10
penggunaan input acak dan distribusi probabilitas. Simulasi Monte Carlo adalah simulasi statistik yang khusus menggunakan bilangan acak random sebagai parameter masukan
input. Teknik Monte Carlo adalah skema model yang menghitung parameter-parameter stochastic atau deterministic dalam sampel acak Hamdy 2007.
Latin Hypercube memilih nilai-nilai secara acak, tetapi penyebaran nilai acak dilakukan merata pada masing-masing asumsi yang ada pada distribusi. Crystal Ball membagi
probabilitas setiap asumsi distribusi ke segmen yang tidak tumpang tindih, masing-masing memiliki probabilitas yang sama seperti Gambar 6.
Gambar 6. Probabilitas latin Hypercube User Manual Crystal Ball 2008
2.6.3 Distribusi Crystal Ball
Crystal Ball merupakan program user friendly atau mudah dioperasikan dan dipahami. di dalam Crystal Ball terdapat beberapa teorema yang dapat digunakan, yaitu Kolmograv-
Sminov, Darling, dan Chi-Square. Program Crytal Ball memiliki tiga macam karakteristik, yaitu:
1. Assumption cell atau sel-sel asumsi.
2. Decision cell atau sel-sel keputusan.
3. Forecast cell atau sel-sel peramalan.
Assumption cell adalah nilai atau variabel yang tidak diketahui pasti masalah yang akan diselesaikan. Sel ini harus berupa nilai numerik dan bukan formula atau teks dan didefinisikan
sebuah distribusi probabilitas yang dapat dipilih, seperti; normal, uniform, exponential, geometric, weibull, beta, hyper geometric, gamma, logistic, pareto, extreme, value, negative,
binomial, dan costum. Decision cell berisi nilai numerik atau angka bukan formula atau teks atau menjelaskan variabel yang memiliki interval nilai tetrtentu sehingga didapat nilai optimal.
Sedangkan forecast cell merupakan sel formula dari assumption cell. Angka yang dihasilkan merupakan suatu variabel random. Variabel random merupakan
variabel yang nilainya ditentukan oleh kesempatan atau peluang. Istilah random disebabkan tidak ada cara untuk memperkirakan angka yang akan muncul.
a
b
Gambar 7. a. Variabel random diskrit dan b. Variabel random kontinu
120
Nilai variabel random kontinu dapat terjadi dalam interval ini
11
Terdapat dua macam variabel random, yaitu diskrit dan kontinu. Variabel random diskrit hanya mengisis nilai-nilai tertentu yang terpisah dalam suatu interval. Jika digambarkan
di atas garis interval, variabel random diskrit akan berupa sederetan titik-titik yang terpisah. Variabel random kontinu akan berupa sederetan titik yang tersambung membentuk garis lurus
Mulyono 2007. Kemunculan nilai variabel random diasumsikan sebagai suatu probabilitas, sehingga kemungkinan kemunculan random variabel yang bersifat discrete dan continue
diartikan sebagai discrete probability dan continues probability Walpole 2007. Discrate probability distribution menggambarkan perbedaan, nilai tak hingga, dan nilai integer.
Disribusi ini terlihat berbeda untuk setiap tinggi kolom. Continue probability distribution mengasumsikan semua nilai berada pada kisaran yang mungkin, termasuk range nilai yang tak
hingga. Distribusi ini memiliki kurva yang solid dan halus. Langkah pertama dalam memilih distribusi probabilitas harus berdasarkan data yang
ada, menggunakan pemahaman secara fisik mengenai kondisi-kondisi variabel data. Tabel 2. menjelaskan distribusi probabilitas berdasarkan Crystal Ball.
Tabel 2. Distribusi probabilitas berdasarkan kegunaan dan bentuk data.
Distribution Condition
Application Examples
Normal Mean value is most likely.
It is symmetrical about the mean.
More likely to be close to the mean than far away.
Natural phenomena.
People’s heights, reproduction rates,
inflation.
Triangular Minimum and maximum are
fixed. It has a most likely value in
his range, which forms a triangle with the minimum
and maximum. When you
know the minimum,
maximum, and most-likely
value, useful with limited
data. Sales estimates,
number of cars sold in a week, inventory
numbers, marketing cost.
Lognormal Upper and lower limits are
unlimited. Distribution is positively
skewed, with most values near lower limit.
Natural logarithm of the distribution is a normal
distribution. Situations
where values are positively
skewed. Real estate prices,
stock prices, pay scales, oil reservoir
size.
Uniform
Discrete Uniform Minimum is fixed.
Maximim is fixed. All values in range are
equally likely to occur. Discrete uniform is the
discrete equivalent of the uniform distribution.
When you know the range
and all possible values are
equally likely. A real estate
appraisal, leak on a pipeline.
Less commonly used distribution are listed below and on the back side of the card
12
Tabel 2 . Lanjutan
Distribution Condition
Application Examples
Binomial
Yes-No For each trial, only 2
outcomes are possible; usually, success or failure.
Trials are independent. Probability is the same from
trial to trial. The Yes-No distribution is
equivalent to the Binomial distribution with one trial.
Describes the number of times
an event occur in a fixed number
of trials, also used for Boolean
logic truefalse or onoff
Number of head in 10 flips on a coin,
likelihood of success or failure.
Beta Minimum and maximum
range is between 0 and a positive value.
Shape can be specified with two positive values, alpha,
and beta. Represents
variability over a fixed range,
describes empirical data.
Representing the reliability of a
companys devices.
BetaPERT Minimum and maximum are
fixed. It has a most likely value in
this range, which forms a triangle with the minimum
and maximum; betaPERT forms a smoothed curve on
the underlying triangle. When you know
the minimum, maximum, and
most likely value, useful
with limited data.
Similar to Triangular, but
especially for project
management
Exponential Distribution describes the
time between occurrences. Distribution is not affected by
previous events. Describes events
that recur randomly.
Time between incoming phone
calls, time between customer arrivals.
Gamma Possible occurrences in any
unit of measurement is not limited.
Occurrences are independent. Average number of
occurrences is constant from unit to unit.
Applied for physical
quantities, such as the time
between events when the event
process is not completely
random. Demand for
expected number of units sold during
lead time, meteorological
processes pollutant
concentrations.
Weibull This flexible distribution can
assume the properties of other disributions.
When shape parameters equal 1, it is identical to
Exponential, when equal to 2, it is identical to Rayleigh.
Fatique and failure tests or
other physical quantities
Failure time in a reliability study,
breaking strength of a material in a
control test
Max Extreme
Min Extreme Conditions and parameters are
complex. See: Castilo, Enrique. Extreme Value Theory in
Engineering. London; Academic Press, 1988.
Describes largest value Max
Extreme or smallest value
min Extereme of a response
over time or the breaking strength
of materials. Largest or smallest
flood flows, rainfall, and
earthquakes, aircraft loads and
tolerances.
13
Tabel 2. Lanjutan
Distribution Condition
Application Examples
Logistic Conditions and parameters are
complex. See: Fishman, G. Springer Series in Operation
Research. NY: Springer-Verlag. 1996.
Descibes growth. Growth of a
population as a function of time,
some chemical reactions.
Student’s t Midpoint values is most-
likely. It is symmetrical about the
mean. Approximates the Normal
distribution when degrees of freedom are equal to or
greater than 30. Econometric data.
Excange rates.
Pareto Conditions and parameters are
complex. See: Fishman, G. Springer Series in Operation
Research. NY: Springer- Verlag. 1996.
Analyzes other distributions
associated with empirical
phenomena. Investigating
distributions associated with
city population sizes, size of
companies, stock prices
fluctuations.
Poisson Number of possible
occurrences is not limited. Occurrences are independent.
Average number of occurrences is the same from
unit to unit. Describes the
number of times an event occur in
a given interval usually time.
Number of telephone calls
per minute, number of defects
per 100 square yards of material.
Hypergeometric Total number of items
population is fixed. Sample size number of trials
is a portion of the population. Probability of success
changes after each trials. Describes the
number of times an event occurs in
a fixed number of trials, but trials
are dependent on previous results.
Chance of a picked part being
defective when selected from a
box without replacing picked
parts to the box for the next trial.
Neg Binomial Number of trials is not fixed.
Trials continue to the γth succes trials never less than
δ. Probability of success is the
same from trial to trial. Models the
distribution of the number of trials
or failures until
the γth successful occurrence
Number of sales calls before you
close 10 order.
Geometric Number of trials is not fixed.
Trials continue until the first success.
Probability of success is the same from trial to trial.
Describes the number of trials
until the first successful
occurrence. Number of times
you spin a roulette wheel before you
win, how many wells to drill
before you hit oil.
Custom Very flexible distribution, used to represent a situation you cannot
describe with other distribution types. Can be either continuous or discrete or a combination of both.
Used to input an entire set of data point from a range of cells. Sumber : User Manual 2008
14
III. METODOLOGI
3.1 Waktu Dan Tempat Pelaksanaan
Penelitian dilakukan mulai dari bulan Maret 2012 hingga juni 2012, bertempat di PT. Krakatau Tirta Industri, Cilegon, Provinsi Banten.
3.2 Alat Dan Bahan
Alat yang digunakan dalam penelitian ini adalah seperangkat komputer dengan program Microsoft Excel, Crystal Ball, dan perangkat lunak statistik. Bahan-bahan yang digunakan
adalah serangkaian data primer dan sekunder untuk mengidentifikasi risiko dan menentukan risiko biaya seperti:
1. Data primer berupa kuesioner dan wawancara
2. Data sekunder berupa rancangan anggaran biaya dan materi proyek modernisasi kontrol
proses tahap II.
3.3 Metode Penelitian
Metode yang digunakan dalam penelitian menggunakan pedoman AustraliaNew Zealand Standard
4360:2004 tentang “Risk Management”. Tahapan penelitian terdiri dari:
3.3.1 Penentuan Konteks Risiko
Penentuan konteks dilakukan dengan melakukan konsultasi dan wawancara kepada para ahli di PT. KTI yang terkait dalam proyek MCP II. Teknik wawancara dilakukan untuk
mengetahui materi dan rancangan anggaran biaya proyek MCP II Modernization Control Process II. Penentuan konteks bertujuan untuk menentukan ruang lingkup manajemen risiko
yang mungkin pada proyek MCP II yang telah dilakukan.
3.3.2 Identifikasi Risiko
Identifikasi risiko merupakan proses mengidentifikasi apa, mengapa, dan bagaimana faktor-faktor yang mempengaruhi terjadinya risiko. Identifikasi risiko merupakan
pengembangan dari konteks risiko. Hasil identifikasi risiko tersebut kemudian disusun dalam bentuk kuesioner Lampiran 1 yang kemudian disebarkan kepada karyawan PT. KTI yang
terkait dalam proyek MCP II.
Tabel 3. Contoh skala ordinal pada kuesioner 1
No. Kriteria
2 1
ya cukup
tidak 1
Apakah anda mengetahui tentang proyek modernisasi kontrol proses tahap II?
Sumber : Data hasil
