Material 1: Basic Decision Theorylbprastdp.staff.ipb.ac.id/files/2010/12/070824_4_buce.pdf · Technique Pengambilan Keputusan dengan Metode AHP Oleh: M. Buce Saleh & Tatang Tiryana

GIS Asia Link 2 :

Course of Rehabilitation and Monitoring

Degraded Forest in Southeast Asia

Technique Pengambilan Keputusan dengan Metode AHPOleh: M. Buce Saleh & Tatang Tiryana

AHP and Land Zoning AHP and Land Zoning

decisiondecision

Muhamad Buce SalehTatang Tiryana

Department of Forest ManagementFaculty of Forestry IPB

Technique Pengambilan Keputusan dengan Metode AHP

Oleh: M. Buce Saleh & Tatang Tiryana

Material DiscussionMaterial Discussion

• Basic Decision Theory

• Basic Theory of AHP

• Case Study on Land use planning in Puncak Bogor


Material 1:

Basic Decision TheoryBasic Decision Theory



Decision urgenciesDecision urgencies……..

• For human’s life:

– Facing the problems:

• Gap between the real and hopes

– Generally the problems are complicated,

– The resources always limited for solving the

problems,

– Then decision should be made – even if we do nothing is a decision


Ways of explaining reality

Sea Snow Sea Snow

PhenomenonPhenomenon



GIS Asia Link 2 :






MODEL Limitation

Characters of MODEL:Characters of MODEL:

1.1. GeneralityGenerality

2.2. PrecisionPrecision

3.3. ReliableReliable

In general a model only fulfilled 2 In general a model only fulfilled 2

among 3 characters model aboveamong 3 characters model above


Decision urgencies …(2)

• Examples:

– How to chose the best school for our children?

– How to chose our vehicles?

– etc?Which one I have to chose …???



Decision urgencies…(3)

• Decision are not easy, because of:– The problems are really complicated– The limitation of human’s brain– Non-linear utilities:

– Most of the criteria are contradictive or conflicting• For example: we want a good car but cheap …????

– Measurement technique are difficult:• We should combine the data with difference scale• No scale for qualitative factors

Cost of promotion (Rp)

Rating sale


How to implement decision?How to implement decision?

• Stages of decision (Herbert Simon, 1960):

IntelligenceIntelligence

DesignDesign

ChoiceChoice

ImplementationImplementation

Are there any problems?

Are there any solution?

Which Alternative solution to chose?

Are the choices effective?



A common use in decisionA common use in decision

• Usually decision made by method:BOGSAT (a Bunch of Old Guys/Gals Sitting Around Talking)

Procedural stages:• Collecting the data/information,• Analyzing the data/information,• Discussing• Take a Decision

GIS Asia Link 2 :




A common use in decision…(2)

• weaknesses of BOGSAT:

– Dominated by only a few people

– Ignore the cognitive limitation:• BOGSAT have to discussed many thing in one time (e.g.

problem, alternatives, goal, criteria, etc),

• Though human brain have a limited capacity (up to 7 items)

– There is a satisfying principles, only concern with the alternatives which proposed – aspiration of the group:

• Limited to find more information for evaluate other alternatives

Sumber: Forman & Selly (2002)



Decision theories development Decision theories development –– other other

subjects:subjects:


Foundation of a decisionFoundation of a decision

•• Value System: a belief that used in natural resources Value System: a belief that used in natural resources management with or without consciousness management with or without consciousness

–– EfficiencyEfficiency

–– PrecautionaryPrecautionary

–– EquityEquity

•• Preference: a knowledge, experiences, etcPreference: a knowledge, experiences, etc

–– Time PreferenceTime Preference

–– Risk PreferenceRisk Preference

•• Information: data or information that we haveInformation: data or information that we have

–– Quantitative/qualitativeQuantitative/qualitative

–– Type of data: nominal, ordinal, cardinal, Type of data: nominal, ordinal, cardinal, ratio2ratio2



Compatibility with/usability of decision principles in DAFs: Compatibility with/usability of decision principles in DAFs: –– weak but not impossible; + possible but not central; * esweak but not impossible; + possible but not central; * essential feature of DAFsential feature of DAF

Level of application: G = Global; I=Inter/SupraLevel of application: G = Global; I=Inter/Supra--national; N=National; R = Regional/Sectoral (Subnational; N=National; R = Regional/Sectoral (Sub--national); L=Local (community); M = Micro (Family, national); L=Local (community); M = Micro (Family,

firm, farm); X = Allfirm, farm); X = All

Typical domain of application: D=Direct intervention; I=IndireTypical domain of application: D=Direct intervention; I=Indirect influence; B=Bothct influence; B=Both

Uncertainty treatment: Rigor: * high; + good; Uncertainty treatment: Rigor: * high; + good; –– moderate/lowmoderate/low

Form: St=Model structure; SA=Sensitivity analysis; Sc=ScenarioForm: St=Model structure; SA=Sensitivity analysis; Sc=Scenarioss

Source:Source: Joseph Alcamo [et al.]. Joseph Alcamo [et al.]. 2003. 2003.

De

cis

ion

Fra

me

wo

rk

De

cis

ion

Fra

mew

ork


Decision Situation (ADAPTED FROM EPA, 2000; STERN & Decision Situation (ADAPTED FROM EPA, 2000; STERN &

FINEBERG, 1996; KFINEBERG, 1996; KØØRNRNØØV & THISSEN, 2000). V & THISSEN, 2000).



History of Decision TheoryHistory of Decision Theory

GIS Asia Link 2 :





Measurement Techniques in DecisionMeasurement Techniques in Decision

•• RankingRanking

•• RatingRating

•• Pairwise comparisonPairwise comparison

•• Utility theoryUtility theory

•• Probability TheoryProbability Theory

•• Posterior/Subjective probability (Bayesian)Posterior/Subjective probability (Bayesian)

•• DempsterDempster--ShaferShafer

•• FuzzyFuzzy



Data as source of decisionData as source of decision

• Decision based on data/information,

• But, be careful on data….!!!

• Human Population : 5000

• Animal Population : 750

• Area : 1250

Total : 7000

Number not always as it is - seen …!


Measurement ScaleMeasurement Scale

• What kind of analysis?

Nominal

Ordinal

IntervalRatio

96

5

21

3

• Number of runner

• Ranking winner

• Performance rating on scale 0 - 10

60

• Human weight



Measurement Scale…(2)

• What operation should be done with number…!!!

Y=aXY=aX+b--Mathematical Function

YesYesNoNox / :

YesYesNoNo+ / -

RatioIntervalOrdinalNominal

ScaleMathematical Operation


Decision MethodDecision Method

• Popular Decision Method PK : “Multi-criteria decision making (MCDM)”

– Consisted of 2 groups:

• MODM (multi-objective decision making):

– Decision space is continue

– Alternatives choices based on several objectives that are

conflicting

• MADM (multi-attibute decision making):

– Decision space is discrete (alternatives is limited and have

determined before)

– Alternatives choices based on attributes (criteria) which are

conflicting for a certain goal



MADM method classificationMADM method classification

©Yoon & Hwang (1995)��

GIS Asia Link 2 :




Characteristics of MADMCharacteristics of MADM

– Alternatives:

• Several action that will be prioritized

• Number of action limited

– Multi Attribute (goals, criteria):

• Used for evaluating the alternatives

• Each problem has specific attribute:

– Examples when we chose the car: price, comfort ability,

safety, etc.

– Measurement scale are different.



Characteristic of MADM…(2)

– Weight for attributes:

• Relative Important level for each attributes

• Can be determined directly or using a certain method (example: pairwise comparison AHP)

– Decision Matrix:

• column represent attributes

• row represent alternatives


How to conduct MADM ?How to conduct MADM ?

• 3 stages :

1) Determined the relevant criteria and alternatives

2) Used numerical scale for relative important level of criteria and alternatives

3) Calculated and Analysis the numerical values to determined priority/ranking of alternatives



Material 2:

Basic Theory of AHPBasic Theory of AHP


AHP?AHP?

• Introduced and developed by Dr. Thomas L. Saaty at early 1970 period

• One of MCDA methods which explained a complicated problem through a simple way as follow: – Structurized the problem into hierarchical form

– Put a numerical value to determined important level for one element compare with other element

– Synthesizing to get priority of alternatives solution



Basic concept AHPBasic concept AHP

• Decomposition : put the problem into hierarchical form

• Priority determination through pairwise comparison

• Priority Synthesizing

• Evaluate inconsistency

GIS Asia Link 2 :




Why AHP?Why AHP?

• Helpful in organizing the elements of probleminto a hierarchy form:– Fit with human natural thinking to analyzed a complex problem

• Determination of important level conducted carefully:– Pairwise comparison more intuitive

• Integration of qualitative and quantitative data:– qualitative: judgment, preference

– quantitative: measurement result (e.g. price, cost, etc)



AHP ProcedureAHP Procedure

1. Problem definition

2. Organized elements of problem into hierarchy

3. Assessment the criteria and alternatives

4. Determined the relative priority

5. Evaluated consistency index

6. Synthesis the alternatives priority


1. Problem definition1. Problem definition

• Problem:

– Gap between the recent condition (what is) and the expectation (what should be)

• Problem Identification:

– What goal/objectives should be achieved?

– What criteria and sub-criteria which are suitable?

– What are alternatives solution?



2. Hierarchy framing2. Hierarchy framing

• Basic Structure AHP:

GoalGoal

K1K1 K2K2 K3K3 K4K4

A1A1

A2A2

A3A3

A1A1

A2A2

A3A3

A1A1

A2A2

A3A3

A1A1

A2A2

A3A3

GoalGoal

CriteriaCriteria

AlternativesAlternatives


2. Hierarchy framingHierarchy framing …(2)

• Structure expanded:

Scenario

Criteria & Sub-criteria

Alternatives

Goal

Actor

Criteria & Sub-criteria

Alternatives

Goal




Generally there are 2 models of AHP:1. Relative measurement model:

• If alternative solution has known exactly (maximum 9 alternative), then

• Alternatives can be compared one to others directly

• Example: choice 3 alternative cars

2. Absolute measurement model:• If alternative solution has not known well or

might be the number is too many

• Alternatives rated by a certain standard

• Example: - rating model for student selection

GIS Asia Link 2 :





• Example of relative model:

GoalGoal

CriteriaCriteria

AlternativeAlternative

The best car for familyThe best car for family

PricePrice PrestigePrestige MultiuseMultiuse

BMWBMW ToyotaKijang

ToyotaKijang

SuzukiKarimun

SuzukiKarimun



3. Criteria and Alternative assessment3. Criteria and Alternative assessment

• Conducted for each hierarchy level to determined the relative important value between one and other element

• Pairwise comparison used:

– Data (measurement results), or generally used

– Scale 1-9



• Example absolute model:

GoalGoal

CriteriaCriteria

Intensity ScaleIntensity Scale

AlternativeAlternative

The best car for familyThe best car for family

PricePrice PrestigePrestige MultiuseMultiuse

Alternative (all type and trade mark in market)

Alternative (all type and trade mark in market)

CheapCheap ExpensExpens LowLow MediumMedium HighHigh LessLess MoreMore



• Struktur hierarki PHTL LEI: model rating

Kelestarian

Kualitas

Lahan dan Air

Kelestarian

Keanekaragaman

Hayati

Kelestarian

Akses & Kontrol

Komuniti

Kelestarian

Integrasi Sosial

dan Budaya

Kelestarian

Hubungan

Tenaga Kerja

Kelestarian

Usaha

Kelestarian

Hasil Hutan

Kelestarian

Sumberdaya

Keterangan :

MK : Manajemen Kawasan

MH : Manajemen Hutan

PK : Penataan

Kelembagaan

Pm-K : Pemantapan Kawasan

Pn-K : Penataan Kawasan

Pg-K : Pengamanan Kawasan

KP : Kelola Produksi

KL : Kelola Lingkungan

KS : Kelola Sosial

PO : Penataan Organisasi

PSDM : Peningkatan

Sumberdaya Manusia

MKeu : Manajemen

Keuangan

SUB-PROSES

PROSES

KRITERIA

PRINSIP

TUJUAN

MH PKMK

Pm-K Pn-K Pg-K PO PSDM

INDIKATORINDIKATOR

NILAI (SKALA INTENSITAS)NILAI (SKALA INTENSITAS)

MKeuKLKP KS

Pengelolaan Hutan

Tanaman Lestari (PHTL)

Kelestarian Fungsi

Produksi

Kelestarian Fungsi

Ekologi/Lingkungan

Kelestarian Fungsi

Sosial Ekonomi dan

Budaya


3. Criteria and Alternative assessmentCriteria and Alternative assessment …(2)

If feel hesitated between two close values2,4,6,8

A absolutely more important than B9

A very clearly more important than B7

A clearly more important than B5

A little more important than B3

Criteria/alternative A and B equally important

1

StatementValue

Reciprocal value (1/3, 1/5,…) used if B more important than A




• Assessment results put on pairwise comparison matrix (PCM)

• Example PCM (1): criteria level

– “Which criteria is more important and how much important?”

11/21/4Prestige

211/3Multiuse

431Price

PrestigeMultiusePriceCriteria

GIS Asia Link 2 :





• Example PCM (2): alternative level

– “From prestige aspect, which car more important and how much important?”

• If n element, then the total number of comparison: n(n-1)/2

11/51/7Karimun

511/3Kijang

731BMW

KarimunKijangBMWPrestige



InterruptionInterruption……!!!!!!

If only determined the weight,

why we should use pairwise

comparison?

I think easier to determined

directly such as for example:

0,4??

But why 0,4? Why not

0,45?

Then here in AHP,

when determined the

weight not

careless…!!!

”it is better to be approximately right than precisely wrong”


3. Criteria and Alternative assessment…(5)

• How if assessment conducted by group?

– Better if group made a consensus:

• Discussion and argues should be done before coming with one value

– Value based on consensus used in PCM

OK, we deal with that price is 3 time more important than prestige..!!!




• How if there is no consensus among the group?

– Used mean geometric from individual assessment:

– Example:1 2. ....n

nx x x x=

A=3B

A=2B

A=5B

A=7B Mean-geo: A = 3,8B

4 (2).(3).(5).(7) 3,8x = =


4. Priority determination4. Priority determination

• Each element (criteria, alternative) in hierarchy has relative weight:

– To show priority/important relatively

• Weight get from PCM:

– Calculate eigen value (eigenvector)



4. Priority determinationPriority determination …(2)

• Calculation methods (eigen value):

– According to Saaty: power method

– Procedure:

• Squared the matrix

• Calculate sum of number for each row, then normalized (divided by total sum)

• Iterated until we get the deviation value between two iteration is very small (not change in 4 decimal)

GIS Asia Link 2 :





• Example weight:

• Iteration -1:

– Squared PCM:

11/21/4Prestige

211/3Multiuse

431Price

PrestigeMultiusePriceCriteria 1 3 4

0,33 1 2

0, 25 0,5 1

1,000 3,000 4,000 1,000 3,000 4,000 3,000 8,000 14,000

0,333 1,000 2,000 0,333 1,000 2,000 1,167 3,000 5,333

0,250 0,500 1,000 0,250 0,500 1,000 0,667 1,750 3,000

x

=

PCM




• Iteration 1:

– Summed value of each row and normalized:

– So, eigen value from iteration -1:

• 0,6263, 0,2380, 0,1357

3,0000 8,0000 14,0000 25,0000 25,0000/39,9167 = 0,6263

1,1667 3,0000 5,3333 9,5000 9,5000/39,9167 = 0,2380

0,6667 1,7500 3,0000 5,4167 5,4167/39,9167 = 0,1357

Sum row normalized

Sum: 39,9167



• Iteration-2:

– Again squared the matrix from iteration -1:

3,0000 8,0000 14,0000 3,0000 8,0000 14,0000

1,1667 3,0000 5,3333 1,1667 3,0000 5,3333

0,6667 1,7500 3,0000 0,6667 1,7500 3,0000

27,6667 72,5000 126,6667

= 10,5556 27,6667 48,3333

6,0417 15,8333 27,6667

x




• Iteration-2 :

– Summed the value for each row and normalized:

– So, eigen value from iteration -2:

• 0,6250, 0,2385, 0,1365

27.6667 72.5000 126.6667 226.8334 226,8334/362,9307 = 0,6250

10.5556 27.6667 48.3333 86,5556 86,5556/362,9307 = 0,2385

6.0417 15.8333 27.6667 49,5417 49,5417/362,9307 = 0,1365

Sum row normalized

Sum : 362,9307


0,6263 0,6250 0,0013

0,2380 0, 2385 0,0005

0,1357 0,1365 0,0008

− =

− = −

− = −


• Now, calculate deviation between eigen valuefrom iteration-1 and iteration-2:

– Deviation not large until 4 decimal



• If iteration-3 continued, we will get the eigen value not change in 4 decimal:

– So, eigen value for criteria level:

– That means the most important criteria is price, follow by multiuse then prestige


0,6250 Harga

0,2385 Serbaguna

0,1365 Prestise

⇒

GIS Asia Link 2 :





Stop…stop..!!! Should we calculated so complicated like

this…???

Not of course…,Just used the software…!!!



5. Consistency evaluation5. Consistency evaluation

• Are pairwise comparison conducted consistently?

• Consistency will guaranteed the weight validity

Consistent:•A = 2B, B = 3C� A = 6C

•Price > Multiuse,Multiuse > Prestige� Price > Prestige

Consistent:•A = 2B, B = 3C� A = 6C

•Price > Multiuse,Multiuse > Prestige� Price > Prestige

Inconsistent:•A = 2B, B = 3C� A = 4C

•Price > Multiuse,Multiuse > Prestige� Price < Prestige

Inconsistent:•A = 2B, B = 3C� A = 4C

•Price > Multiuse,Multiuse > Prestige� Price < Prestige


5. Consistency evaluationConsistency evaluation …(2)

• Inconsistency might be happen when:

–Wrong entry data

– Information not enough

– Less concentration

– Facts are really not always consistent:

• Team A outmatch Team B, and Team B outmatch Team C, but

• May be Team C outmatch Team A




• AHP tolerate inconsistency:

– Measure by consistency index (CI) dan ratio consistency (CR):

– Random value (RI):

– Good Consistency : CR CR ≤≤ 0,10,1

CICR

RI=



• Example consistency evaluation:– Is the assessment consistent?

– From the calculation we have eigen value (priority vector):

11/21/4Prestige

211/3Multiuse

431Price

Prestige

MultiusePriceCriteria1 3 4

0,33 1 2

0,25 0,5 1

PCM

0, 62500,23850,1365

PriceMultiusePrestige




Next Procedure:• Calculate weighted sum vector : multiply matrix with eigen

value (priority vector)

• Divide the weight sum vector with priority vector:

1 3 4 0,6250 1,8865

0,33 1 2 0, 2385 = 0,7178

0, 25 0,5 1 0,1365 0,4120

x

1,8865 0,6250 3,0184

0,7178 : 0,2385 = 3,0096

0,4120 0,1365 3,0183

GIS Asia Link 2 :





Next Procedure:• Calculate maximum eigen value :

• Calculate consistency index (CI):

max

(3,0184)+(3,0096)+(3,0183) = =3,0154

3λ

max 3, 0154 30, 0077

1 3 1

nCI

n

λ − −= = =

− −




Next Procedure:• Calculate consistency ratio (CR):

(Note: RI value for n=3 we get from table)

– Conclusion: assessment of the criteria level was consistent because CR value < 0,1

0,00770,0132

0,58

CICR

RI= = =



Once more… feel dizzy?Just used the software…!!!



6. Synthesis Alternative Priority6. Synthesis Alternative Priority

• For example we get relative priority as follow:

Chose Ideal CarChose Ideal Car

Price(0,625)

Price(0,625)

Multiuse(0,238)

Multiuse(0,238)

Prestige(0,136)

Prestige(0,136)

1. Karimun (0,648)2. Kijang (0,230)3. BMW (0,122)

1. Karimun (0,648)2. Kijang (0,230)3. BMW (0,122)

1. Kijang (0,625) 2. Karimun (0,238)3. BMW (0,136)

1. Kijang (0,625) 2. Karimun (0,238)3. BMW (0,136)

1. BMW (0,649)2. Kijang (0,279)3. Karimun (0,072)

1. BMW (0,649)2. Kijang (0,279)3. Karimun (0,072)

So, which is car we have to chose…???


6. Synthesis Alternative PrioritySynthesis Alternative Priority …(2)

• If we want to get total priority for

alternatives, we have to synthesis:

– Combine all relative priority vector

– One of the method is weighted summation, compromise method, etc



6. Synthesis Alternative PrioritySynthesis Alternative Priority …(3)

• Example synthesis:

0,072

0,279

0,649

Prestige

(0,136)

0,4700,2380,648Karimun

0,3330,6250,230Kijang

0,1970,1360,122BMW

Multiuse

(0,238)

Price

(0,625)Alternative

Alternative Priority

CriteriaWeight

Calculation:For BMW: (0,122).(0,625) + (0,136).(0.238) + (0,649).(0.136) = 0,197

GIS Asia Link 2 :




Oh I have to chose just Karimun …!!!

6. Synthesis Alternative PrioritySynthesis Alternative Priority…(4)

• After all, then:

– Total Priority level is: Karimun > Kijang > BMW



Material 3:

Case Study: Implementation Case Study: Implementation

on Land use Planning in on Land use Planning in

Puncak Area, BogorPuncak Area, Bogor


Thank you bu Syar, pa Hadi and pa Lilik …!

PENERAPAN PENERAPAN MULTI CRITERIA DECISION MAKINGMULTI CRITERIA DECISION MAKING(MCDM) DAN (MCDM) DAN GEOGRAPHICAL INFORMATION GEOGRAPHICAL INFORMATION SYSTEMSYSTEM (GIS) PADA EVALUASI PERUNTUKAN (GIS) PADA EVALUASI PERUNTUKAN LAHAN LAHAN (Studi Kasus: DAS Ciliwung Hulu, Kab. Bogor, Jawa Barat)(Studi Kasus: DAS Ciliwung Hulu, Kab. Bogor, Jawa Barat)

By: SYARTINILIASupervisor :Ir. Hadi Susilo Arifin, MS., Ph. D.Dr. Ir. Lilik Budi Prasetyo, M.Sc


#

########

###

## ##############

#########

#######

#

#

#####

###

###

#

#

#

#

#

##

#

#

#

#

#

#

# #

#

#

######

#

##

#

#

#

#

#

Ground truth point# GPS track 2002 Sungai C illiwung

Legenda :

N

0 1 2 3 Kilometers

Pulau Jawa

Province Jawa Barat

DAS Ciliwung Hulu, Kabupaten Bogor, Jawa Barat


Protected Area CriteriaProtected Area Criteria

Source : - Ketentuan teknis kawasan lindung dalam RTRW Bopunjur (Bappeda, 2000) dengan berpedoman pada Keppres No.32 tahun 1990 tentang Pengelolaan Kawasan Lindung dan SK Gubernur Jawa Barat No. 413.21/SK.222-HUK/91

tentang Kriteria Lokasi dan Standar Teknis Penataan Ruang di Kawasan Puncak.

Land suitability for Protected Area

Slope High Land creep area

Elevation Soil River bank Important Catchment

Area

>2000 m>40% Litosol,slope > 15%

100 m both side of river

Regosol, slope> 15%



Land suitability for Build up and Cultivation Land suitability for Build up and Cultivation AreaArea

Settlement

Rice field>2000

Dry agricultureSlow1500-2000>20

ShrubsFast1000-150016-20

Tea estateMedium700-100010-15

ForestGood300-700<10SettlementSettlement

Rice field

Dry agriculture>25

ShrubsSlow>200016-25Tea estateFast1500-20008-15

ForestMedium-Good1000-1500<8Estate/ plantation

Settlement

Rice field>2000>25Dry agriculture1500-200016-25

ShrubsSlow1000-15009-15

Tea estateFast700-10003-8

ForestMedium-Good300-700< 3Dry land agriculture

Settlement

Rice field>2000>25

Dry agriculture1500-200016-25

ShrubsFast1000-15009-15

Tea estateMedium-Good700-10003-8

Forestslowslow300300--700700< 3< 3Rice field

Land coverDrainageElevation)Slope (%)

CriteriaLand use

GIS Asia Link 2 :




CULTIVATION AREACULTIVATION AREA

•• Weight and intensity scale: AHPWeight and intensity scale: AHP

Level 3:

Value

Land suitability for rice field

Slope (%)

< 3

3-8

>25

9-15

16-25

Drainage

Slow

Medm-Good

Fast

Elevation (m)

300-700

700-1000

1000-1500

1500-2000

>2000

Penggunaan/pe

nutupan lahan

Forest

Tea estate

Shrubs

Ricef

Dry Agr

Settlement

Level 2:Weight



Slope (%)

< 3

3-8

>25

9-15

16-25

Drainage

Baik-sedang

Cepat

Terhambat

Elevasi (m)

300-700

700-1000

1000-1500

1500-2000

>2000

Land cover

Hutan

Perkebunan teh

Semak belukar

Sawah

Ladang

Pemukiman

Kesesuaian lahan untuk pertanian lahan kering

Level 3:Nilai

Level 2 :Bobot

DRY LAND AGRICULTURE AREADRY LAND AGRICULTURE AREA


Slope (%)

16-25

< 8

Drainage

Baik

Sedang-cepat

Terhambat

Elevasi (m)

1000-1500

1500-2000

>2000

Land cover

Hutan

Perkebunan teh

Semak belukar

Sawah

Ladang

Pemukiman

Kesesuaian lahan untuk perkebunan

8-15

>25

Level 3 :Nilai

Level 2 :Bobot

PLANTATION AREAPLANTATION AREA



Level 3 :Nilai

Level 2 :Bobot

Slope (%)

< 10

>20

10-15

16-20

Drainage

Baik

Cepat

Terhambat

Elevasi (m)

300-700

700-1000

1000-1500

1500-2000

>2000

Land cover

Hutan

Perkebunan teh

Semak belukar

Sawah

Ladang

Pemukiman

Kesesuaian lahan untuk pemukiman

Sedang

SETTLEMENT AREASETTLEMENT AREA


Example: weight and intensity scale Example: weight and intensity scale for Rice field Areafor Rice field Area

0.0

0.2

0.4

0.6

0.8

1.0

<3 % 3-8 % 9-15 % 16-25 % >25 %

Slope

Valu

e

B= 0.217

0.0

0.2

0.4

0.6

0.8

1.0

300-700 700-1000 1000-1500 1500-2000 >2000

Elevasi (m)

Valu

e

B= 0.149

0.0

0.2

0.4

0.6

0.8

1.0

Terhambat Sedang-baik Cepat

Drainase

Valu

e

B = 0.461

0.0

0.2

0.4

0.6

0.8

1.0

Hutan Perkebunan

Teh

Semak

belukar

Sawah Ladang Pemukiman

Land cover

Valu

e

B= 0.172



� We use sensitivity analysis with 3 different values, such as: p = 1, 2 and (e.g. p>10).

MODEL SINTHESISMODEL SINTHESIS

( )p

I

i

pk

ii

p

ip xxd

/1

1

*

−= ∑

=

β

∞

dp = distance metrics

= weight for criteria based on preference level, where > 0, = ideal point p

p = distance parameter, range from 1 to

iβi

β 1=∑ iβ∗

x∞

Multi Criteria Decision Making (MCDM)Multi Criteria Decision Making (MCDM)Compromise Programming (CP)

GIS Asia Link 2 :




Distance Matrix value calculation for GIS



Slope

Digital Elevation Model

(DEM)

Aspect

Database SpatialDatabase Spatial


Drainage Land cover



Example: for cultivation area modelExample: for cultivation area model


Cultivation area ModelCultivation area Model

Ric

e fie

ld

Dry la

nd

ag

ricu

lture



Esta

te

Settle

ment

GIS Asia Link 2 :




Cu

ltiva

tion

Are

a w

ith p

= 1

, 2, 1

0C

ultiv

atio

n A

rea w

ith p

= 1

, 2, 1

0



THANK YOU FOR BEING DIZZY…..

Waaah… SO COMPLICATED…!?!?

Documents

Material 1: Basic Decision Theorylbprastdp.staff.ipb.ac.id/files/2010/12/070824_4_buce.pdf · Technique Pengambilan Keputusan dengan Metode AHP Oleh: M. Buce Saleh & Tatang Tiryana