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    Wyświetlanie 1-3 z 3
    Tytuł:
    Comparative Study of Techniques Used in Prediction of Student Performance
    Autorzy:
    Chauhan, Minakshi
    Gupta, Varsha
    Powiązania:
    https://bibliotekanauki.pl/articles/1159721.pdf
    Data publikacji:
    2018
    Wydawca:
    Przedsiębiorstwo Wydawnictw Naukowych Darwin / Scientific Publishing House DARWIN
    Tematy:
    Classification
    Clustering
    Data Mining Techniques
    Educational Data Mining
    Fuzzy Logic
    Opis:
    Providing high quality education is a major concern for higher educational institutions. The quality of education in higher institutions can be assessed by the teaching and learning process. The quality of the teaching learning process depends on the performance of instructor as well as performance of students involved. Analysis and prediction of student performance is key step to identify the poor academic performance. On the basis of prediction, the corrective actions must be taken to improve performance of students and enhance the quality of education system. In this study we surveyed the techniques commonly used to predict the performance of students and also analysed the factors affecting the student academic performance.
    Źródło:
    World Scientific News; 2018, 113; 185-193
    2392-2192
    Pojawia się w:
    World Scientific News
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Implementation of Big Data Concept for Variability Mapping Control of Financing Assessment of Informal Sector Workers in Bogor City
    Autorzy:
    Salmah, Salmah
    Andria, Fredi
    Wahyudin, Irfan
    Powiązania:
    https://bibliotekanauki.pl/articles/1065325.pdf
    Data publikacji:
    2019
    Wydawca:
    Przedsiębiorstwo Wydawnictw Naukowych Darwin / Scientific Publishing House DARWIN
    Tematy:
    Big Data
    Cluster
    Informal Worker Sector
    K-Means Clustering
    Opis:
    At present risks and uncertainties occur in protecting health for the community. This requires a national health insurance program that can guarantee health care costs. One of the program participants is a resident who works in the informal sector. This group is vulnerable as well as the potential for the implementation of health insurance programs. However, the level of participation of informal sector workers is still low, so an analysis of the constraints affecting it is needed. This study aims to identify categories of informal sector workers and analyze various obstacles faced by informal sector workers to become health insurance participants in the city of Bogor. The method used is the concept of big data with K-means clustering data mining techniques to group informal sector workers along with the constraints that exist in each of these groups. The results showed that there were 3 clusters with very low Social Security Administrator (BPJS) health ownership, namely cluster 1, cluster 3, and cluster 5. Each cluster had different constraints. Cluster 1 has constraints on the number of dependents it has, Cluster 3 has constraints on the gender side that are dominated by women, while Cluster 5 has constraints on the low-income side. Each cluster has a different obstacle resolution recommendation, namely for cluster 1 by registering workers in JKN contribution recipient (PBI) participants, cluster 2 by giving outreach to women who have only focused on men, and for clusters 5 by involving the community as a forum for the empowerment of informal sector workers.
    Źródło:
    World Scientific News; 2019, 135; 261-282
    2392-2192
    Pojawia się w:
    World Scientific News
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Life Insurance Customers segmentation using fuzzy clustering
    Autorzy:
    Jandaghi, Gholamreza
    Moazzez, Hashem
    Moradpour, Zahra
    Powiązania:
    https://bibliotekanauki.pl/articles/1193938.pdf
    Data publikacji:
    2015
    Wydawca:
    Przedsiębiorstwo Wydawnictw Naukowych Darwin / Scientific Publishing House DARWIN
    Tematy:
    Market segmentation
    customer segmentation
    data mining
    fuzzy clustering
    life insurance
    Opis:
    One of the important issues in service organizations is to identify the customers, understanding their difference and ranking them. Recently, the customer value as a quantitative parameter has been used for segmenting customers. A practical solution for analytical development is using analytical techniques such as dynamic clustering algorithms and programs to explore the dynamics in consumer preferences. The aim of this research is to understand the current customer behavior and suggest a suitable policy for new customers in order to attain the highest benefits and customer satisfaction. To identify such market in life insurance customers, We have used the FKM.pf.niose fuzzy clustering technique for classifying the customers based on their demographic and behavioral data of 1071 people in the period April to October 2014. Results show the optimal number of clusters is 3. These three clusters can be named as: investment, security of life and a combination of both. Some suggestions are presented to improve the performance of the insurance company.
    Źródło:
    World Scientific News; 2015, 21; 24-35
    2392-2192
    Pojawia się w:
    World Scientific News
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-3 z 3

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