scholarly journals ANALISIS FAKTOR-FAKTOR YANG MEMPENGARUHI PENERIMA BERAS RASKIN MENGGUNAKAN REGRESI LOGISTIK BINER DENGAN GUI R

2021 ◽  
Vol 10 (1) ◽  
pp. 31-43
Author(s):  
Agustinus Salomo Parsaulian ◽  
Tarno Tarno ◽  
Dwi Ispriyanti
Keyword(s):  
Logistic Regression ◽  
Low Income ◽  
Binary Logistic Regression ◽  
Education Level ◽  
Final Model ◽  
Poor Households ◽  
Logistic Regression Method ◽  
House Floor ◽  
The Relationship

The Rice Subsidy Program for Low-Income Communities or the Raskin Program is one of the government's programs to eradicate poverty. However, in practice, determining the criteria for Raskin recipients is a complicated problem. The Raskin program is a cross-sectoral national program both horizontally and vertically, to help meet the rice needs of low-income citizens. Determining the criteria for Raskin recipients is often a complicated issue. This study aims to analyze the classification of the Target Households (RTS) for the Raskin Program. The method used is binary logistic regression by utilizing R GUI. Binary logistic regression method is a method to find the relationship between independent and dependent variables, with a binary or dichotomous dependent variable. The data used is the March 2018 National Socio-Economic Survey (Susenas) data for Brebes Regency. The independent variables used in this study are the criteria for determining poor households, namely the area of the house, floor type of the house, wall type of the house, defecation facilities, lighting used, fuel used, ability to buy meat/milk, education level of the head of the household, and the capacity of installed electricity in the main residence. The results of the analysis show that in the final model, the variables that significantly affect the classification of RTS are the ability to eat healthy food, the capacity of installed electricity in the main residence, the education level of the head of the household, and defecation facilities with an accuracy value of 85.4%.Keywords: Raskin Program, Binary Logistic Regression, R GUI

scholarly journals On the Comparison of Deep Learning Neural Network and Binary Logistic Regression for Classifying the Acceptance Status of Bidikmisi Scholarship Applicants in East Java

MATEMATIKA ◽  
2018 ◽  
Vol 34 (3) ◽  
pp. 83-90
Author(s):  
Nita Cahyani ◽  
Kartika Fithriasari ◽  
Irhamah Irhamah ◽  
Nur Iriawan
Keyword(s):  
Neural Network ◽  
Logistic Regression ◽  
Deep Learning ◽  
Binary Logistic Regression ◽  
Training Data ◽  
Regression Methods ◽  
Logistic Regression Method ◽  
Validation Procedure ◽  
Deep Learning Neural Network

Neural Network and Binary Logistic Regression are modern and classical data mining analysis tools that can be used to classify data on Bidikmisi scholarship acceptance in East Java Province, Indonesia. One form of Neural Network model available for various applications is the Resilient Backpropagation Neural Network (Resilient BPNN). This study aims to compare the performance of the Resilient BPNN method as a Deep Learning Neural Network and Binary Logistic Regression method in determining the classification of Bidikmisi scholarship acceptance in East Java Province. After preprocessing data and dividing them into two parts, i.e. sets of testing and training data, with 10-foldcross-validation procedure, the Resilient BPNN and Binary Logistic Regression methods are implemented. The result shows that Resilient BPNN with two hidden layers is the best platformnetwork model. The classificationG-mean resulted by these both methods is that Resilient BPNN with two hidden layers is more representative with better performance than Binary Logistic Regression. The Resilient BPNN is recommended to be used topredict acceptance of Bidikmisi applicants yearly.

scholarly journals KLASIFIKASI PEMBERIAN KREDIT SEPEDA MOTOR MENGGUNAKAN METODE REGRESI LOGISTIK BINER DAN CHI-SQUARED AUTOMATIC INTERACTION DETECTION (CHAID) DENGAN GUI R (Studi Kasus: Kredit Sepeda Motor di PT X)

2021 ◽  
Vol 10 (2) ◽  
pp. 159-169
Author(s):  
Chalimatus Sa'diah ◽  
Tatik Widiharih ◽  
Arief Rachman Hakim
Keyword(s):  
Logistic Regression ◽  
Home Ownership ◽  
Binary Logistic Regression ◽  
Study Data ◽  
Regression Method ◽  
Interaction Detection ◽  
Chi Squared ◽  
Logistic Regression Method ◽  
Credit Data

One of the factors causing the bankruptcy of a company is bad credit. Therefore, prospective customers need to be selected so that bad credit cases can be minimized. This study aims to determine the classification of credit granting to prospective customers of company X in order to reduce the risk of bad credit. The method used is the binary logistic regression method and the Chi-Squared Automatic Interaction Detection (CHAID) method. In this study, data used in November 2019 were 690 motorcycle credit data for company X in Gresik. The independent variables in this study are the factors that affect bad credit such as gender, marital status, education, employment, income, expenses, home ownership status and the dependent variable is credit status (bad and current). The analysis results show that the binary logistic regression has an accuracy value of 76.38% with an APER of 23.62%, while CHAID has an accuracy value of 93.19% with an APER of 6.81%. The accuracy value of the CHAID method is greater than the binary logistic regression method, while the APER value of the CHAID method is smaller than the binary logistic regression method. So it can be concluded that the CHAID method is better than the binary logistic regression method in classifying bad credit at company X. Keywords: Credit, Classification, Binary Logistic Regression, CHAID.

scholarly journals Bivariate binary analysis on composite index of anthropometric failure of under-five children and household wealth-index

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Demeke Lakew Workie ◽  
Lijalem Melie Tesfaw
Keyword(s):  
Logistic Regression ◽  
Composite Index ◽  
Binary Logistic Regression ◽  
Wealth Index ◽  
Household Wealth ◽  
Education Level ◽  
Multiple Birth ◽  
Under Five ◽  
Under Five Children ◽  
Study Results

Abstract Background Malnutrition is the most common cause of mortality and morbidity of children in low and middle income countries including Ethiopia and household wealth index shares the highest contribution. Thus, in this study it is aimed to conduct bivariate binary logistic regression analysis by accounting the possible dependency of child composite index anthropometric failure and household wealth index. Methods In this study the data from Ethiopian Demographic and Health Survey (EDHS) 2016 involved 9411 under five children was considered. Child Composite Index Anthropometric Failure (CIAF) measures the aggregate child undernourished derived from the conventional anthropometric indices (stunting, underweight and wasting). The correlation between CIAF and wealth index was checked and significant correlation found. To address the dependency between the two outcome variables bivariate binary logistic regression was used to analyze the determinants of child CAIF and household wealth index jointly. Results Study results show that region, place of residence, religion, education level of women and husband/partner, sex of child, source of drinking water, household size and number of under five children in the household, mothers body mass index, multiple birth and anemia level of child had significant association with child CIAF. Female children were 0.82 times less likely to be CIAF compared to male and multiple birth children were more likely to be CIAF compared to single birth. Children from Oromia, Somalie, Gambela, SNNPR, Harari and Addis Ababa region were 0.6, 0.56, 0.67, 0.52, 0.6 and 0.44 times less likely to be CIAF compared to Tigray. A household from rural area were 15.49 times more likely poor compared to a household. The estimated odds of children whose mothers attended primary, and secondary and higher education was 0.82, and 0.52 times respectively the estimated odds of children from mothers who had never attended formal education. Conclusion The prevalence of children with composite index anthropometric failure was high and closely tied with the household wealth index. Among the determinants, region, religion, family education level, and anemia level of child were statistically significant determinants of both CIAF and household wealth index. Thus, the authors recommend to concerned bodies and policymakers work on household wealth index to reduce the prevalence of child composite anthropometric failure.

Abstract 3734: Presurgical fMRI and Morbidity Outcomes in Patients with Vascular Lesions

Stroke ◽  
2012 ◽  
Vol 43 (suppl_1) ◽  
Author(s):  
Andrew C Radtke ◽  
Joshua Pankratz ◽  
Ryan Holdsworth ◽  
Dovile Baniulis ◽  
Nicole Kornder ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Preoperative Planning ◽  
Binary Logistic Regression ◽  
Vascular Lesion ◽  
Vascular Lesions ◽  
Motor Deficits ◽  
Binary Logistic Regression Analysis ◽  
Language Mapping ◽  
Motor Mapping ◽  
The Relationship

Background fMRI is being increasingly used as an adjunct imaging technique for preoperative planning for patients with various brain lesions. The proximity of the lesion to eloquent cortex is a major factor in guiding surgical planning. Our group has previously reported significant association between the distance between brain tumor periphery and area of fMRI activation (Lesion-Activation Distance; LAD) and morbidity and mortality outcomes. This study investigated the relationship between vascular lesion LAD and morbidity. Methods This study was a retrospective analysis of data from patients with vascular lesions [arteriovenous malformations (AVMs) (n=49), and cavernomas (n=57)], who had received fMRI as part of their preoperative planning. The preoperative fMRI included motor mapping (n=87) and/or language mapping (n=102). The fMRI paradigms were chosen based on observed preoperative weakness (aphasia, paresis) and anticipated functional areas of the brain that may be affected by treatment. Results Multiple logistic regression analyses showed that a model that combines Age and Language LAD was a significant predictor of postoperative deficits (p= 0.04). Broca’s LAD(1-2 cm) X Age was a significant predictor of postoperative deficits (change in odds ratio (OR) =0.82, CI:0.68-0.98). The relationship between Brocas’s LAD and postoperative aphasia and Broca’s LAD and pre and postoperative aphasia trended towards significance (p = .08 and p =.07 respectively). Wernicke’s LAD, independently or combined with Age, was not a significant predictor of postoperative deficits. Binary logistic regression analysis for SMC LAD and postop deficits did not reach significance (p =.10). There were no significant differences in postoperative language or motor deficits as a function of gender or handedness. Conclusions These results suggest that both age and the proximity of a vascular lesion to language LAD are factors that can help predict postoperative outcomes, especially for Broca’s LAD. The lack of similar results when investigating the relationship between Wernicke’s LAD and postoperative deficits suggests potential brain reorganization and/or robustness of this brain region. These results have implications for the potential use of fMRI as a presurgical tool for language mapping in patients with vascular lesions.

scholarly journals Prediksi Not Operational Transaction Menggunakan Logistic Regression pada Bank XYZ di Kota Kupang

AITI ◽  
2020 ◽  
Vol 17 (1) ◽  
pp. 42-55
Author(s):  
Radius Tanone ◽  
Arnold B Emmanuel
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Regression Method ◽  
Learning Approach ◽  
Know How ◽  
Independent Variables ◽  
Machine Learning Approach ◽  
Logistic Regression Method ◽  
Python Programming

Bank XYZ is one of the banks in Kupang City, East Nusa Tenggara Province which has several ATM machines and is placed in several merchant locations. The existing ATM machine is one of the goals of customers and non-customers in conducting transactions at the ATM machine. The placement of the ATM machines sometimes makes the machine not used optimally by the customer to transact, causing the disposal of machine resources and a condition called Not Operational Transaction (NOP). With the data consisting of several independent variables with numeric types, it is necessary to know how the classification of the dependent variable is NOP. Machine learning approach with Logistic Regression method is the solution in doing this classification. Some research steps are carried out by collecting data, analyzing using machine learning using python programming and writing reports. The results obtained with this machine learning approach is the resulting prediction value of 0.507 for its classification. This means that in the future XYZ Bank can classify NOP conditions based on the behavior of customers or non-customers in making transactions using Bank XYZ ATM machines.  

scholarly journals Loyalitas Konsumen Terhadap Konsumsi Kopi Tubruk di Meulaboh

2017 ◽  
Vol 2 (2) ◽  
pp. 147-155
Author(s):  
Dina Fida ◽  
Ismayani Ismayani ◽  
Fajri Jakfar
Keyword(s):  
Logistic Regression ◽  
Customer Satisfaction ◽  
Customer Loyalty ◽  
Binary Logistic Regression ◽  
Validity And Reliability ◽  
Chi Square ◽  
Plantation Species ◽  
Long Time ◽  
Coffee Powder ◽  
The Relationship

Abstrak.Kopi adalah salah satu jenis tanaman perkebunan yang sudah lama dibudidayakan dan memiliki nilai ekonomi yang dapat menghasilkan keuntungan. Kopi tubruk merupakan kopi tradisional yang umumnya lebih keras karena bubuk kopi murni yang langsung diseduh dengan air  mendidih, teksturnya lebih kasar, lebih banyak mengandung ampas, aroma kopi yang lebih menyengat, serta tingkat kekentalan yang bisa disesuaikan dengan lidah penikmatnya. Loyalitas konsumen pada umumnya merupakan suatu sikap konsumen yang loyal terhadap pilihan dan penggunaan produk dalam waktu yang lama dan untuk masa yang akan datang, Sedangkan kepuasan konsumen ialah perasaan senang atau kecewa seseorang yang berasal dari perbandingan antara kesannya terhadap kinerja (hasil) sesuatu produk dengan harapannya. Pada penelitian ini bertujuan untuk mengetahui faktor-faktor yang mempengaruhi loyalitas konsumen terhadap minuman kopi tubruk di Meulaboh, Mengetahui hubungan antara tingkat kepuasan konsumen dengan loyalitas konsumen terhadap konsumsi kopi tubruk di Meulaboh.Adapun metode yang digunakan adalah Uji Validitas dan Reliabilitas,Regresi Logistik Biner dan Chi Square.Hasil Regresi Biner Logistik menunjukkan bahwa citarasa, harga, kualitas pelayanan dan lokasi merupakan faktor yang mempengaruhi loyalitas konsumen terhadap minuman kopi tubruk.Hasil Uji Chi-Square menunjukkan bahwa adanya hubungan yang signifikan antara kepuasan konsumen dengan loyalitas konsumen terhadap minuman kopi tubruk di Meulaboh.Consumer Loyality To The Consumption Of Coffee Brewed In MeulabohAbstract.Coffee is one of the plantation species has long been cultivated and have a mutually beneficial economic value.The brewed coffee is a traditional coffee are generally harder for pure coffee powder that instantly brewed with boiling water, rough texture, contains more dregs, pungent coffee aroma, as well as the level of consistency that can be adapted to the tongue of the audience.Consumer loyalty is generally a loyal consumer attitudes towards choice and use of the product for a long time and for the future.While customer satisfaction is feeling happy or disappointed someone who comes from a comparison between her impression of the performance (yield) of a product with expectations.In this study aims to determine the factors that influence consumer loyalty to the coffee beverage brewed in Meulaboh.Determine the relationship between the level of customer satisfaction and customer loyalty towards the consumption of instant coffee in Meulaboh.The methods used are validity and reliability, Binary Logistic Regression and Chi Square.Binary logistic regression results show that the simultaneous testing of the factors that influence loyalty is taste, price, service quality, and location. While testing only partially furnished variables that influence loyalty.Chi-Square test results indicate that there is a significant relationship between customer satisfaction and customer loyalty so that customer satisfaction is influenced flavors that suit the tastes of consumers. 

scholarly journals Relationship Between Unbound Bilirubin Levels and Acute Bilirubin Encephalopathy in Exchange Transfusion Neonates

2021 ◽  
Author(s):  
Yiyi Ding ◽  
Shuo Wang ◽  
Rui Guo ◽  
Aizhen Zhang ◽  
Yufang Zhu
Keyword(s):  
Logistic Regression ◽  
Blood Glucose ◽  
Odds Ratio ◽  
Exchange Transfusion ◽  
Binary Logistic Regression ◽  
P Value ◽  
Bilirubin Encephalopathy ◽  
Unbound Bilirubin ◽  
Independent Variable ◽  
The Relationship

Abstract BACKGROUND: Evidence regarding the relationship between unbound bilirubin levels and acute bilirubin encephalopathy was limited. Therefore, this study set out to investigate whether the unbound bilirubin level was independently related to acute bilirubin encephalopathy in children who underwent exchange transfusion after adjusting for other covariates. METHODS: A total of 46 neonates who underwent exchange transfusion were involved in The First People's Hospital Of Changde City in China from 2016-1-1 to 2018-12-31. The target independent variable and the dependent variable were unbound bilirubin levels measured at baseline and acute bilirubin encephalopathy respectively. Covariates involved in this study included sex, age, birth weight, blood glucose, red blood cell, hemolysis, receive phototherapy before exchange transfusion. RESULTS: The average gestational age of 46 selected participants was 38.6 ± 1.3 weeks old, the average age was 146.5 ± 86.9 hours old, 52.17% of them were male. Result of fully-adjusted binary logistic regression showed unbound bilirubin levels were positively associated with risk of acute bilirubin encephalopathy after adjusting confounders (Odds ratio = 1.41, 95% confidence intervals 1.05-1.91, P value <0.05). CONCLUSION: Unbound bilirubin levels are associated with neonatal acute bilirubin encephalopathy. The mechanism of unbound bilirubin levels leading to neonatal acute bilirubin encephalopathy needs to be further explored.

scholarly journals Classification of Soccer and Basketball Players’ Jumping Performance Characteristics: A Logistic Regression Approach

Sports ◽  
2019 ◽  
Vol 7 (7) ◽  
pp. 163 ◽  
Author(s):  
Christos Chalitsios ◽  
Thomas Nikodelis ◽  
Vassilios Panoutsakopoulos ◽  
Christos Chassanidis ◽  
Iraklis Kollias
Keyword(s):  
Logistic Regression ◽  
Statistical Significance ◽  
Center Of Mass ◽  
Binary Logistic Regression ◽  
Vertical Displacement ◽  
Rate Of Force Development ◽  
Basketball Players ◽  
Impulse Generation ◽  
Force Development

This study aimed to examine countermovement jump (CMJ) kinetic data using logistic regression, in order to distinguish sports-related mechanical profiles. Eighty-one professional basketball and soccer athletes participated, each performing three CMJs on a force platform. Inferential parametric and nonparametric statistics were performed to explore group differences. Binary logistic regression was used to model the response variable (soccer or not soccer). Statistical significance (p < 0.05) was reached for differences between groups in maximum braking rate of force development (RFDDmax, U79 = 1035), mean braking rate of force development (RFDDavg, U79 = 1038), propulsive impulse (IMPU, t79 = 2.375), minimum value of vertical displacement for center of mass (SBCMmin, t79 = 3.135), and time difference (% of impulse time; ΔΤ) between the peak value of maximum force value (FUmax) and SBCMmin (U79 = 1188). Logistic regression showed that RFDDavg, impulse during the downward phase (IMPD), IMPU, and ΔΤ were all significant predictors. The model showed that soccer group membership could be strongly related to IMPU, with the odds ratio being 6.48 times higher from the basketball group, whereas RFDDavg, IMPD, and ΔΤ were related to basketball group. The results imply that soccer players execute CMJ differently compared to basketball players, exhibiting increased countermovement depth and impulse generation during the propulsive phase.

Comparison between students and residents on determinants of willingness to separate waste and waste separation behaviour in Zhengzhou, China

2017 ◽  
Vol 35 (9) ◽  
pp. 949-957 ◽  
Author(s):  
Xiaoping Dai ◽  
Yuping Han ◽  
Xiaohong Zhang ◽  
Wei Hu ◽  
Liangji Huang ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Undergraduate Students ◽  
Regression Models ◽  
Binary Logistic Regression ◽  
Education Level ◽  
Factors Affecting ◽  
Logistic Regression Models ◽  
Management Policies ◽  
Waste Separation ◽  
Do So

A better understanding of willingness to separate waste and waste separation behaviour can aid the design and improvement of waste management policies. Based on the intercept questionnaire survey data of undergraduate students and residents in Zhengzhou City of China, this article compared factors affecting the willingness and behaviour of students and residents to participate in waste separation using two binary logistic regression models. Improvement opportunities for waste separation were also discussed. Binary logistic regression results indicate that knowledge of and attitude to waste separation and acceptance of waste education significantly affect the willingness of undergraduate students to separate waste, and demographic factors, such as gender, age, education level, and income, significantly affect the willingness of residents to do so. Presence of waste-specific bins and attitude to waste separation are drivers of waste separation behaviour for both students and residents. Improved education about waste separation and facilities are effective to stimulate waste separation, and charging on unsorted waste may be an effective way to improve it in Zhengzhou.

scholarly journals Association between Violent Crime and Psychosis in Men Serving Prison Terms

2017 ◽  
Vol 20 ◽  
Author(s):  
Javier Saavedra ◽  
Marcelino López ◽  
M. Eva Trigo
Keyword(s):  
Logistic Regression ◽  
Personality Disorders ◽  
Violent Crime ◽  
Suicide Attempts ◽  
Prison Population ◽  
Positive Symptoms ◽  
Violent Crimes ◽  
Final Model ◽  
Functional Psychoses ◽  
The Relationship

AbstractPsychosis has been associated with committing violent crimes. However, it has been reported that the association is mediated by toxin consumption, personality disorders, and positive symptoms. This study will examine the relationship between different psychological disorders and sociodemographic variables, and violent crime perpetration in a sample of 472 men serving prison terms in Andalusia, Spain. A correlation-based, retrospective study was conducted and data were analyzed through logistic regression. The sample is representative of the Andalusian prison population, with a 95% level of confidence and .02% precision. Inmates were sampled and diagnosed by expert clinicians using the SCID-I and the IPDE-II. We computed bivariate correlations between the aforementioned variables and perpetration of violent crimes (murder, homicide, attempted murder, and injury) to later apply logistic regression and find adjusted odds ratios. We confirmed the association between diagnosis of functional psychoses and violent crime, with a significant adjusted odds ratio in the last model (OR = 3.71; p = .010). Other significant variables that acted like risk factors include suicide attempts (OR = 2.04; p = .046), having received care at a mental health facility in the year before imprisonment (OR = 3.83; p = .008), and more strongly than the psychosis diagnosis, low level of education (OR = 10.32; p = .029). Toxin consumption and personality disorders were not significant in the final model.

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