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Abstrak :
Implementasi manajernen risiko dalam dunia perbankan di Indonesia pada saat ini sudah mcrupakan suatu kewajiban yang tidak dapat ditawar-tawar lagi, karena Bank Indonesia sudah mengeluarkan peraturan yaitu Peraturan Bank Indonesia (PBI) nomor 5/8/PBI 2003 mengenai Penerapan Manajemen Risiko Bagi Bank Umum dan Surat Edaran BI Nomor 5/21/DPNP tanggal 29 September 2003 mengenai Penerapan Manajemen Risiko Bagi Bank Umum yang mulai efektif pada tanggal 29 September 2003. Perbankan sangat rentan terhadap risiko kredit yang timbul akibal dari bisnis yang digelutinya atau yang dijalaninya, oleh karena itu perbankan perlu mengembangkan suatu sistem yang dapat memonitor atau mengendalikan risiko kredit tersebut. Risiko kredit adalah risiko gagal bayar atau terjadinya default dimana suatu counterpary/borrower tidak dapat mengembalikan kewajibannya termasuk biaya over head-nya. Credit scoring adalah suatu model yang digunakan perbankan untuk mengetahui layak atau tidaknya suatu debitur untuk diberikan pinjaman. Dari berbagai macam definisi maka dapat disimpulkan bahwa Credit Scoring Model adalah suatu penilaian terhadap debitur untuk menentukan Probability of Default berdasarkan faktor-faktor/variabel-variabel tertentu yang dapat dikuantifikasikan ke dalam bentuk skor, dimana skor tersebut adalah suatu alat untuk mengetahui dan mengklasifikasikan debitur ke dalam dua kategori yaitu good debitur dan bad debitur. Dalam credit scoring ini terdapat empat macam jenis pendekatan yaitu pendekatan linear probability model, logic model, probit model, dan discriminant analysis model. Dalam penulisan karya ilmiah ini pendekatan yang digunakan adalah pendekatan model probit dan model logit dimana untuk pendekatan model logit menggunakan logistic distribution function dan untuk pendekatan model probit menggunakan normal distribution function. Untuk kedua model tersebut menggunakan variabel yang memiliki nilai 0 atau 1 (dummy variable). Berdasarkan pengolahan data dalam penelitian ini ternyata kedua model memiliki hasil yang tidak berbeda dalam menentukan nilai probability of default. Untuk kedua metode tersebut ternyata variabel yang memiliki tingkat signifikansi a= 5% berjumlah 13 variabel yang artinya hanya 13 variabel itu saja yang sangat berpengaruh terhadap probability of default. Ketiga belas variabel tersebut adalah CS1, CS3, ED4, IC2, ID14, ID17, ID20, MB, MR, TN2, TN3, TN4 dan TN5. Cut-off point yang digunakan penulis dalam karya akhir ini adalah 0.4. Correct estimates yang didapat dari model logit 87.93% sementara untuk model probit 87.95%. Error Type I model logit adalah 0.28% dan untuk model probit sebesar 0.19%. Error Type I untuk melihat berapa observasi yang ditolak (reject) padahal seharusnya diterima. Sementara Error Type II model logit adalah 11.79% dan untuk model probit 11.86 %. Error Type II untuk melihat berapa observasi yang disetujui padahal seharusnya ditolak. ...... Risk Management Implementation is a must in Banking Area in Indonesia caused Bank of Indonesia has been issued regulation, name is Bank of Indonesia Regulation (PBI) number 5/8/PBI 2003 contents of Risk Management Implementation for Common Bank and Letter Issued Bank of Indonesia number 5/21/DPNP date of September 29, 2003 contents of Risk Management Implementation for Common Bank which come into effective as per September 29, 2003. Banking is a risky area for credit risk which comes from business that had been running itself, therefore banking needs to develop a system which can be monitored and handled credit risk itself, Credit risk is a default happened where a borrower or counterparty are not able to pay back their responsibility including over head cost. Credit scoring is a model which used in banking to know whether a borrower or counterparty acceptable or not to get a loan. From all of kind of definition in summary credit scoring model is a value for customer or borrower to define probability of default based on limited variables which could be quantified into a score, where the score is a tool to find and classified borrower into two categories those are good borrower and bad borrower. In credit scoring there are four models: linear probability model, Logic model, probit model, and discriminant analysis model. In this thesis models which used are probit model and logit model where as for logit model using logistic distribution function and For probit model using normal distribution function. Both models are using variable which have 0 values and 1 value (dummy variable). Based on processing data in this observation unfortunately both kind of models have results which not too different significantly to determine probability of default. In fact for both methods total variables which have significancy level for a = 5% are 13 variables means that only 13 variables have most influenced for probability of default. The thirteen of variables are CS 1, CS3, ED4, 1C2, 1014, ID 17, ID20, MB, MR, TN2, TN3, TN4 and TN5. Cut-off point which used in this thesis is 0.4. Correct estimate from logic model is 87.93 % meanwhile for probit model is 87.95 %. Error type 1 for logit model is 0.28 % and for probit model is 0.19 %. Function of error type 1 is to find/know how many observation have to be rejected otherwise have to be accepted, vice versa meanwhile function of error type II is to find/know how many observation have to he accepted otherwise/unfortunately have to be rejected.

Abstrak :
This book presents an integrative approach tomathematical and statistical modelling in ecology and evolutionary biology. After an introductory chapter, the book devotes one chapter for movement ecology, one for population ecology, one for community ecology, and one for genetics and evolutionary ecology. Each chapter starts with a conceptual section, which provides the necessary biological background and motivates the modelling approaches. The next three sections present mathematical modelling approaches, followed by one section devoted to statistical approaches. Each chapter ends with a perspectives section, which summarizes the key messages and discusses the limitations of the approaches considered. To illustrate how the very same modelling approaches apply in different fields of ecology and evolutionary biology, the book uses movement models as a building block to construct single-species models of population dynamics, the models of which are further expanded to models of species communities and to models of evolutionary dynamics. In all chapters, the book starts by making assumptions at the level of individuals, leading to individual-based simulationmodels. To derive analytical insights and to compare the behaviours of different types of models, the book shows how the individual-based models can be simplified, e.g. to yield models formulated directly at the population level. The book has a special emphasis on the integration of models with data. To achieve this, it applies statistical methods to data generated by mathematical models, and thus asks to what extent does the data contain signals of the underlying mechanisms.

Abstrak :
This book consists of the identification, characterization, and modeling of electromagnetic interferences in substations for the deployment of wireless sensor networks. The authors present in chapter 3 the measurement setup to record sequences of impulsive noise samples in the ISM band of interest. The setup can measure substation impulsive noise, in wide band, with enough samples per time window and enough precision to allow a statistical study of the noise. During the measurement campaign, the authors recorded around 120 noise sequences in different substations and for four ranges of equipment voltage, which are 25 kV, 230 kV, 315 kV and 735 kV. A characterization process is proposed, by which physical characteristics of partial discharge can be measured in terms of first- and second-order statistics. From the measurement campaign, the authors infer the characteristics of substation impulsive noise as a function of the substation equipment voltage, and can provide representative parameters for the four voltage ranges and for several existing impulsive noise models.