scholarly journals Prediksi Tingkat Kepuasan dalam Pembelajaran Daring Menggunakan Algoritma Naïve Bayes

2021 ◽  
pp. 88-94
Author(s):  
Abdi Rahim Damanik ◽  
S Sumijan ◽  
Gunadi Widi Nurcahyo
Keyword(s):  
Online Education ◽  
Student Satisfaction ◽  
Communication Technology ◽  
Good Accuracy ◽  
Naive Bayes ◽  
Student Evaluation ◽  
Prediction Method ◽  
Naïve Bayes ◽  
Evaluation Module

The growth of learning at this time is influenced by advances in data and communication technology. One of the data technologies that functioned in the world of learning during the COVID-19 pandemic was online education. Online education is used as a liaison between lecturers and students in an internet network that can be accessed at any time. The online media used are Whatsapp, Google Classroom, Google Meet, Cloud x and the Zoom application. This research aims to predict the level of student satisfaction in online education as well as to distribute donations to large academies in making policies related to improving the quality of education online. The information used was obtained by distributing questionnaires to 110 students of the 2020/2021 class. The parameters in the questionnaire are lecturer communication, online education atmosphere, student evaluation, module delivery. Naïve Bayes is a prediction method for finding simple probabilities based on the Bayes theorem with a strong assumption of independence. Rapid Miner is one of the tools used for testing information and viewing the results of accuracy based on revolutionary information. The results of the test using 80 training information and 30 testing information show very good accuracy.

Klasifikasi Tahap Kematangan Pisang Ambon Berdasarkan Warna Menggunakan Naive Bayes

2018 ◽  
Vol 5 (2) ◽  
pp. 60-67 ◽  
Author(s):  
Dwi Yulianto ◽  
Retno Nugroho Whidhiasih ◽  
Maimunah Maimunah
Keyword(s):  
Naive Bayes ◽  
Fruit Production ◽  
Naïve Bayes ◽  
Primary Data ◽  
Banana Fruit ◽  
Bayes Method ◽  
Classification Image ◽  
Average Accuracy ◽  
The Government

ABSTRACT   Banana fruit is a commodity that contributes a great value to both national and international fruit production achievement. The government through the National Standardization Agency establishes standards to maintain the quality of bananas. The purpose of this Project is to classify the stages of maturity of Ambon banana base on the color index using Naïve Bayes method in accordance with the regulations of SNI 7422:2009. Naive Bayes is used as a method in the classification process by comparing the probability values generated from the variable value of each model to determine the stage of Ambon banana maturity. The data used is the primary data image of 105 pieces of Ambon banana. By using 3 models which consists of different variables obtained the same greatest average accuracy by using the 2nd model which has 9 variable values (r, g, b, v, * a, * b, entropy, energy, and homogeneity) and the 3rd model has 7 variable values (r, g, b, v , * a, entropy and homogeneity) that is 90.48%.   Keywords: banana maturity, classification, image processing     ABSTRAK   Buah pisang merupakan komoditas yang memberikan kontribusi besar terhadap angka produksi buah nasional maupun internasional. Pemerintah melalui Badan Standarisasi Nasional menetapkan standar untuk buah pisang, menjaga mutu  buah pisang. Tujuan dari penelitian ini adalah klasifikasi tahapan kematangan dari buah pisang ambon berdasarkan indeks warna menggunakan metode Naïve Bayes  sesuai dengan SNI 7422:2009. Naive bayes digunakan sebagai metode dalam proses pengklasifikasian dengan cara membandingkan nilai probabilitas yang dihasilkan dari nilai variabel penduga setiap model untuk menentukan tahap kematangan pisang ambon. Data yang digunakan adalah data primer citra pisang ambon sebanyak 105. Dengan menggunakan 3 buah model yang terdiri dari variabel penduga yang berbeda didapatkan akurasi rata-rata terbesar yang sama yaitu dengan menggunakan model ke-2 yang mempunyai 9 nilai variabel (r, g, b, v, *a, *b, entropi, energi, dan homogenitas) dan model ke-3 yang mempunyai 7 nilai variabel (r, g, b, v, *a, entropi dan homogenitas) yaitu sebesar 90.48%.   Kata Kunci : kematangan pisang,  klasifikasi, pengolahan citra

scholarly journals Voting Based Classification Method for Diabetes Prediction

2019 ◽  
Vol 8 (2S6) ◽  
pp. 913-918
Keyword(s):  
Support Vector Machine ◽  
Naive Bayes ◽  
Research Work ◽  
Prediction Method ◽  
Naïve Bayes ◽  
Support Vector ◽  
Decision Tree Classifier ◽  
Prediction Analysis ◽  
Tree Classifier ◽  
Diabetes Prediction

This research work is based on the diabetes prediction analysis. The prediction analysis technique has the three steps which are dataset input, feature extraction and classification. In this previous system, the Support Vector Machine and naïve bayes are applied for the diabetes prediction. In this research work, voting based method is applied for the diabetes prediction. The voting based method is the ensemble based which is applied for the diabetes prediction method. In the voting method, three classifiers are applied which are Support Vector Machine, naïve bayes and decision tree classifier. The existing and proposed methods are implemented in python and results in terms of accuracy, precision-recall and execution time. It is analyzed that voting based method give high performance as compared to other classifiers.

scholarly journals Quality Assessment of Online Learning in Regional Higher Education Systems

2021 ◽  
Vol 17 (3) ◽  
pp. 929-943
Author(s):  
Olga A. Gritsova ◽  
Elena V. Tissen
Keyword(s):  
Higher Education ◽  
Online Learning ◽  
Online Education ◽  
Quality Assessment ◽  
Student Satisfaction ◽  
Educational Process ◽  
Learning Mechanisms ◽  
Education Systems ◽  
Higher Education Systems

The quality of online learning mechanisms, widely implemented due to the COVID-19 pandemic, is a significant issue for regional higher education systems. The research aims to assess student satisfaction with the quality of online education by identifying discrepancies between their requirements and the actual learning process. In order to examine the gaps between students’ expectations and perceptions, a new approach was proposed based on the integrated use of Gap analysis and SERVQUAL methodology, combining qualitative and quantitative aspects. SERVQUAL questionnaires for measuring student satisfaction with online learning include the following criteria: tangibles, reliability, responsiveness, assurance, empathy. Full- and part-time undergraduates of humanitarian and socio-economic departments of two universities participated in the study. Ural Federal University bachelors, learning via Moodle and Microsoft Teams platforms, could directly communicate with their peers and professors, while students of National Research Nuclear University MEPhI were engaged in massive open online courses (MOOC). As a result, all five criteria were analysed in the proposed model for quality assessment of online learning to reveal the gaps between students’ expectations and perceptions of the educational process. Significant discrepancies in the «empathy» and «responsiveness» criteria in both groups demonstrate low student satisfaction with the quality of communication and individualisation of learning. The research findings can be used to construct resource allocation models for implementing educational programmes and developing support measures for regional higher education institutions.

scholarly journals Bearing Remaining Useful Life Prediction Based on Naive Bayes and Weibull Distributions

Entropy ◽  
2018 ◽  
Vol 20 (12) ◽  
pp. 944
Author(s):  
Nannan Zhang ◽  
Lifeng Wu ◽  
Zhonghua Wang ◽  
Yong Guan
Keyword(s):  
Naive Bayes ◽  
Prediction Method ◽  
Naïve Bayes ◽  
Remaining Useful Life ◽  
Statistical Characteristics ◽  
Mechanical Equipment ◽  
Weibull Distributions ◽  
Health States ◽  
Testing Stage ◽  
Useful Life

Bearing plays an important role in mechanical equipment, and its remaining useful life (RUL) prediction is an important research topic of mechanical equipment. To accurately predict the RUL of bearing, this paper proposes a data-driven RUL prediction method. First, the statistical method is used to extract the features of the signal, and the root mean square (RMS) is regarded as the main performance degradation index. Second, the correlation coefficient is used to select the statistical characteristics that have high correlation with the RMS. Then, In order to avoid the fluctuation of the statistical feature, the improved Weibull distributions (WD) algorithm is used to fit the fluctuation feature of bearing at different recession stages, which is used as input of Naive Bayes (NB) training stage. During the testing stage, the true fluctuation feature of the bearings are used as the input of NB. After the NB testing, five classes are obtained: health states and four states for bearing degradation. Finally, the exponential smoothing algorithm is used to smooth the five classes, and to predict the RUL of bearing. The experimental results show that the proposed method is effective for RUL prediction of bearing.

scholarly journals Word sense disambiguation to improve precision for ambiguous queries

2012 ◽  
Vol 2 (4) ◽  
Author(s):  
Adrian-Gabriel Chifu ◽  
Radu-Tudor Ionescu
Keyword(s):  
Information Retrieval ◽  
Ad Hoc ◽  
Naive Bayes ◽  
Word Sense Disambiguation ◽  
Naïve Bayes ◽  
Word Sense ◽  
Interdisciplinary Approaches ◽  
Sense Disambiguation ◽  
Ranking Technique

AbstractSuccess in Information Retrieval (IR) depends on many variables. Several interdisciplinary approaches try to improve the quality of the results obtained by an IR system. In this paper we propose a new way of using word sense disambiguation (WSD) in IR. The method we develop is based on Naïve Bayes classification and can be used both as a filtering and as a re-ranking technique. We show on the TREC ad-hoc collection that WSD is useful in the case of queries which are difficult due to sense ambiguity. Our interest regards improving the precision after 5, 10 and 30 retrieved documents (P@5, P@10, P@30), respectively, for such lowest precision queries.

scholarly journals Prediksi Kemenangan eSport DOTA 2 Berdasarkan Data Pertandingan

AVITEC ◽  
2020 ◽  
Vol 2 (1) ◽  
Author(s):  
Eduardus Hardika Sandy Atmaja
Keyword(s):  
Data Mining ◽  
Naive Bayes ◽  
Open Data ◽  
Prediction Method ◽  
Naïve Bayes ◽  
Professional Communities ◽  
Accurate Analysis ◽  
Great Demand ◽  
Analysis System ◽  
General Society

DOTA 2 is one of the eSports that are in great demand both by the general society and the game professional communities. They compete with each other to develop the best strategy to defeat all enemies they faced. In order to develop the best strategy, a good and accurate analysis system is needed. Data mining can be used to solve these problems by digging valuable information from dataset using certain method. Prediction method is one of the methods in data mining that is most appropriate for finding the winning predictions for the DOTA 2 game. One method that is quite simple and can be used is Naive Bayes. The results of this study indicate that Naive Bayes can make predictions well with an accuracy of 98,804 %. The data used in this research as much as 50000 that obtained from open data. It is expected that this research can assist players in providing information for developing game strategies.

scholarly journals Educational Data Mining for Predicting Student Graduation Using the Naïve Bayes Classifier Algorithm

2020 ◽  
Vol 4 (1) ◽  
pp. 95-101 ◽  
Author(s):  
Edi Sutoyo ◽  
Ahmad Almaarif
Keyword(s):  
Data Mining ◽  
Naive Bayes ◽  
Educational Data Mining ◽  
Naïve Bayes ◽  
Drop Out ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier ◽  
Student Graduation

The quality of students can be seen from the academic achievements, which are evidence of the efforts made by students. Student academic achievement is evaluated at the end of each semester to determine the learning outcomes that have been achieved. If a student cannot meet certain academic criteria that are stated by fulfilling the requirements to continue his studies, the student may have the potential to not graduate on time or even Drop Out (DO). The high number of students who do not graduate on time or DO in higher education institutions can be minimized by detecting students who are at risk in the early stages of education and is supported by making policies that can direct students to complete their education. Also, if the time for completion of student studies can be predicted then the handling of students will be more effective. One technique for making predictions that can be used is data mining techniques. Therefore, in this study, the Naive Bayes Classifier (NBC) algorithm will be used to predict student graduation at Telkom University. The dataset was obtained from the Information Systems Directorate (SISFO), Telkom University which contained 4000 instance data. The results of this study prove that NBC was successfully implemented to predict student graduation. Prediction of the graduation of these students is able to produce an accuracy of 73,725%, precision 0.742, recall 0.736 and F-measure of 0.735.

scholarly journals Line Bot Chat Filtering using Naïve Bayes Algorithm

2019 ◽  
Vol 8 (12) ◽  
pp. 4877-4882
Keyword(s):  
Good Accuracy ◽  
Naive Bayes ◽  
Instant Messaging ◽  
Naïve Bayes ◽  
Spam Filtering ◽  
Training Process ◽  
Huge Amount ◽  
Instant Messenger ◽  
Bayes Algorithm ◽  
Incoming Message

Instant messaging has changed and simplified the way people communicate, whether in professional or personal life. Most communication is done through instant messaging, and it is common for people to miss important information. This is due to the huge amount of incoming message notifications, so users tend to accidentally ignore them. This is also experienced by Universitas Multimedia Nusantara (UMN) student committees who communicate via LINE instant messenger. This research showed LINE bot was made by using the Naive Bayes algorithm to classify between important messages and unimportant messages on the committee group. The Naive Bayes algorithm is a classification algorithm based on probability and statistical methods. The Naive Bayes algorithm is chosen because it is widely implemented in spam filtering; the method is simple and has good accuracy. The classification process is done by calculating the probability of chat in each class based on the value of the word likelihood which generated in the training process. This research produces spam precision and spam recall as 94.2% and 95.6% respectively

scholarly journals QUESTIONNAIRE «TEACHER THROUGH THE EYES OF STUDENTS» AS A TOOL FOR IMPLEMENTATION OF EDUCATION QUALITY IN UNIVERSITIES

2019 ◽  
Author(s):  
Tetiana Kuprii
Keyword(s):  
Student Satisfaction ◽  
Student Evaluation ◽  
Learning Technologies ◽  
Educational Process ◽  
Professional Activity ◽  
Practical Implementation ◽  
Sociological Research ◽  
Leadership Studies ◽  
Educational Product

The article deals with the effectiveness of the questionnaire "Teacher through the eyes of students" and the practical implementation of the form as a tool for assessing the personal and professional qualities of teachers and their direct links with the student in the context of pedagogical, sociological, managerial dimensions and cross-cultural sociological research. Student evaluation is required to adjust actions in educational processes and to make changes to the organization’s management, educational programs, and learning technologies. The efficiency and effectiveness of the educational process in higher education depends crucially on two key people - the teacher and the student. Students are the main consumers of educational outcomes. Therefore, in order to determine the degree of student satisfaction, the quality of teaching and teaching methods and the correspondence of the real learning process to the expectations of students, teaching makes it possible to make adjustments to the content of the course, improve yourself as an author and improve the quality of the educational product provided, which will undoubtedly lead to improvement of the process , which both students and teachers are interested in. According to the university leadership, studies of this type will help to increase the effectiveness of teachers’ professional activity, stimulate cooperation between both sides of the educational process. Different approaches to the formation of questions of the questionnaire of world universities with emphasis on pedagogical skills, contact with the audience, students’ interest in subjects, relevance to future professional activity are revealed. The author of the research proposed his own questionnaire, tested in a pilot sociological research and theoretically useful for educators and the administrative corps, which will encourage the teacher to improve himself.

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.

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