scholarly journals Klasifikasi Tingkat Kepuasan Mahasiswa Terhadap Pembelajaran Secara Daring Menggunakan Algoritma Naïve Bayes

2021 ◽  
Vol 6 (3) ◽  
pp. 161-170
Author(s):  
Ami Natuzzuhriyyah ◽  
Nisa Nafisah ◽  
Rini Mayasari
Keyword(s):  
Student Satisfaction ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Educational Institutions ◽  
Ministry Of Education ◽  
Learning Satisfaction ◽  
Naive Algorithm ◽  
Auc Value ◽  
The Republic ◽  
Bayes Algorithm

Since the spread of Covid-19 in Indonesia, in early March 2020, the activities of Educational Institutions have not been disrupted. As conventional learning. Learning at Singaperbangsa University began with regulation from the Ministry of Education and Culture of the Republic of Indonesia, from learning that boldly affects concentration, influences concentration, such as signals, learning atmosphere, and teaching methods, so that factors affect the level of student satisfaction in learning. This study aims to determine the level of student satisfaction with learning who dares to use the Bayes naive algorithm using RapidMiner tools with results obtained with an accuracy rate of 76.92%, class precision of 100.00%, class recall 57.14%, and an AUC value of 0.881 or close to, so the resulting model is good. In other words, the results obtained using the Naïve Bayes algorithm can be used as material for making decisions about the level of online learning satisfaction.

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 Penerapan Metode Naive Bayes dalam Memprediksi Kepuasan Mahasiswa Terhadap Cara Pengajaran Dosen

2019 ◽  
Vol 1 ◽  
pp. 287
Author(s):  
Desi Ratna Sari ◽  
Dedy Hartama ◽  
Irfan Sudahri Damanik ◽  
Anjar Wanto
Keyword(s):  
Higher Education ◽  
Student Satisfaction ◽  
Teaching Methods ◽  
Naive Bayes ◽  
Teaching Method ◽  
Higher Education Institutions ◽  
Naïve Bayes ◽  
Training Data ◽  
Testing Data ◽  
Bayes Algorithm

This research aims to classify in determining student satisfaction with teaching methods at STIKOM Tunas Bangsa. Data obtained from the results of the 2015 and 2016 semester student questionnaires were odd, with a sample of 80 students. Attributes used are 4, namely communication (C1), Building learning atmosphere (C2), Assessment of students (C3) and delivery of material (C4). The method used in this study is the Naïve Bayes Algorithm and is processed using RapidMiner studio 5.3 software to determine student satisfaction with teaching methods. Training data used 100 data while testing data used in manual calculations as much as 5 data. From the results of data testing the five data expressed satisfaction with the way teaching lecturers at STIKOM Tunas Bangsa. While the training data that is processed with RapidMiner has an accuracy of 92.00%. With this analysis, it is expected to be able to help higher education institutions to evaluate the performance of lecturers, especially in evaluating one of the three triharma colleges, namely the teaching method of lecturers.

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 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.

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 SENTIMENT ANALYSIS ON E-SPORTS FOR EDUCATION CURRICULUM USING NAIVE BAYES AND SUPPORT VECTOR MACHINE

2020 ◽  
Vol 13 (2) ◽  
pp. 109-122
Author(s):  
Rian Ardianto ◽  
Tri Rivanie ◽  
Yuris Alkhalifi ◽  
Fitra Septia Nugraha ◽  
Windu Gata
Keyword(s):  
Social Media ◽  
Support Vector Machine ◽  
Naive Bayes ◽  
Marketing Research ◽  
Naïve Bayes ◽  
Support Vector ◽  
The Public ◽  
Accuracy Difference ◽  
Auc Value ◽  
Bayes Algorithm

The development of e-sports education is not just playing games, but about start making, development, marketing, research and other forms education aimed at training skills and providing knowledge in fostering character. The opinions expressed by the public can take form support, criticism and input. Very large volume of comments need to be analyzed accurately in order separate positive and negative sentiments. This research was conducted to measure opinions or separate positive and negative sentiments towards e-sports education, so that valuable information can be sought from social media. Data used in this study was obtained by crawling on social media Twitter. This study uses a classification algorithm, Naïve Bayes and Support Vector Machine. Comparison two algorithms produces predictions obtained that the Naïve Bayes algorithm with SMOTE gets accuracy value 70.32%, and AUC value 0.954. While Support Vector Machine with SMOTE gets accuracy value 66.92% and AUC value 0.832. From these results can be concluded that Naïve Bayes algorithm has a higher accuracy compared to Support Vector Machine algorithm, it can be seen that the accuracy difference between naïve Bayes and the vector machine support is 3.4%. Naïve Bayes algorithm can thus better predict the achievement of e-sports for students' learning curriculum.

Algorithm Comparation of Naive Bayes and Support Vector Machine based on Particle Swarm Optimization in Sentiment Analysis of Freight Forwarding Services

2020 ◽  
Vol 4 (2) ◽  
pp. 362-369
Author(s):  
Sharazita Dyah Anggita ◽  
Ikmah
Keyword(s):  
Support Vector Machine ◽  
Sentiment Analysis ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Support Vector ◽  
The Public ◽  
Svm Algorithm ◽  
Bayes Algorithm ◽  
Freight Forwarding ◽  
Improved Accuracy

The needs of the community for freight forwarding are now starting to increase with the marketplace. User opinion about freight forwarding services is currently carried out by the public through many things one of them is social media Twitter. By sentiment analysis, the tendency of an opinion will be able to be seen whether it has a positive or negative tendency. The methods that can be applied to sentiment analysis are the Naive Bayes Algorithm and Support Vector Machine (SVM). This research will implement the two algorithms that are optimized using the PSO algorithms in sentiment analysis. Testing will be done by setting parameters on the PSO in each classifier algorithm. The results of the research that have been done can produce an increase in the accreditation of 15.11% on the optimization of the PSO-based Naive Bayes algorithm. Improved accuracy on the PSO-based SVM algorithm worth 1.74% in the sigmoid kernel.

Analysis of Sentiment of Moving a National Capital with Feature Selection Naive Bayes Algorithm and Support Vector Machine

2020 ◽  
Vol 4 (3) ◽  
pp. 504-512
Author(s):  
Faried Zamachsari ◽  
Gabriel Vangeran Saragih ◽  
Susafa'ati ◽  
Windu Gata
Keyword(s):  
Social Media ◽  
Support Vector Machine ◽  
Feature Selection ◽  
Public Opinion ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Capital City ◽  
Support Vector ◽  
National Capital ◽  
Bayes Algorithm

The decision to move Indonesia's capital city to East Kalimantan received mixed responses on social media. When the poverty rate is still high and the country's finances are difficult to be a factor in disapproval of the relocation of the national capital. Twitter as one of the popular social media, is used by the public to express these opinions. How is the tendency of community responses related to the move of the National Capital and how to do public opinion sentiment analysis related to the move of the National Capital with Feature Selection Naive Bayes Algorithm and Support Vector Machine to get the highest accuracy value is the goal in this study. Sentiment analysis data will take from public opinion using Indonesian from Twitter social media tweets in a crawling manner. Search words used are #IbuKotaBaru and #PindahIbuKota. The stages of the research consisted of collecting data through social media Twitter, polarity, preprocessing consisting of the process of transform case, cleansing, tokenizing, filtering and stemming. The use of feature selection to increase the accuracy value will then enter the ratio that has been determined to be used by data testing and training. The next step is the comparison between the Support Vector Machine and Naive Bayes methods to determine which method is more accurate. In the data period above it was found 24.26% positive sentiment 75.74% negative sentiment related to the move of a new capital city. Accuracy results using Rapid Miner software, the best accuracy value of Naive Bayes with Feature Selection is at a ratio of 9:1 with an accuracy of 88.24% while the best accuracy results Support Vector Machine with Feature Selection is at a ratio of 5:5 with an accuracy of 78.77%.

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