Application of Data Mining Technology in Financial Intervention Based on Data Fusion Information Entropy

Finance, as the core of the modern economy, supports sustained economic growth through financing and distribution. With the continuous development of the market economy, finance plays an increasingly important role in economic development. A new economic and financial phenomenon, known as financial intervention, has emerged in recent years, which has created a series of new problems, promoting the rapid increase both in credit and investment and causing many problems on normal operation of financial bodies. In the long run, it will inevitably affect the stability and soundness of the entire economic and financial system. In order to maximize the effect of financial intervention, in response to the above problems, this article uses a series of US practices in financial intervention as the survey content, combined with the loan data provided by the US government financial intervention department, and mines the data of the general C4.5 algorithm of the decision tree algorithm. Generate a decision tree and convert it into classification rules. Next, we will discover the laws hidden behind the loan data, further discover information that may violate relevant financial policies, provide a reliable basis for financial intervention, and improve the efficiency of financial intervention. Experiments show that the method used in this article can effectively solve the above problems and has certain practicability in fiscal intervention. With stratified sampling, the risky accuracy rate increased by 10%, probably because stratified sampling increased the number of high-risk samples.

DATA MINING ALGORITHM C4.5 CLASSIFICATION DETERMINATION CREDIT ELIGIBILITY FOR JAYA BERSAMA COOPERATIVES (KORJABE)

This study uses the C4.5 classification algorithm to determine creditworthness, clasification aims to divide the assigned object intoin a number of categories called classes. In this study, the authorusing data mining and C4.5 algorithm as the selection method. The criteria used are loan installments, prospective customer income, termloan time, status of prospective customers. This study resulted in a classification modeldecision tree using the C4.5 algorithm is included in the Excellent category Classification with an accuracy value of 98.33% and a classification error of 1.67%,so that this study uses 70% training data and 30% test data. From resultthe calculation obtained shows that the C4.5 algorithm can be usedto determine the feasibility of granting credit to Koperasi Jaya customers Together (KORJABE). Keywords: Analysis, Credit Eligibility, C4 Algorithm, Data Mining, Method Abstrak: Penelitian ini menggunakan metode Algoritma C4.5 klasifikasi untuk menentukan kelayakan kredit, klasifikasi bertujuan untuk membagi objek yang ditetapkan ke dalam satu nomor kategori yang disebut kelas. Dalam penelitian ini, penulis menggunankan data mining dan algoritma C4.5 sebagai metode pemilihannya. Kriteria yang digunakan yaitu , angsuran pinjaman,penghasilan calon nasabah,jangka waktu pinjaman ,status calon nasabah. Penelitian ini menghasillkan model klasifikasi pohon keputusan menggunakan algoritma C4.5 termasuk dalam kategori Excellent Classification dengan nilai akurasi sebesar 98,33% dan klasifikasi eror 1,67%, sehingga penelitian ini kan menggunakan data latih 70% dan data uji 30%. Dari hasil perhitungan yang diperoleh menunjukan bahwa algoritma C4.5 dapat digunakan untuk menen tukan kelayakan pemberian kredit kepada nasabah Koperasi Jaya Bersama (KORJABE). Kata kunci: Algoritma C4.5, Analisis, Data Mining, Kelayakan Kredit, Metode

Application of Data Mining in Determining Social Assistance Recipients With C4.5 Algorithm in Maligas Mountain District

The Gunung Maligas District Office is a government agency tasked with running a government program, namely the Social Assistance Receipt program, to run the social assistance program, many residents complain that they do not receive assistance, while some residents who are considered capable actually get assistance, where each aid program is have different criteria in determining the recipient. Due to the large number of existing aid programs with different criteria in determining the acceptance of the aid program, of course, local government staff will have difficulty in conducting the selection process. So we need a system that is able to help local government staff to more easily determine the recipients of the social assistance. Based on the historical data of beneficiaries, recommendations for the classification of beneficiaries can be made that will assist government staff. Classification can be done using the C4.5 algorithm. In this study, it has parameters, namely, occupation, income, housing conditions and number of dependents. By applying the C4.5 data mining algorithm, it is hoped that it will make it easier and faster for government staff to determine the recipients of social assistance at the Gunung Maligas District Office.

Analisis Perbandingan Akurasi Algoritma Naïve Bayes Dan C4.5 untuk Klasifikasi Diabetes

Diabetes is a metabolic disease in which blood sugar rises high. If blood sugar is not controlled properly, it can cause a variety of critical diseases, one of which is diabetes. The purpose of this study was to find out the results of comparing the performance values of Naïve Bayes and C4.5 algorithms with 7 different scenarios in the classification of diabetes that will be tested for accuracy, precision, and recall performance. The method used in this study is descriptive, and the source of skunder data obtained from the data of diabetic patients available on Kaggle with the format .csv issued by Ishan Dutta as many as 520 data and 17 fields. The tool used for data analysis is Rapidminer for the process of classification and performance testing of Naïve Bayes algorithm and C4.5 Algorithm. Our results showed that the C4.5 algorithm (scenario 4) had good results in the classification of diabetes compared to Naïve Bayes' algorithm (scenario 2) where the performance of the C4.5 algorithm had an accuracy of 99.03%, precision 100%, and recall 98.18%.

Construction and Application of Reservoir Flood Control Operation Rules Using the Decision Tree Algorithm

Current conventional and optimal reservoir flood control operation methods insufficiently utilize historical reservoir operation data, which include rainfall, runoff generation, and inflow from the watershed, as well as the operational experience of decision makers over many years. Therefore, this study proposed and evaluated a new method for extracting reservoir flood control operation rules from historical operation data using the C4.5 algorithm. Thus, in this paper, the C4.5 algorithm is first introduced; then, the generation of the flood control operation dataset, the construction of decision tree-based (DT-based) rules, and the subsequent design of a real-time operating scheme are detailed. A case study of the Rizhao Reservoir is then employed to demonstrate the feasibility and even superiority of the operating scheme formulated using DT-based rules. Compared with previously proposed conventional and optimal reservoir operation methods, the DT-based method has the advantages of strong and convenient adaptability, enabling decision makers to effectively guide real-time reservoir operation.

Optimization of Management Mode of Small- and Medium-Sized Enterprises Based on Decision Tree Model

Decision tree algorithm is a common classification algorithm in data mining technology, and its results are usually expressed in the form of if-then rules. The C4.5 algorithm is one of the decision tree algorithms, which has the advantages of easy to understand and high accuracy, and the concept of information gain rate is added compared with its predecessor ID3 algorithm. After theoretical analysis, C4.5 algorithm is chosen to analyze the performance appraisal results, and the decision tree for performance appraisal is generated by collecting data, data preprocessing, calculating information gain rate, determining splitting attributes, and postpruning. The system is developed in B/S architecture, and an R&D project management system and platform that can realize performance assessment analysis are built by means of visualization tools, decision tree algorithm, and dynamic web pages. The system includes information storage, task management, report generation, role authority control, information visualization, and other management information system functional modules. They can realize the project management functions such as project establishment and management, task flow, employee information filling and management, performance assessment system establishment, report generation of various dimensions, management cockpit construction. With decision tree algorithm as the core technology, the system obtains scientific and reliable project management information with high accuracy and realizes data visualization, which can assist enterprises to establish a good management system in the era of big data.

Research on Audit Data Analysis and Decision Tree Algorithm for Benefit Distribution of Enterprise Financing Alliance

When traditional methods analyze the audit data of enterprise financing alliance, there are some problems, such as long algorithm modeling time and low accuracy of interest distribution algorithm of enterprise financing alliance. Therefore, this paper proposes an analysis method of interest distribution of enterprise audit data financing alliance based on the decision tree algorithm. The audit data collection process of enterprise financing alliance is given, and the continuous attributes of audit data are discretized by the C4.5 algorithm. We perform enterprise financing alliance audit data analysis, remove inconsistencies from audit data through data cleaning, and finally realize enterprise financing alliance audit data analysis based on the improved C4.5 algorithm. The experimental results show that this method can shorten the modeling time and improve the accuracy of interest distribution algorithm of enterprise financing alliance. We achieved an average accuracy of 84.7% with the C4.5 algorithm while 84.35% with NBTree.

Penerapan Metode C4.5 Berbasis Particle Swarm Optimization Untuk Memprediksi Penjualan Obat Pada Apotek Bunda Azka

The application of the C4.5 Algorithm based on Particle Swarm Optimization to classify the level of sales of drugs that are often sold at the Bunda Azka Pharmacy, is a strategic thing to reduce the problems experienced by the pharmacy. Classify the level of sales of drugs sold using the C4.5 method. based on particle swarm optimization, to find out whether the C4.5 method based on particle swarm optimization (PSO) can optimize drug sales in the future. This research method uses a descriptive method, namely by conducting case study research by studying activities in the field, observing and interviewing stakeholders. in the initial step of this research is the determination of the attributes that will be processed into data mining with the help of rapidminer tools, this study the author uses the KDD model as a standardization in the data mining process. at the pharmacy. The data will later be processed using the c4.5 algorithm based on Particle Swarm Optimization to find the accuracy results of the prediction of the data. The data sample used is the number of 65 drug transaction records at the Bunda Azka Pharmacy. In the test results, the accuracy of Particle Swarm Optimization was 78.10%, for class recall drug sales was 72.50% and after using Particle Swarm Optimization increased to 78.33%, while precision had an accuracy of 77.92% and after using Particle Swarm Optimization increased to 80.33%. From the results of testing with Particle Swarm Optimization, there is an increase in accuracy of 7.15% from the research application of the C4.5 Method Based on Particle Swarm Optimization to Predict Drug Sales at Bunda Azka Pharmacy.

A Comparative Analysis of C4.5 Classification Algorithm, Naïve Bayes and Support Vector Machine Based on Particle Swarm Optimization (PSO) for Heart Disease Prediction

Heart disease is a general term for all of types of the disorders which is affects the heart. This research aims to compare several classification algorithms known as the C4.5 algorithm, Naïve Bayes, and Support Vector Machine. The algorithm is about to optimize of the heart disease predicting by applying Particle Swarm Optimization (PSO). Based on the test results, the accuracy value of the C4.5 algorithm is about 74.12% and Naïve Bayes algorithm accuracy value is about 85.26% and the last the Support Vector Machine algorithm is about 85.26%. From the three of algorithms above then continue to do an optimization by using Particle Swarm Optimization. The data is shown that Naïve Bayes algorithm with Particle Swarm Optimization has the highest value based on accuracy value of 86.30%, AUC of 0.895 and precision of 87.01%, while the highest recall value is Support Vector Machine algorithm with Particle Swarm Optimization of 96.00%. Based on the results of the research has been done, the algorithm is expected can be applied as an alternative for problem solving, especially in predicting of the heart disease.