scholarly journals Analisis Rekomendasi Calon Debitur Motor pada PT.XYZ menggunakan Algortima C 4.5

2020 ◽  
Vol 7 (4) ◽  
pp. 673
Author(s):  
Lilis Nurellisa ◽  
Devi Fitrianah
Keyword(s):  
Decision Making ◽  
Decision Tree ◽  
Supervised Learning ◽  
Data Driven ◽  
Classification Method ◽  
Service Company ◽  
C4.5 Algorithm ◽  
Application Data ◽  
Using Data ◽  
Existing Data

<p class="Abstrak">PT.XYZ merupakan perusahaan jasa pembiayaan atau <em>leasing</em> dengan berkonsentrasi kepada pembiayaan sepeda motor. Dalam bisnisnya PT.XYZ sering sekali dihadapkan oleh masalah kredit macet atau bahkan penipuan. Hal ini dikarenakan kesalahan dalam pemberian kredit kepada calon debitur yang tidak potensial. Jika tidak ditangani hal ini tentu saja akan berdampak buruk bagi perusahaan. Perusahaan mengalami penurunan kemampuan dalam membayar angsuran pinjaman ke perbankan bahkan dapat berdampak pada kebangkrutan. Dalam hal ini PT.XYZ perlu melalukan analisis untuk menentukan calon debitur yang potensial dengan menggunakan data driven method atau pendekatan berbasis kepada data. Yaitu pengambilan keputusan dengan melihat data pengajuan kredit yang pernah ada sebelumnya yang disebut juga sebagai <em>supervised learning</em>. Algoritma yang digunakan adalah algoritma C4.5 karena algoritma ini dapat mengklasifikasi data yang sudah ada sebelumnya. Dengan algoritma ini akan dihasilkan sebuah pohon keputusan yang akan membantu PT.XYZ dalam pengambilan keputusan. Dengan pengujian menggunakan 3587 sampel data pengajuan kredit dalam kurun waktu 1 tahun akurasi yang didapatkan ialah 97,96%. Dengan begitu hal ini menunjukkan bahwa metode klasifikasi menggunakan algoritma C4.4 berhasil diimplementasikan dengan baik. Hal ini diharapkan dapat membantu PT.XYZ dalam merekomendasikan calon debitur yang potensial.</p><p class="Abstrak"> </p><p class="Abstrak"><em><strong>Abstract</strong></em></p><p><em>PT. XYZ is a finance or leasing service company by concentrating on motorcycle financing. In its business, PT. XYZ is often faced with problems of bad credit or even fraud. This is due to an error in giving credit to potential debtors. If it is not handled this, of course, will have a bad impact on the company. Companies experiencing a decline in the ability to repay loan installments to banks can even have an impact on bankruptcy. In this case, PT. XYZ needs to do an analysis to determine potential debtors using data-driven methods or data-based approaches. That is decision making by looking at credit application data that has never been before, which is also called supervised learning. The algorithm used is the C4.5 algorithm because this algorithm can classify pre-existing data. With this algorithm, a decision tree will be produced that will help PT. XYZ in decision making. By testing using 3587 samples of credit filing data within a period of 1 year the accuracy obtained was 97.96%. That way this shows that the classification method using the C4.4 algorithm is successfully implemented properly. This is expected to help PT. XYZ in recommending potential debtors.</em></p><p class="Abstrak"><em><strong><br /></strong></em></p>

scholarly journals PREDICTION FOR COOPERATIVE CREDIT ELIGIBILITY USING DATA MINING CLASSIFICATION WITH C4.5 ALGORITHM

2021 ◽  
Vol 2 (2) ◽  
pp. 67-74
Author(s):  
Yogiek Indra Kurniawan ◽  
Annastalia Fatikasari ◽  
Muhammad Luthfi Hidayat ◽  
Mohamad Waluyo
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Classification Method ◽  
Test Results ◽  
Savings And Loans ◽  
Manual Method ◽  
Average Value ◽  
C4.5 Algorithm ◽  
History Of ◽  
Using Data

BMT Artha Mandiri is a cooperative that provides savings and loans services. In providing credit, BMT Artha Mandiri still uses the manual method, namely by looking at the ledger and history of each customer, to find out whether the applicant is worthy or not worthy of credit so that it is not effective and efficient. The purpose of this research is to make an application that can predict whether a prospective customer is eligible or not to be given credit. Predictions are made using the data mining classification method, namely the C4.5 algorithm based on the supporting data each customer has to classify which factors have the most influence on the level of credit payments in the cooperative. In a built application, the C4.5 algorithm produces a decision tree that is easy to interpret based on the existing variables. In the application, there are features that can be used to make decisions about customers who will apply for credit at the cooperative. The blackbox test results on the application show that the application has been able to run as expected, while the results of the algorithm test also show that the application has been able to implement the C4.5 algorithm correctly. In addition, the results of testing for accuracy show that the maximum average value of Accuracy is 79.19%.

scholarly journals Analysis of Decision Making Process in Moneyball: The Art of Winning an Unfair Game

The Winners ◽  
2015 ◽  
Vol 16 (1) ◽  
pp. 57
Author(s):  
Mochamad Sandy Triady ◽  
Ami Fitri Utami
Keyword(s):  
Decision Making ◽  
Excellent Result ◽  
Data Driven ◽  
Decision Making Process ◽  
Data Driven Decision Making ◽  
General Manager ◽  
Use Of Data ◽  
Bottom Position ◽  
Using Data ◽  
Baseball Team

Billy Beanes’s success in using data-driven decision making in baseball industry is wonderfully written by Michael Lewis in Moneyball. As a general manager in baseball team that were in the bottom position of the league from the financial side to acquire the players, Beane, along with his partner, explored the use of data in choosing the team’s player. They figured out how to determine the worth of every player.The process was not smooth, due to the condition of baseball industry that was not common with using advanced statistic in acquiring   players. Many teams still use the old paradigm that rely on experts’ judgments, intuition, or experience in decision making process. Moneyball approached that using data-driven decision making gave excellent result for Beane’s team. The team won 20 gamessequently in the 2002 season and also spent the lowest cost per win than other teams.This paper attempts to review the principles of Moneyball – The Art of Winning an Unfair Game as a process of decision making and gives what we can learn from the story in order to win the games, the unfair games.

Data Science Tools Application for Business Processes Modelling in Aviation

2019 ◽  
pp. 176-190
Author(s):  
Maryna Nehrey ◽  
Taras Hnot
Keyword(s):  
Decision Making ◽  
Logistic Regression ◽  
Unsupervised Learning ◽  
Decision Trees ◽  
Supervised Learning ◽  
Data Science ◽  
Business Processes ◽  
Business Decision ◽  
Logistic Regression Models ◽  
Using Data

Successful business involves making decisions under uncertainty using a lot of information. Modern modeling approaches based on data science algorithms are a necessity for the effective management of business processes in aviation. Data science involves principles, processes, and techniques for understanding business processes through the analysis of data. The main goal of this chapter is to improve decision making using data science algorithms. There are sets of frequently used algorithms described in the chapter: linear, logistic regression models, decision trees as a classical example of supervised learning, and k-means and hierarchical clustering as unsupervised learning. Application of data science algorithms gives an opportunity for deep analyses and understanding of business processes in aviation, gives structuring of problems, provides systematization of business processes. Business processes modeling, based on the data science algorithms, enables us to substantiate solutions and even automate the processes of business decision making.

Data Science Tools Application for Business Processes Modelling in Aviation

2021 ◽  
pp. 617-631
Author(s):  
Maryna Nehrey ◽  
Taras Hnot
Keyword(s):  
Decision Making ◽  
Logistic Regression ◽  
Unsupervised Learning ◽  
Decision Trees ◽  
Supervised Learning ◽  
Data Science ◽  
Business Processes ◽  
Business Decision ◽  
Logistic Regression Models ◽  
Using Data

Successful business involves making decisions under uncertainty using a lot of information. Modern modeling approaches based on data science algorithms are a necessity for the effective management of business processes in aviation. Data science involves principles, processes, and techniques for understanding business processes through the analysis of data. The main goal of this chapter is to improve decision making using data science algorithms. There are sets of frequently used algorithms described in the chapter: linear, logistic regression models, decision trees as a classical example of supervised learning, and k-means and hierarchical clustering as unsupervised learning. Application of data science algorithms gives an opportunity for deep analyses and understanding of business processes in aviation, gives structuring of problems, provides systematization of business processes. Business processes modeling, based on the data science algorithms, enables us to substantiate solutions and even automate the processes of business decision making.

scholarly journals The Rapid Adoption of Data-Driven Decision-Making

2016 ◽  
Vol 106 (5) ◽  
pp. 133-139 ◽  
Author(s):  
Erik Brynjolfsson ◽  
Kristina McElheran
Keyword(s):  
Decision Making ◽  
Companion Paper ◽  
Census Bureau ◽  
Data Driven ◽  
Data Driven Decision Making ◽  
Manufacturing Plants ◽  
Using Data ◽  
Large Representative ◽  
The U.S ◽  
Case Based

We provide a systematic empirical study of the diffusion and adoption patterns of data-driven decision making (DDD) in the U.S. Using data collected by the Census Bureau for a large representative sample of manufacturing plants, we find that DDD rates nearly tripled (11%-30%) between 2005 and 2010. This rapid diffusion, along with results from a companion paper, are consistent with case-based evidence that DDD tends to be productivity-enhancing. Yet certain plants are significantly more likely to adopt than others. Key correlates of adoption are size, presence of potential complements such as information technology and educated workers, and firm learning.

scholarly journals COVID-19 Risk Minimization Decision Making Strategy Using Data-Driven Model

2021 ◽  
Vol 3 (1) ◽  
pp. 57-66
Author(s):  
Akey Sungheetha
Keyword(s):  
Decision Making ◽  
Data Driven ◽  
Risk Minimization ◽  
Proposed Model ◽  
Epidemic Intelligence ◽  
Spread Dynamics ◽  
Using Data ◽  
The Impact ◽  
Global Strategies

In order to establish social resilient and sustainable cities during the pandemic outbreak, it is essential to forecast the epidemic trends and trace infection by means of data-driven solution addressing the requirements of local operational defense applications and global strategies. The smartphone based Digital Proximity Tracing Technology (DPTT) has obtained a great deal of interest with the ongoing COVID-19 pandemic in terms of mitigation, containing and monitoring with the population acceptance insights and effectiveness of the function. The DPTTs and Data-Driven Epidemic Intelligence Strategies (DDEIS) are compared in this paper to identify the shortcomings and propose a novel solution to overcome them. In terms of epidemic resurgence risk minimization, guaranteeing public health safety and quick return of cities to normalcy, a social as well as technological solution may be provided by incorporating the key features of DDEIS. The role of human behavior is taken into consideration while assessing its limitations and benefits for policy making as well as individual decision making. The epidemiological model of SEIR (Susceptible–Exposed–Infectious–Recovered) provides preliminary data for the preferences of users in a DPTT. The impact of the proposed model on the spread dynamics of Covid-19 is evaluated and the results are presented.

scholarly journals Data-driven decision-making in creating class rosters

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Rebecca Wolf ◽  
Joseph M. Reilly ◽  
Steven M. Ross
Keyword(s):  
Decision Making ◽  
Student Learning ◽  
Low Income ◽  
School Leaders ◽  
Data Driven ◽  
Data Driven Decision Making ◽  
Content Type ◽  
Use Of Data ◽  
Using Data ◽  
Rich Data

PurposeThis article informs school leaders and staffs about existing research findings on the use of data-driven decision-making in creating class rosters. Given that teachers are the most important school-based educational resource, decisions regarding the assignment of students to particular classes and teachers are highly impactful for student learning. Classroom compositions of peers can also influence student learning.Design/methodology/approachA literature review was conducted on the use of data-driven decision-making in the rostering process. The review addressed the merits of using various quantitative metrics in the rostering process.FindingsFindings revealed that, despite often being purposeful about rostering, school leaders and staffs have generally not engaged in data-driven decision-making in creating class rosters. Using data-driven rostering may have benefits, such as limiting the questionable practice of assigning the least effective teachers in the school to the youngest or lowest performing students. School leaders and staffs may also work to minimize negative peer effects due to concentrating low-achieving, low-income, or disruptive students in any one class. Any data-driven system used in rostering, however, would need to be adequately complex to account for multiple influences on student learning. Based on the research reviewed, quantitative data alone may not be sufficient for effective rostering decisions.Practical implicationsGiven the rich data available to school leaders and staffs, data-driven decision-making could inform rostering and contribute to more efficacious and equitable classroom assignments.Originality/valueThis article is the first to summarize relevant research across multiple bodies of literature on the opportunities for and challenges of using data-driven decision-making in creating class rosters.

Research Initiatives in Accounting Education: Research Relevance and Research Productivity

2020 ◽  
Vol 35 (4) ◽  
pp. 111-124
Author(s):  
Jared S. Moon ◽  
David A. Wood
Keyword(s):  
Decision Making ◽  
Research Productivity ◽  
Accounting Education ◽  
Data Driven ◽  
Faculty Research ◽  
Faculty Research Productivity ◽  
Research Findings ◽  
Using Data ◽  
Research Initiatives ◽  
Inform Decision Making

ABSTRACT Research in accounting education has evolved to include, among other areas, research relevance, faculty research productivity, and the use of journal lists. These topics offer new areas for research, including investigating the benefits and risks of relevant/irrelevant research, how effectively faculty research is evaluated, the potential consequences of using journal lists, and much more. Although these areas have significant and wide-ranging effects on faculty, much more empirical data are needed to inform decision making. This paper highlights these issues and makes suggestions for additional research to help the academy make better decisions by using data-driven research findings.

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