scholarly journals PERBANDINGAN ANALISIS DISKRIMINAN DAN REGRESI LOGISTIK UNTUK MENGKLASIFIKASIKAN KELAYAKAN VISITASI PELAMAR BIDIKMISI

2020 ◽  
Vol 9 (1) ◽  
pp. 14
Author(s):  
NISA HIDAYATI ◽  
I KOMANG GDE SUKARSA ◽  
DESAK PUTU EKA NILAKUSMAWATI
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Discriminant Analysis ◽  
Logistic Regression Analysis ◽  
Classification Accuracy ◽  
Validation Data ◽  
Parental Income ◽  
Significant Independent Variable ◽  
Independent Variable

The purpose of this study is to compare discriminant analysis and logistic regression to classify the feasibility of the Bidikmisi applicant's visitation based on the classification accuracy. The results showed that the assumptions of homogeneity covariance matricies between the groups are unequal, The significant independent variable is the combined amount of parental income , parents income divided by the number of family dependents , and electricity bills , and then the results of the classification of validation data from the logistic regression analysis of 98,21% higher than the discriminant analysis of 94,64%.

scholarly journals Perbandingan Analisis Diskriminan dan Analisis Regresi Logistik Ordinal dalam Prediksi Klasifikasi Kondisi Kesehatan Bank

2017 ◽  
Vol 7 (2) ◽  
pp. 92
Author(s):  
Fajri Zufa ◽  
Sigit Nugroho ◽  
Mudin Simanihuruk
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Discriminant Analysis ◽  
Logistic Regression Analysis ◽  
Prediction Accuracy ◽  
Secondary Data ◽  
Ordinal Logistic Regression ◽  
Capital Adequacy ◽  
Ordinal Logistic Regression Analysis

The purpose of this research is to compare the accuracy of bank classification prediction based on Capital Adequacy Ratio (CAR), Earning Asset Quality (EAQ), Non Performing Loan (NPL), Return on Assets (ROA), Net Interest Margin (NIM), Short Term Mismatch (STM) and Loan to Deposit Ratio (LDR). Discriminant analysis and ordinal logistic regression analysis are compared in classifying the prediction. The data used are secondary data, namely data classification of bank conditions in Indonesia in 2014 obtained from research institute PT Infovesta Utama. Based on Apparent Error Rate (APER) score obtained, it can be said that discriminant analysis is better in predicting the classification of bank conditions in Indonesia than that of ordinal logistic regression analysis. Discriminant analysis has the average prediction accuracy of 80%, while ordinal logistic regression analysis has the average prediction accuracy of 74,38%.

scholarly journals PERBANDINGAN REGRESI LOGISTIK MULTINOMINAL DAN ANALISIS DISKRIMINAN

2021 ◽  
Vol 1 (2) ◽  
pp. 135-150
Author(s):  
Rifda Nabila ◽  
Risdiana Himmati ◽  
Rendra Erdkhadifa
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Discriminant Analysis ◽  
Logistic Regression Analysis ◽  
Multinomial Logistic Regression ◽  
Regression Method ◽  
Destination Image ◽  
Tourism Destination ◽  
Multinomial Logistic Regression Analysis ◽  
Logistic Regression Method

Abstrak: Tujuan dari penelitian ini adalah untuk membandingkan analisis regresi logistik multinomial dan analisis diskriminan untuk mengelompokkan keputusan kunjungan wisata halal di Jawa Tengah berdasarkan ketepatan pengelompokan. Analisis statistik yang digunakan adalah regresi logistik multinomial dan analisis diskriminan. Kedua analisis tersebut dapat digunakan sebagai metode pengelompokan objek, sehingga keduanya dapat dibandingkan berdasarkan ketepatan pengelompokkannya. Penelitian ini membandingkan analisis regresi logistik multinomial dan analisis diskriminan dalam pengelompokan keputusan kunjungan wisata halal. Data yang digunakan adalah worship facilities, halalness, general Islamic mortality, dan tourism destination image. Hasil analisis menggunakan metode regresi logistik multinomial menunjukkan faktor-faktor yang secara signifikan mempengaruhi pengelompokan keputusan kunjungan wisata halal adalah variabel tourism destination image, variabel halalness, dan variabel general Islamic morality. Sedangkan dengan analisis diskriminan menunjukkan bahwa semua variabel prediktor yakni worship facilities, halalness, general Islamic mortality, dan tourism destination image memberikan pengaruh secara signifikan terhadap pengklasifikasian keputusan mengunjungi destinasi wisata halal. Penelitian ini menunjukkan bahwa metode regresi logistik multinomial lebih baik untuk pengelompokkan keputusan kunjungan wisata halal dibandingan metode analisis diskriminan, dengan presetnase ketepatan pengelompokkan pada metode regresi logit multinomial sebesar 59,5%  dan analisis diskriminan sebesar 53,5%. Analisis regresi logistik multinominal lebih mudah digunakan dalam proses pengelompokan keputusan kunjuangan wisata halal karena tidak mempertimbangkan asumsi yang harus dipenuhi. Kata Kunci: Analisis Diskriminan; Regresi Logistik Multinominal; Keputusan Mengunjungi   Abstract: The purpose of this study is to compare multinomial logistic regression analysis and discriminant analysis to classify decisions on halal tourism visits in Central Java based on grouping accuracy. Statistical analysis used is multinomial logistic regression and discriminant analysis. The two analyzes can be used as a method of grouping objects, so that they can be compared based on the accuracy of the grouping. This study compares multinomial logistic regression analysis and discriminant analysis in grouping decisions for halal tourism visits. The data used are worship facilities, halalness, general Islamic mortality, and tourism destination image. The results of the analysis using the multinomial logistic regression method show that the factors that significantly influence the grouping of decisions for halal tourism visits are the tourism destination image variable, the halalness variable, and the general Islamic morality variable. Meanwhile, discriminant analysis shows that all predictor variables namely worship facilities, halalness, general Islamic mortality, and tourism destination image have a significant influence on the classification of decisions to visit halal tourist destinations. This study shows that the multinomial logistic regression method is better for grouping decisions on halal tourist visits than the discriminant analysis method, with a preset percentage of grouping accuracy in the multinomial logit regression method of 59.5% and discriminant analysis of 53.5%. Multinominal logistic regression analysis is easier to use in the process of grouping halal tourism travel decisions because it does not consider the assumptions that must be met. Keywords: Discriminant Analysis; Multinomial Logistic Regression; Visiting decision.

scholarly journals Bullying Victimization among Mexican Adolescents: Psychosocial Differences from an Ecological Approach

2020 ◽  
Vol 17 (13) ◽  
pp. 4831
Author(s):  
Silvana Mabel Nuñez-Fadda ◽  
Remberto Castro-Castañeda ◽  
Esperanza Vargas-Jiménez ◽  
Gonzalo Musitu-Ochoa ◽  
Juan Evaristo Callejas-Jerónimo
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Psychological Distress ◽  
Discriminant Analysis ◽  
Logistic Regression Analysis ◽  
Positive Attitude ◽  
Bullying Victimization ◽  
Open Communication ◽  
Individual Variables ◽  
Psychosocial Differences

This transversal study over a random representative sample of 1687 Mexican students attending public and private secondary schools (54% girls, 12–17 years old, M = 13.65. DT = 1.14) aimed to analyze psychosocial differences between victims and non-victims of bullying from the bioecological model. It included individual variables (ontosystem), familiar, community, and scholar factors (microsystem), and gender (macrosystem) to perform a multivariate discriminant analysis and a logistic regression analysis. The discriminant analysis found that psychological distress, offensive communication with mother and father, and a positive attitude toward social norms transgression characterized the high victimization cluster. For the non-victims, the discriminant variables were community implication, positive attitude toward institutional authority, and open communication with the mother. These variables allowed for correctly predicting membership in 76% of the cases. Logistic regression analysis found that psychological distress, offensive communication with the father, and being a boy increased the probability of high victimization, while a positive attitude toward authority, open communication with the mother, and being a girl decrease this probability. These results highlight the importance of open and offensive communication between adolescents and their parents on psychological distress, attitude toward authority, community implication, and bullying victimization.

Development and External Validation of Diagnostic Model for Coronary Microvascular Obstruction: Algorithm Development and Validation (Preprint)

2020 ◽  
Author(s):  
Yong Li ◽  
Shuzheng Lyu
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Logistic Regression Analysis ◽  
Microvascular Obstruction ◽  
External Validation ◽  
Diagnostic Model ◽  
Total Occlusion ◽  
Validation Data ◽  
Data Set ◽  
Acute Stemi

BACKGROUND Prevention of coronary microvascular obstruction /no-reflow phenomenon(CMVO/NR) is a crucial step in improving prognosis of patients with acute ST segment elevation myocardial infarction (STEMI )during primary percutaneous coronary intervention (PPCI). OBJECTIVE The objective of our study was to develop and externally validate a diagnostic model of CMVO/NR in patients with acute STEMI underwent PPCI. METHODS Design: Multivariate logistic regression of a cohort of acute STEMI patients. Setting: Emergency department ward of a university hospital. Participants: Diagnostic model development: Totally 1232 acute STEMI patients who were consecutively treated with PPCI from November 2007 to December 2013. External validation: Totally 1301 acute STEMI patients who were treated with PPCI from January 2014 to June 2018. Outcomes: CMVO/NR during PPCI. We used logistic regression analysis to analyze the risk factors of CMVO/NR in the development data set. We developed a diagnostic model of CMVO/NR and constructed a nomogram.We assessed the predictive performance of the diagnostic model in the validation data sets by examining measures of discrimination, calibration, and decision curve analysis (DCA). RESULTS A total of 147 out of 1,232 participants (11.9%) presented CMVO/NR in the development dataset.The strongest predictors of CMVO/NR were age, periprocedural bradycardia, using thrombus aspiration devices during procedure and total occlusion of culprit vessel. Logistic regression analysis showed that the differences between two group with and without CMVO/NR in age( odds ratios (OR)1.031; 95% confidence interval(CI), 1.015 ~1.048 ; P <.001), periprocedural bradycardia (OR 2.151;95% CI,1.472~ 3.143 ; P <.001) , total occlusion of the culprit vessel (OR 1.842;95% CI, 1.095~ 3.1 ; P =.021) , and using thrombus aspirationdevices during procedure (OR 1.631; 95% CI, 1.029~ 2.584 ; P =.037).We developed a diagnostic model of CMVO/NR. The area under the receiver operating characteristic curve (AUC) was .6833±.023. We constructed a nomogram. CMVO/NR occurred in 120 out of 1,301 participants (9.2%) in the validation data set. The AUC was .6547±.025. Discrimination, calibration, and DCA were satisfactory. Date of approved by ethic committee:16 May 2019. Date of data collection start: 1 June 2019. Numbers recruited as of submission of the manuscript:2,533. CONCLUSIONS We developed and externally validated a diagnostic model of CMVO/NR during PPCI. CLINICALTRIAL We registered this study with WHO International Clinical Trials Registry Platform on 16 May 2019. Registration number: ChiCTR1900023213. http://www.chictr.org.cn/edit.aspx?pid=39057&htm=4.

Importance of events per independent variable in logistic regression analysis

2012 ◽  
Vol 40 (4) ◽  
pp. 1392-1393
Author(s):  
Jurjan Aman ◽  
Geerten P. van Nieuw Amerongen ◽  
A. B. Johan Groeneveld
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Logistic Regression Analysis ◽  
Independent Variable

scholarly journals Novel Method of Classification in Knee Osteoarthritis: Machine Learning Application Versus Logistic Regression Model

2020 ◽  
Vol 44 (6) ◽  
pp. 415-427
Author(s):  
Jung Ho Yang ◽  
Jae Hyeon Park ◽  
Seong-Ho Jang ◽  
Jaesung Cho
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Knee Osteoarthritis ◽  
Logistic Regression Analysis ◽  
Support Vector ◽  
Svm Classifier ◽  
Machine Learning Method ◽  
Learning Method

Objective To present new classification methods of knee osteoarthritis (KOA) using machine learning and compare its performance with conventional statistical methods as classification techniques using machine learning have recently been developed.Methods A total of 84 KOA patients and 97 normal participants were recruited. KOA patients were clustered into three groups according to the Kellgren-Lawrence (K-L) grading system. All subjects completed gait trials under the same experimental conditions. Machine learning-based classification using the support vector machine (SVM) classifier was performed to classify KOA patients and the severity of KOA. Logistic regression analysis was also performed to compare the results in classifying KOA patients with machine learning method.Results In the classification between KOA patients and normal subjects, the accuracy of classification was higher in machine learning method than in logistic regression analysis. In the classification of KOA severity, accuracy was enhanced through the feature selection process in the machine learning method. The most significant gait feature for classification was flexion and extension of the knee in the swing phase in the machine learning method.Conclusion The machine learning method is thought to be a new approach to complement conventional logistic regression analysis in the classification of KOA patients. It can be clinically used for diagnosis and gait correction of KOA patients.

scholarly journals PENGARUH FAKTOR AKUNTANSI TERHADAP PREDIKSI PERINGKAT OBLIGASI

2017 ◽  
Vol 20 (1) ◽  
pp. 52
Author(s):  
Henny Henny
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Logistic Regression Analysis ◽  
Sampling Method ◽  
Stock Exchange ◽  
Corporate Bond ◽  
Bond Ratings ◽  
Purposive Sampling ◽  
Independent Variable ◽  
Selection Of

Tujuan dari penelitian ini adalah untuk mengungkapkan faktor akuntansi yang mempengaruhi prediksi peringkat obligasi pada perusahaan non keuangan di Bursa Efek Indonesia. Data diambil dari perusahaan non keuangan yang terdaftar di Bursa Efek Indonesia. Pemilihan sampel sebanyak 20 perusahaan dengan menggunakan purposive sampling method. Metode penelitian yang digunakan untuk menguji hipotesis adalah logistic regression analysis metode stepwise untuk menguji faktor akuntansi yaitu variabel independen leverage, liquidity, profitability, productivity dan growth terhadap variabel dependen prediksi peringkat obligasi. Hasil dari penelitian ini adalah variabel profitability dan productivity memiliki pengaruh terhadap prediksi peringkat obligasi perusahaan non keuangan.The purpose of this study was to reveal the accounting factors that affect the predictions bond ratings on non-financial companies in the Indonesia Stock Exchange. Data taken from non-financial companies listed on the Indonesia Stock Exchange. The selection of a sample of 20 companies using purposive sampling method. The method used to test the hypothesis is a stepwise method logistic regression analysis to examine of accounting factors namely the independent variable leverage, liquidity, profitability, productivity and growth on the dependent variable prediction bond ratings. Results from this study are variable profitability and productivity have an influence on the prediction of non-financial corporate bond ratings

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