scholarly journals Probabilitas Pembayaran Kredit Tepat Waktu Mengggunakan Algoritma Naïve Bayes Pada Koperasi Serba Usaha Daruzzakah Rensing Lombok Timur

2021 ◽  
Vol 4 (2) ◽  
pp. 194-204
Author(s):  
Nurhidayati Nurhidayati ◽  
◽  
Suhartini Suhartini ◽  
Keyword(s):  
Naive Bayes ◽  
Naïve Bayes ◽  
Savings And Loans ◽  
Cooperative Principle ◽  
Alternative Means ◽  
Auc Value ◽  
Bayes Algorithm ◽  
Using Data ◽  
Number Of Customers ◽  
The Village

In general, the notion of a cooperative is a business entity that is owned and managed by its members. Meanwhile, multi-business cooperatives are cooperatives whose business activities are in various economic aspects such as savings and loans, production, consumption and services, which consist of people or cooperative legal entities by basing their business activities on the cooperative principle as well as a people's economic movement based on the principle of kinship. This research took place in one of the cooperatives in the village of Rensing, East Lombok, with the cooperative name "Daruzzakah". This cooperative is a multi-business cooperative with one type of activity is to provide savings and loans or credit to its members. The purpose of this cooperative is as an alternative means of borrowing money or credit as well as trying to prevent its members from loan sharks. However, in practice there are problems, namely the number of delays and credit payments that are not on time. Judging from the large number of customers who borrow funds, a strategy is needed to be able to fulfill all of these activities, the increasing number of prospective customers applying for credit with different economic conditions, requiring accuracy in making credit decisions. To avoid this, it should be necessary to analyze member data to determine the feasibility of providing credit, so that it can be classified as whether or not to get a loan. Data analysis can be done using data mining techniques. For this reason, the authors try to provide solutions to these problems by applying the naïve Bayes algorithm in predicting and determining creditworthiness. The Naive Bayes algorithm has been widely used by previous researchers and has high accuracy values. In this study, the Naive Bayes algorithm was used and resulted in an accuracy value of 96.45% with an AUC value of 0.942 which means it is a good classification.

scholarly journals Penentuan Penilaian Kredit Menggunakan Metode Naive Bayes Berbasis Particle Swarm Optimization

2017 ◽  
Vol 1 (1) ◽  
pp. 48
Author(s):  
Rinawati Rinawati
Keyword(s):  
Particle Swarm Optimization ◽  
Naive Bayes ◽  
Credit Ratings ◽  
Particle Swarm ◽  
Naïve Bayes ◽  
Swarm Optimization ◽  
Data Mining Algorithms ◽  
Auc Value ◽  
Bayes Algorithm ◽  
Selection Of

Bad credit is one of the credit risk faced by the financial and banking industry. Bad credit can be avoided by means of an accurate credit analysis of the debtor. The accuracy of credit ratings is crucial to the profitability of financial institutions. Improved accuracy of credit ratings can be done by doing the selection of attributes, because the selection of attributes reduce the dimensionality of the data so that operation of the data mining algorithms can be run more effectively and more cepat.Banyak research has been conducted to determine credit ratings. One of the methods most widely used method of Naive Bayes. In this study will be used method Naive Bayes and will do the selection of attributes by using particle swarm optimization to determine credit ratings. After testing the results obtained are Naive Bayes produce accuracy value of 72.40% and AUC value of 0.765. Then be optimized by using particle swarm optimization results show values higher accuracy is equal to 75.90% and AUC value of 0.773. So as to achieve the increased accuracy of 3.5%, and increased the AUC of 0.008. By looking at the accuracy and AUC values, the Naive Bayes algorithm based on particle swarm optimization into the classification category enough.

scholarly journals Application of the Naïve Bayes Algorithm for Student Graduation Analysis

2018 ◽  
Vol 7 (4.15) ◽  
pp. 421
Author(s):  
Erick Akhmad Fahmi Alfa’izy ◽  
Khairil Anam ◽  
Naidah Naing ◽  
Rosanita Tritias Utami ◽  
Nur Anim Jauhariyah ◽  
...  
Keyword(s):  
Naive Bayes ◽  
Naïve Bayes ◽  
Training Data ◽  
College System ◽  
Student Graduation ◽  
Bayes Algorithm ◽  
Using Data ◽  
Analysis System ◽  
Law Student

Design an analysis system to find out graduation by comparing previous data and existing data to overcome errors in a college system. By taking data records that are already available to be processed using the naïve Bayes algorithm. This research was conducted at Universitas Maarif Hasyim Latif. In this case, the object of research is to analyze the data of students with naïve Bayes algorithms to find out their graduation. For sampling the data taken is the previous Faculty of Law Student data to be used as training data, to retrieve the entire data using data records that are already available in the Directorate of Information Systems. That the naïve Bayes algorithm can be used in the classification of data in the form of a string or textual. This is based on researchers' trials in taking examples of calculations that have been done before. To compare the results of the classification of graduation analysis using the naïve Bayes algorithm testing is done with a sample of data in the form of training data compared to data testing. From the calculations that have been made, the accuracy is 77.78%. 

scholarly journals ALGORITMA KLASIFIKASI NAIVE BAYES DAN SUPPORT VECTOR MACHINE DALAM LAYANAN KOMPLAIN MAHASISWA

2020 ◽  
Vol 5 (2) ◽  
pp. 211-220 ◽  
Author(s):  
Hermanto Hermanto ◽  
Ali Mustopa ◽  
Antonius Yadi Kuntoro
Keyword(s):  
Support Vector Machine ◽  
Text Mining ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Support Vector ◽  
Machine Method ◽  
The Status ◽  
Auc Value ◽  
Bayes Algorithm

Service in the world of education is an important element for the creation of an academic atmosphere that is conducive to the implementation of a successful teaching and learning process. The process of service to students there is a tendency to be implemented not following the minimum service standards that must be provided to students so that students tend to complain about the services provided. Submission of criticism, complaints, input, or suggestions for dissatisfaction and problems that exist in the university environment is still very limited. Complaints can be constructive if submitted to the right place and party. In this research the data processing of email complaints from students conducted at the academic student body (students.bsi.ac.id). Student complaint data that will be processed is data in the form of * .xls complaint file. Before text data is analyzed using text mining methods, the pre-processing text needs to be done including tokenizing, case folding, stopwords, and stemming. After pre-processing, the classification method is then performed in classifying each complaint category and dividing the status into two parts, namely complaint and not complaint so that the status becomes a normal condition in text mining research. The purpose of this study is to obtain the most accurate algorithm in the classification of student complaints and can find out the results of the classification of the Naïve Bayes algorithm method and Support vector Machine used and compared. In this study, the results of testing by measuring the performance of these two algorithms using Cross-Validation, Confusion Matrix, and ROC Curves. The obtained Support vector Machine algorithm has the highest accuracy value compared to Naïve Bayes. AUC value = 0.922. for the Support vector machine method using the student academic data collection dataset (students.bsi.ac.id) has 84.45%, from the Naïve Bayes algorithm has an accuracy rate of about 69.75% and AUC value = 0.679.

scholarly journals ANALISIS KINERJA ALGORITMA C4.5 DAN NAÏVE BAYES UNTUK MEMPREDIKSI PRESTASI SISWA SEKOLAH MENENGAH KEJURUAN

2019 ◽  
Vol 5 (1) ◽  
pp. 23-28
Author(s):  
Astrid Noviriandini ◽  
Nurajijah Nurajijah
Keyword(s):  
Student Achievement ◽  
Decision Tree ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Tree Model ◽  
Learning Achievement ◽  
Achievement Prediction ◽  
C4.5 Decision Tree ◽  
Auc Value ◽  
Bayes Algorithm

This research informs students and teachers to anticipate early in following the learning period in order to get maximum learning outcomes. The method used is C4.5 decision tree algorithm and Naïve Bayes algorithm. The purpose of this study was to compare and evaluate the decision tree model C4.5 as the selected algorithm and Naïve Bayes to find out algorithms that have higher accuracy in predicting student achievement. Learning achievement can be measured by the value of report cards. After comparison of the two algorithms, the results of the learning achievement prediction are obtained. The results showed that the Naïve Bayes algorithm had an accuracy value of 95.67% and the AUC value of 0.999 was included in Excellent Clasification, for the C4.5 algorithm the accuracy value was 90.91% and the AUC value of 0.639 was included in the state of Poor Clasification. Thus the Naïve Bayes algorithm can better predict student achievement.

scholarly journals Perbandingan Akurasi, Recall, dan Presisi Klasifikasi pada Algoritma C4.5, Random Forest, SVM dan Naive Bayes

2021 ◽  
Vol 5 (2) ◽  
pp. 640
Author(s):  
Mulkan Azhari ◽  
Zakaria Situmorang ◽  
Rika Rosnelly
Keyword(s):  
Random Forest ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Training Data ◽  
Random Forest Algorithm ◽  
Svm Algorithm ◽  
Testing Data ◽  
C4.5 Algorithm ◽  
Bayes Algorithm ◽  
Using Data

In this study aims to compare the performance of several classification algorithms namely C4.5, Random Forest, SVM, and naive bayes. Research data in the form of JISC participant data amounting to 200 data. Training data amounted to 140 (70%) and testing data amounted to 60 (30%). Classification simulation using data mining tools in the form of rapidminer. The results showed that . In the C4.5 algorithm obtained accuracy of 86.67%. Random Forest algorithm obtained accuracy of 83.33%. In SVM algorithm obtained accuracy of 95%. Naive Bayes' algorithm obtained an accuracy of 86.67%. The highest algorithm accuracy is in SVM algorithm and the smallest is in random forest algorithm

PENERAPAN DATA MINING MENGGUNAKAN METODE TEKNIK CLASSIFICATION UNTUK MELIHAT POTENSI KEPATUHAN WAJIB PAJAK BUMI DAN BANGUNAN

2019 ◽  
Vol 20 (2) ◽  
pp. 157-168
Author(s):  
Qoriani Widayati
Keyword(s):  
Data Mining ◽  
Regional Development ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Regional Government ◽  
Data Mining Techniques ◽  
Know How ◽  
Bayes Algorithm ◽  
Using Data

The goverment implements development in Indonesia, requires substantial funds. The entry of cash from the Land and Building Tax is the most important part for the development of a region, with the results that have been obtained by the regional government can increase regional development with various infrastructures that help the community to carry out various activities and make the area more advanced. One type of tax is the Land and Building Tax (PBB). With the increasing number of taxpayers and data paying contributions directly into the treasury of state finances, the UPT BPPD of SU II Subdistrict of Palembang city did not know how many obedient and non-compliant taxpayers. In this study using data mining techniques, namely classification by applying the Naive Bayes algorithm and getting from the number of taxpayers as many as 1,647 taxpayers with an accuracy of 99.33% which has the potential to not be on time in 16 ulu villages at 0,437 and sub-district households with data of 0.229.

scholarly journals Prediksi Kelulusan Mahasiswa menggunakan Algoritma Naive Bayes (Studi Kasus 5 PTS di Banda Aceh)

2019 ◽  
Vol 3 (2) ◽  
pp. 59
Author(s):  
Munawir Munawir ◽  
Taufiq Iqbal
Keyword(s):  
Data Mining ◽  
Research Method ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Data Mining Algorithm ◽  
Standard Data ◽  
Industry Standard ◽  
Bayes Algorithm ◽  
Using Data ◽  
Banda Aceh

The e-questionnaire application that researchers built using CodeIgniter and React-Js This study aims to data mining by using rapidminer tools to collect student data from the Feeder application page from the class of 2010-2014 which is assumed that the student class has been declared graduated in 2018. The data was collected from 5 (five) Private Universities in the City Banda Aceh. then by observing the graduation level using data mining can bring a considerable contribution to educational institutions, in an effort to improve curriculum competency in Higher Education, it is expected that the results of data mining can make reference to curriculum standards as a form of graduate competency improvement. The research method uses the Cross-Industry Standard Process for Data Mining (CRISP-DM) which is used as a standard data mining process as well as a research method with stages starting from Business understanding, data understanding, data preparation, modeling, evaluation, and deployment. The results showed that the data mining algorithm for graduation prediction based on the selected pass accuracy attribute revealed that the prediction level was uniform with the algorithm used, Naïve Bayes, prediction accuracy was 84%. The data attributes that were found to have significantly influenced the classification process were the GPA and Study Length. The results obtained that students who graduated by 60% are students who are educated in ASM Nusantara and AMIK Indonesia, while in Banda Aceh STIES and Serambi University Mecca the prediction of graduation is 52%. Another thing is different from STIA Iskandar Thani where the prediction of graduating is only 48% and not passing on time is 52%. The results of this prediction can reveal and become a recommendation for prospective students or academics to increase the quantity of graduates and increase student confidence in tertiary institutions.Keywords:Prediction, Student Graduation, Naive Bayes Algorithm. 

IMPLEMENTASI DATA MINING UNTUK MEMPREDIKSI PEMESANAN DRIVER GO-JEK ONLINE DENGAN MENGGUNAKAN METODE NAIVE BAYES (STUDI KASUS: PT. GO-JEK INDONESIA)

2018 ◽  
Vol 2 (1) ◽  
Author(s):  
Delisman Laia ◽  
Efori Buulolo ◽  
Matias Julyus Fika Sirait
Keyword(s):  
Data Mining ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Training Data ◽  
Transportation Industry ◽  
Data Set ◽  
Data Mining Algorithms ◽  
Taxi Service ◽  
Bayes Algorithm ◽  
Using Data

PT. Go-Jek Indonesia is a service company. Go-jek online is a technology-based motorcycle taxi service that leads the transportation industry revolution. Predictions on ordering go-jek drivers using data mining algorithms are used to solve problems faced by the company PT. Go-Jek Indonesia to predict the level of ordering of online go-to drivers. In determining the crowded and lonely time. The proposed method is Naive Bayes. Naive Bayes algorithm aims to classify data in certain classes. The purpose of this study is to look at the prediction patterns of each of the attributes contained in the data set by using the naive algorithm and testing the training data on testing data to see whether the data pattern is good or not. what will be predicted is to collect the data of the previous driver ordering, which is based on the day, time for one month. The Naive Bayes algorithm is used to predict the ordering of online go-to-go drivers that will be experienced every day by seeing each order such as morning, afternoon and evening. The results of this study are to make it easier for the company to analyze the data of each go-jek driver booking in taking policies to ensure that both drivers and consumers or customers.Keywords: Go-jek Driver, Data Mining, Naive Bayes

scholarly journals Klasifikasi Opini Masyarakat Terhadap Jasa Ekspedisi JNE dengan Naïve Bayes

2018 ◽  
Vol 8 (1) ◽  
pp. 92
Author(s):  
Fithri Selva Jumeilah
Keyword(s):  
Naive Bayes ◽  
Probability Model ◽  
Confusion Matrix ◽  
Naïve Bayes ◽  
Service Users ◽  
Average Percentage ◽  
Online Sales ◽  
Bayes Algorithm ◽  
Using Data

The large number of online sales transactions has increased the number of service users. One of the companies engaged in the delivery service in Indonesia is Tiki Nugraha Ekakurir or more known JNE. Currently, JNE service users reach 14.000.000 per month. JNE has used many media communications with its customers one of them with Twitter. The number of followers of JNECare is 108,000 and the number of tweets is 375,000. The number of comments for people who can be used to see what they think of JNE is an inseparable comment is a negative, positive or neutral category. To simplify the grouping of comments, the data will be classified using the Naïve Bayes method present in Rstudio. The amount data used on the internet is 1725 tweets. The data will be divided into allegations of 70% data training as much as 1208 data and 30% data testing or as many as 517 data. Before the data is classified the previous data must go through the process of preprocessing that is changing all the letters into lowercase and other letters other than letters and spaces (case folding), tokenizing words, and the removal of the word common (stopword remove). After the data is cleared the data will be labeled manually one by one and new data can be used for the training process to get the probability model for each category. Probailitas obtained by using Naïve bayes algorithm. Models obtained from the training will be used using data testing. The categories obtained from the test will be used to process the data used by using the confusion matrix and will calculate the accuracy, precision and recall. From the results of the classification of JNE comments obtained that Naïve Bayes was able to classify the data well. This is evidenced by the average percentage accuracy of 85%, 78% precision and 67% recall.

scholarly journals Prediksi Calon Pendonor Darah Potensial Dengan Algoritma Naïve Bayes, K-Nearest Neighbors dan Decision Tree C4.5

2020 ◽  
Vol 7 (1) ◽  
pp. 7
Author(s):  
Hermanto Wahono ◽  
Dwiza Riana
Keyword(s):  
Decision Tree ◽  
Naive Bayes ◽  
Blood Donation ◽  
Nearest Neighbors ◽  
Naïve Bayes ◽  
K Nearest Neighbors ◽  
C4.5 Algorithm ◽  
Auc Value ◽  
Bayes Algorithm ◽  
Feasibility Test

Blood donation is a process of taking blood from donors that is declared feasible, in terms of various factors including age, weight, blood pressure, hemoglobin levels, and donor status which are taken into consideration during the feasibility test. This study was conducted to find the most appropriate method with high accuracy and Area Under Curve (AUC) values using 3710 blood donor datasets from the Bekasi City PMI, processed using the Naïve Bayes algorithm method, K-Nearest Neighbors and Decision Tree C4.5. The analysis shows that the Decision Tree C4.5 algorithm shows higher accuracy of 93.83% compared to Naïve Bayes algorithm which shows an accuracy value of 85.15% and the K-Nearest Neighbors algorithm with an accuracy value of 84.10%. In addition to these values, Decision Tree C4.5 is also visually superior where the Decision Tree has an output model tree that shows attribute relationships and has an AUC value of 0.978, Naïve Bayes with an AUC value of 0.927 and K-Nearest Neighbors with an AUC value of 0.816.

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