Hasil Pencarian  ::  Simpan CSV :: Kembali

"This new book provides a unified, in-depth, readable introduction to the multipredictor regression methods most widely used in biostatistics, linear models for continuous outcomes, logistic models for binary outcomes, the Cox model for right-censored survival times, repeated-measures models for longitudinal and hierarchical outcomes, and generalized linear models for counts and other outcomes."

"This highly anticipated second edition features new chapters and sections, 225 new references, and comprehensive R software. In keeping with the previous edition, this book is about the art and science of data analysis and predictive modeling, which entails choosing and using multiple tools. Instead of presenting isolated techniques, this text emphasizes problem solving strategies that address the many issues arising when developing multivariable models using real data and not standard textbook examples. It includes imputation methods for dealing with missing data effectively, methods for fitting nonlinear relationships and for making the estimation of transformations a formal part of the modeling process, methods for dealing with too many variables to analyze and not enough observations, and powerful model validation techniques based on the bootstrap. The reader will gain a keen understanding of predictive accuracy, and the harm of categorizing continuous predictors or outcomes. This text realistically deals with model uncertainty, and its effects on inference, to achieve safe data mining. It also presents many graphical methods for communicating complex regression models to non-statisticians."

"Penelitian ini bertujuan untuk mengidentifikasi zona kerentanan longsor pada Kabupaten Lebak, Provinsi Banten dengan menggunakan dua model yakni Frequency Ratio dan Logistic Regression. Penelitian ini menggunakan 44 data titik longsor yang terjadi pada daerah penelitian, titik longsor tersebut dibagi menjadi dua bagian yakni 35 titik untuk mengindentifikasi zona rentan longsor dan sisanya sebanyak 9 titik digunakan untuk validasi. Zona rentan longsor tersebut dapat diketahui dengan menganalisis faktor-faktor pemicu terjadinya longsor, pada penelitian ini faktor pemicu tersebut terdiri atas sudut lereng, aspek lereng, elevasi, Normalized Differential Vegetation Index (NDVI), curvature, jarak terhadap kelurusan, jarak terhadap sungai, penggunaan lahan, litologi dan curah hujan. Nilai curah hujan yang digunakan pada peneltian ini adalah jumlah curah hujan rata-rata setiap bulannya yang terjadi selama 10 tahun pada daerah penelitian, sehingga akan dihasilkan peta zona rentan longsor setiap bulannya pada daerah penelitian. Hasil dari analisis dengan kedua model tersebut kemudian dibagi atas 3 tingkat kerentanan yakni rendah, menengah, dan tinggi serta nilai AUC yang didapatkan oleh kedua model tersebut setiap bulannya mencapai diatas 50%.

---

This study aims to identify landslide susceptibility zones in Lebak Regency, Banten Province by using two models Frequency Ratio and Logistic Regression. This study uses 44 data of landslide points that occur in the study area, the landslide points are divided into two parts, 35 points to identify landslide susceptibility zones and 9 points are used for validation. The landslide susceptibility zone can be identified by analyzing factors that maybe trigger landslides, in this study the trigger factors consist of slope angle, slope aspect, elevation, Normalized Differential Vegetation Index (NDVI), curvature, distance to straightness, distance to rivers, usage land, lithology and rainfall. The rainfall value used in this research is the average monthly rainfall that occurs for 10 years in the study area, so a monthly landslide susceptibility zone map will be produced in the study area. The results of the analysis with the two models were then divided into 3 vulnerability zones low, intermediate, and high, and the AUC value obtained by the two models each month reached above 50%."

""A new edition of the definitive guide to logistic regression modeling for health science and other applicationsThis thoroughly expanded Third Edition provides an easily accessible introduction to the logistic regression (LR) model and highlights the power of this model by examining the relationship between a dichotomous outcome and a set of covariables. Applied Logistic Regression, Third Edition emphasizes applications in the health sciences and handpicks topics that best suit the use of modern statistical software. The book provides readers with state-of-the-art techniques for building, interpreting, and assessing the performance of LR models. New and updated features include: A chapter on the analysis of correlated outcome data. A wealth of additional material for topics ranging from Bayesian methods to assessing model fit Rich data sets from real-world studies that demonstrate each method under discussion. Detailed examples and interpretation of the presented results as well as exercises throughout Applied Logistic Regression, Third Edition is a must-have guide for professionals and researchers who need to model nominal or ordinal scaled outcome variables in public health, medicine, and the social sciences as well as a wide range of other fields and disciplines"--"This Third Edition continues to focus on applications and interpretation of results from fitting regression models to categorical response variables"--"

"Credit scoring adalah sebuah sistem yang digunakan kreditor seperti bank dan perusahaan asuransi untuk menentukan apakah pemohon kredit termasuk dalam grup good credit yakni grup yang kemungkinan besar akan membayar utangnya tepat waktu atau bad credit yang merupakan grup dengan kemungkinan besar tidak membayar utangnya tepat waktu. Salah satu metode yang paling sering digunakan dalam pembuatan model credit scoring adalah binary logistic regression.  Namun, seiring dengan kemajuan komputasi, banyak metode lain yang berkembang saat ini untuk dipakai dalam pembuatan model credit scoring yakni, metode gradient boosting. Pada skripsi ini dilakukan implementasi metode binary logistic regression dan gradient boosting dalam pemodelan credit scoring. Hasil yang didapatkan dengan menggunakan data 537.667 debitur dengan rincian 535.705 good credits dan 1.962 bad credits adalah pada data train penggunaan gradient boosting memberikan nilai tingkat akurasi 79,65%, uji KS 0,5389 dan AUROC/AUC 0,8393. Sementara pada data test penggunaan gradient boosting memberikan nilai tingkat akurasi 79,92%, uji KS 0,5345 dan AUROC/AUC 0,8313.  Nilai-nilai tersebut lebih tinggi dibandingkan dengan penggunaan binary logistic regression baik pada data train maupun data test. Berdasarkan nilai uji AUC, metode gradient boosting tergolong klasifikasi yang baik, sedangkan metode binary logistic regression> tergolong klasifikasi yang cukup. Hasil simulasi ini menunjukkan untuk data yang digunakan, metode gradient boosting memberikan hasil yang lebih baik dari sisi akurasi, uji KS, dan AUROC/AUC daripada binary logistic regression. Dengan kata lain, metode gradient boosting dapat meningkatkan discriminant power, yakni kemampuan untuk membandingkan target yang lebih baik dibandingkan dengan metode binary logistic regression.

---

Credit scoring is a system used by creditors such as banks and insurance companies to determine whether credit applicants are included in the good credit group, namely the group that is most likely to pay its debts on time or the bad credit group which is the group that is most likely to not pay its debts on time. One of the most frequently used methods in making credit scoring models is binary logistic regression. However, along with the progress of computation, many other methods are currently being developed to be used in making credit scoring models, namely, the gradient boosting method. In this thesis, we will compare the binary logistic regression and gradient boosting methods in credit scoring model. The results obtained using data from 537,667 debtors with details of 535,705 good credits and 1,962 bad credits are the train data using gradient boosting gives an accuracy rate of 79.65%, KS test 0.5389 and AUROC/AUC 0.8393. Meanwhile, the test data using gradient boosting gives an accuracy rate of 79.92%, KS test 0.5345, and AUROC/AUC 0.8313. These values ​​are higher than the use of binary logistic regression in both the train and test data. Based on the AUC test value, the gradient boosting method is a good classifier, while the binary logistic regression method is an acceptable classifier. The results of this simulation show that for the data used, the gradient boosting method gives better results in terms of accuracy, KS test, and AUROC/AUC than binary logistic regression. In other words, the gradient boosting method can increase discriminant power or the ability to compare targets better than the binary logistic regression method."

"This very popular textbook is now in its third edition. Whether students or working professionals, readers apprciate its unique "lecture book" format. They often say the book reads like they are listening to an outstanding lecture. This edition includes three new chapters, an updated computer appendix, and an expanded section about modeling guidelines that consider causal diagrams. --Like previous editions, this textbook provides a highly readable description of fundamental and more advanced concepts and methods of logistic regression. It is suitable for researchers and statisticians in medical and other life sciences as well as academicians teaching second-level regression methods courses. --The Computer Appendix provides step-by-step instructions for using STATA (version 10.0), SAS (version 9.2), and SPSS (version 16) for procedures described in the main text. --Book Jacket."

"Gerakan Tanah merupakan bencana alam yang paling sering terjadi di Indonesia khususnya di daerah Kabupaten Tasikmalaya, Jawa Barat. BPBD Kabupaten Tasikmalaya, dari Januari hingga September 2021 terdapat 260 kejadian bencana. Dari total kejadian bencana itu, 51 persen atau 133 kejadian di antaranya bencana gerakan tanah. Penelitian ini bertujuan untuk mengetahui zona kerentanan gerakan tanah berdasarkan parameter-parameter yang ada untuk menghasilkan peta persebaran zona kerentanan gerakan tanah di daerah Kabupaten Tasikmalaya dengan bantuan Sistem Informasi Geografis (SIG). Selain itu, penelitian ini juga berfokus pada pengaruh cell size terhadap nilai AUC pada daerah penelitian. Oleh karena itu digunakan beberapa cell size untuk mengetahui pengaruh tersebut. Adapun cell size yang digunakan adalah 15, 20, 25, 30 dan 35. Penelitian ini menggunakan 2 metode dalam menentukan peta zona gerakan gerakan tanah yaitu metode frequency ratio dan logistic regression. Frequency ratio bertujuan untuk mengetahui tingkat signifikan dari setiap kelas faktor. Sementara itu logistic regression menghasilkan nilai probabilitas gerakan tanah dan nilai signifikan dari setiap faktor penyebab gerakan tanah. Nilai probabilitas gerakan tanah bernilai 0 dan 1 semakin mendekati angka satu maka semakin tinggi tingkat zona kerentanannya. Terdapat 125 data kejadian gerakan tanah yang terdapat pada daerah penelitian dimana akan dibagi menjadi 80% data training dan 20% data validasi. Adapun parameter-parameter pendukung pada gerakan tanah adalah litologi, aspek lereng, kemiringan lereng, elevasi, penggunaan lahan, curah hujan, jarak dari kelurusan, jarak dari sungai, kelengkungan (curvature) dan NDVI. Kemudian akan dilakukan uji model. Uji model ini didapatkan dari grafik AUC. Uji ini bertujuan untuk mengetahui apakah peta dapat diterapkan atau tidak. Pada penelitian ini, model pada frequency ratio memiliki nilai AUC berkisar 0,73 – 0,81 sedangkan pada model logistic regression memiliki nilai AUC berkisar 0,58 – 0,85. Dari hasil nilai AUC tersebut model frequency ratio termasuk kedalam model sedang – baik sedangkan pada model logistic regression termasuk kedalam model buruk – sedang. Kedua model ini dapat diterapkan pada daerah penelitian.

---

Landslide is the most frequent natural disaster in Indonesia, especially in the Tasikmalaya Regency, West Java. BPBD Tasikmalaya Regency, from January to September 2021 there were 260 disaster events. Of the total disaster events, 51 percent or 133 incidents were landslides. This study aims to determine the vulnerability zones of ground movement based on existing parameters to produce a map of the distribution of ground movement vulnerability zones in the Tasikmalaya Regency area with the help of a Geographic Information System (GIS). In addition, this study also focuses on the effect of cell size on AUC values in the study area. Therefore, several cell sizes are used to determine the effect. The cell sizes used are 15, 20, 25, 30 and 35. This study uses 2 methods in determining the ground motion zone map, namely the frequency ratio method and logistic regression. Frequency ratio aims to determine the significant level of each factor class. Meanwhile, logistic regression produces probability values of ground motion and significant values of each factor causing ground motion. The value of the probability of ground motion is 0 and 1, the closer to number one, the higher the level of the zone of susceptibility. There are 125 data on ground motion events in the research area which will be divided into 80% training data and 20% validation data. The supporting parameters for ground motion are lithology, slope aspect, slope, elevation, land use, rainfall, distance from fault, distance from river, curvature and NDVI. Then a model test will be carried out. This model test is obtained from the AUC graph. This test aims to determine whether the map can be applied or not. In this study, the frequency ratio model has an AUC value ranging from 0.73 to 0.81 while the logistic regression model has an AUC value ranging from 0.58 to 0.85. From the results of the AUC value, the frequency ratio model is included in the medium - good model, while the logistic regression model is included in the bad - medium model. Both of these models can be applied to the research area. "