kudang Boro Seminar, MSc. Dr. Ir. Yuli Suharnoto, MSc.
Supervisor Co-Supervisor
Endorsed by,
Program Coordinator Dean of the Graduate School
Dr. Ir. Tania June, MSc. Prof. Dr. Ir. Khairil A. Notodiputro, MS
TABLE OF CONTENTS
STATEMENT ... i
ACKNOWLEDGEMENT ... ii
CURRICULUM VITAE ... iv
ABSTRACT ... v
TABLE OF CONTENTS ... vii
LIST OF TABLES ... xii
LIST OF FIGURES ... xiii
LIST OF APPENDIXES ... xvi
I. INTRODUCTION ... 1
1.1 Background ... 1
1.2 Objectives ... 2
1.3 Problem Identification ... 3
1.4 Location ... 3
1.5 Outcome ... 4
1.6 Expected Benefit ... 5
1.7 Scope of Research ... 5
II. LITERATURE PREVIEWS ... 6
2.1 Information System ... 6
2.2 Tools for Analysis ... 8
2.2.1 Decision Support System ... 8
2.2.2 Database Model ... 13
2.2.3 Geographic Information System ... 14
2.3 Industrial Pollutions ... 17
2.4 Water Pollutants ... 19
2.5 Storet Method ... 19
2.6 Global Pollution Policy ... 20
2.8 The Statement of Clean River (Superkasih) ... 21
2.9 System Development ... 22
2.10 The Stage of Activity ... 24
III. METHODOLOGY ... 26
3.1 Need Assessment ... 26
3.2 User Identification ... 27
3.3 Data Collection Method ... 27
3.4 Time & Location ... 28
3.5 Database ... 29
3.6 Graphical User interface ... 31
3.7 Hardware & Software Requirement ... 32
3.8 Source of Data ... 32
3.9 Public Perception Processed With Fuzzy Method ... 33
3.10 Water Quality Standard ... 35
3.11 Water Quality Evaluation ... 35
3.11.1 The Government Regulation ... 36
3.11.2 River Water Quality ... 36
3.11.2.1 Parameter of the Water Quality ... 36
3.11.2.2 Determination of Water Quality Parameter ... 36
3.11.2.3 Grouping of Water Quality Characteristics ... 36
3.11.2.4 Monitoring the Water Quality ... 37
3.11.3 Evaluation of Water Quality Status ... 38
3.11.3.1 Evaluation of Water Class & Status ... 38
3.11.3.2 Evaluation of Water Quality Status Using Storet Method ... 39
3.11.3.3 Evaluation of Water Quality Status Using Pollution Index Method ... 41
3.11.3.4 Calculation Sample of Pollution Index ... 42
3.11.3.5 Water Quality Monitoring ... 42
3.11.3.6 Industrial and Other Activities with Waste Water Effluent ... 43
3.12 Location of Wastewater Monitoring ... 43
3.12.1 Information of Monitoring Site ... 43
LIST OF TABLES
LIST OF FIGURES
LIST
OF
APPENDIX
I. INTRODUCTION
1.1. Background
Natural resources exploitation and sustainable development are two main extreme
poles which have interdependence between those two items, within their negative or
positive impact. There are two main keywords in this discussion, e.g. environmental and
economic perspectives, where both of them should be in balance. It is not easy to keep it in
balance; usually the imbalance situation is caused by over exploitation, limitation of
resources (or carrying capacity) and may be the weaknesses of environmental management
itself. The imbalance situation is usually triggers to the occurrence of negative impacts. To
prevent negative impacts, the environmental management is required, especially for the
production activity in industrial sector.
Nowadays, environmental degradation by industrial activities tends face a complex
problems. On the other hand saving and maintaining the environment needs a strong
concern and also becomes global issues. Environmental management has become one of
the important activity related to the sustainable development in Indonesia. Therefore,
according that phenomenon, the government should have a good strategy, any efforts and
also breakthrough to solve that problem. The main issues of pollution by industrial
activities are industrial wastewater and river water quality. According to environmental
monitoring in 2004, more than 50 percent of parameters DO, BOD, COD, Fecal Coli and
Total Coliform were not achieving Class I of water quality criteria (Government Regulation
The Ministry of Environment has issued the environmental programs, especially for
controlling water pollution content from industry through Clean River Program (Prokasih). The aims of Prokasih are to improve the river water quality and to protect the river function
based on the class usage. But at the time of study, the Government still doesn’t have a
sound application system instrument supporting for monitoring toward achieving the goal
(by efficiency, effectiveness and powerfully monitoring to the industrial pollution, etc).
This study is aimed to develop an industrial wastewater monitoring application
system, called IWMS System (Industrial Wastewater Monitoring Support System). IWMS
System should give some spatial information to support the government decision for the
industry monitoring (Spatial Decision Support System). It is designed upon industrial
profile, pollution monitoring reports and the Government Regulation in the format of Water
Quality Standard.
In the study also, the source of pollutant will be bounded only from industry
considering that they are giving the highest contribution of water pollution in the river. The
other reason, most of the industries discharge some waste water to the river everyday, and
also some industry still didn’t have waste-water treatment plan or may be not installed yet.
The focus of research only on industry and its pollution in the Upper Citarum River Basin
as a case study, but should be replicated to another river basin, especially in Indonesia.
1.2 Objectives
The objectives of this study are:
(1) To develop the DSS of industrial wastewater monitoring system as an application
(2) To implement the IWMS as a prototype of the system
1.3. Problem Identification
Main issues for industrial wastewater pollutions are:
(1) Most of the industry discharges its waste water to the river without any controllable
measure.
(2) Majority of peoples rely on a river as water resource supplies to fulfill their daily need,
as a consequence they are very concern about river water quantity and quality.
(3) The river must be protected and conserved by the government, through the control of
industrial activities by imposing regulation and do regular monitoring.
(4) At the time of study, the government didn’t have a sound application system to monitor
industrial waste water pollution in the location of study.
According the above problem identifications, the government needs some system
application to support the decision for controlling the industrial wastewater and their
activities.
1.4. Location
Upper Citarum River Basin is selected as the study area, due to:
(1) The river is one of the high priority river basin in Indonesia,
(2) The region cover the Capital City of West Java, which have a lot of the important
activities
(3) Many industries are located in this river basin, along with the negative impacts which
(4) The subject of study not too wide, and can be replicated to the other river basin
especially in Indonesia.
(5) There is a special management to achieve the sustainable development, without
disruption to the economic growth in this area.
Figure 1. Study Area
1.5. Outcome
The result of this system development is software to support decision related to
industrial wastewater monitoring in the environmental management by using both spatial
and non spatial (attribute data) information. And also to support the government (including
local government) and some industries related to make better planning and arranging for
1.6. Expected Benefit
The expected benefit of this research as follow:
(1) Able to support the information of industrial profile and environmental status
(2) Able to do spatial analysis of industry distribution and river pollution.
(3) Able to get information of the polluted industry and river status
1.7 . Scope of Research
There are three kinds of scope in this research e.g. (1) scope of level, (2) scope of
boundary, and (3) scope of time period. Scopes of level are support to the local
government decision (related to giving of industrial license and river protection) and central
government (related to general environmental management). Scope of boundary is case
study in the Upper Citarum River Basin; and scope of time period is monthly (or depends
II. LITERATURE REVIEW
To Develop the Spatial DSS and Information system needs several theories:
(1) Information System, (2) Decision Support System, (3) System Development Life Cycle,
(4) Geographical Information System, (5) Database, (6) Industrial Pollution, (7) Water
Pollutant, and (8) The Government Regulation of Environmental Management. This
section presents several theories applied in this research.
2.1 Information System
An information system is an organization of people, hardware, software,
communication networks and a data resource that collects, transforms, and disseminates
[image:35.612.155.471.404.676.2]information in organization (O’Brien, 2002).
Figure 2 show that there are five components of information systems, i.e. (1) people, (2) software, (3) hardware, (4) data and (5) network resources. According to
O’Brien (2002), people resources include end-user (people who are use an information
systems or the information it produces) and information system specialist (people who
develop and operate information systems). Hardware resources include all physical devices
and materials used in information processing. Software resources include all sets of
information processing instructions. Data is more than the raw material of information and
includes wide variety of data type, how the data be organized (database) and knowledge
bases. Network resources emphasize that communication network are a fundamental
resource component of all information systems and include communication media and
network support.
The Study is focusing on the two segments of Information System components, i.e.
software and data resources. The concepts of software includes not only the sets of
operating instruction called programs, which direct and control computer hardware, but also
the sets of information processing instructions needed by people, called procedures. Data
are vital organizational resources that should be managed. Most organizations could not
survive without quality data about their internal operations and external environment.
2.2. Tools for Analysis
2.2.1. Decision Support System
Turban (1995) stated that decision support system is an interactive, flexible and
adaptable computer based information system, especially developed for supporting the
solutions of a non-structured management problem for improved decision making. A
Decision Support System allows decision-makers to combine personal judgment with
computer output, in a user-machine interface, to produce meaningful information for
support in a decision-making process. Such systems are capable of assisting in solution of
all problems (structured, semi-structured and unstructured) using all information available
on request. They use quantitative models and database elements for problem solving and an
integral part of the decision-maker’s approach to problem identification and solution
(Simonovic, 1998).
By definition, decision-making is a process of choosing among alternative courses of action for the purpose of achieving a goal or goals (Turban 1995). Managerial
decision-making is synonymous with the whole process of management: planning, directing,
controlling, and organizing which involves a series of decision-making activities. Decision
Support System (DSS) is an interactive, flexible and adaptable Computer-Based
Information System (CBIS), specially developed for supporting the solution of a particular
management problem for improved decision-making (Turban 1995).
According to Sol in Terfai and Schrimpf (2004), decision support is the
development of approaches for applying information systems technology to increase the
human judgment in the performance of tasks that have elements, which cannot be specified
in advance. Actually, there are many definitions of a DSS. There is a general agreement
that these systems focus on decisions and on supporting rather than replacing the user's
decision-making process. There is also a general consensus in the definitions of DSS that
both database and model components are usually required to fully support decisions. Many
of today's DSS focus on problem solving rather than on supporting the modeling process,
but the main goal of a DSS should be to provide decision makers with tools for interactively
exploring, designing and analyzing decision situations. Users should be able to perform the
following functions: they can analyze decision situations according to their personal styles
and knowledge; they can build and compare various quantitative models; they can adapt
these models to changing conditions; can evaluate different aspects of their activities using
[image:38.612.163.461.407.692.2]a variety of different means (Terfai and Schrimpf, 2004).
DSS is composed of several software components: Data Management, Model
Management, Communication (Dialog) Subsystem, and Knowledge Management (Turban,
1995):
(1) Data Management: The data management includes the database which contains relevant
data for the situation and is managed by software called database management system
(DBMS), where DBMS containing relevant data and computer program utilities to
manage a database.
(2) Model Management: A software package that includes various models: statistics,
mathematics, economics, environmental, qualitative models that provide system’s
analytical capabilities. Model Management System, Modeling Language, Model
Directory, Model Execution, Integration, and Command or other quantitative models
that provide the system’s analytical capabilities and an appropriate software
management
(3) Dialog Management: includes user interface that enables easy, interactive and
communicative interaction between users and DSS. Dialog Management is managed by
software called dialog management system (DGMS).
(4) Knowledge Management: a subsystem that supports logical interconnection and
integration between data and model management. This optional subsystem can support
any of the other subsystem or act as an independent component.
Within the framework of management information systems (Mittra in Simonovic,
1998) the DSS has four primary characteristics:
(2) It is flexible and responds quickly to questions;
(3) It provides “what if” scenarios; and
(4) It considers the specific requirements of the decision-makers.
In the period since DSS came to prominence there has been considerable growth in
the importance of geographic information systems (GIS). This growth in GIS reflects the
decreased cost of the required technology and the increasing availability of appropriate
spatial data. Recent improvements in mainstream computer technologies facilitate this
spread of the use of spatial data. These include inexpensive gigabyte sized hard disks, large
high-resolution color monitors, graphics accelerators and CD-ROM storage. This explosion
in the use of computer technology can also be seen in other areas, where a virtuous circle of
declining hardware costs leads to larger software sales and therefore reduced software costs.
Little (1970) “model-based set of procedures for processing data and judgments to
assist a manager in his decision making” Assumption: that the system is computer-based
and extends the user’s capabilities. Moore and Chang (1980), DSS are (1) Extendible
systems, (2) Capable of supporting ad hoc data analysis and decision modeling, (3)
Oriented toward future planning, and (4) Used at irregular, unplanned intervals. Bonczek et
al. (1991), DSS is a computer-based system consisting of (1) A language system --
communication between the user and DSS components, (2) A knowledge system, and (3) A
problem-processing system - the link between the other two components. Keen (1987) said
that DSS apply “to situations where a ‘final’ system can be developed only through an
adaptive process of learning and evolution”. Generally the Central Issue in DSS is support
A DSS is an interactive, flexible, and adaptable CBIS, specially developed for
supporting the solution of a non-structured management problem for improved decision
making. It utilizes data, it provides easy user interface, and it allows for the decision
maker’s own insights. DSS may utilize models, is built by an interactive process (frequently
by end-users), supports all the phases of the decision-making, and may include a knowledge
component.
Most DSS have some of the following ideal features (Turban 1995): (1) Supporting
structured, semi-structured, and unstructured problems by bringing human judgment and
computerized information, (2) Supporting various managerial levels, ranging from top
executive to line managers, (3) Supporting individuals as well as groups (organizations), (4)
Supporting interdependent and/or sequential decisions, (5) Supporting all phases of decision
process: (a) intelligence, (b) design, (c) choice, (d) implementation, (6) Supporting a variety
of decision making processes and styles, there is a fit between the DSS and the attributes of
the individual decision makers (e.g., the vocabulary and decision style), (7) Adaptive over
time and easy to use, and (8) Utilizing models and knowledge.
Characteristics and Capabilities of DSS are: (1) Provide support in semi-structured
and unstructured situations, includes human judgment and computerized information (2)
Support for various managerial levels, (3) Support to individuals and groups, (4) Support to
interdependent and/or sequential decisions, (5) Support all phases of the decision-making
process, (6) Support a variety of decision-making processes and styles, (7) Are adaptive, (8)
Have user friendly interfaces, (9) Goal: improve effectiveness of decision making, (10) The
decision maker controls the decision-making process, (11) End-users can build simple
sources, formats, and types. Decision makers can make better, more consistent decisions in
a timely manner.
Several benefits of DSS can be enumerated as follows: (1) Ability to support fast
and objective solution of problems, (2) Ability to explore several alternative solutions under
different strategies under different configurations, (3) New insights and learning, (4)
Improved management control and performance, (5) Cost savings, (6) Reusable and
replicable: DSS can be reused for solving similar problems and be replicated for many
users, and (7) Improved workgroup cooperation.
Table 1. Supports provided by DSS (Turban, 1995)
DSS Support Answers to Questions:
Raw data and status access What is…?
General analysis capabilities What is/Why? …
Representation models What will be? …
Causal models (forecasting, diagnosis)What will be/ Why? …
Solution suggestions, evaluation What if/How? …
Solution selection What is best? What is good enough? …
2.2.2 Database Model
Database is a collection of non-redundant data, which is shareable among different
applications representing needs of individual or group users (Laurini, 1996). The
organization of database can be described in terms of records, fields, and keys. Record is a
group of related fields that stores data about a subject, called the master record or activity,
which is known as the transaction record (Power, 2003). Database model is a collection of
constraints. The various database models can be specified into thee groups: object-based
logical models, record-based logical models, and physical models.
2.2.3 Geographic Information System
A Geographic Information System (GIS) is a specific information system applied to
geographic data and mainly referred to as a system of hardware, software and procedures
designed to support the capture, management, manipulation, analysis, modeling and display
of spatially-referenced data for solving complex planning and management problems
(Burrough, 1986). A geographical Information System (GIS) is a powerful for handling
spatial data. It is used for storing, retrieving, maintaining, manipulating, analyzing, and
producing the digital format of spatial data. Moreover, it could produce a spatial data in a
hardcopy format (Aronoff, 1991).
In GIS environment, there are two types of common data that should be taken into
account, i.e. spatial data and non-spatial data. Spatial data provides information about the
feature referred to geographical orientation, size, and relative position from other features.
Non-spatial data is complementary information to spatial data, which provides some further
information. Since GIS has been introduced in 1960 and due to the user demand for
mapped data focused attention on data availability, accuracy, and standards, as well as data
structure issues, GIS has served an important role as an integrating technology. The
capability in providing data spatial and non-spatial that are cannot be fulfilled by another
application, considering GIS, as an application for a user needs. The ultimate need, GIS has
been linked to models, decision support systems and expert systems in order to make these
GIS applications have been developed for wider application of digital data;
encourage more sectors to invest in GIS technology that can be run on their existing
computer. The growth of GIS application has been paralleled by the extraordinary gains of
computer performance. Furthermore, the range of commercially available products of
information technology that candidate for the implementation of a GIS has widened,
including CAD (computer assisted drafting), DBMS (database management system),
geo-processing, remote sensing, GPS (global positioning system), Multimedia, network
communication and EDI (electronic data interchange).
There are three important stages of working with geographic data (de By, 2000):
(1) Data entry. The early stage in which data about the study phenomenon is collected and
prepared to be entered into the system.
(2) Data analysis. The middle stage in which collected data is carefully reviewed, and for
instance, attempts are made to discover patterns.
(3) Data presentation. The final stage in which the results of earlier analysis are presented
in an appropriate way.
Data GIS demonstrated the advantage of organizing, managing, and distributing
geographic information culled from various databases while maintaining data integrity and
focusing on project direction. In the framework of decision making perception, GIS
evolves around its decision support capabilities including query functions, statistical
analysis capabilities, spreadsheet analysis, graphics and mapping function for evaluating
decision options and assessing the optimal and most suitable alternative (United Nations,
GIS is gaining importance and widespread acceptance as a tools for decision support
in land, infrastructure, resources, environmental management and spatial analysis, and in
urban and regional development planning. With the development of GIS, environmental
and natural resource managers increasingly have at their disposal information systems in
which data are more readily accessible, more easily combined and more flexibly modified
to meet the needs of environmental and natural resource decision making. It is thus
reasonable to expect a better informed more explicitly reasoned, decision-making process.
But despite the proliferation of GIS software systems and the surge of public interest in the
application of the system to resolve the real world problems, the technology has commonly
seen as complex, inaccessible, and alienating to the decision makers (Sharifi, 2002).
Table 2. Computerized support for decision making (adopted from Turban, 1995)
Phase Description Traditional Tools Spatial
Tools Early Compute, “Crunch Numbers”,
Summarize, Organize
Early computer programs, Management Science Models
Computerized Cartography Intermediate Find, Organize & Display Decision
Relevant Information
Database Management System, MIS
Workstation GIS Current Perform Decision relevant computations
on decision relevant information: organize and display the results, Query based and user friendly approach, “What If “ analysis
Financial Models, Spreadsheets, trend, exploration, operations research models, Decision Support System
Spatial Decision Support System
Spatial Decision Support System (SDSS) can therefore be seen as an important
subset of DSS, whose potential for rapid growth has been facilitated by technical
developments (Table 2). The availability of appropriate inexpensive technology for manipulating spatial data enables SDSS applications to be created. The benefits of using
GIS software is becoming increasingly suitable for use as a generator for a SDSS. As GIS
designers gain a greater awareness of decision-making possibilities, their systems will be
designed to facilitate interaction with models. GIS software provides a sophisticated
interface for spatial information. Even limited functionality GIS software will provide the
ability to zoom and to display or highlight different features. GIS provides database support
that is designed to allow for the effective storage of spatial data. Furthermore GIS software
provides a link between the interface and database to allow the user to easily query spatial
data.
2.3 Industrial Pollutions
Scientists tend to define pollution differently to economist. For the economist,
pollution is an external cost and occurs only when one or more individuals suffer a loss of
welfare (Pearce, et. al. 1990). Even then, economist do not typically recommend the
elimination of externality become they argue that the optimal externality is not zero
(Pearce, et. al, 1990). The idea of “zero pollution” is not, however, absurd. At least two
considerations make it more reasonable than it appears at first sight. These are (a) the fact
that the environment tends to have positive assimilative capacity, and (b) the fact that it is
possible, to some extent, to divorce economic activity from waste flows. Affecting the
environment by introducing pollution abatement (Pearce, et. al, 1990).
Industry plays critical role in economic development and in enhancing the economic
welfare of society. Industry produces a wide range of consumer goods and, more
importantly, a whole range of intermediate and capital goods for other sector and branches
of economy (such as agriculture, services, mining, construction and utilities) as well as
Despite the obvious benefits of industrial development, it frequently results in
damage to the environment and human health. According to Faisal et.al. (2000), industries
cause environmental degradation throughout the life cycle of a product starting from
exploration of raw materials and energy resources to disposal of wastes and end products.
A conceptual model of generation of pollution at various stages of production process is
[image:47.612.94.512.247.475.2]shown in Figure 5.
Figure 5. A conceptual model of generation of pollution (Faisal et. al., 2000)
Industry generates both traditional and newly emerging pollutants in three major
forms, namely gaseous, liquid and solid wastes, including hazardous wastes. The following
sections are summaries derived from Faisal et. Al. (2000), Davis and Cornwell (1991), Park
and Labys (1998), Hettige et. Al. (1994) and Spellman (1999), about the major known
In this research, we would like to discuss and more concern about water pollution
caused by industrial activities (see Figure 6).
Figure 6. Specific Impact in This Research
2.4 Water Pollutants
The most essential of water pollutant parameters are BOD (Biological Oxygen
Demand) and COD (Chemical Oxygen Demand). BOD is defined, as the amount of
oxygen needed by aerobic decomposers to breakdown the organic materials in a given
volume of water at a certain temperature over a specified time period. Rather same within
the BOD definition, but for COD the amount of oxygen needed by anaerobic decomposers
to breakdown the inorganic materials.
BOD is caused by organic water pollutants that are oxidized by naturally occurring
microorganisms. This ‘biological oxygen demand’ removes dissolved oxygen from the
water and can seriously damage some fish species, which have adapted to the previous
dissolved oxygen level. Low levels of dissolved oxygen may enable disease-causing
pathogens to survive longer in water. Organic water pollutants can also accelerate the
growth of algae, which will crowd out other plant species. The eventual death and
decomposition of the algae is another source of oxygen depletion as well as noxious smells
and unsightly scum. The most common measure for BOD is the amount of oxygen used by
Industrial Activities
Water Pollution Air Pollution Toxic & Hazardous Waste
- Decreasing of River Water Quality - Decreasing of Air Quality
- Soil Pollution
microorganisms to oxidize the organic waste in a standard sample of pollutant during a
five-day period. (5-day BOD).
2.5 Storet Method
STORET (short for STOrage and RETrieval) is an EPA developed database for
water quality, biological, and physical data that is used by state environmental agencies,
EPA and other federal agencies, universities, and private citizens.
2.6 Global Pollution Policy
Pollution arising from one region can change damage in another region. This Trans
boundary pollution takes on the features of an externality between the “emitter” and the
“recipient” (Peace and Turner, 1990). The typical ‘image” is that polluters are firms and
individual people (Peace et.al, 1990). It is wrong to think of polluters only as firms,
individual’s polluter, so do government.
Table 3. Relationships between Emitter and Receptor
No External generator Externality Sufferer
1 Firm Firm
2 Firm Individuals
3 Individuals Firm
4 Individuals Individuals
5 Government Firm
6 Government Individuals
2.7 The Clean River Program (Prokasih)
the Act No. 22/1999 on which the districts are given more autonomous status, it is expected
that the environmental management could be also handled directly by the local government.
Based on the initiative of the programs, the action programs can be divided into two
categories. First, top-down initiative such as, clean river program (Prokasih), clean air
program, sustainable coastal and marine program, Proper and Superkasih program. Second,
bottom-up initiative is among others 4-R (reduce, reuse, recover, recycle) program and tree
bank program.
Clean river program (Prokasih) declared in early 1990s. Provincial and local
government involved in this program shall regularly submit information on monitoring
result of water quality of rivers. In general, the monitoring result conducted under Prokasih
showed that the water quality of rivers is improving.
Prokasih is aimed to reduce of pollution load entering rivers, improve of river
quality and, improve of resources and institutions (regulations, human resources, budgets
etc.) in the management of the environment and river water quality. It includes activities to
reduce the pollution load and discharges into the rivers. Prokasih activities are carried out
by local Governments under the co-ordination of Ministry of Environmental, operating in
cooperation with the Department of Interior Affairs and related technical agencies.
The clean rivers program initially covered 8 provinces and now covers 17
provinces. It includes 36 river basins and about 1500 industries. It is being extended to
2.8 The Statement of Clean River (Superkasih)
In addition to the Proper, the government through decree from the chairman of
Environmental Impact Management Agency (Bapedal) introduced the Superkasih program.
Superkasih stand for Surat Pernyataan Kali Bersih, which means a letter of intent to clean
river. It is an alternative strategy that is developed based on the voluntary commitment of
the industries to process their product through clean production. The program is developed
based on several considerations, among others 1) increasing number and type of industries
along watershed system, 2) increasing pollution especially
Top down programs that need greater support are among others, Proper and
Prokasih program. Constrains faced in the implementation of pollution prevention and
reduction strategies lie in the lack of institutional capability especially in the provincial and
district levels, and inadequate industrial-stakeholders’ participation which results from
weakness in local organizations and lack of awareness of the issues.
2.9 System Development
The system approach to problem solving uses a systems orientation to define
problem and opportunities and develop solutions. When the systems approach to problem
solving is applied to the development of information system, it called information system
development or application development. Most computer based information systems are
conceived, designed, and implemented using some form of systematic development
process. In this process, end user and information specialists design information systems
based on an analysis of the information requirements of organization. Thus a major part of
The traditional system development is the waterfall model or known as system
development life cycle (SDLC). O’Brien (2002) describes in Figure 7 that SDLC includes the steps of (1) investigation, (2) analysis, (3) design, (4) implementation and (5)
[image:52.612.91.534.187.405.2]maintenance.
Figure 7. System Development Life Cycle - SDLC (O’Brien, 2002)
Investigation stage intended to understand the business problem or opportunity.
Analysis stage describes what a system should do to meet the information needs of user.
Design stage specifies how the system will accomplish this objective. Once the new
information systems have been designed, it must be implemented. The final stage is
maintenance, which involves the monitoring, evaluation, and modifying of a system.
In many case, the traditional SDLC have to be modified because its limitation such
as the SDLC approach is costly and time consuming, inflexible, and discourage change, and
ill-suited to decision making. One alternative approach that can be used is prototyping.
model, or prototypes, of new application in an interactive, iterative process that can be used
by both systems analysts and end-user. Prototyping is an interactive process that combines
[image:53.612.116.506.160.375.2]steps of the traditional systems development.
Figure 8. Prototyping Development Stages (O’Brien, 2002)
The advantages of prototyping are users are involved in design and captures
requirements in concrete form. Prototyping makes the development process faster and
easier for system analyst, especially for projects where end-user requirements are hard to
define.
2.10 The Stage of Activity
In this research, the following step will be taken:
(1) Need Assessment, this stage is needed for getting initial information before
developing the system, and will do through focus group discussion among
stakeholders (in environmental sectors).
(2) Problem Analysis, in order to understand several problems faced by the
government in environmental management, in this stage try to know the root of
(3) Understanding the existing condition, to understand the procedure and
monitoring activity of industrial pollution.
(4) Data Mining & Collecting, to collect the industrial data and information of
pollution
(5) The general design, to design of Graphical User Interface (GUI) related to
spatial information system.
(6) Database Structuring, to develop the structure of database (industrial profile
and pollution).
(7) Preparing the Formulation, to prepare the formulation which is used by
system according to the several criteria and parameters.
(8) System Analysis, to develop the system analysis (based on user needs) and data
base design (conceptual design, logical design and physical design).
(9) Fuzzy System Design, to develop the qualitative decision analysis according to
stakeholders opinion (related to river load condition)
(10) Coding the spatial program, writing the code for the sub system of spatial
information (using digital map).
(11) Coding the Non Spatial Program, writing the code for the sub system of
database (non spatial information: industrial profile, pollution etc.)
(12) System Prototype, to make system prototype through to combine between
spatial and non spatial system to the one application system, called
IPMS-System (Industrial Pollution Monitoring Support System).
(13) System Testing, by using Beta Test to know the performance of system
prototype (until valid).
(14) Data Inputting, to input data and other information related to measurement
result (from industry outlet and river body).
(15) Reporting, according the printout of system (as output the system and end of
result), the result will write down as a complete thesis.
III. METHODOLOGY
Spatial-Decision Support System was developed to support the Government to
monitor the industrial wastewater and the river water quality. As an information system,
this system was developed by using SDLC approach (System Development Life Cycle).
3.1 Need Assessment
In the beginning of the analysis and general design phase, intending to elicit an
understanding of the scope of a study, a needs assessment was performed to understand the
project process, to know what they want to accomplish with the automation, and to involve
them at an early stage of the implementation. There are two kind of analysis in this stage,
e.g.: system analysis and data need analysis. System analysis, means that the principle of
database structure based on the output plan. Data Need Analysis, means that in this
analysis will be done identification of data type, data availability, data format, group of
data/variable, and data reading technique. Data will be used in database structuring should
be made in the same format, which can be done going through standardization all kind of
data. There are some questions arise during the need assessment of spatial decision support
system for industrial wastewater monitoring. The questions are shown in Table 4.
Table 4. Question from need assessment
User as decision maker Researcher
How to do the effective monitor- ing industrial and river water pollution?
• What is the existing information of pollution was covered?
• It’s complete or not?
• Can we display, share or access those data?
From literature review, it was found that monitoring the water pollution needs a system to
be implemented. To achieve the goals Spatial Decision Support System will be developed
and used.
3.2 User Identification
User identification analysis is needed for defining the specific target and appropriate
information. This should be done for designing the system. There are four categories of
users:
(1)Central Government (KMLH) as a decision maker needs information of industrial
profile and its pollution status.
(2)Local Government (Pemda) in order to release of industrial license and permitting
(3)Environmental Agency (BPLHD) in order to make good planning and monitoring of
environmental sectors.
(4) Industry in order to support regular data of industrial waste water and to get information
of water quality status.
(5) Public in order to know the industrial and river pollutions status
3.3 Data Collection Method
Data collecting will be done according to data need analysis going through to make listing of data which is suitable to level of need, especially kind of data which is support to
constructing database system. Data collecting only secondary data type, where it’s resource
from institution/department related to environmental aspects, and also from the existing
Figure 9. Stage of System Development Figure 10. Stage of Database Structuring
3.4 Time & Location
This research will be conducted from March to August 2006 at Bogor Agricultural
University. The location of study is Upper Citarum River Basin, West Java – Indonesia.
Geographically, it is located at 60 44’ 36”– 70 14’ 30” South Latitude and 1070 21’ 35” –
1070 50’ 54” East Longitude. This area has 3 districts/regions, 61 sub districts and 474
Figure 11. Research Location
3.5 Database
Database design involved defining how graphic will be symbolized (e.g. color, size,
symbols, etc), how graphic files will be structured, how non graphic attribute files will be
structured, what is the active layer, in what scale shall the layers expose, how GIS products
will be presented (e.g. map sheet layouts report format etc), and what management and
security restriction will be imposed on file access. Database design proceeds through the
[image:58.612.156.469.68.280.2]steps illustrated in figure below:
In this research, there are two typical of sub system: non-spatial and spatial sub
system:
(1) Database Sub System (Non spatial):
a. Program Interfacing: Visual Basic 6.0
b. Database: MS Access 2000
c. Crystal Report Ver. 9.0
d. Database Structure: according to need assessment result
e. Base line/Reference: Government Regulations.
f. Kind of Data: Administration, Industry Profile, water quality measurement (River &
Effluent)
(2) Spatial Sub System:
a. Map Object, ArcView 3.3, ERMapper
b. Base map (Prototype): Digital Base Map Bakosurtanal, scale: 1 : 25.000 (year 2000)
c. Landsat Imagery (for really land viewing, as soon as possible and depend on
requirement)
There are three main activities of the database system design with the following
activities (Rao, 1993):
(1)Conceptual design: identify data content, describe data, define features and entities, list
attributes and characteristics of each entity.
(2)Logical design: converting the conceptual design to the logical design of the GIS
database, include logical process modeling and logical data modeling.
(3)Physical design: design of the DSS application system. It describes the actual software
and hardware application, including how data is processed and organized on a particular
type of machine.
Hybrid architecture manages geospatial data independently and in different software
module from the non-spatial data (Worboys and Duckham, 2004). Spatial and non-spatial
data in the designed industry and river database have to be linked up for better analysis and
visualization of desired output. The link is provided by interface with connectivity function
to other related database. Non-spatial database will be designed in MS Access and link up
with the spatial data through ActiveX Data Object (ADO). ADO is familiar to database
programmers using Microsoft Visual Basic.
Figure 13. Hybrid system design
3.6 Graphical User interface
Spatial DSS Application Development - preparing applications identified in the
Needs Assessment, which require additional programming using the macro language or
other supporting programming languages. Several models have been introduced for system
development. In this research used prototyping model. Prototyping is the rapid
development and testing of working model or prototypes of new application in an
interactive, iterative process that can be used by both systems analyst and end user.
Prototyping makes the development process faster and easier for system analyst, especially
Graphical user interface is an application that can be used by users and it has
specific functions. The system development can be integrated and operated in a personal
computer. The interface has been developed using Microsoft Visual Basic and supported by
database reference (ADO) and several ActiveX components, i.e. ESRI MapObject.
3.7 Hardware & Software Requirement
The hardware used for this study is one unit of personal computer with Pentium IV
processor, 512 MB RAM and 40 GB hard disc. This system was developed by using
[image:61.612.99.519.346.556.2]several software employed to accomplish this research are shown in Table 5.
Table 5 Software Components will be used
No Software Function
1 AutoCAD Map 2000i Preprocessing raw digital data in dxf format. Converting from dxf into shape format.
2 ArcView GIS 3.3 GIS application Spatial data analysis, Viewing and updating attribute data.
3 MapObject Ver. 2.1
Active X Developing user interface
3 MS Access Database application Developing attribute data, Storing database as tables.
4 Visual Basic Programming software Developing user interface and database programming
5 Crystal Report Ver. 9.0 Designing The Output (Report) 6 ERMapper Ver. 6.4 Processing the satellite imagery 7 MS Visio Create the Flowchart & ER Diagram
3.8 Source of Data
Mainly the spatial data used for this research acquired from Bakosurtanal. There are
two kinds of data, i.e.:
(2) Spatial data: - Vector: administration boundary, industry, river network, road,.
The sources of secondary data from:
(1) Current data from PROKASIH (Clean River Program)
(2) The government Regulation Document
(3) Monthly Report from Industry
(4) Other sources of data
3.9 Public Perception Processed with Fuzzy Method
There are some condition related to the opinions of public/stakeholders, especially
for river segment which has low capacity. Although some industry not so polluted,
sometimes a river segment has a bad condition (caused by low discharge, high domestic
pollution, etc.). In many chances, we need some stakeholders/public opinion related to
these rivers condition. By using Fuzzy Logic, the best choice of some alternatives can be
resulted. More stakeholders gives opinion is better, because computer can compute it,
resulting only the best alternative within certain Alpha-Cut Value as a valid result.
In this research, there are five alternatives can be chosen as follow:
Table 6. Five alternatives for River Segment Evaluation
Description L1 L2 L3 L4 L5
River condition Very Good Good Fair Moderate Bad
Description Level 1 is the best choice among five river (an overview to the river condition). It means that the intention usage of this river perform to drinking water or any other use with the similar requirements.
Level 2 is the second choice where the river can be used for service water, recreational, gardening, or any other use with the similar requirements.
Level 3 is the third choice where the river can be used for fresh water aquacultures, farming or any other use with the similar requirements
Level 4 is the forth choice where the river can be used for irrigation water or any other use with the similar requirements.
Making the decision for each status of river segment related to stakeholder’s opinion
can use Fuzzy Method. This approach is used to get the best alternative from public
side/point of view. For instance some alternative called L1, L2…L5. Which one the best
alternative? Each audience has the different choice among some those alternatives.
The method can be explained step by step as follow:
• Step 1. Defining the alternatives, e.g. L1, L2, L3, L4, L5 (Opinion 1,2,3,4,5)
• Step 2: Opinion Collecting according to stakeholders. For instance: P1 = (L1, L3, L2, L4, L5)
P2 = (L2, L1, L3, L4, L5) …. etc. for each stakeholder (P3, P4 … Pn)
After this step, We have a lot of alternatives of opinion or solution from many audience
(depend on the number of people opinion or total respondent).
• Step 3: To calculate a lot of opinion where L1 better than L2 and so on.
• Step 4:
To calculate the degree or level of alternatives group Li on Lj
Formulae: S(Li,Lj) = N(Li,Lj)/n ... (1)
Where, N(Li,Lj): Number of audience, whose choose Li on Lj.
• Step 5:
To make relation matix of alternatives choicing using fuzzy
• Step 6:
To calculate the α-cuts value according to matrix result above.
• Step 7:
To give some recomendation/decision based on the α-cuts value. Fuzzy system will
give one result of decision only, which are exactly and more satisfy from the
3.10 Water Quality Standard
Water quality standard has been calculated following the calculation below:
Maximum Pollution Load
BPM = (Cm)j x Dm x f ………...………….. (2)
Notes:
BPM = Tolerable Maximum Pollutant Load, (kg parameter per day).
(Cm)j = Maximum concentration of parameter j (mg/l).
Dm = Discharge of Maximum Liquid Waste (Liters liquid waste per second per hectare).
f = conversion factor = 1 kg/1.000.000 mg * 24 hours/day x 3600 second/hours =
0,086
Actual Pollution Load can be calculated as below:
BPA = (CA)j x (DA) x f ... (3)
Notes:
BPA = Actual Pollution Load (kg parameter per day)
(CA)j = Actual concentration of parameter j (mg/l).
DA = Actual Waste Discharge (liter/s)
f = Conversion factor = 0,086
3.11 Water Quality Evaluation
According to the water quality standard above, the evaluation of Pollution Load is BPA
3.11.1 The Government Regulation
References of the regulation can be listed at follows:
(1)Government Regulation No. 82/2001 about Water Quality Management and Water
Pollution Control.
(2)Environmental Ministerial Decree No. 115/2003 about the Guidance of Water Quality
Status Calculation.
(3)Regional Regulation - West Java Province No. 3/2004 about Water Quality
Management and Water Pollution Control.
(4)Regional Regulation about Guidance of Water Quality Status Calculation.
3.11.2 River Water Quality
3.11.2.1. Parameter of the Water Quality
According to the Government Regulation (PP 82/2001), generally the classification of parameters of water quality is classified into 4 main groups, which are: Physics,
Chemistry, Biology & Microbiology and Radioactivity, the later separating chemistry
parameter into Inorganic Chemistry and Organic Chemistry.
3.11.2.2 Determination of Water Quality Parameter
The number of water quality parameters depends on the usage and class of water.
There is a standard on this subject. Water Quality Standard, called BMA (Baku Mutu Air)
which is effectively used in West Java was based on the classification of water as the Group
A, B, C, D and Group BDC as well Group CD.
3.11.2.3 Grouping of Water Quality Characteristics
(1) Physics parameter, Anion and Cation;
(2) Cation parameters, Anion and pH;
(3) Anorganic parameters, Non metal
(4) Biodegradable organic and Dissolved Oxygen: BOD & COD;
(5) Metals and Heavy metals;
(6) Non pesticide Organic
