scholarly journals Talent Performance Analysis Using People Analytics Approach

2021 ◽  
Vol 4 (1) ◽  
pp. 216-230
Author(s):  
Fahreza Nasril ◽  
Dian Indiyati ◽  
Gadang Ramantoko
Keyword(s):  
Logistic Regression ◽  
Multinomial Logistic Regression ◽  
Research Question ◽  
Development Program ◽  
Individual Performance ◽  
Study Data ◽  
Regression Method ◽  
Process Data ◽  
Logistic Regression Method ◽  
The Right

The purpose of this study was to answer the research question "How is the prediction of Talent Performance in the following year with the application of People Analytics?" and knowing the description of employees who are potential talents, the resulting performance contributions, to the description of the development and retention efforts needed by Talent in order to be able to maintain their future performance and position as Talents compared to the previous People Analytics method using predictive analysis, namely prediction of Talent Performance in the year next. In this study, data analysis using the Multivariate Logistic Regression method is used to get the Prediction of the Performance of Talents who become the object of research in the form of individual performance quickly and precisely in accordance with the patterns drawn by individual Performance score data in previous years. And can provide insight regarding the projected strategies that need to be done to maintain the improvement of individual talent performance in the years of the assessment period. It also helps management in making decisions about the right Talent development program and determining which Talents are priorities. The population in this study were the talents of employees of PT. Angkasa Pura II (Persero) with a managerial level consisting of: Senior Leader, Middle Leader, and First Line Leader who has a Person Grade (PG) range of 13 to 21. The sample used is Middle Leader level talent with specified criteria and through a process data cleansing. The results of this study indicate that the variable that significantly affects the performance of the following year is the performance of the previous 2 years. Then prediction analysis can be done using these independent variables with the Multinomial Logistic Regression method, and to get prediction results with better accuracy can be done by the Random Forest method.

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 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 Analysis of Decision to Work of Female Workers in Indonesia

2019 ◽  
Vol 8 (3) ◽  
pp. 290-300
Author(s):  
Andi Yhudo Wijayanto ◽  
Dyah Wulan Sari
Keyword(s):  
Logistic Regression ◽  
Multinomial Logistic Regression ◽  
Economic Structure ◽  
Industrial Sector ◽  
Regression Method ◽  
Tertiary Sector ◽  
Female Workers ◽  
Secondary Sector ◽  
Logistic Regression Method ◽  
Work Contract

This study analyze the influence of demographic, social, and economic characteristics on the decision to work of female workers in Indonesia. Based on the data of the National Labor Force Survey (Sakernas) August 2017, this study is conducted by using multinomial logistic regression method to achieve the objective. The result shows the characteristics of demographic, social, and economic have significant effect on the decision to work of female workers in Indonesia. Female workers tend to work in tertiary sector than primary sector. This finding is in line with the trend of increasing female workers in the tertiary sector which is one of the factor that influence the shift in economic structure in Indonesia. Another finding is that the increase of wage reduce the probability of female workers to work in the secondary sector. This condition is probably related to the existence of rules of minimum wage and work contract which bind female workers in the secondary sector, especially in large and medium industrial sector.

scholarly journals PEMODELAN FAKTOR - FAKTOR PENYEBAB KEPARAHAN KORBAN KECELAKAAN LALU LINTAS DI KOTA AMBON DENGAN MENGGUNAKAN MODEL REGRESI LOGISTIK MULTINOMIAL

2019 ◽  
Vol 1 (1) ◽  
pp. 17-26
Author(s):  
Miranti Miranti ◽  
F. Y. Rumlawang ◽  
F. Kondolembang
Keyword(s):  
Logistic Regression ◽  
Traffic Accident ◽  
Traffic Accidents ◽  
Multinomial Logistic Regression ◽  
Regression Method ◽  
Predictor Variables ◽  
Death Toll ◽  
Logistic Regression Method ◽  
Significant Factors ◽  
Response Variables

Traffic accidents are one of the main causes of the highest increase in mortality in Indonesia. This problem needs attention to anticipate the fall of the death toll in a traffic accident. So in this study, there are response variables and several predictor variables. The purpose of this study was to find out what factors influence the severity of traffic accident victims in Ambon city based on categories and model the severity of traffic accident victims in Ambon city based on significant factors using the Multinomial Logistic Regression method. In this study, the results obtained are factors that significantly affect the severity of the traffic accident victims are sex variables (X1), age (X1), education (X3) and type of vehicle (X5).

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 Prognostic algorithms for the progression of chronic heart failure depending on the clinical phenotype

2019 ◽  
Vol 18 (3) ◽  
pp. 41-47
Author(s):  
E. A. Polunina ◽  
L. P. Voronina ◽  
E. A. Popov ◽  
I. S. Belyakova ◽  
O. S. Polunina ◽  
...  
Keyword(s):  
Heart Failure ◽  
Logistic Regression ◽  
Chronic Heart Failure ◽  
Clinical Phenotype ◽  
Left Ventricular ◽  
Regression Method ◽  
Left Ventricular Ejection ◽  
Control Group ◽  
Ventricular Ejection Fraction ◽  
Logistic Regression Method

Aim. To develop a mathematical equation (algorithm) to predict the development of chronic heart failure (CHF) for three years, depending on the clinical phenotype.Material and methods. Three hundred forty five patients with CHF with a different left ventricular ejection fraction (preserved, mean, low) were examined. The control group included somatically healthy individuals (n=60). In all patients, 48 parameters that most widely characterize the pathogenesis of CHF (gender-anamnestic, clinical, instrumental, biochemical) were analyzed. To isolate phenotypes, dispersive and cluster analysis was used: the hierarchical classification method and the k-means method. In the development of algorithms we used binary logistic regression method. We used ROC curve to assess the quality of the obtained algorithms.Results. We identified four phenotypes in patients with CHF: fibro-rigid, fibro-inflammatory, inflammatory-destructive, dilated-maladaptive. For the first three phenotypes, a mathematical logistic regression method was used to develop mathematical models for predicting the progression of CHF for three years, with the release of predictors for each phenotype. Belonging to the dilatedmaladaptive phenotype according to the results of the analysis is already an indicator of an unfavorable prognosis in patients with CHF.Conclusion. The developed algorithms based on the selected phenotypes have high diagnostic sensitivity and specificity and can be recommended for use in clinical practice.

Effects of a Bolster Curvature on an Automobile Seat Fit

2021 ◽  
Vol 65 (1) ◽  
pp. 423-426
Author(s):  
Baekhee Lee ◽  
Byoung-Keon (Daniel) Park ◽  
Kihyo Jung ◽  
Jangwoon Park
Keyword(s):  
Logistic Regression ◽  
Design Process ◽  
Cross Sections ◽  
Classification Accuracy ◽  
Regression Method ◽  
Digital Design ◽  
Cross Sectional ◽  
Development Costs ◽  
Logistic Regression Method ◽  
Vehicle Seat

Vehicle-seat dimensions measured at specific cross-sections have been historically utilized as shape determinants to evaluate a driver’s seat fit. The present study is intended to quantify the relationships between seat fits and the seat dimensions for designing an ergonomic vehicle seat. Eight seat engineers evaluated seat fits for 54 different driver seats based on their expertise. Five seat dimensions were measured at six cross-sectional planes using a custom-built, computerized program. The best-subset-logistic-regression method was employed to model the relationships between the seat fit and the seat dimensions. As a result, significant seat dimensions, such as insert width, bolster height, and/or bolster curvature, on the subjective seat fit (e.g., loose-fit, right-fit, and tight-fit) were quantified. The developed models showed 98% overall classification accuracy throughout the cross-sectional planes. The models promote a digital design process of an automobile seat, which would increase the efficiency of the process and reduce the development costs.

Modeling Species Distribution

2011 ◽  
pp. 171-197 ◽  
Author(s):  
Yongyut Trisurat ◽  
Albertus G. Toxopeus
Keyword(s):  
Logistic Regression ◽  
Maximum Entropy ◽  
Habitat Suitability ◽  
Logistic Regression Model ◽  
Entropy Method ◽  
Regression Method ◽  
Logistic Regression Method ◽  
Suitable Habitats ◽  
Suitable Area ◽  
Available Information

The results show that among the three approaches, the potentially suitable habitats derived from cartographic overlay cover the largest area and are likely to overestimate existing occurrence areas. The logistic regression model predicts approximately 56% as suitable area, while maximum entropy results covers approximately 9% of the sanctuary. Although the results show large differences in the suitable areas, it should not be concluded that any one method always proves better than the others. Utilization of any method is dependent on the situation and available information. If species observations are limited, the cartographic overlay or habitat suitability is recommended. The logistic regression method is recommended when adequate presence and absence data are available. If presence-only data is available, a niche-based model or the maximum entropy method (MAXENT) is highly recommended.

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