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All Journal Enrichment: Journal of Multidisciplinary Research and Development
Ade Kania Ningsih
Universitas Jenderal Achmad Yani, Indonesia
Humanities Economics, Econometrics & Finance Engineering Health Professions Social Sciences

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Consumer segmentation using K-Medians algorithm on transaction data based on LRFMP (length, recency, frequency, monetary, periodecity) Akbar Dena Maulana; Ade Kania Ningsih; Gunawan Abdillah
Enrichment: Journal of Multidisciplinary Research and Development Vol. 1 No. 8 (2023): Enrichment: Journal of Multidisciplinary Research and Development
Publisher : International Journal Labs

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55324/enrichment.v1i8.70

Abstract

Consumer loyalty has a crucial role for companies, especially in conditions of competition between companies. Success in retaining loyal customers is crucial. For this reason, customer loyalty analysis is needed to identify the level of consumer compliance with the company. In this case, consumer segmentation is also an important step to group consumers with similar characteristics to facilitate the management process. One of the analysis methods used is the LRFMP (Length, Recency, Frequency, Monetary, Periodecity) model, which examines consumer purchasing patterns based on various factors such as relationship length, last transaction time span, number of transactions, total money spent, and purchase regularity. The K-Medians grouping method was also used in this study. The data used is the history of purchase transactions in e-commerce for 373 days. From the application of LRFMP analysis and the K-Medians method, 4 clusters were obtained. The number of consumers in cluster 1 is 1183, cluster 2 is 1221, cluster 3 is 1206, and cluster 4 is 1102. The interpretation of the LRFMP model shows that 25.1% of consumers have high loyalty potential, 25.9% of consumers have low loyalty potential, 25.6% of consumers have high loyalty potential, and 23.4% of consumers have medium loyalty potential.
Classification of Sentiment Towards BPJS Services Using the C50 Algorithm Amellia Fahezha Cahyaningrum; Yulison Herry Chrisnanto; Ade Kania Ningsih
Enrichment: Journal of Multidisciplinary Research and Development Vol. 1 No. 8 (2023): Enrichment: Journal of Multidisciplinary Research and Development
Publisher : International Journal Labs

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55324/enrichment.v1i8.71

Abstract

This public health insurance program for all Indonesian people is supervised by the Social Security Administering Body (BPJS) for Health, an Air-Owned Enterprise. Thus, it will be easier for the public to find information about what policies the government has implemented to regulate BPJS. One of them is that people can find information on the social network Twitter. Due to its ease and simplicity of use, the number of tweets can easily grow quickly, which is why Twitter is more popular among Indonesians for communicating. Twitter is widely used as a promotional medium as well as a means of expressing opinions regarding criticism, suggestions, issues, and opinions of a public nature such as the views of netizens on new government policies and so on. One of them is in BPJS services, the large number of BPJS users causes BPJS to provide feedback services to users to find out how many good and bad responses to BPJS services. Sentiment classification is a branch of text mining. Sentiment classification is very basic in the evaluation process of a topic problem. Then the sentiment classification has the main objective of finding out the polarity of positive, and negative sentiment. The c50 algorithm method is one of the methods that can be used in the classification of BPJS service sentiment. In this research, the classification of BPJS service sentiment through Twitter media was carried out using the C50 algorithm method.
Co-Authors Akbar Dena Maulana Amellia Fahezha Cahyaningrum Gunawan Abdillah Yulison Herry Chrisnanto