scholarly journals Analisis Loyalitas Pelanggan Berbasis Model Recency, Frequency, dan Monetary (RFM) dan Decision Tree pada PT. Solo

2020 ◽  
Vol 7 (5) ◽  
pp. 943
Author(s):  
Basri Basri ◽  
Windu Gata ◽  
Risnandar Risnandar
Keyword(s):  
Decision Tree ◽  
Customer Loyalty ◽  
State Of The Art ◽  
Model Cluster ◽  
Customer Data ◽  
Decision Tree Classification ◽  
Clustering Model ◽  
Rfm Model ◽  
Tree Models ◽  
Cluster 2

<p>Perkembangan bisnis alat tulis kantor dan sekolah saat ini banyak yang menjanjikan, maka banyak bermunculan pemasok baru dalam bisnis Alat Tulis Kantor dan Sekolah (ATKS). PT Solo yang bergerak di bidang bisnis ATKS harus memiliki strategi dalam setiap persaingan usaha, khususnya dalam meraih loyalitas pelanggan. Loyalitas pelanggan sering dipengaruhi oleh faktor jumlah aktivitas transaksi, nilai nominal transaksi, waktu transaksi di perusahaan, dan atribut <em>outlet</em>. Penelitian ini mengusulkan model <em>Recency</em>, <em>Frequency</em>, dan <em>Monetary</em> (RFM) yang dikombinasikan dengan <em>Decision Tree</em>. Model RFM digunakan untuk proses klasterisasi data pelanggan berdasarkan jumlah transaksi, nilai nominal transaksi, waktu transaksi, dan atribut outlet. Sedangkan <em>Decision Tree</em> dapat menggambarkan tingkat loyalitas pelanggan. Data transaksi dalam penelitian ini dilakukan sepanjang 1 Januari hingga 31 Desember 2018 terhadap 1.203 pelanggan dan 18.087 transaki melalui faktur pembelian. Hasil penelitian ini menunjukan bahwa <em>state-of-the-art </em>pada<em> </em>model RFM dan <em>Decision Tree</em> yang diusulkan lebih unggul dibandingkan hanya dengan menggunakan model RFM saja. <em>Cluster</em> ke-1 memiliki 860 pelanggan menghasilkan loyalitas pelanggan sedang (biru), <em>cluster</em> ke-2 memiliki 69 pelanggan menghasilkan loyalitas pelanggan yang tinggi (hijau), dan <em>cluster</em> ke-3 memiliki 274 pelanggan menghasilkan loyalitas pelanggan yang rendah (merah). Model klasterisasi RFM dan klasifikasi <em>Decision Tree </em>telah menghasilkan atribut <em>outlet</em> yang berpengaruh terhadap nilai akurasi sebesar 67,54%.</p><p> </p><p><em><strong>Abstract</strong></em></p><p class="Judul2"> </p><p class="Judul2"><em>The development of office and school stationery business at this time, many promising, so many new suppliers have sprung up in the office and school stationery business. PT Solo, which has the office and school stationery business, must have a strategy in every business competition, especially in achieving customer loyalty. Customer loyalty is often influenced by factors in the number of transaction activities, transaction nominal value, transaction time at the company, and outlet attributes. This research proposes a Recency, Frequency, and Monetary (RFM) model combined with a Decision Tree. RFM model is used to process customer data clustering based on number of transactions, transaction nominal value, transaction time, and outlet attributes. Whereas Decision Tree can describe the level of customer loyalty. Transaction data in this study were conducted from 1 January to 31 December 2018 to the 1,203 customers and 18,087 transactions through purchase invoices. The results of this study indicate that the state-of-the-art in the proposed RFM and Decision Tree models is outperform compared to only using the RFM model. Cluster 1 has 860 customers resulting in moderate customer loyalty (blue), Cluster 2 has 69 customers resulting in high customer loyalty (green), and Cluster 3 has 274 customers resulting in lower customer loyalty (red). RFM clustering model and Decision Tree classification have produced outlet attributes that affect the accuracy value of 67.54%.</em></p><p><em><strong><br /></strong></em></p>

scholarly journals Decision Tree Classification Model for Popularity Forecast of Chinese Colleges

2014 ◽  
Vol 2014 ◽  
pp. 1-7
Author(s):  
Xiangxiang Zeng ◽  
Sisi Yuan ◽  
You Li ◽  
Quan Zou
Keyword(s):  
Time Series ◽  
Decision Tree ◽  
Classification Model ◽  
Prospective Students ◽  
Final Model ◽  
Decision Tree Classification ◽  
Tree Construction ◽  
Tree Models ◽  
Educational Field ◽  
Existing Data

Prospective students generally select their preferred college on the basis of popularity. Thus, this study uses survey data to build decision tree models for forecasting the popularity of a number of Chinese colleges in each district. We first extract a feature called “popularity change ratio” from existing data and then use a simplified but efficient algorithm based on “gain ratio” for decision tree construction. The final model is evaluated using common evaluation methods. This research is the first of its type in the educational field and represents a novel use of decision tree models with time series attributes for forecasting the popularity of Chinese colleges. Experimental analyses demonstrated encouraging results, proving the practical viability of the approach.

Penerapan Metode Klasifikasi Decision Tree dan Algoritma C4.5 dalam Memprediksi Kriteria Nasabah Kredit Mega Auto Finance

2020 ◽  
Vol 7 (2) ◽  
pp. 200
Author(s):  
Puji Santoso ◽  
Rudy Setiawan
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Microsoft Excel ◽  
Customer Data ◽  
Data Mining Techniques ◽  
C4.5 Algorithm ◽  
Marketing Costs ◽  
Excel Format ◽  
Data Mining Application

One of the tasks in the field of marketing finance is to analyze customer data to find out which customers have the potential to do credit again. The method used to analyze customer data is by classifying all customers who have completed their credit installments into marketing targets, so this method causes high operational marketing costs. Therefore this research was conducted to help solve the above problems by designing a data mining application that serves to predict the criteria of credit customers with the potential to lend (credit) to Mega Auto Finance. The Mega Auto finance Fund Section located in Kotim Regency is a place chosen by researchers as a case study, assuming the Mega Auto finance Fund Section has experienced the same problems as described above. Data mining techniques that are applied to the application built is a classification while the classification method used is the Decision Tree (decision tree). While the algorithm used as a decision tree forming algorithm is the C4.5 Algorithm. The data processed in this study is the installment data of Mega Auto finance loan customers in July 2018 in Microsoft Excel format. The results of this study are an application that can facilitate the Mega Auto finance Funds Section in obtaining credit marketing targets in the future

A Practical Tutorial for Decision Tree Induction

2021 ◽  
Vol 54 (1) ◽  
pp. 1-38
Author(s):  
Víctor Adrián Sosa Hernández ◽  
Raúl Monroy ◽  
Miguel Angel Medina-Pérez ◽  
Octavio Loyola-González ◽  
Francisco Herrera
Keyword(s):  
Decision Tree ◽  
Decision Trees ◽  
Machine Learning Techniques ◽  
Evaluation Measures ◽  
Decision Tree Induction ◽  
Learning Techniques ◽  
Tree Models ◽  
Evaluation Measure ◽  
Main Components ◽  
Support Decision Making

Experts from different domains have resorted to machine learning techniques to produce explainable models that support decision-making. Among existing techniques, decision trees have been useful in many application domains for classification. Decision trees can make decisions in a language that is closer to that of the experts. Many researchers have attempted to create better decision tree models by improving the components of the induction algorithm. One of the main components that have been studied and improved is the evaluation measure for candidate splits. In this article, we introduce a tutorial that explains decision tree induction. Then, we present an experimental framework to assess the performance of 21 evaluation measures that produce different C4.5 variants considering 110 databases, two performance measures, and 10× 10-fold cross-validation. Furthermore, we compare and rank the evaluation measures by using a Bayesian statistical analysis. From our experimental results, we present the first two performance rankings in the literature of C4.5 variants. Moreover, we organize the evaluation measures into two groups according to their performance. Finally, we introduce meta-models that automatically determine the group of evaluation measures to produce a C4.5 variant for a new database and some further opportunities for decision tree models.

A DECISION TREE-BASED CLASSIFICATION APPROACH TO RULE EXTRACTION FOR SECURITY ANALYSIS

2006 ◽  
Vol 05 (01) ◽  
pp. 227-240 ◽  
Author(s):  
N. REN ◽  
M. ZARGHAM ◽  
S. RAHIMI
Keyword(s):  
Decision Tree ◽  
High Performance ◽  
Security Analysis ◽  
Predictive Performance ◽  
Classification Model ◽  
Stock Selection ◽  
Selection Rules ◽  
Stock Portfolios ◽  
Decision Tree Classification ◽  
C4.5 Decision Tree

Stock selection rules are extensively utilized as the guideline to construct high performance stock portfolios. However, the predictive performance of the rules developed by some economic experts in the past has decreased dramatically for the current stock market. In this paper, C4.5 decision tree classification method was adopted to construct a model for stock prediction based on the fundamental stock data, from which a set of stock selection rules was derived. The experimental results showed that the generated rules have exceptional predictive performance. Moreover, it also demonstrated that the C4.5 decision tree classification model can work efficiently on the high noise stock data domain.

Truck Parking Usage Patterns by Facility Amenity Availability

2020 ◽  
Vol 2674 (10) ◽  
pp. 749-763
Author(s):  
Sharif Mahmud ◽  
Taslima Akter ◽  
Sarah Hernandez
Keyword(s):  
Stated Preference ◽  
Critical Issue ◽  
Time Of Day ◽  
Geographic Region ◽  
Clustering Model ◽  
Global Positioning ◽  
Usage Patterns ◽  
Transportation Research ◽  
Parking Facilities ◽  
Cluster 2

Truck parking is currently ranked by the American Transportation Research Institute (ATRI) as the fifth most critical issue for the trucking industry and, more importantly, as the second most important issue for truck drivers. Part of the problem can be attributed to inadequate supply of parking and federal hours of service (HOS) regulations. Recent truck driver stated-preference surveys reveal that amenities including restrooms, fuel, and showers are important considerations while seeking available parking. A link between parking usage patterns and facility amenity bundles can guide transportation agency investments in relation to the design and type of parking facilities with high potential to mitigate overcrowding issues, and can be used for predictive modeling in real-time parking availability algorithms and information systems. This paper used historical, anonymous truck global positioning system (GPS) data to determine the extent to which hourly parking usage patterns, that is, average parking duration, percentage of parked trucks, and parking usage ratio, vary by amenity availability. A K-means clustering model grouped parking facilities by time of day parking usage patterns, season, and geographic region. Each cluster, represented by parking usage patterns, was then tied to unique amenity bundles. Three usage pattern clusters were identified: overnight usage with long parking durations ( Cluster 1), off-peak usage with long parking durations, ( Cluster 2), and off-peak usage with short parking durations ( Cluster 3). In general, overnight and longer duration parking was associated with facilities that had fewer amenities, notably without showers, while peak and off-peak hours and shorter duration parking was associated with full-service facilities.

Improved differentiation classification of variable precision artificial intelligence higher education management

2021 ◽  
pp. 1-10
Author(s):  
Chao Dong ◽  
Yan Guo
Keyword(s):  
Artificial Intelligence ◽  
Higher Education ◽  
Data Mining ◽  
Decision Tree ◽  
Classification Accuracy ◽  
Attribute Selection ◽  
Higher Education Management ◽  
Education Management ◽  
Decision Tree Classification

The wide application of artificial intelligence technology in various fields has accelerated the pace of people exploring the hidden information behind large amounts of data. People hope to use data mining methods to conduct effective research on higher education management, and decision tree classification algorithm as a data analysis method in data mining technology, high-precision classification accuracy, intuitive decision results, and high generalization ability make it become a more ideal method of higher education management. Aiming at the sensitivity of data processing and decision tree classification to noisy data, this paper proposes corresponding improvements, and proposes a variable precision rough set attribute selection standard based on scale function, which considers both the weighted approximation accuracy and attribute value of the attribute. The number improves the anti-interference ability of noise data, reduces the bias in attribute selection, and improves the classification accuracy. At the same time, the suppression factor threshold, support and confidence are introduced in the tree pre-pruning process, which simplifies the tree structure. The comparative experiments on standard data sets show that the improved algorithm proposed in this paper is better than other decision tree algorithms and can effectively realize the differentiated classification of higher education management.

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