Clustering Student's Satisfaction in Complex Adaptive Blended Learning with the Six Value System Using the K-Means Algorithm

Sudrajat, Dadang; Dana, Raditya Danar; Rahaningsih, Nining; Dikananda, Arif Rinaldi; Kurnia, Dian Ade

doi:10.13189/ujer.2019.070920

Cited by 12 publications

(10 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Before the development of hybrid learning methods, there was one study of blended learning conducted at three vocational schools in Cirebon City with the results obtained were the level of satisfaction with the model measured by Very Satisfied, Satisfied, Moderately or Dissatisfied. The aspect measured by satisfaction consists of six components, namely Institution, Teacher, Learner, Content, Learning Support and Technology [12].…”

Section: Related Researchmentioning

confidence: 99%

Hybrid Learning Predictions on Learning Quality Using Multiple Linear Regression

Sudrajat¹,

Purnamasari²,

Dikananda³

et al. 2023

ISI

View full text Add to dashboard Cite

Indonesia declares COVID-19 Pandemic by the World Health Organization (WHO) from March 2020. This has a very impact, one of which is on the continuity of the world of education. This research aims to foretell the impact of hybrid teaching methods used at SMK Cendikia Cirebon City during the COVID-19 period on student achievement. Teachers' materials, honesty, enthusiasm, and IT backing are all factors in determining the quality of education. Multiple linear regression with root-mean-squared error as the dependent variable is used in this study (RMSE). Experiments conducted on 122 participants yielded an RMSE of 0.375 and a correlation level of 0.440 for each attribute, with test samples comprising 10% and training samples comprising 90%. As a result, the use of this multiple linear regression model can be suggested for foreseeing the introduction of a hybrid learning model to enhance educational quality.

show abstract

Section: Related Researchmentioning

confidence: 99%

Hybrid Learning Predictions on Learning Quality Using Multiple Linear Regression

Sudrajat¹,

Purnamasari²,

Dikananda³

et al. 2023

ISI

View full text Add to dashboard Cite

show abstract

“…Sebelum pengembangan metode hybrid learning, terdapat satu penelitian tentang blended learning yang dilakukan di tiga SMK di Kota Cirebon dengan hasil yang diperoleh adalah tingkat kepuasan dengan model yang diukur dengan Sangat Puas, Puas, Sedang atau Tidak Puas. Aspek yang diukur dengan kepuasan terdiri dari enam komponen, yaitu Institusi, Guru, Pembelajar, Konten, Dukungan Pembelajaran dan Teknologi [12].…”

Section: Pendahuluanunclassified

Klasifikasi Mutu Pembelajaran Hybrid berdasarkan Algoritma C.45, Random Forest dan Naïve Bayes dengan Optimasi Bootsrap Areggating (Bagging) pada masa COVID-19

et al. 2022

View full text Add to dashboard Cite

This study aims to find the best model from the 3 (three) models chosen, namely Random Forest, Naïve Bayes and C4.5 against the classification of hybrid learning quality held at SMK Cendekia Cirebon City during the COVID-19 period. The research method is carried out by referring to the approach or technique of machine learning with the stages of data collection, data preparation, data transformation, modeling and evaluation. The final results of this study are measured by the level of accuracy of hybrid learning quality aspects, namely teachers, subject matter, integrity, student motivation, and technology resources with bad, sufficient, good and very good categories.. Based on the experiments that have been carried out, the accuracy results for the Random Forest algorithm were obtained by 93.92%, Naïve Bayes by 93.22% and C4.5 by 77.16%. Thus this study recommends the best classification algorithm model based on dataset processing to measure this hybrid learning quality model is Random Forest

show abstract

“…Tahapan penelitian yang digunakan untuk menganalisis data dalam penerapan ini menggunakan proses tahapan knowledge discovery in databases (KDD) yang terdiri dari Data, Data Cleaning, Data transformation, Data mining, Pattern evolution, knowledge [4]- [6] Gambar 1. Tahapan Penelitian [7]- [10] Berdasarkan gambar 1 diatas menjelaskan bahwa tahapan penelitian ini dibagi menjadi 5 (lima) tahapan sebagai berikut [8], [11], [12] Pada tahap ini dilakukan evolusi terhadap hasil-hasil prediksi yang diperoleh algoritma decision tree. Kriteria Evaluasi dengan metode Confusion Matrix, Accuracy dan error digunakan untuk mendapatkan Nilai performansi.…”

Section: Metodologi Penelitian 21 Tahapan Penelitianunclassified

Klasifikasi Kondisi Gizi Bayi Bawah Lima Tahun Pada Posyandu Melati Dengan Menggunakan Algoritma Decision Tree

Zami

Nurdiawan

Dwilestari

2022

json

View full text Add to dashboard Cite

One of the health problems in Cirebon is about nutritional status. This happens because the increase and decrease in the number of children under five who experience nutritional status problems each year is uncertain. Toddlers are a group of people who are vulnerable to nutrition. The incidence of malnutrition if not addressed will cause a bad impact for toddlers. The impacts include death and chronic infection. Early detection of undernourished children (malnutrition and malnutrition) can be done with an examination of weight for age (W/U) to monitor the child's weight, the parameters used to calculate the nutritional status of toddlers include age, weight and height/length. To classify the nutritional status of children under five, a knowledge or scientific study is needed that can classify data based on the data from measurements and weighing. This study uses 8 criteria, namely Name, Address, Mother's Name, Gender, Age, Weight, Height, Status Classification. The accuracy results obtained are 98.86% with details, namely the Prediction Results of Malnutrition and it turns out that the True Malnutrition is 13 data. Poor Nutrition Prediction Results and turns out to be True Normal by 1 Data. Normal Prediction Results and it turns out to be True Malnutrition is 1 Data. Normal Prediction Results and turns out to be True Normal of 161 data. The results of the classification of infant levels based on age, infants aged 0 months to 10 months had normal nutrition, while infants aged 10 months to 19.5 months were prone to malnutrition for infants, and those aged more than 19.5 months had poor nutrition. normal.

show abstract

Clustering Student's Satisfaction in Complex Adaptive Blended Learning with the Six Value System Using the K-Means Algorithm

Cited by 12 publications

References 0 publications

Hybrid Learning Predictions on Learning Quality Using Multiple Linear Regression

Hybrid Learning Predictions on Learning Quality Using Multiple Linear Regression

Klasifikasi Mutu Pembelajaran Hybrid berdasarkan Algoritma C.45, Random Forest dan Naïve Bayes dengan Optimasi Bootsrap Areggating (Bagging) pada masa COVID-19

Klasifikasi Kondisi Gizi Bayi Bawah Lima Tahun Pada Posyandu Melati Dengan Menggunakan Algoritma Decision Tree

Contact Info

Product

Resources

About