scholarly journals Prediction of Crime Occurrence using Multinomial Logistic Regression

2020 ◽  
Vol 9 (3) ◽  
pp. 1432-1435
Keyword(s):  
Logistic Regression ◽  
Data Analysis ◽  
Systematic Approach ◽  
Multinomial Logistic Regression ◽  
Heat Map ◽  
Criminal Offender ◽  
Logistic Regression Method ◽  
Hidden Patterns ◽  
Class Labels ◽  
Causes Of Crime

In order to uncover hidden patterns and correlations, data analysis examines large amounts of data. Analysis of crime isa systematic approach to the identification and analysis of crime patterns and itstrends. This plays a role in the planning of problems with crime and in formulating strategies for crime prevention. Instead of focusing on causes of crime such as criminal offender background, this work focuses primarily crime factors happened on every day. This work can predict the category of crime that has a higher likelihood of occurrence in those areas and can visualize in the form of histogram and heat map by category of crime, crime by day of week and month. The study depends on a lot of variables like class, latitude, longitude, etc. For forecast, the multinomial logistic regression method is used. For weekdays, the district and the hour of the accident are used as predictors.This algorithm is used because its target variable has more than two values and no ordering in the response variable.This provides greater efficiency for handling datasets with multi class labels. This forecast can be helpful in predicting the occurrence of crime in vulnerable areas, which in turn minimizes the crime rate by providing the patrol in those areas.

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 Systematic Approach to Perform Task Centric Exploratory Data Analysis with Case study

2021 ◽  
Vol 10 (3) ◽  
pp. 1920-1927
Keyword(s):  
Data Analysis ◽  
Systematic Approach ◽  
Exploratory Data Analysis ◽  
Data Organization ◽  
Interactive Programming ◽  
Different Types ◽  
Exploratory Data ◽  
Hidden Patterns ◽  
Visualization Techniques

Exploratorydata analysis is a method to summarize main characteristics of data, and also to understand data more deeply using visualization techniques. This paper focuses on defining systematic approach in the form of well-defined sequence of steps to explore data in various aspects. Every organization produces lot of data. Organization needs to analyze this data very carefully to extract hidden patterns in the data. Task Centric EDA[2]produces actionable insights as outcome to improve business process.This uses Pythonprogramming language and Jupyter Notebook for data analysis. Python is an object oriented and interactive programming language, which contains rich sets of libraries likepandas, MATplotlib, seaborn[10]etc. We have used different types of charts and various types of parameters to analyze retail dataset and to improve sales using precision marketing.

scholarly journals FINANCIAL STATEMENT QUALITY AND INVESTMENT EFFICIENCY

ACCRUALS ◽  
2019 ◽  
Vol 3 (1) ◽  
pp. 131-138
Author(s):  
Indah Umiyati ◽  
Riyanto Riyanto
Keyword(s):  
Logistic Regression ◽  
Data Analysis ◽  
Stock Exchange ◽  
Multinomial Logistic Regression ◽  
Financial Statements ◽  
Financial Statement ◽  
Investment Efficiency ◽  
The Relationship

This study aims to analyze the relationship between financial statements quality with reduced over-investment and under-investment. The study was conducted on companies listed on the Indonesia Stock Exchange in 2008-2015 totaling 1,525 years. The analysis was done using an estimation of multinomial logistic regression. The results of the data analysis show that financial statements quality does not have an influence on the reduced opportunities for over-investment or under investment

scholarly journals Company's Decision on Virtual Exhibition During Covid-19 Pandemic

2021 ◽  
pp. 207-215
Author(s):  
Pesta Elrida Lumbantoruan ◽  
Wahyudi David
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Data Analysis ◽  
Multinomial Logistic Regression ◽  
Analysis Method ◽  
Multinomial Logistic Regression Analysis ◽  
Time Representation ◽  
Direct Sales ◽  
Online Platforms

The exhibition is one of the promotional tools that increase company branding, product brand as well as awareness of direct sales. Exhibitions are considered investment activities carried out by companies. During pandemic Covid-19, offline exhibitions are impossible to be hosted. As alternative to hosted exhibitions by using virtual or online platforms. However, there is still a need to study to evaluate which factors have a stronger influence and relationship on the exhibition decision. The data analysis method used is multinomial logistic regression analysis. The results showed that the quality of the exhibition, popularity, time, representation, and price determine the company's decision to join an exhibition. It concludes that the company's decision is influenced by these factors 65% and three factors are having a significant influence for joining the exhibition.

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).

Coming Together Through Falling Apart

2019 ◽  
Vol 40 (2) ◽  
pp. 151-168
Author(s):  
Heather Churchill ◽  
Jeremy M. Ridenour
Keyword(s):  
Data Analysis ◽  
Qualitative Data ◽  
Structural Changes ◽  
Systematic Approach ◽  
Qualitative Approach ◽  
Psychological Assessments ◽  
Transference And Countertransference ◽  
Testing Data ◽  
Psychoanalytically Oriented Psychotherapy

Abstract. Assessing change during long-term psychotherapy can be a challenging and uncertain task. Psychological assessments can be a valuable tool and can offer a perspective from outside the therapy dyad, independent of the powerful and distorting influences of transference and countertransference. Subtle structural changes that may not yet have manifested behaviorally can also be assessed. However, it can be difficult to find a balance between a rigorous, systematic approach to data, while also allowing for the richness of the patient’s internal world to emerge. In this article, the authors discuss a primarily qualitative approach to the data and demonstrate the ways in which this kind of approach can deepen the understanding of the more subtle or complex changes a particular patient is undergoing while in treatment, as well as provide more detail about the nature of an individual’s internal world. The authors also outline several developmental frameworks that focus on the ways a patient constructs their reality and can guide the interpretation of qualitative data. The authors then analyze testing data from a patient in long-term psychoanalytically oriented psychotherapy in order to demonstrate an approach to data analysis and to show an example of how change can unfold over long-term treatments.

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