Hasil Pencarian  ::  Simpan CSV :: Kembali

"In order to characterize the distribution patten of rotifers Brachionus spp.in North Sulawesi,sample collections have been conducted at four locations,two at east coast and other two at West coast of North Sulawesi peninsula,which are connected to Maluku and Sulawesi Seas,respectively...."

"In order to understand biodiversity,distribution and abundance among the tropical anguillid eels in the Indonesian waters,inshore migration mechanism of the juvenile anguillid eel (glas eel) to the estuaries of western,central and eastern region of Indonesian waters were examined using both morphology and genetic analysis...."

"Ten surface sediment samples were collected from Jakarta bay to study the horizontal distribution of dinoflagellate resting cysts in this area.Overall results had shown unique species composition and diversity of dinoflagellate cyst assemblages...."

"This study was conducted in PT. Coca-Coca Distribution Indonesia (PT.CCDI) that evaluated the change from direct selling, in 2007, to become Distribution Center Matahari Balaraja, in 2008. It uses SCOR 9.0 process mapping and performance metrics to analyze whether this change is better for PT. CCDI. The advantages are reducing outbound transportation cost about 204 million after subtracting with distribution fee for Matahari DC, centralized discount management. The disadvantages are decreasing in Service Level and Perfect Order Fulfillment, and increasing cash-to-cash cycle time. Due to some advantages, this study also recommends evaluation with other parameter such as upside supply chain flexibility and upside supply chain adaptability."

"Pada skripsi ini dibahas mengenai perluasan dari distribusi Chen distribusi XTG Distribusi XTG merupakan perluasan dari distribusi Chen dengan penambahan scale parameter Distribusi XTG merupakan salah satu distribusi probabilitas yang memiliki fungsi hazard berbentuk bathtub Distribusi XTG diperoleh dengan melakukan transformasi variabel random dengan perkalian skalar yaitu untuk dan dimana Karakteristik dari distribusi XTG yang dibahas adalah pdf fungsi distribusi fungsi survival fungsi hazard momen mean dan variansi Selain itu juga dibahas mengenai estimasi parameter dengan Weibull Probability Paper WPP plot dan maximum likelihood estimator Subkasus dari distribusi XTG adalah distribusi Chen distribusi Exponential Power dan distribusi Weibull Pada akhirnya 2 data mengenai waktu tunggu terjadinya kerusakan pada lampu dan pada suatu perangkat elektronik akan digunakan sebagai ilustrasi.

---

XTG distribution is a distribution obtained by extending the Chen distribution which is one of the bathtub hazard shaped distribution It is extended from the Chen distribution by adding the scale parameter In doing this a scalar multiplication ransformation is applied to the random variable i e T=αY for α>0 and Y~Chen (η,β)where η=λα. The caracteristics explained are pdf distribution function survival function hazard rate moment mean and variance Moreover parameter estimation using Weibull Probability Paper WPP plot and maximum likelihood estimator are also presented Subcases of the XTG distribution are the Chen distribution Exponential Power distribution and Weibull distribution Finally two data about failure times for lightbulb and electronic devices are used as illustration. "

"ABSTRACTOverdispersi sering kali menjadi kendala dalam memodelkan count data dikarenakan distribusi Poisson yang sering digunakan untuk memodelkan count data tidak dapat menanggulangi data overdispersi. Telah diperkenalkan beberapa distribusi yang dapat digunakan sebagai alternatif dari distribusi Poisson dalam menanggulangi overdispersi pada data. Namun, distribusi yang ditawarkan tesebut memiliki kompleksitas yang lebih tinggi dibanding distribusi Poisson dalam hal jumlah parameter yang digunakan. Untuk itu, ditawarkan distribusi baru yang memiliki sebaran mirip dengan distribusi Poisson, yaitu distribusi Lindley. Namun, distribusi Lindley merupakan distribusi kontinu sehingga tidak dapat digunakan untuk memodelkan count data. Oleh karena itu, dilakukan diskritisasi pada distribusi Lindley menggunakan metode yang mempertahankan fungsi survival dari distribusi Lindley. Distribusi hasil dari diskritisasi distribusi Lindley tersebut memiliki satu parameter dan dapat digunakan untuk memodelkan data overdispersi sehingga cocok digunakan sebagai alternatif dari distribusi Poisson dalam memodelkan count data yang overdispersi. Distribusi hasil dari diskritisasi distribusi Lindley tersebut biasa disebut distribusi Discrete Lindley. Dalam penulisan ini diperoleh karakteristik dari distribusi Discrete Lindley yang unimodal, menceng kanan, memiliki kelancipan yang tinggi, dan overdispersi. Berdasarkan simulasi numerik, diperoleh pula karakteristik dari parameter distribusi Discrete Lindley yang memiliki bias dan MSE besar pada sekitaran nilai parameter exp(-1).

---

ABSTRACTOverdispersion often being a problem in modeling count data because the Poisson distribution that is often used to modeling count data cannot conquer the overdispersion data. Several distributions have been introduced to be used as an alternative to the Poisson distribution on conquering dispersion in data. However, that alternative distribution has higher complexity than Poisson distribution in the number of parameters used. Therefore, a new distribution with similar distribution to Poisson is offered, that is Lindley distribution. Lindley distribution is a continuous distribution, then it cannot be used to modeling count data. Hence, discretization on Lindley distribution should be done using a method that maintain the survival function of Lindley distribution. Result distribution from discretization on Lindley distribution has one parameter and can be used to modeling overdispersion data so that distribution is appropriate to be used as an alternative to Poisson distribution in modeling overdispersed count data. The result distribution of Lindley distribution discretization is commonly called Discrete Lindley distribution. In this paper, characteristics of Discrete Lindley distribution that are obtained are unimodal, right skew, high fluidity and overdispersion. Based on numerical simulation, another charasteristic of parameter is also obtained from Discrete Lindley distribution that has a large bias and MSE when parameter value around exp(-1)."

"ABSTRAKCount data tidak selalu bersifat ekuidispersi. Sehingga, distribusi Poisson tidak dapat digunakan untuk memodelkan count data tersebut. Beberapa distribusi alternatif dari distribusi Poisson telah dikenalkan untuk memodelkan data overdispersi. Namun, distribusi tersebut memiliki kompleksitas yang lebih tinggi dalam jumlah parameter distribusi. Perlu dilakukan modifikasi pada distribusi Poisson agar distribusi yang terbentuk bisa merepresentasikan data overdispersi. Salah satu caranya yaitu dengan melakukan pencampuran distribusi antara distribusi Poisson dengan distribusi Lindley. Distribusi yang terbentuk yaitu distribusi Poisson-Lindley. Namun, distribusi Poisson-Lindley belum dapat mengatasi data underdispersi. Selain itu terdapat data asli yang tidak memiliki observasi bernilai nol. Dengan demikian, untuk mendapatkan distribusi yang lebih fleksibel agar lebih cocok dengan count data tersebut, perlu dilakukan modifikasi pada distribusi Poisson-Lindley dengan menerapkan metode zero-truncated. Distribusi baru yang terbentuk yaitu distribusi Zero-truncated Poisson-Lindley. Distribusi baru tersebut dapat mengatasi data yang tidak memiliki observasi bernilai nol dalam kondisi overdispersi maupun underdispersi. Dalam skripsi ini, didapat karakteristik dari distribusi Zero truncated Poisson-Lindley dan penaksiran parameter distribusi menggunakan metode maximum likelihood.

---

ABSTRACT

Not every count data has equal-dispersion. As a result, Poisson distribution is no longer appropriate to be used for count data modelling. Several distributions have been introduced to be used as an alternative to Poisson distribution on handling the over-dispersion in data. In general, the alternative distributions have higher complexity in the number of parameters. Modification needs to be done in Poisson distribution so that the distribution can represent the condition of the over-dispersion in data. By doing mixing Poisson and Lindley distribution, a new distribution called Poisson-Lindley is developed. However, Poisson-Lindley distribution cannot handle data that exhibits under-dispersion. On the other hand, there is real data that has no zero-count. Therefore, in order to obtain a more flexible distribution to fit count data that has no zero count, a modification needs to be done in Poisson Lindley distribution by applying a zero truncated method in Poisson-Lindley distribution. The newly formed distribution is named Zero-truncated Poisson Lindley distribution. It can handle the condition when the data has no zero-count both in over-dispersion and under-dispersion. In this paper, characteristics of Zero truncated Poisson Lindley distribution are obtained and estimate distribution parameters using the maximum likelihood method."