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"Menurut badan kesehatan dunia World Health Organization (WHO) pada tahun 2015, sebanyak 70% penyebab kematian pada penyakit jantung disebabkan oleh penyakit jantung koroner (PJK). Tercatat 17,5 juta kematian atau setara dengan 30,0 % dari total kematian di dunia disebabkan oleh penyakit jantung koroner (WHO, 2017). Penyakit jantung koroner merupakan gangguan fungsi jantung yang disebabkan adanya plaque yang menumpuk di dalam pembuluh darah arteri sehingga mengganggu supply oksigen ke jantung. Hal ini menyebabkan aliran darah ke otot jantung menjadi berkurang dan terjadi defisiensi oksigen. Pada keadaan yang lebih serius dapat mengakibatkan serangan jantung. Faktor risiko penyakit jantung koroner diantaranya adalah Usia, Jenis Kelamin, Hipertensi, Kolesterol, Riwayat Keluarga dan sebagainya. Jika kemungkinan seseorang untuk menderita penyakit jantung koroner dapat diprediksi sejak awal berdasarkan faktor risiko yang ada, maka tingkat kematian akibat penyakit jantung koroner dapat ditekan menjadi lebih rendah.Tesis ini mengusulkan Model Regresi Logistik Fuzzy untuk memprediksi kemungkinan seseorang untuk menderita penyakit jantung koroner. Tahap pertama dari penelitian ini adalah membangun model prediksi, kemudian mengestimasi nilai parameter dengan menggunakan metode least square. Selanjutnya pada tahap ketiga mengaplikasikan model yang didapatkan untuk memprediksi penyakit jantung koroner. Setelah itu melakukan uji kelayakan atau kesesuaian model dengan metode Mean Degree of Membership dan yang terakhir menghitung akurasi prediksi dengan menggunakan Confusion Matrix.

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According to the World Health Organization (WHO) in 2015, as many as 70% of the causes of death in heart disease were caused by coronary heart disease (CHD). It was recorded that 17.5 million deaths or the equivalent of 30.0% of the world's total deaths were caused by coronary heart disease (WHO, 2017). Coronary heart disease is a disorder of heart function caused by plaque that builds up in the arteries so it interferes with oxygen supply to the heart. This causes blood flow to be reduced and oxygen deficiency occurs. In more serious situations it can prevent heart attacks. Risk factors for coronary heart disease are Age, Gender, Hypertension, Cholesterol, Family History and so on. If there is someone who is a victim of coronary heart disease can be predicted from the beginning, then there is likely to arise more.

This thesis proposes a Fuzzy Logistic Regression Model to predict the possibility of a person suffering from coronary heart disease. The first stage of this research is to build a predictive model, then estimate the parameter values using the least square method. Furthermore, in the third stage, apply a model to predict coronary heart disease. After that, test the feasibility or suitability of the model with the Mean Degree of Membership method and finally calculate the prediction accuracy using the Confusion Matrix."

"Penyakit Jantung Koroner (PJK) merupakan penyebab utama kematian di seluruh dunia termasuk di Indonesia. Berdasarkan Sample Registration System (SRS), PJK menjadi penyebab kematian tertinggi di Indonesia pada semua umur pada tahun 2014 setelah stroke yaitu sebesar 12,9%. Terdapat beberapa faktor risiko yang menyebabkan PJK seperti merokok, umur, obesitas, jenis kelamin, diabetes, dan lain-lain. Penelitian ini menggunakan 3 model yaitu model Bootstrapping parametic regresi logistik, model Bootstrapping nonparametic regresi logistik dan model regresi logistik. Bootstrapping digunakan untuk meningkatkan akurasi hasil klasifikasi pada model. Metode Bootstrapping merupakan metode yang dilakukan dengan cara resampling dan replicate data awal. Data yang digunakan adalah data yang berasal dari Rumah Sakit Ibnu Sina Yarsi Padang pada bulan Januari tahun 2020. Berdasarkan penelitian ini, dapat disimpulkan bahwasannya akurasi, sensitivity, specivity pada model Bootstrapping parametic regresi logistic adalah 83.87%, 83.33%, dan 84.21% dan model Bootstrapping nonparametic regresi logistik adalah 74%, 72.72%, 75% lebih baik dibandingkan dengan model regresi logistic adalah 71%, 77.8%, dan 61.58%.

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Coronary Heart Disease (CHD) is the leading cause of death worldwide, including in Indonesia. Based on the Sample Registration System (SRS), CHD is the leading cause of death in Indonesia at all ages in 2014 after stroke, amounting to 12.9%. There are several risk factors that cause CHD such as smoking, age, obesity, gender, diabetes, and others. This study used 3 models, namely the parametric Bootstrapping logistic regression model, the nonparametric Bootstrapping logistic regression

model and the logistic regression model. Then 3 models are compared to see the accuracy of each model. Bootstrapping method is a method that is done by resampling and replicating the initial data. The data used are data from the Ibnu Sina Yarsi Hospital Padang in January 2020. Based on this research, it can be concluded that the accuracy, sensitivity, specivity of the logistic regression parametric Bootstrapping model is 83.87%, 83.33%, and 84.21%, then nonparametric logistic regression Bootstrapping model 74%, 72.72%, 75%, both of them are better than the logistic regression model 71%, 77.8%, dan 61.58%."

"Kanker merupakan salah satu penyebab kematian yang paling sering terjadi di seluruh dunia. Salah satu jenis kanker yang dapat mengancam terutama pada wanita adalah kanker payudara. Terlambatnya pendeteksian dini pada penderita kanker payudara menyebabkan sulitnya penanganan untuk proses penyembuhan dan besarnya angka kemungkinan kematian. Metode machine learning banyak diaplikasikan dalam kasus pendeteksian dini karena metode machine learning cukup efektif untuk mendiagnosis suatu penyakit. Pada penelitian ini digunakan metode Bayesian Logistic Regression untuk memprediksi kanker payudara. Metode Bayesian digunakan untuk menghitung bobot dari setiap parameter dari data pada regresi logistik. Data yang digunakan pada penelitian ini adalah data Wisconsin Breast Cancer Database (WBCD, 1992) yang dapat diakses melalui UCI Machine Learning Repository. Berdasarkan hasil uji coba, metode Bayesian Logistik Regression memperoleh akurasi sebesar 96,85%, precision, recall dan F-1 score sebsar 95,44%. Hasil simulasi tersebut menunjukkan bahwa Bayesian Logistic Regression cukup baik untuk membantu praktisi medis dalam mendiagnosis kanker payudara.

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Cancer is one of the most common cause of death in the world. One type of cancer that can be threaten women is breast cancer. The delay in early detection in patient with breast cancer can cause difficulty in recovery process and high mortality rate. Machine learning technique is widely applied in cases of early detection, because machine learning technique is quite effective in diagnose a disease. In this study, the Bayesian Logistic Regression method was used to predict breast cancer. The Bayesian method is used to calculate the weight of each parameter from the data in logistic regression. The data that used in this study is the Wisconsin Breast Cancer Database from UCI Machine Learning Repository. Based on the results of the experiment, Bayesian Logistic Regression method give 96.85% accuracy, and 95,44% precision, recall and F-1 score. These performance results show that the Bayesian Logistic Regression is good enough to help medical experts in diagnosing breast cancer.

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"ABSTRAKKredit merupakan salah satu bentuk penyaluran dana yang dilakukan oleh lembaga keuangan perbankan. Berbagai jenis kredit ditawarkan oleh pihak-pihak yang memberikan pinjaman, salah satu jenis kredit yang paling diminati adalah kredit uang. Dalam memberikan kredit, pihak bank tidak akan begitu saja dalam memberikan kredit. Model teknologi credit scoring dapat dimanfaatkan untuk menyaring peminjam. Model logistic regression dapat digunakan untuk menghubungkan probabilitas kegagalan pinjaman kredit macet dengan menggunakan data calon peminjam yang diperlukan seperti besar pendapatan perbulan, besar pinjaman, usia calon peminjam, klasifikasi pekerjaan, jenis tempat tinggal dan kepemilikan jaminan. Atribut-atribut tersebut akan dievaluasi oleh bilangan fuzzy. Sehingga diharapkan metode fuzzy logistic regression dapat digunakan untuk menentukan probabilitas kredit macet dimana dengan probabilitas tersebut dapat diketahui apakah pinjaman yang diajukan calon peminjam akan masuk kedalam kategori kredit macet atau kredit lancar.

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ABSTRACTCredit is one form of distribution of funds by financial institutions banking. Various types of loans offered by the parties are on loan, one type of credit the most popular is credit money. In providing credit, the bank will not just provide credit. Model of credit scoring technology can be used to screen borrowers. Logistic regression models can be used to connect the probability of failure of loans bad loans using data from the prospective borrower required such a large monthly income, loan size, the age of prospective borrowers, job classification, type of dwelling and ownership guarantee. The attributes will be evaluated by fuzzy numbers. So expect fuzzy logistic regression method can be used to determine the probability of bad loans in which the probability can be known whether the proposed loan to potential borrowers will be entered into the category of bad credit or good credit."

"ABSTRACTPenyakit Parkinson terbagi dalam dua subtipe, yaitu Tremor Dominant (TD) dan Postural Instability/Gait Dominant (PIGD). Tiap subtipe memiliki perbedaan dalam penanganan klinis, sehingga perlu dilakukan klasifikasi subtipe penyakit Parkinson. Dalam Statistika, ada beberapa model yang membahas klasifikasi diantaranya adalah decision tree, regresi logistik, dan logit leaf model (LLM). LLM merupakan model campuran dari decision tree dan regresi logistik yang diusulkan oleh De Caigny et al. (2018). Penulisan ini membahas klasifikasi subtipe penyakit Parkinson menggunakan model klasifikasi statistika beserta penanganan masalah imbalanced data yang terjadi pada data penyakit Parkinson. Diperoleh model klasifikasi regresi logistik dengan melakukan proses SMOTE ± = 600, = 200 untuk menangani masalah imbalanced data. Model tersebut memberikan akurasi sebesar 98,83%, sensitivitas sebesar 98,41%, dan spesifisitas sebesar 99,07%.

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ABSTRACTParkinsons Disease has two sub-types which are Tremor Dominant (TD) and Postural Instability/Gait Difficulty (PIGD). Each subtype has the difference in clinical treatment, so it is necessary to classify Parkinsons Disease subtypes. In Statistics, there are statistical models for classifying such as decision tree, logistic regression, and logit leaf model (LLM). LLM is a hybrid model from decision tree and logistic regression that proposed by (De Caigny et al., 2018). In this thesis discuss Parkinsons Disease Classification using statistical models with imbalanced data problem handling happen in Parkinson`s Disease data. For the result, logistic regression by processing SMOTE ± = 600, = 200 to handle data imbalanced problem. The model provides an accuracy of 98,83%, sensitivity of 98.41%, and specificity of 99.07%."

"Dalam dua tahun terakhir pandemi corona virus disease 2019 (COVID-19) telah menginfeksi > 220 juta orang dan 5 juta orang meninggal. Di Indonesia > 4 juta orang terinfeksi dan > 140.000 orang meninggal. Pada puncak pandemi, kebutuhan perawatan tidak seimbang dengan sarana rumah sakit sehingga WHO menganjurkan untuk memprioritaskan pasien secara ekual. Untuk itu diperlukan prediktor luaran pasien COVID-19. Penelitian ini bertujuan menyusun prediktor luaran pasien COVID-19 menggunakan regresi logistik dan machine learning.Penelitian terdiri atas 2 tahap. Tahap pertama adalah kohort retrospektif untuk menyusun prediktor kematian di rumah sakit dengan regresi logistik dan machine learning (decision tree, random forest, support vectore machine, gradient boost and extreme gradient boost). Pasien terkonfirmasi COVID-19 diinput di data registri REG-COVID-19 pada bulan Maret–Juli 2020 di RS Persahabatan (RSP) dan RS Universitas Indonesia (RSUI). Tahap kedua adalah kohort prospektif pada pasien COVID-19 di RSP, RSUI dan RSPI Suliati Saroso pada bulan Maret–Mei 2021. Data yang diinput adalah data demografi, gejala klinis, komorbid, laboratorium, skor Brixia dari radiografi toraks, luaran pasien dari perawatan dan lama rawat.Pada tahap penyusunan diperoleh 271 subjek untuk analisis machine learning, 239 subjek untuk model 1, sebanyak 180 subjek model 2, dan 152 subjek model 3 dan model 4. Hasil analisis regresi logistik model 1 terdiri atas 7 variabel yaitu demam, diabetes melitus, frekuensi napas, saturasi O2, leukosit, SGOT dan CRP dengan AUC 0,930. Model 2 memberikan hasil hampir sama tetapi SGOT menjadi SGPT dengan AUC 0,926. Model 3 memiliki AUC 0,919 dan model 4 memberikan AUC 0,924 dengan variabel D dimer > 2000 menjadi salah satu prediktor. Validasi semua model regresi logistik dan machine learning menunjukkan penurunan AUC, tetapi tidak berbeda bermakna (uji perbandingan AUC, p = 0,683–0,736). Perbandingan model regresi logistik dan machine learning juga tidak berbeda bermakna (uji perbandingan AUC dengan rumus Hanley, p = 0,492–0,923).Disimpulkan prediksi kematian pasien COVID-19 menggunakan regresi logistik dan machine learning memiliki akurasi yang baik sehingga regresi logistik dan machine learning dapat dijadikan prediktor luaran pasien COVID-19.

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Corona virus disease 2019 (COVID-19) pandemic has lasted almost 2 years worldwide with more than two hundred million world population were infected and almost 5 million (2%) death. In Indonesia, there have been more than 4 million people were infected with more than 140.000 (3.5%) death. At the peak of the outbreak there were discrepancy between health care facilities and demands. WHO recommended to prioritize patient equally, to avoid patient discrimination by social class, race, and gender. The best prediction tool should be valid, reliable and feasible. Many studies develop assessment with logistic regression and machine learning with the goal to improve accuracy. Some study showed variety of predictors in outcome prediction, in this study we developed and validated assessment tool to predict hospital mortality comparing logistic regression and machine learning, included support vector machine (SVM), decision tree (DT), random forest (RF), gradient boost (GB) and extreme gradient boost (XGB). Our study was conducted in 2 stages. The first stage study was cohort retrospective to develop assessment tool to predict hospital mortality by comparing logistic regression and machine learning among hospitalized COVID-19 patients from March to July 2020. The second was cohort prospective study among the same population, to validate the tools. The development data were collected from Persahabatan hospital and Universitas Indonesia hospital who registered in REG-COVID-19, 271subjects were eligible for machine learning analysis and 239 subjects for logistic regression data set 1; 180 subjects for data set 2; 152 for data set 3 and 4. Analysis of data set 1 resulted in 8 variables as mortality prediction include fever, DM, respiratory rate (RR), oxygen saturation, leucocyte, ALT > 42, CRP > 88, with AUC 0,930. Data set 2 resulted in similar variables except AST, with AUC 0,926. Data set 3 resulted in 6 variables with AUC 0,919 and Data set 4 resulted in 7 variables included fever, HR, RR, leucocyte, age above 52, CRP > 86 and D-dimer > 2000 with AUC 0,924. Validation of all models showed decreasing AUC. Machine learning analysis resulted in 5 models with the best was XGB among all set data with AUC between 0,8–0,9. There were decreasing of AUC of all models, but not statistically different (p 0.683–0.736). Comparing developed models with logistic regression and machine learning showed there were differences but not statistically significant. (p 0.492-0.923)"

""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"--"

"Prediksi harga saham merupakan hal yang sangat penting bagi investor karena sangat berguna untuk menentukan nilai masa depan dari suatu perusahaan yang sahamnya sedang diperdagangkan di bursa efek. Investor akan mendapatkan keuntungan yang besar dengan prediksi yang tepat, sebaliknya investor akan mendapatkan kerugian jika prediksi yang digunakan tidak tepat. Pada skripsi ini, akan dibahas pembuatan model prediksi Adaptive Neuro Fuzzy Inference System ANFIS dengan menggunakan variabel indikator teknikal terbaik berdasarkan Support Vector Regression SVR yang dilihat dari kecenderungan data historis saham 25 perusahaan dari sub sektor Bank, sektor Keuangan, yang tercatat di Bursa Efek Indonesia. Melalui metode ini, akan didapatkan nilai akurasi model yang cukup baik sedemikian sehingga dapat menjadi rekomendasi bagi investor dalam melakukan prediksi harga saham berdasarkan variabel indikator teknikal terpilih.

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Forecasting stock price has become an important issue for stock investors because it is very useful to determine the future value of a company whose its share are traded on the stock exchange. Investors will get a profit with a sharp predictions, otherwise they will get loss if the predictions is inappropriately used. This undergraduate thesis will study how to make a model prediction Adaptive Neruo Fuzzy Inference System ANFIS using the best technical indicators. These technical indicators chosen by using Support Vector Regression SVR referred from the tendencies of stock time series data for 25 companies of Banking sub sector, Financial sector, that listed on Indonesian Stock Exchange. Through this method, analyst will get the value of the model rsquo s accuracy, that is good enough. So that it could be a recommendation for investors for forecasting the stock prices using this method with the selected technical indicators."

"Model regresi logistik umum digunakan untuk memodelkan variabel respon berupa variabel kategorik dengan sejumlah variabel prediktor. Kontribusi dari variabel prediktor terhadap variabel respon dinyatakan melalui koefisien regresi (beta), sehingga beta memiliki peran yang penting dalam penggunaan model. Oleh karena itu, perlu dilakukan estimasi nilai beta. Pada skripsi ini dibahas mengenai estimasi beta menggunakan metode Bayesian. Metode Bayesian adalah metode penaksiran parameter yang memanfaatkan gabungan informasi dari data sampel dan informasi terdahulu/prior mengenai karakteristik parameter yang akan ditaksir sehingga metode Bayesian dapat mengatasi masalah jika kualitas data sampel kurang mendukung pengamatan. Prosedur penaksiran parameter tersebut meliputi spesifikasi distribusi prior, digunakan prior non-konjugat, pembentukan fungsi likelihood, dan pembentukan distribusi posterior. Lalu, metode Bayesian Logistic Regression tersebut akan digunakan dalam menganalisa data pasien kanker nasofaring (KNF) pasca radiasi, untuk menilai signifikansi dari komponen skor Zulewski dalam memprediksi ada tidaknya hipotiroid yang merupakan efek samping jangka panjang dari radiasi yang diberikan untuk KNF. Berdasarkan Markov Chain Monte Carlo dengan Gibbs Sampling, diperoleh hasil estimasi yang konvergen. Hasil yang diperoleh adalah tidak ada komponen skor Zulewski yang lebih signifikan antara satu dengan yang lainnya. Diperlukan tambahan informasi dari pengukuran selain komponen skor Zulewski untuk dapat menentukan apakah seorang pasien KNF akan mengalami hipotiroid atau tidak.

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Logistic regression models are commonly used to model response variables in the form of categorical variables with a number of predictor variables. The contribution of the predictor variable to the response variable is expressed through a regression coefficient (beta) so that beta has an important role in the use of the model. Therefore, it is necessary to estimate the value of beta. This thesis will discuss the estimated beta using the Bayesian method. Bayesian Method is a parameter estimation method that utilizes a combination of information from sample data and prior information about the characteristics of the parameters to be estimated so that the Bayesian method can overcome the problem if the quality of the sample data does not support observation. The parameter estimation procedure includes the prior distribution specification, which is to use non-conjugate prior, the formation of the likelihood function, and the formation of the posterior distribution. Then, the Bayesian Logistic Regression method will be used in analyzing post-radiation nasopharyngeal cancer (NPC) patient data, to determine the significance of the Zulewski’s score component in predicting the presence or absence of hypothyroidism which is a long-term side effect of radiation given to NPC. Based on Markov Chain Monte Carlo with Gibbs Sampling, a convergent estimate is obtained. The result is that there is no component of Zulewski’s score that is more significant between one another. Additional information is needed from measurements other than the Zulewski’s score component to be able to determine whether a NPC patient will have hypothyroidism or not."

"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."