scholarly journals Analisa Kepuasan Pelanggan Menggunakan Klasifikasi Data Mining

2020 ◽  
Vol 2 (1) ◽  
pp. 41-48
Author(s):  
F Fauziah ◽  
Dedy Hartama ◽  
Irfan Sudahri Damanik
Keyword(s):  
Data Mining ◽  
Customer Satisfaction ◽  
Accuracy Rate ◽  
Level Of Satisfaction ◽  
C4.5 Algorithm ◽  
Data Source

The research objective was to obtain a model of rules in classifying the level of customer satisfaction at Indiis Cafe Pematangsiantar. By knowing the level of customer satisfaction, the owner of Indiis Cafe can improve the service if it is not good and further improve the service when the level of satisfaction is good. This study measures the level of customer satisfaction at Indiis Cafe Pematangsiantar. The method used in this study is the C4.5 Algorithm, where the data source used is a questionnaire / questionnaire given to customers at Indiis Cafe Pematangsiantar. The variables used include (1) Product, (2) Price, (3) Place (4) Promotion and (5) Service. The results of the study obtained 15 rules for the classification of customer satisfaction levels with 8 satisfied status rules and 7 unsatisfied rules. The C4.5 algorithm can be used in the case of customer satisfaction levels with an accuracy rate of 98.00%. From the results of the analysis is expected to be applied so that it can be used as a decision to improve service to customers.

scholarly journals Metode Data Mining Klasifikasi Pada Kualitas Pelayanan Terhadap Nasabah Bank Syariah Mandiri dengan Model C4.5

2021 ◽  
Vol 6 (1) ◽  
pp. 150
Author(s):  
Ari Supriadi ◽  
Poningsih P ◽  
Hendry Qurniawan
Keyword(s):  
Data Mining ◽  
Customer Satisfaction ◽  
Survival Data ◽  
Mining Method ◽  
Economic Activities ◽  
Banking Services ◽  
New Knowledge ◽  
C4.5 Algorithm ◽  
Data Source

Customer satisfaction is the most important thing in assessing the level of management and services provided by the bank to its customers. The existence of banking services in society is indeed more profitable, especially in the economic sector, where economic actors are more free to carry out the process of economic activities to support survival. Data mining is an analysis of observations of large amounts of data to find relationships that are not known beforehand, data processed by the data mining method will produce a new knowledge sourced from old data, the results of processing can be used to determine future decisions. Using the C4.5 algorithm will predict which aspects are more dominant towards customer satisfaction. The data source of this research was collected based on a questionnaire (questionnaire) filled out by customers of Bank Syariah Mandiri in Pematangsiantar City. Data will be processed by calculating the value of entropy, calculating the gain value. So that the final results obtained in the form of a decision tree are expected to be input to the Bank Syariah Mandiri in Pematangsiantar City in maintaining the quality of its services to customers and improving the quality so that customers are always satisfied with the services provided

scholarly journals Implementation of C4.5 Algorithm for Critical Land Prediction in Agricultural Cultivation Areas in Pemali Jratun Watershed

2019 ◽  
Vol 2 (2) ◽  
pp. 67
Author(s):  
Deden Istiawan ◽  
Laelatul Khikmah
Keyword(s):  
Complex System ◽  
Land Use ◽  
Policy Making ◽  
Critical Level ◽  
Production Functions ◽  
Accuracy Rate ◽  
Physical Systems ◽  
C4.5 Algorithm ◽  
The Impact

Watershed is a complex system that is built on physical systems, biological systems and human systems that are related to each other. Each component has a distinctive nature and its existence is related to other components so as to form a unified ecosystem. Land use that does not pay attention to the conservation requirements of land and water causes land degradation which ultimately results in critical land. The impact of critical land is not only the withdrawal of soil properties, but also results in a decrease in production functions. Prediction of the critical level of land is needed to reduce the level of damage to the watershed, so that it can be used for policy making by the relevant agencies. In this research C4.5 algorithm will be applied to predictions of critical land in agricultural cultivation areas using critical land parameters. Based on the results of the research on critical land classification of agricultural cultivation areas in the jratun pemali watershed it can be concluded that the C.45 algorithm can be implemented to predict critical land in agricultural cultivation areas with an accuracy rate of 92.47%.

scholarly journals Penerapan Algoritma C5.0 Untuk Prediksi Kelulusan Pembelajaran Mahasiswa Pada Matakuliah Arsitektur Sistem Komputer

2021 ◽  
Vol 5 (3) ◽  
pp. 1166
Author(s):  
Muchamad Sobri Sungkar ◽  
M Taufik Qurohman
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Extraction Process ◽  
Study Program ◽  
Data Set ◽  
C4.5 Algorithm ◽  
Previous Algorithm ◽  
Process Prediction ◽  
Computer System Architecture

Computer system architecture is one of the subjects that must be taken in the informatics engineering study program. In the study program the graduation of each student in the course is one of the important aspects that must be evaluated every semester. Graduation for each student / I in the course is an illustration that the learning process delivered is going well and also the material presented by the lecturer in charge of the course can be digested by students. Graduation of each student in the course can be predicted based on the habit pattern of the students. Data mining is an alternative process that can be done to find out habit patterns based on the data that has been collected. Data mining itself is an extraction process on a collection of data that produces valuable information for companies, agencies or organizations that can be used in the decision-making process. Prediction of graduation with data mining can be solved by classifying the data set. The C5.0 algorithm is an improvement algorithm from the C4.5 algorithm where the process is almost the same, only the C5.0 algorithm has advantages over the previous algorithm. The results of the C5.0 algorithm are in the form of a decision tree or a rule that is formed based on the entropy or gain value. The prediction process is carried out based on the classification of the C5.0 algorithm by using the attributes of Attendance Value, Assignment Value, UTS Value and UAS Value. The final result of the C5.0 algorithm classification process is a decision tree with rules in it. The performance of the C5.0 algorithm gets a high accuracy rate of 93.33%

scholarly journals Penerapan Algoritma C4.5 pada Penentuan Tingkat Pemahaman Mahasiswa Terhadap Matakuliah

2019 ◽  
Vol 1 ◽  
pp. 307
Author(s):  
Nurul Rofiqo ◽  
Agus Perdana Windarto ◽  
Eka Irawan
Keyword(s):  
Information System ◽  
Computer Science ◽  
Decision Trees ◽  
Private Universities ◽  
Accuracy Rate ◽  
Lecture Material ◽  
C4.5 Algorithm ◽  
Data Source ◽  
Level Of Understanding ◽  
North Sumatra

This study aims to classify the level of understanding of students at STIKOM Tunas Bangsa Pematangsiantar. STIKOM Tunas Bangsa is one of the private universities in North Sumatra that is engaged in the field of computer science. In carrying out lecture activities, students are required to understand each lecture material provided by the lecturer. There are several things that can affect the level of understanding of students in receiving lecture material. The data source was obtained from the results of the fifth semester and seven student questionnaires in the STIKOM Tunas Bangsa Information System department. Attributes used are as many as five, namely communication, learning atmosphere, learning media, appearance and how to teach. The method used in the research is C4.5 Algorithm and assisted by RapidMiner software to make decision trees. From the results of the study, there were 14 rules for classification in determining the level of understanding with 9 rules, the best status and 5 rules did not understand. C4.5 algorithm can be used in the case of determining the level of understanding of students at STIKOM Tunas Bangsa with an accuracy rate of 87.10%. With this analysis it is expected to be a motivation for students to be able to understand the course well.

scholarly journals Identification of Poison using C4.5 Algorithm

2020 ◽  
pp. 218-222
Author(s):  
Lai Lai Yee ◽  
Myo Ma Ma
Keyword(s):  
Data Mining ◽  
Test Data ◽  
Knowledge Worker ◽  
Training Data ◽  
Independent Data ◽  
Classification Rules ◽  
Natural Evolution ◽  
C4.5 Algorithm ◽  
Other Information

Data mining is the task of discovering interesting patterns from large amounts of data where the data can be stored in databases, data warehouses or other information repositories. This can be viewed as a result of the natural evolution of information technology. The key point is that data mining is the application of these and other AI and statistical techniques to common business problems in a fashion that makes these techniques available to the skilled knowledge worker as well as the trained statistics professional. This paper is classification system for Toxicology using C4.5. Firstly, the input data are randomly partitioned into two independent data, a training data and a test data. And then two third of the data are allocated to the training data and the remaining one third is allocated to the test data. Final step is C4.5 Algorithm Process, the training data is used to derive C4.5 algorithm. Classification Process, test data are used to estimate the accuracy of the classification rules. If the accuracy is considered acceptable the rules can be applied to the classification of new data.

scholarly journals Categorization of Data Mining Systems and Data Secrecy Towards Wireless Sensor Networks

2019 ◽  
pp. 818-826
Author(s):  
Shoban Babu Sriramoju
Keyword(s):  
Data Mining ◽  
Wireless Sensor Networks ◽  
Source Monitoring ◽  
Wireless Sensor ◽  
Monitoring Systems ◽  
Efficient Access ◽  
Database Administration ◽  
Data Source ◽  
Large Corpus

The reliable database administration systems have actually been very vital assets for monitoring of a large corpus of data as well as particularly for reliable as well as efficient access of particular information from a huge collection whenever required. The expansion of data source monitoring systems has actually also contributed to recent huge gathering of all kind of info. This paper deals with the classification of data mining systems and also issues available in Data Mining.

scholarly journals Feature Selection in Classification of Blood Sugar Disease Using Particle Swarm Optimization (PSO) on C4.5 Algorithm

2020 ◽  
Vol 4 (3) ◽  
pp. 569-575
Author(s):  
Dwi Meylitasari Tarigan ◽  
Dian Palupi Rini ◽  
Samsuryadi
Keyword(s):  
Data Mining ◽  
Particle Swarm Optimization ◽  
Blood Sugar ◽  
Blood Sugar Level ◽  
Public Awareness ◽  
Particle Swarm ◽  
Data Mining Technique ◽  
Swarm Optimization ◽  
C4.5 Algorithm

Diabetes Mellitus (DM) is a disease caused by blood sugar level increased were higher than the maximum limit. Food consumed tends to contain uncontrolled sugar which could cause the drastic increase of blood sugar level. It is necessary to efforts, to increasing the public awareness to controlling blood sugar and the risks of increasing blood sugar level so as to determine of preventive and early detection measures One of used of data mining technique is information technology in the health sector which used a lot as a decision maker to predicting and diagnosing a several disease.  This research aims to optimizing the features on classification of the data mining with the C4.5 algorithm using Particle Swarm Optimization (PSO) to detect the blood sugar level in patient. The dataset used is the effect of physical activity to the Blood Sugar Level at H. Abdul Manan Simatupang Kisaran Regional Public Hospital.  The amount of dataset used is 42 record with 10 attributes.  The result of this research obtained that the Particle Swarm Optimization (PSO) may increasing the accuracy performance of C4.5 from 86% to 95%.  Whereas the evaluation result of the AUC Value increasing from 0,917 to 0,950. From those 10 attributes which are then selection with using PSO into 7 attributes used to determine the prediction of sugar level.  Therefore the Algorithm C4.5 using the Particle Swarm Optimization (PSO) may provide the best solution to the accuracy of detection blood sugar levels.

scholarly journals Penerapan Data Mining Klasifikasi Tingkat Pemahaman Siswa Pada Pelajaran Matematika

2021 ◽  
Vol 5 (1) ◽  
pp. 9
Author(s):  
Tri Novika ◽  
Poningsih Poningsih ◽  
Harly Okprana ◽  
Agus Perdana Windarto ◽  
Hasudungan Siahaan
Keyword(s):  
Junior High School ◽  
Eighth Graders ◽  
Student Comprehension ◽  
New Information ◽  
Student Interests ◽  
C4.5 Algorithm ◽  
Data Source ◽  
Status Classification ◽  
Math Problems

The purpose of the research is to classify the concept of understanding students in Mathematics lessons. In the learning process teaching students understanding learning materials is very important. The attainment of student understanding is a function of the being of an educator. Many formulas and concepts to understand make it difficult for students to solve math problems. The data source was obtained from the results of a math comprehension questionnaire of eighth graders at Tamansiswa Tapian Dolok Private Junior High School. The classification method used is the C4.5 Algorithm and assisted with RapidMiner software. Attributes used are student interests, how students learn, student motivation, how to teach teachers, learning media, and infrastructure facilities. The results of the calculation of entropy values and attribute gains obtained 15 rules of mathematical comprehension decisions with 9 rules of understanding status and 6 rules of inconsistency status. Classification modeling with C4.5 Algorithm on RapidMiner obtained 96.00% accuracy Classification with C4.5 Algorithm can be applied and provide new information about the classification of student comprehension concepts in math lessons

scholarly journals PENENTUAN TALENTA KARYAWAN BERDASARKAN MENGGUNAKAN KONSEP DATA MINING

2016 ◽  
Vol 2 (1) ◽  
pp. 34
Author(s):  
Ni G.A.P. Harry Saptarini
Keyword(s):  
Data Mining ◽  
Human Resources ◽  
Decision Maker ◽  
Conventional Method ◽  
Input Data ◽  
Linguistic Terms ◽  
Linguistic Term ◽  
C4.5 Algorithm ◽  
The Right

The human resources competency is one aspect that most affect company performances. Knowing its competence will help the decision maker to place the right man on the right place. However it can be done by analyzing the human talent. One of the conventional method that common to use is C4.5 algorithm. Conventional C4.5 method uses data crisp. In this study, it used a linguistic term as an input data because the talent test expressed using the language (linguistic term) and it is a set of data in fuzzy form. To generate the input data in the form of fuzzy was done by fuzzify the data preprocessing and further preprocessing of data results will be used for the construction of decision trees using C4.5 algorithm is then the process is called fuzzy C4.5. The result of this research is that the number of linguistic terms of attribute effect directly and significantly to system accuracy. The accuracy of Fuzzy C4.5 algorithm with 5 linguistic terms (90.1099%) is less than Fuzzy C4.5 accuracy with 3 linguistic terms (96.7033%). Fuzzy C4.5 accuracy with 3 linguistic terms has the same accuracy with conventional C4.5 (96.7033%), so we can use as an alternative solution to build classification of employee talent in PNB.

Customer Satisfaction towards Financial Inclusion: A Case Study of Rural Cooperative Banks in Belgaum District

2016 ◽  
Vol 3 (1) ◽  
Author(s):  
Praveen A. Korbu
Keyword(s):  
Customer Satisfaction ◽  
Rural Areas ◽  
Financial Inclusion ◽  
Bank Account ◽  
Banking Services ◽  
Level Of Satisfaction ◽  
Gender Education ◽  
Cooperative Banks

Co-operative banks are playing an important role in the achievement of the goal of financial inclusion. These banks with their extensive branch network and localized operational base, also engage in recreation of the development process, credit delivery and deposits mobilization in rural areas. Through their variety of services, they are reaching vulnerable sections of the society. In this paper, an attempt has been made to study the customer satisfaction towards financial inclusion by rural co-operative banks. The sample units constitute 100 cooperative banks (registered under the KCS Act, 1956) and 300 Customers from three regions (i.e. Belagavi, Chikkodi, Bailhongal headed by ARCS) of Belgaum District selected randomly. The study focuses on the classification of customers on the basis of age, gender, education, occupation, annual income and type of accounts they hold. This research finds reasons for not possessing a bank account, level of awareness of customers towards banking services, factors determining satisfaction of their bank, and the level of satisfaction of customers towards banking services. The study concludes with suitable suggestions to improve the customer satisfaction.

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