scholarly journals Penerapan Metode K-Means Untuk Menganalisis Minat Nasabah

2021 ◽  
Vol 5 (3) ◽  
pp. 1187
Author(s):  
Juniar Hutagalung ◽  
Fifin Sonata
Keyword(s):  
Data Mining ◽  
Health Insurance ◽  
Insurance Companies ◽  
Data Types ◽  
Clustering Techniques ◽  
Constraint Factors ◽  
Processing Data ◽  
Fire Insurance ◽  
Potential Customers ◽  
Cluster 2

Insurance is a mechanism of protection or protection from the risk of loss by transferring the risk to another party. Sometimes a product that has just emerged becomes a product that is superior in terms of sales, so that interest in a product is not absolutely measured from the year the product was released. The constraint factors include the marketing of the product when it was launched. Offering products with low premiums along with the benefits that customers want. However, insurance companies still have difficulty in classifying superior products that are in great demand by prospective customers. For this reason, a technique for grouping insurance products is needed to make it easier for companies to see superior products and choose products that suit the needs of their customers. Analyzing and processing data using the K-Means method in the clustering of insurance products is the aim of this study. The application of the K-Means algorithm is to help calculate the purity value from the results of the clustering carried out so that the clustering of insurance products is in accordance with the needs of its customers. The application of the K-Means method with clustering techniques for data mining produces information on insurance products that are more attractive to potential customers. This is very appropriate in grouping data types because it is easier to implement and its application can filter quickly and precisely. Calculations using the K-Means method with a data sample of 55 customers obtained 3 clusters, namely cluster 1 for fire insurance which has 30 customers, cluster 2 for accident insurance 24 people and cluster 3 for health insurance 1 person.

scholarly journals Penerapan Teknik Clustering Data Mining untuk Memprediksi Kesesuaian Jurusan Siswa (Studi Kasus SMA PGRI 1 Subang)

Faktor Exacta ◽  
2020 ◽  
Vol 13 (2) ◽  
pp. 125
Author(s):  
Tubagus Riko Rivanthio ◽  
Mardhiya Ramdhani
Keyword(s):  
Data Mining ◽  
Data Processing ◽  
Private School ◽  
Similar Data ◽  
Clustering Techniques ◽  
Academic Achievements ◽  
Processing Data ◽  
Using Data ◽  
Clustering Data ◽  
Selection Of

<p>SMA PGRI 1 Subang is a private school that has several missions, one of which is the establishment of academic and non-academic achievements. In an effort to achieve the mission must supervise student achievement. The effort he did was to provide understanding in the selection of majors in accordance with the interests and talents of students. But in the activity of providing understanding, the school does not yet have a model that can evaluate the interests and talents of students to choose majors. The model can be obtained using student data processing. Data processing can be done using data mining, namely data mining clustering techniques. The technique will produce a model in the selection of majors. This clustering process is the process of grouping similar data based on the similarity of data held by students. The research method used is the CRISP-DM method which has 6 stages consisting of: Business Understanding, Data Understanding, Data Processing, Modeling, Evaluation, and Dissemination. The data that is processed is 620 data consisting of class of students in 2014, 2015, 2016. The results of processing using clustering obtained 6 clusters that have different models for each cluster. The results of this study can be used by schools in recommending courses chosen by students according to students' interests and talents, so students can learn optimally.</p><strong><em>Key words</em></strong>: clustering, dataMining, suitability, majors, students

The Use of Data Mining by Private Health Insurance Companies and Customers’ Privacy

2015 ◽  
Vol 24 (3) ◽  
pp. 281-292 ◽  
Author(s):  
YESLAM AL-SAGGAF
Keyword(s):  
Data Mining ◽  
Health Insurance ◽  
Private Health Insurance ◽  
Personal Information ◽  
Qualitative Interviews ◽  
Ethical Analysis ◽  
Insurance Companies ◽  
Australian Health ◽  
Use Of Data ◽  
Health Insurance Companies

Abstract:This article examines privacy threats arising from the use of data mining by private Australian health insurance companies. Qualitative interviews were conducted with key experts, and Australian governmental and nongovernmental websites relevant to private health insurance were searched. Using Rationale, a critical thinking tool, the themes and considerations elicited through this empirical approach were developed into an argument about the use of data mining by private health insurance companies. The argument is followed by an ethical analysis guided by classical philosophical theories—utilitarianism, Mill’s harm principle, Kant’s deontological theory, and Helen Nissenbaum’s contextual integrity framework. Both the argument and the ethical analysis find the use of data mining by private health insurance companies in Australia to be unethical. Although private health insurance companies in Australia cannot use data mining for risk rating to cherry-pick customers and cannot use customers’ personal information for unintended purposes, this article nonetheless concludes that the secondary use of customers’ personal information and the absence of customers’ consent still suggest that the use of data mining by private health insurance companies is wrong.

Penerapan Data Mining Untuk Prediksi Penjualan Mobil Menggunakan Metode K-Means Clustering

2020 ◽  
Vol 3 (3) ◽  
pp. 187-201
Author(s):  
Sufajar Butsianto ◽  
Nindi Tya Mayangwulan
Keyword(s):  
Data Mining ◽  
Clustering Data ◽  
Cluster 2

Penggunaan mobil di Indonesia setiap tahunnya selalu meningkat dan membuat perusahaan otomotif berlomba-lomba dalam peningkatan penjualannya. Tujuan dari penelitian ini untuk mengelompokan data penjualan kedalam sebuah cluster dengan metode Data Mining Algoritma K-Means Clustering. Data Penjualan nantinya akan dikelompokan berdasarkan kemiripan data tersebut sehingga data dengan karakteristik yang sama akan berada dalam satu cluster. Atribut yang digunakan adalah brand dan penjualan. Cluster yang terbentuk setelah dilakukan proses K-Means Clustering terbagi menjadi tiga cluster yaitu Cluster 0 jumlah anggota 235 dengan presentase 26% dikategorikan Laris, Cluster 1 jumlah anggota 604 dengan presentase 67% dikategorikan Kurang Laris, dan Cluster 2 jumlah angota 61 dengan presentase 7% dikategorikan Paling Laris, dari proses clustering diatas dapat diperoleh validasi DBI (Davies Bouldin Index) dengan nilai 0,341

scholarly journals Which health-related reasons lead to prehospital emergency care and how does subjective emergency status connect to subsequent care?

2021 ◽  
Author(s):  
Silke Piedmont ◽  
Anna Katharina Reinhold ◽  
Jens-Oliver Bock ◽  
Enno Swart ◽  
Bernt-Peter Robra
Keyword(s):  
Health Insurance ◽  
Emergency Care ◽  
Third Party ◽  
Insurance Companies ◽  
Prehospital Emergency Care ◽  
Health Related ◽  
Life Threatening ◽  
Prehospital Emergency ◽  
Health Insurance Companies ◽  
Reported Data

Abstract Objectives/Background In many countries, the use of emergency medical services (EMS) increases steadily each year. At the same time, the percentage of life-threatening complaints decreases. To redesign the system, an assessment and consideration of the patients’ perspectives is helpful. Methods We conducted a paper-based survey of German EMS patients who had at least one case of prehospital emergency care in 2016. Four health insurance companies sent out the questionnaire to 1312 insured persons. We linked the self-reported data of 254 respondents to corresponding claims data provided by their health insurance companies. The analysis focuses a.) how strongly patients tend to call EMS for themselves and others given different health-related scenarios, b.) self-perceived health complaints in their own index case of prehospital emergency care and c.) subjective emergency status in combination with so-called “objective” characteristics of subsequent EMS and inpatient care. We report principal diagnoses of (1) respondents, (2) 57,240 EMS users who are not part of the survey and (3) all 20,063,689 inpatients in German hospitals. Diagnoses for group 1 and 2 only cover the inpatient stay that started on the day of the last EMS use in 2016. Results According to the survey, the threshold to call an ambulance is lower for someone else than for oneself. In 89% of all cases during their own EMS use, a third party called the ambulance. The most common, self-reported complaints were pain (38%), problems with heart and circulation (32%), and loss of consciousness (17%). The majority of respondents indicated that their EMS use was due to an emergency (89%). We could detect no or only weak associations between patients’ subjective urgency and different items for objective care. Conclusion Dispatchers can possibly optimize or reduce the disposition of EMS staff and vehicles if they spoke directly to the patients more often. Nonetheless, there is need for further research on how strongly the patients’ perceived urgency may affect the disposition, rapidness of the service and transport targets.

scholarly journals Health Insurance as a Healthcare Financing Mechanism in India: Key Strategic Insights and a Business Model Perspective

2021 ◽  
pp. 025609092110270
Author(s):  
Rohit Kumar ◽  
Aditya Duggirala
Keyword(s):  
Health Insurance ◽  
Business Model ◽  
Critical Success Factors ◽  
Success Factors ◽  
Insurance Industry ◽  
Insurance Companies ◽  
Healthcare Financing ◽  
Strategic Choices ◽  
Critical Success ◽  
Health Insurance Industry

This study provides strategic insights and a business model perspective on health insurance as a vehicle for financing healthcare. It uses both primary (expert interview) and secondary data to investigate the overall disease burden and healthcare industry trends and track healthcare financing through the health insurance mechanism in India. To identify the critical success factors and to gain a business model perspective within the health insurance industry, telephonic and face-to-face interviews were held with 27 experts in the healthcare, insurance, and strategic management field. The study’s findings suggest that the growth of health insurance as a healthcare financing mechanism in India has been challenged continuously and impacted by multiple changes in the health insurance and healthcare industry over the last decade. One of the critical challenges faced by insurance companies is the high incurred claim ratio. We find the Indian health insurance industry to be very competitive and that the focus on critical success factors can help insurance companies gain a competitive advantage. The health insurance business model is unique, with varying configurations, and broadly comprises strategic choices and consequences. In this article, drawing from the strategic management literature on the resource-based view (RBV) and insights gained from the interviews of healthcare and health insurance experts, we highlight the six critical success factors relevant for competing in the health insurance business. We also list five strategic choices that can help health insurance companies improve their profitability and gain a sustained competitive advantage. We recommend that the insurance companies design and develop an innovative business model centred around lowering the claim ratio and simultaneously increasing the customer willingness to pay. To increase the customer willingness to pay and reduce the claim ratio, the insurance companies should focus on the six critical success factors and invest in the five strategic choices.

Healthcare of the Netherlands

2022 ◽  
pp. 67-69
Author(s):  
Elena Vladimirovna Frolova ◽  
Keyword(s):  
Health Insurance ◽  
The Netherlands ◽  
Western Europe ◽  
Private Insurance ◽  
The State ◽  
Insurance Companies ◽  
Malignant Neoplasms ◽  
Average Life Expectancy ◽  
Medical Reform ◽  
Cause Of Deaths

The Netherlands is a state located in Western Europe bordering Germany and Belgium. The population of the country is just over 17million people. In terms of GDP, theNetherlands is among the twenty richest countries in the world, and in terms of exports, it is in the top ten. The average life expectancy in theNetherlands is 81.4 years; in the structure ofmortality, malignant neoplasms come out on top, which distinguishes the state from other European countries, where the main cause of deaths is cardiovascular diseases. The compulsory health insurance system was introduced in the country in 2006 after the medical reform. A distinctive feature of the Dutch healthcare system is its relative autonomy from the state, which performs only the function of an external controller, and all other powers belong to the municipal authorities. As a result, several private insurance companies have been admitted to health insurance in the Netherlands, which create healthy competition among themselves, thereby contributing to better quality and more affordable healthcare.

Data mining experiments on hop processing data

2005 ◽  
Author(s):  
J.W. Grzymala-Busse ◽  
Z.S. Hippe ◽  
T. Mroczek ◽  
E. Roj ◽  
B. Skowronski
Keyword(s):  
Data Mining ◽  
Processing Data

The Pioneer in American Life Insurance Marketing

1938 ◽  
Vol 12 (5) ◽  
pp. 65-75
Author(s):  
J. Owen Stalson
Keyword(s):  
United States ◽  
Eighteenth Century ◽  
North America ◽  
Life Insurance ◽  
General Public ◽  
Colonial America ◽  
The United States ◽  
Insurance Companies ◽  
Insurance Company ◽  
Fire Insurance

Colonial America gave little thought to life insurance selling. The colonists secured protection against marine risks from private underwriters, first in London, eventually at home. It has been asserted that Philadelphia had no fire insurance until 1752; Boston none before 1795. The first corporations formed in this country for insuring lives were those of the Presbyterian Ministers Fund (1759) and a similar company organized for the benefit of Episcopal ministers (1769). Neither of these corporations offered insurance to the general public. In the last decade of the eighteenth century many insurance companies were formed in the United States. At least five were chartered to underwrite life risks, but only one, The Insurance Company of North America, appears to have accepted any. There is no basis for saying that any of these early companies tried to sell life insurance.

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