Resep Nasi Goreng terlur isi tempe

Resep Memasak Cumi

Oke guys, what are you doing now? bagi yang nganggur dari pada bingung2 enaknya mau ngapain. Atau bagi yang laper tapi bingung mau makan apa, mending ikutin resep ini aja nih, kita masak cumi ala aku, hahahah
oke, le'ts try together me! Are you Ready? :D

Bahan :
1/4kg Cumi. Buang kantung tintanya, cuci bersih dan potong-potong
2 lembar daun pandan
1/2 biji cabe rawit merah
2 sdt gula merah
1 buah jeruk nipis
5 sdm saus tiram
1/2 garam

Bahan untuk membuat bumbu :
4 buah cabe
4 butir bawang merah
3 siung bawang putih
1/2 sdt terasi
1/4 cangkir air asam kental
1/2 sdm merica bubuk

Cara Memasak :

1. Cumi terlebih dahulu di cuci bersih kemudian campurkan dengan air jeruk nipis, diamkan selama kurang lebih 30 menit agar cumi tidak amis.
2. Panaskan minyak, tumis bumbu halus hingga wangi
3. Masukkan cumi-cumi, daun pandan, cabe rawit, & garam, aduk sesekali
4. Masukan 1/4 cangkir air asam kental (air rendaman asam)\
5. Masukan gula merah, aduk rata
6. Masak hingga 5 menit lalu masukkan gula merah & saus tiram. aduk rata
7. Angkat dan sajikan untuk keluarga tercinta. Selamat mencoba :D

Bingung mesti ngasih nama cumi apa, jadiiii terserah deh yaaah, cobain aja buat bikin sendiri di rumah, dan rasakan nikmatnya hehe ;)

Semoga bermanfaat bagi yang sedang belajar masak, atau sengaja mencoba resep baru untuk keluarga di rumah... 

Masak? 

KATAKAN

It's enjoyeble of me !

Bukber SAPELA di Ramadhan tahun 2013 M

Tentu kehendak Allah yang menjadikan kami bersaudara, hingga sepakat dihati masing-masing bahwa kami keluarga.

Ya Allah, ini sungguh luar biasa untukku..

Terimakasih atas karuniaMu ya Allah, memberi apa yang sekiranya aku butuhkan, yah.. mereka sangat aku butuhkan, butuh dalam konteks apa? aku rasa, untuk jiwa, rasanya jiwa ini sedikitnya ternafkahi dengan adanya mereka di perjalanan hidupku ini :) Setelah Engkau beri aku keluarga yang sangat harmonis, saudara satu keturunan yang begitu sangat mencinta, Engkaupun tak habis-habisnya menghendaki aku untuk memiliki mereka, keluarga Saung Pelangi.

Tanggal 3 Agustus 2013, 

di kediaman Neneng Maulida. Cilegon-Banten.

Meski gak semua keluarga sapela bisa kumpul, 5 diantaranya berhalangan untuk hadir.

Alhamdulillah bisa bersilaturahmi dengan keluarga neneng disana, banyak yg aku baru tau soal neneng adikku ini, yah aku mengganggapnya sebagai adik, dia manis menurutku, senyumnya itu loh, masya Allah manis sekali. Gak ada alasan yang membuatku untuk tidak sayang padanya:)

Sebulan lamanya kami KKM di desa masing-masing, terpencae-pencar. 

Dengan acara buka bersama di kediaman Neneng ini, kami melepas rindu dengan banyak bercerita soal pengalaman di lokasi KKM masing-masing. 

Tuker kado, foto bersama. yah mungkin suatu saat nanti, aku akan sangat merindukan saat-saat bersama dengan mereka. Harapku semoga tali persaudaraan dan silaturahmi kami senantiasa terjaga, sampai nenek kakek, bahkan hingga ajal menjemput. Semoga akan banyak doa yang terbang untuk setiap kami, karena bagiku, media doa yang bisa menjamin bahwa kita akan terus bersatu:) karena bukan ikatan darah yang menyebabkan kita menjadi saudara bahkan keluarga, tapi ikatan hati melalu doa, yah dari situlah kami menjadi bersaudara:) 

Allah, Ya Latief, sampaikanlah pada hati-hati mereka, bahwa aku sangat sayang mereka :') dengan caraku yang mungkin tidak sampai untuk menyentuh hati-hatinya, dengan caranku yang mungkin cuek dengan setiap masalah mereka, dengan caraku yang mungkin terkadang kesannya terlalu ramai untuk bicara, maka ku mohon, hanya Engkau yang mampu untuk menyampaikan ini, aku... Aku sayang mereka karena Engkau Ya Allah, Selamatkan mereka di dunia ini, selamatkan mereka di akhirat kelak ;') Aamiin

Udah lama gak jengukin

aaaaaaaaaaaaaaaaaagh, maaf ya blog udh lama gak jengukin kamu, jadi bersarang laba-laba gini deh yaah..

Ikan-ikan jadi mandiri banget gak aku kasih makan, hehe

Jadi kangen rasanya :D

Meski gak nulis di sini aku, aku sempet coret2 di Tumblr loh, 

ini dia, Tumblr aku, klik ini silahkan

Areas under the Normal Curve

The curve of any continuous probability distribution or density function is constructed so that the area under the curve bounded by the two ordinatos x — x\ and x — x2 equals the probability that the random variable X assumes a value between x = x\ and x = x2. Thus, for the normal curve in Figure 6.6,

In Figures 6.3, 6.4, and 6.5 we saw how the normal curve is dependent on the mean and the standard deviation of the distribution under investigation. The area under the curve between any two ordinates must then also depend on the values p and a. This is evident in Figure 6.7, where we have shaded regions corresponding

to P(xi < X < x-2) for two curves with different means and variances. The P{x\ < X < x2), where X is the random variable describing distribution A, is indicated by the darker shaded area. If X is the random variable describing distribution B, then P(x.\ < X < x2) is given by the entire shaded region. Obviously, the two

shaded regions are different in size; therefore, the probability associated with each distribution will be different for the two given values ol X. The difficulty encountered in solving integrals of normal density functions necessitates the tabulation of normal curve areas for quick reference. However, it 

Definition

The distribution of a normal random variable with mean 0 and variance 1 is called

a standard normal distribution.

The original and transformed distributions are illustrated in Figure 6.8. Since

all the values of X falling between x\ and x2 have corresponding z values between

z\ and z2, the area under the X-curve between the ordinates x = x\ and x = x2 in

Figure 6.8 equals the area under the Z-curve between the transformed ordinates

z = z\ and z — z2.

We have now reduced the required number of tables of normal-curve areas to

one, that of the standard normal distribution. Table A.3 indicates the area under

the standard normal curve corresponding to P(Z < z) for values of z ranging from

—3.49 to 3.49. To illustrate the use of this table, let us find the probability that Z

is less than 1.74. First, we locate a value of z equal to 1.7 in the left column, then

move across the row to the column under 0.04, where we read 0.9591. Therefore,

P(Z < 1.74) = 0.9591. To find a z value corresponding to a given probability, the

process is reversed. For example, the z value leaving an area of 0.2148 under the

curve to the left of z is seen to be —0.79

Sebaran Seragam Kontinu

One of the simplest continuous distributions in all of statistics is the continuous

uniform distribution. This distribution is characterized by a density function

that is "flat," and thus the probability is uniform in a closed interval, say [A, B].

Although applications of the continuous uniform distribution are not as abundant

as they are for other distributions discussed in this chapter, it is appropriate for

the novice to begin this introduction to continuous distributions with the uniform

distribution.

Uniform Distribution

It should be emphasized to the reader that the density function forms a rectangle

with base B — A and constant height -g^j- As a result, the uniform distribution

is often called the rectangular distribution. The density function for a uniform

random variable on the interval [1, 3] is shown in Figure 6.1.

Probabilities are simple to calculate for the uniform distribution due to the

simple nature of the density function. However, note that the application of this

distribution is based on the assumption that the probability of falling in an interval

of fixed length within [A, B] is constant.

Example :

Suppose that a large conference room for a certain company can be reserved for no

more than 4 hours. However, the use of the conference room is such that both long

and short conferences occur quite often. In fact, it can be assumed that length X

of a conference has a uniform distribution on the interval [0, 4].

(                  (a)    What is the probability density function?

                                                                                       

(b)   What is the probability that any given conference lasts at least 3 hours?

Solution: (a) The appropriate density function for the uniformly distributed random variable

X in this situation is

Sebaran Poisson

Experiments yielding numerical values of a random variable X, the number of

outcomes occurring during a given time interval or in a specified region, are called

Poisson experiments. The given time interval may be of any length, such as

a minute, a day, a week, a month, or even a year. Hence a Poisson experiment

can generate observations for the random variable X representing the number of

telephone calls per hour received by an office, the number of days school is closed

due to snow during the winter, or the number of postponed games due to rain

during a baseball season. The specified region could be a line segment, an area,

a volume, or perhaps a piece of material. In such instances X might represent

the number of field mice per acre, the number of bacteria in a given culture, or

the number of typing errors per page. A Poisson experiment is derived from the

Poisson process and possesses the following properties:

Properties of Poisson Process

1. The number of outcomes occurring in one time interval or specified region is

independent of the number that occurs in any other disjoint time interval or

region of space. In this way wc say that the Poisson process has no memory.

2. The probability that a single outcome will occur during a very short time

interval or in a small region is proportional to the length of the time interval

or the size of the region and does not depend on the number of outcomes

occurring outside this time interval or region.

3. The probability that more than one outcome will occur in such a short time

interval or fall in such a small region is negligible.

The number X of outcomes occurring during a Poisson experiment is called a

Poisson random variable, and its probability distribution is called the Poisson

distribution. The mean number of outcomes is computed from p = Xt, where

t is the specific "time," "distance," "area," or "volume" of interest. Since its

probabilities depend on A, the rate of occurrence of outcomes, we shall denote

them by the symbol p(x; Xt). The derivation of the formula for p(x; Xt), based on

the three properties of a Poisson process listed above, is beyond the scope of this

book. The following concept is used for computing Poisson probabilities.

Poisson Distribution

The probability distribution of the Poisson random variable X, representing

the number of outcomes occurring in a given time interval or specified region

denoted by t, is P(x;λt) =  ,    x = 0, 1, 2, ...

where A is the average number of outcomes per unit time, distance, area, or volume, and  e=2.71828

for a few selected values of At ranging from 0.1 to 18. We illustrate the use of this

table with the following two examples.

During a laboratory experiment the average number of radioactive particles passing

through a counter in 1 millisecond is 4. What is the probability that 6 particles

enter the counter in a given millisecond?

Solution: Using the Poisson distribution with x = 6 and Xt = 4 and Table A.2, we have

P(6,4) = =  -   

          = 0.8893 – 0.7851

          = 0.1042

Example :

Ten is the average number of oil tankers arriving each day at a certain port city.

The facilities at the port can handle at most 15 tankers per day. What is the

probability that on a given day tankers have to be turned away?

Solution: Let X be the number of tankers arriving each day. Then, using Table A.2, we have

P(X>15) = 1 – P(X= 1) = 0,0487

Like the binomial distribution, the Poisson distribution is used for quality control,

quality assurance, and acceptance sampling. In addition, certain important

continuous distributions used in reliability theory and queuing theory depend on

the Poisson process. Some of these distributions arc discussed and developed in

Chapter 6.

 Teorem

Both the mean and variance of the Poisson distribution p(x; Xt) are Xt.

The proof of this Theorem is found in Appendix A.26.

In Example 5.20, where Xt — 4, we also have a² — 4 and hence a = 2. Using

Chebyshev's theorem, we can state that our random variable has a probability of at

least 3/4 of falling in the interval p±2a = 4± (2)(2), or from 0 to 8. Therefore, we

conclude that at least three-fourths of the time the number of radioactive particles

entering the counter will be anywhere from 0 to 8 during a given millisecond.

The Poisson Distribution as a Limiting Form of the Binomial

It should be evident from the three principles of the Poisson process that the

Poisson distribution relates to the binomial distribution. Although the Poisson

usually finds applications in space and time problems as illustrated by Examples

5.20 and 5.21, it can be viewed as a limiting form of the binomial distribution.

In the case of the binomial, if n is quite large and p is small, the conditions

begin to simulate the continuous space or time region implications of the Poisson

process. The independence among Bernoulli trials in the binomial case is consistent

with property 2 of the Poisson process. Allowing the parameter p to be close to

zero relates to property 3 of the Poisson process. Indeed, if n is large and p is

close to 0, the Poisson distribution can be used, with p = np, to approximate

binomial probabilities. If p is close to 1, we can still use the Poisson distribution

to approximate binomial probabilities by interchanging what we have defined to

be a success and a failure, thereby changing p to a value close to 0.

Teorem

Let X be a binomial random variable with probability distribution b(x;n,p). When n →∞, p → 0, and η =np remains constant, b(x;n,p) —>p(x;.µ).