AHP and Land Zoning decision

AHP and Land Zoning decision Muhamad Buce Saleh Tatang Tiryana Department of Forest Management Faculty of Forestry IPB

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

Material Discussion • Basic Decision Theory • Basic Theory of AHP • Case Study on Land use planning in Puncak Bogor


Material 1:

Basic Decision Theory


Decision 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 Phenomenon




MODEL Limitation Characters of MODEL: 1. Generality 2. Precision 3. Reliable In general a model only fulfilled 2 among 3 characters model above


Decision urgencies …(2) • Examples: – How to chose the best school for our children? – How to chose our vehicles? Which one I have to chose …??? – etc?


Decision urgencies…(3) • Decision are not easy, because of:

Rating sale

– The problems are really complicated – The limitation of human’s brain – Non-linear utilities:

Cost of promotion (Rp)

– 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


How to implement decision? • Stages of decision (Herbert Simon, 1960): Intelligence

Are there any problems?

Design

Are there any solution?

Choice Implementation Technique Pengambilan Keputusan dengan Metode AHP Oleh: M. Buce Saleh & Tatang Tiryana

Which Alternative solution to chose?

Are the choices effective?

A 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


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) Technique Pengambilan Keputusan dengan Metode AHP Oleh: M. Buce Saleh & Tatang Tiryana

Decision theories development – other subjects:


Foundation of a decision • Value System: a belief that used in natural resources management with or without consciousness – Efficiency – Precautionary – Equity • Preference: a knowledge, experiences, etc – Time Preference – Risk Preference • Information: data or information that we have – Quantitative/qualitative – Type of data: nominal, ordinal, cardinal, ratio2 Technique Pengambilan Keputusan dengan Metode AHP Oleh: M. Buce Saleh & Tatang Tiryana

Decision Framework Compatibility with/usability of decision principles in DAFs: – weak but not impossible; + possible but not central; * essential essential feature of DAF Level of application: G = Global; I=Inter/SupraI=Inter/Supra-national; N=National; R = Regional/Sectoral (Sub(Sub-national); L=Local (community); M = Micro (Family, firm, farm); X = All Typical domain of application: D=Direct intervention; I=Indirect I=Indirect influence; B=Both Uncertainty treatment: Rigor: * high; + good; – moderate/low Form: St=Model structure; SA=Sensitivity analysis; Sc=Scenarios Sc=Scenarios Source: Joseph Alcamo [et al.]. 2003.


Decision Situation (ADAPTED FROM EPA, 2000; STERN & FINEBERG, 1996; KØRNØV & THISSEN, 2000).


History of Decision Theory


Measurement Techniques in Decision • • • • • • • •

Ranking Rating Pairwise comparison Utility theory Probability Theory Posterior/Subjective probability (Bayesian) Dempster-Shafer Fuzzy


Data 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 …! Technique Pengambilan Keputusan dengan Metode AHP Oleh: M. Buce Saleh & Tatang Tiryana

Measurement Scale • What kind of analysis? Interval

Ratio 60

Ordinal Nominal • Number of runner

9

• Human weight

6 5 1

2

3


• Ranking winner

• Performance rating on scale 0 - 10

Measurement Scale…(2) • What operation should be done with number…!!! Mathematical Operation

Scale Nominal Ordinal

Interval

Ratio

+/-

No

No

Yes

Yes

x/:

No

No

Yes

Yes

Mathematical Function

-

-

Y=aX+b

Y=aX


Decision 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 Technique Pengambilan Keputusan dengan Metode AHP Oleh: M. Buce Saleh & Tatang Tiryana

MADM method classification

) Technique Pengambilan Keputusan dengan Metode AHP Oleh: M. Buce Saleh & Tatang Tiryana

©Yoon & Hwang (1995)

Characteristics 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. Technique Pengambilan Keputusan dengan Metode AHP Oleh: M. Buce Saleh & Tatang Tiryana

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 ? • 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 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 AHP • Decomposition : put the problem into hierarchical form • Priority determination through pairwise comparison • Priority Synthesizing • Evaluate inconsistency


Why AHP? • Helpful in organizing the elements of problem into 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) Technique Pengambilan Keputusan dengan Metode AHP Oleh: M. Buce Saleh & Tatang Tiryana

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

1. 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 framing • Basic Structure AHP: Goal Goal

Goal Goal Criteria Criteria

K1 K1

K2 K2

K3 K3

K4 K4

Alternatives Alternatives

A1 A1 A2 A2

A1 A1 A2 A2

A1 A1 A2 A2

A1 A1 A2 A2

A3 A3

A3 A3

A3 A3

A3 A3


2. Hierarchy framing …(2) • Structure expanded: Goal

Goal

Scenario

Actor

Criteria & Sub-criteria

Criteria & Sub-criteria

Alternatives

Alternatives


2. Hierarchy framing …(3) 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


2. Hierarchy framing …(4) • Example of relative model: The Thebest bestcar carfor forfamily family

Goal Goal

Criteria Criteria

Price Price

Alternative Alternative


BMW BMW

Prestige Prestige

Toyota Toyota Kijang Kijang

Multiuse Multiuse

Suzuki Suzuki Karimun Karimun

2. Hierarchy framing …(5) • Example absolute model: The Thebest bestcar carfor forfamily family

Goal Goal

Criteria Criteria

Price Price

Intensity IntensityScale Scale

Cheap Expens Cheap Expens

Alternative Alternative Technique Pengambilan Keputusan dengan Metode AHP Oleh: M. Buce Saleh & Tatang Tiryana

Prestige Prestige

Low Low

Medium Medium High High

Multiuse Multiuse

Less Less

Alternative Alternative (all (alltype typeand andtrade trademark markin inmarket) market)

More More

2. Hierarchy framing …(6) • Struktur hierarki PHTL LEI: model rating Pengelolaan Hutan Tanaman Lestari (PHTL)

TUJUAN

KRITERIA

Kelestarian Sumberdaya

Kelestarian Hasil Hutan

Kelestarian Fungsi Sosial Ekonomi dan Budaya

Kelestarian Fungsi Ekologi/Lingkungan

Kelestarian Fungsi Produksi

PRINSIP

Kelestarian Usaha

Kelestarian Kualitas Lahan dan Air

Kelestarian Keanekaragaman Hayati

Kelestarian Akses & Kontrol Komuniti

Kelestarian Integrasi Sosial dan Budaya

Kelestarian Hubungan Tenaga Kerja

Keterangan :

MK

PROSES

SUB-PROSES

Pm-K

Pn-K

PK

MH

Pg-K

KP

KL

KS

INDIKATOR INDIKATOR

PO

PSDM

MK MH PK

MKeu

Pm-K Pn-K Pg-K KP KL KS PO PSDM MKeu


NILAI (SKALA INTENSITAS) NILAI (SKALA INTENSITAS)

: Manajemen Kawasan : Manajemen Hutan : Penataan Kelembagaan : Pemantapan Kawasan : Penataan Kawasan : Pengamanan Kawasan : Kelola Produksi : Kelola Lingkungan : Kelola Sosial : Penataan Organisasi : Peningkatan Sumberdaya Manusia : Manajemen Keuangan

3. 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


3. Criteria and Alternative assessment …(2) Value 1 3 5 7 9 2,4,6,8

Statement Criteria/alternative A and B equally important A little more important than B A clearly more important than B A very clearly more important than B A absolutely more important than B If feel hesitated between two close values

Reciprocal value (1/3, 1/5,…) used if B more important than A Technique Pengambilan Keputusan dengan Metode AHP Oleh: M. Buce Saleh & Tatang Tiryana

3. Criteria and Alternative assessment …(3) • Assessment results put on pairwise comparison matrix (PCM) • Example PCM (1): criteria level – “Which criteria is more important and how much important?” Criteria

Price

Multiuse

Prestige

Price Multiuse Prestige

1 1/3 1/4

3 1 1/2

4 2 1


3. Criteria and Alternative assessment …(4) • Example PCM (2): alternative level – “From prestige aspect, which car more important and how much important?” Prestige

BMW

Kijang

Karimun

BMW Kijang Karimun

1 1/3 1/7

3 1 1/5

7 5 1

• If n element, then the total number of comparison: n(n-1)/2 Technique Pengambilan Keputusan dengan Metode AHP Oleh: M. Buce Saleh & Tatang Tiryana

Interruption…!!! 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” Technique Pengambilan Keputusan dengan Metode AHP Oleh: M. Buce Saleh & Tatang Tiryana

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..!!!


3. Criteria and Alternative assessment …(6)

• How if there is no consensus among the group? – Used mean geometric from individual assessment: – Example: x = n x1.x2....xn A=2B A=7B

A=3B A=5B


Mean-geo: A = 3,8B

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

4. 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 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)


4. Priority determination …(3) • Example weight: Criteria

Price

Multiuse

Prestige

1

3

4

Multiuse

1/3

1

2

Prestige

1/4

1/2

1

Price

PCM

3 4⎤ ⎡ 1 ⎢ 0,33 1 2 ⎥ ⎢ ⎥ ⎢⎣0, 25 0,5 1 ⎥⎦

• Iteration -1: – Squared PCM: ⎡1,000 3,000 4,000⎤ ⎡1,000 3,000 4,000⎤ ⎡3,000 8,000 14,000⎤ ⎢0,333 1,000 2,000⎥ x ⎢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 ⎥⎦ Technique Pengambilan Keputusan dengan Metode AHP Oleh: M. Buce Saleh & Tatang Tiryana

4. Priority determination …(4) • Iteration 1: – Summed value of each row and normalized: Sum row

⎡3,0000 8,0000 14,0000⎤ ⎢1,1667 3,0000 5,3333 ⎥ ⎢ ⎥ ⎢⎣0,6667 1,7500 3,0000 ⎥⎦

25,0000

25,0000/39,9167 = 0,6263

9,5000 5,4167

9,5000/39,9167 = 0,2380 5,4167/39,9167 = 0,1357

Sum: 39,9167

– So, eigen value from iteration -1: • 0,6263, 0,2380, 0,1357


normalized

4. Priority determination …(5) • 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 ⎥ x ⎢ 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 ⎥⎦


4. Priority determination …(6) • Iteration-2 : – Summed the value for each row and normalized: ⎡27.6667 72.5000 126.6667⎤ ⎢10.5556 27.6667 48.3333 ⎥ ⎢ ⎥ ⎢⎣ 6.0417 15.8333 27.6667 ⎥⎦

Sum row

normalized

226.8334 86,5556

226,8334/362,9307 = 0,6250 86,5556/362,9307 = 0,2385

49,5417

49,5417/362,9307 = 0,1365

Sum : 362,9307

– So, eigen value from iteration -2: • 0,6250, 0,2385, 0,1365


4. Priority determination …(7) • Now, calculate deviation between eigen value from iteration-1 and iteration-2: 0, 6263 − 0, 6250 =

0, 0013

0, 2380 − 0, 2385 = −0, 0005 0,1357 − 0,1365 = −0, 0008 – Deviation not large until 4 decimal


4. Priority determination …(8) • If iteration-3 continued, we will get the eigen value not change in 4 decimal: – So, eigen value for criteria level:

⎡0, 6250 ⎤ ⎡ Harga ⎤ ⎢0, 2385⎥ ⇒ ⎢Serbaguna ⎥ ⎢ ⎥ ⎢ ⎥ ⎢⎣ 0,1365 ⎥⎦ ⎢⎣ Prestise ⎥⎦ – That means the most important criteria is price, follow by multiuse then prestige


4. Priority determination …(9) Stop…stop..!!! Should we calculated so complicated like this…???


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

5. Consistency evaluation • Are pairwise comparison conducted consistently? 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

• Consistency will guaranteed the weight validity Technique Pengambilan Keputusan dengan Metode AHP Oleh: M. Buce Saleh & Tatang Tiryana

5. Consistency 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


5. Consistency evaluation …(3) • AHP tolerate inconsistency: – Measure by consistency index (CI) dan ratio consistency (CR): CI CR = RI

– Random value (RI):

– Good Consistency : CR ≤ 0,1 Technique Pengambilan Keputusan dengan Metode AHP Oleh: M. Buce Saleh & Tatang Tiryana

5. Consistency evaluation …(4) • Example consistency evaluation: – Is the assessment consistent? Price

Multiuse

Prestig e

1

3

4

Multiuse

1/3

1

2

Prestige

1/4

1/2

1

Criteria Price

PCM

3 4⎤ ⎡ 1 ⎢ 0,33 1 2 ⎥ ⎢ ⎥ ⎢⎣0, 25 0,5 1 ⎥⎦

– From the calculation we have eigen value (priority vector): 0, 6250 Price 0,2385 Multiuse 0,1365 Prestige Technique Pengambilan Keputusan dengan Metode AHP Oleh: M. Buce Saleh & Tatang Tiryana

5. Consistency evaluation …(5) Next Procedure: • Calculate weighted sum vector : multiply matrix with eigen value (priority vector)

3 4 ⎤ ⎡ 0, 6250 ⎤ ⎡ 1 ⎡1,8865 ⎤ ⎢ 0, 33 1 2 ⎥ x ⎢ 0, 2385 ⎥ = ⎢ 0,7178 ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢⎣ 0, 25 0, 5 1 ⎥⎦ ⎢⎣ 0,1365 ⎥⎦ ⎢⎣ 0,4120 ⎥⎦ • Divide the weight sum vector with priority vector:

⎡1,8865 ⎤ ⎡ 0, 6250 ⎤ ⎡3, 0184⎤ ⎢0,7178 ⎥ : ⎢ 0, 2385⎥ = ⎢3, 0096 ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢⎣0,4120 ⎥⎦ ⎢⎣ 0,1365 ⎥⎦ ⎢⎣3, 0183⎥⎦ Technique Pengambilan Keputusan dengan Metode AHP Oleh: M. Buce Saleh & Tatang Tiryana

5. Consistency evaluation …(6) Next Procedure: • Calculate maximum eigen value :

λmax

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

• Calculate consistency index (CI):

CI =

λ max − n n −1


3, 0154 − 3 = = 0, 0077 3 −1

5. Consistency evaluation …(7) Next Procedure: • Calculate consistency ratio (CR):

CI 0, 0077 CR = = = 0, 0132 RI 0,58 (Note: RI value for n=3 we get from table)

– Conclusion: assessment of the criteria level was consistent because CR value < 0,1 Technique Pengambilan Keputusan dengan Metode AHP Oleh: M. Buce Saleh & Tatang Tiryana

5. Consistency evaluation …(8) Once more… feel dizzy? Just used the software…!!!


6. Synthesis Alternative Priority • For example we get relative priority as follow: Chose Ideal Car Chose Ideal Car

Price Price (0,625) (0,625)

Multiuse Multiuse (0,238) (0,238)

1. 1. Karimun Karimun(0,648) (0,648) 1. 1. Kijang Kijang(0,625) (0,625) 2. Kijang (0,230) 2. Karimun 2. Kijang (0,230) 2. Karimun(0,238) (0,238) 3. 3. 3. BMW BMW(0,122) (0,122) 3. BMW BMW(0,136) (0,136)

Prestige Prestige (0,136) (0,136)

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

So, which is car we have to chose…??? Technique Pengambilan Keputusan dengan Metode AHP Oleh: M. Buce Saleh & Tatang Tiryana

6. Synthesis 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 Priority …(3) • Example synthesis: Weight

Criteria

Alternative Priority

Price (0,625)

Multiuse (0,238)

Prestige (0,136)

BMW

0,122

0,136

0,649

0,197

Kijang

0,230

0,625

0,279

0,333

Karimun

0,648

0,238

0,072

0,470

Alternative

Calculation: For BMW: (0,122).(0,625) + (0,136).(0.238) + (0,649).(0.136) = 0,197 Technique Pengambilan Keputusan dengan Metode AHP Oleh: M. Buce Saleh & Tatang Tiryana

6. Synthesis Alternative Priority …(4) • After all, then: – Total Priority level is: Karimun > Kijang > BMW Oh I have to chose just Karimun …!!!


Material 3:

Case Study: Implementation on Land use Planning in Puncak Area, Bogor Technique Pengambilan Keputusan dengan Metode AHP Oleh: M. Buce Saleh & Tatang Tiryana

Thank you bu Syar, pa Hadi and pa Lilik …! PENERAPAN MULTI CRITERIA DECISION MAKING (MCDM) DAN GEOGRAPHICAL INFORMATION SYSTEM (GIS) PADA EVALUASI PERUNTUKAN LAHAN (Studi Kasus: DAS Ciliwung Hulu, Kab. Bogor, Jawa Barat)

By: SYARTINILIA Supervisor : Ir. Hadi Susilo Arifin, MS., Ph. D. Dr. Ir. Lilik Budi Prasetyo, M.Sc Technique Pengambilan Keputusan dengan Metode AHP Oleh: M. Buce Saleh & Tatang Tiryana

Province Jawa Barat

Pulau Jawa

# # #

#

#

#

#

#

# # # ##

##

#

#

#

#

#

# # #

# # # # ## #

### # ##

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

## # #

N

Legenda : #

Ground truth point

GPS track 2002

Sungai Cilliwung

0

1

2

3 Kilometers

DAS Ciliwung Hulu, Kabupaten Bogor, Jawa Barat Technique Pengambilan Keputusan dengan Metode AHP Oleh: M. Buce Saleh & Tatang Tiryana

Protected Area Criteria Land suitability for Protected Area

Slope

Elevation

>40%

>2000 m

Soil

Litosol, slope > 15%

Regosol, slope > 15%

River bank

Important Catchment Area

High Land creep area

100 m both side of river

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

Land use

Slope (%)

Criteria Elevation) Drainage

Rice field

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

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

slow Medium-Good Fast

Dry land agriculture

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

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

Medium-Good Fast Slow

Estate/ plantation

<8 8-15 16-25 >25

1000-1500 1500-2000 >2000

Medium-Good Fast Slow

Settlement

<10 10-15 16-20 >20

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

Good Medium Fast Slow


Land cover Forest Tea estate Shrubs Dry agriculture Rice field Settlement Forest Tea estate Shrubs Dry agriculture Rice field Settlement Forest Tea estate Shrubs Dry agriculture Rice field Settlement Forest Tea estate Shrubs Dry agriculture Rice field Settlement

Land suitability for Build up and Cultivation Area

CULTIVATION AREA • Weight and intensity scale: AHP Land suitability for rice field Level 2: Weight

Level 3: Value

Penggunaan/pe nutupan lahan

Slope (%)

Elevation (m)

Drainage

<3

300-700

Slow

3-8

700-1000

Medm-Good

Tea estate

9-15

1000-1500

Fast

Shrubs

16-25

1500-2000

Ricef

>25

>2000

Dry Agr

Forest

Settlement Technique Pengambilan Keputusan dengan Metode AHP Oleh: M. Buce Saleh & Tatang Tiryana

DRY LAND AGRICULTURE AREA Kesesuaian lahan untuk pertanian lahan kering

Level 2 : Bobot

Level 3: Nilai

Slope (%)

Elevasi (m)

Drainage

Land cover

<3

300-700

Baik-sedang

3-8

700-1000

Cepat

Perkebunan teh

9-15

1000-1500

Terhambat

Semak belukar

16-25

1500-2000

Sawah

>25

>2000

Ladang

Hutan

Pemukiman Technique Pengambilan Keputusan dengan Metode AHP Oleh: M. Buce Saleh & Tatang Tiryana

PLANTATION AREA Kesesuaian lahan untuk perkebunan

Level 2 : Bobot

Level 3 : Nilai

Slope (%)

Elevasi (m)

Drainage

Land cover

Hutan

<8

1000-1500

Baik

8-15

1500-2000

Sedang-cepat

Perkebunan teh

16-25

>2000

Terhambat

Semak belukar

>25

Sawah Ladang Pemukiman


SETTLEMENT AREA Kesesuaian lahan untuk pemukiman

Level 2 : Bobot

Level 3 : Nilai

Slope (%)

Elevasi (m)

Drainage

Land cover

< 10

300-700

Baik

10-15

700-1000

Sedang

Perkebunan teh

16-20

1000-1500

Cepat

Semak belukar

>20

1500-2000

Terhambat

>2000

Hutan

Sawah Ladang Pemukiman


Example: weight and intensity scale for Rice field Area B= 0.217

1.0

0.8

0.8

0.6

0.6

Value

Value

1.0

0.4

B= 0.149

0.4 0.2

0.2

0.0

0.0

300-700

<3 %

3-8 %

9-15 %

16-25 %

700-1000

1000-1500 1500-2000

>2000

>25 % Elevasi (m )

Slope 1.0

1.0

B= 0.172

B = 0.461

0.8

Value

Value

0.8 0.6 0.4

0.6 0.4

0.2

0.2

0.0

0.0

Terhambat

Sedang-baik

Cepat

Drainase


Hutan

P erkebunan Teh

Semak belukar

Sawah

Land cover

Ladang

P emukiman

MODEL SINTHESIS Multi Criteria Decision Making (MCDM) Compromise Programming (CP) ⎡ d p = ⎢∑ β i p x i* − x ik ⎣ i =1 I

dp = βi∗ = x = p = z

(

)

p

⎤ ⎥ ⎦

1/ p

distance metrics weight for criteria based on preference level, where ideal point p distance parameter, range from 1 to ∞

β i> 0, ∑ β i = 1

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


Distance Matrix value calculation for GIS


Database Spatial Digital Elevation Model (DEM)

Slope


Aspect

Drainage


Land cover

Example: for cultivation area model


Cultivation area Model

Dry land agriculture

Rice field Technique Pengambilan Keputusan dengan Metode AHP Oleh: M. Buce Saleh & Tatang Tiryana

Settlement

Estate Technique Pengambilan Keputusan dengan Metode AHP Oleh: M. Buce Saleh & Tatang Tiryana

Cultivation Area with p= 1, 2, 10 Technique Pengambilan Keputusan dengan Metode AHP Oleh: M. Buce Saleh & Tatang Tiryana

THANK YOU FOR BEING DIZZY…..

Waaah… SO COMPLICATED…!?!?


AHP and Land Zoning decision

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