Prediksi Tingkat Kepuasan dalam Pembelajaran Daring Menggunakan Algoritma Naïve Bayes

Damanik, Abdi Rahim; Sumijan, S; Nurcahyo, Gunadi Widi

doi:10.37034/jsisfotek.v3i3.137

Cited by 4 publications

(4 citation statements)

References 2 publications

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“…Abdi, Sumijan, dan Gunadi dengan jurnalnya yang berjudul "Prediksi Tingkat Kepuasan dalam Pembelajaran Daring Menggunakan Algoritma Naive Bayes" menyimpulkan bahwa dari hasil pengujian akhir yang dilakukan dari data sampel kuesioner dengan menggunakan atribut komunikasi, suasana pembelajaran, penilaian mahasiswa, dan penyampaian materi menggunakan metode Naive Bayes yang didapatkan nilai akurasi sebesar 100% dengan nilai precision sebesar 100% dan nilai recall sebesar 100% [12].…”

Section: Diskusiunclassified

Data Mining Technique Using Naïve Bayes Algorithm to Predict Shopee Consumer Satisfaction Among Millennial Generation

Hant

Hendry

2022

J. Tek. Inform. (JUTIF)

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Shopee is one of the largest e-commerce platforms currently being used by Millennials. The use of Shopee itself makes it very easy for consumers to process transactions. Shopee itself is committed to maintaining and improving customer satisfaction so they don't switch to other competitors. However, it is undeniable that there are still many cases that can harm consumers when using the platform. With the cases that occur, it is very possible that there will be a big influence on the level of consumer satisfaction on the platform. Consumers will feel satisfied when the product or service used can meet consumer expectations. This study was made with the aim of predicting the level of consumer satisfaction of Shopee Indonesia among the Millennial Generation. This study applies data mining using the Naive Bayes Algorithm. The Naive Bayes algorithm itself is a simple probability classification that can calculate all possibilities by combining a number of combinations and the frequency of a value from the database obtained. The attributes used in conducting this research include Name, Gender, Age, Price, Performance and Efficiency, Fulfillment, Reliability, Control and Security, Responsiveness, Compensation, Contact, and Description of Satisfaction Value. In this study, the results obtained from several input attributes that create a causal relationship when classifying satisfied and dissatisfied consumers. The results obtained can provide benefits for the Shopee company in increasing customer satisfaction. After carrying out the testing process, it can be concluded that the Naive Bayes Algorithm is an algorithm that is suitable for use in the classification process for measuring Shopee Indonesia's consumer satisfaction level among the Millennial Generation, with an accuracy rate of 89.65%.

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Section: Diskusiunclassified

Data Mining Technique Using Naïve Bayes Algorithm to Predict Shopee Consumer Satisfaction Among Millennial Generation

Hant

Hendry

2022

J. Tek. Inform. (JUTIF)

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“…The basic concept of the nave Bayes algorithm method is to be able to predict future opportunities based on past experience data and this theory is known as the Bayes theorem. This concept is combined with Naive, which applies independent attribute values [10]. The Nave Bayes algorithm has been used in previous research such as predict the level of satisfaction in online learning with the results of the study being satisfied and completely satisfied, namely 27, dissatisfied and completely satisfied, 0, satisfied and actually dissatisfied, 0, satisfied.…”

Section: Introductionmentioning

confidence: 99%

Application of Data Mining using Naive Bayes for Student Success Rates in Learning

Wijaya

Kumar²,

Wau³

et al. 2022

mib

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Education is a very important part of human life because through education quality human resources will be formed. Quality education can be read and measured by the achievement of various indicators. However, achieving these indicators is not easy, because learning success is influenced by several factors. One of the factors that can affect the success of learning is the learning system. To understand the level of student success in learning, a data mining processing technique is needed. The algorithm that will be used in this research is the naive Bayes algorithm. This study uses 601 datasets per year from Academic Year 2019/2020 to Academic Year 2021/2022, the data used are attendance score data, assignment scores, mid-exam scores, semester exam scores, and averages. The test is divided into 3, namely testing for the Academic Year 2019/2020 dataset, testing for the Academic Year 2020/2021 dataset, and testing for Academic Year 2021/2022 using the split validation operator. The test results using the Academic Year 2019/2020 – Academic Year 2020/2021 student score dataset have an accuracy value of 95.01% while the Academic Year 2021/2022 student score dataset has an accuracy value of 97.79%.

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“…Covid-19 has an impact on all lines of economic, social and educational life (Damanik, Sumijan, & Nurcahyo, 2021). In the world of education there has been a significant change in teaching and learning activities in connection with the policies of the ministry of education and culture give an appeal to stop teaching and learning activities face-to-face at school and college and replaced by doing distance learning or online.…”

Section: Introductionmentioning

confidence: 99%

Classification of Student Satisfaction With Online Lecture

Ruhyana

Mardiana

Amsury

et al. 2021

JRisInf

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Abstra Covid-19 has had a significant impact on people's lives, resulting in the paralysis of almost the entire economy and education, especially in the education sector, resulting in many students being unable to carry out teaching and learning activities at schools or universities. Based on this, the Ministry of Education and Culture has issued an appeal to stop face-to-face teaching and learning activities at schools and universities and replace them with distance or online learning. Resulting in teaching and learning activities to be less than optimal for students or students, there is dissatisfaction with the distance or online learning system, the purpose of this study is to measure the level of student satisfaction with online lectures by applying data mining techniques, classifying the level of online learning satisfaction using an online learning approach. k-NN algorithm and Decision Tree with 100 questionnaire data that has been collected from active students who carry out online lectures with an accuracy rate of 96.00% from the k-NN algorithm and a satisfied precision value of 95.51%, a satisfied recall value of 98.84% on a precision value the dissatisfied class is 90.91%, the recall value of the dissatisfied class is 71.43%. While the accuracy results using the Decision Tree algorithm approach is lower with an accuracy of 95.00%. based on research results that the level of student satisfaction with distance learning or online is quite high. Keywords: covid 19, data mining, online, k-NN, decision tree

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Prediksi Tingkat Kepuasan dalam Pembelajaran Daring Menggunakan Algoritma Naïve Bayes

Cited by 4 publications

References 2 publications

Data Mining Technique Using Naïve Bayes Algorithm to Predict Shopee Consumer Satisfaction Among Millennial Generation

Data Mining Technique Using Naïve Bayes Algorithm to Predict Shopee Consumer Satisfaction Among Millennial Generation

Application of Data Mining using Naive Bayes for Student Success Rates in Learning

Classification of Student Satisfaction With Online Lecture

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