Tommy Tanu Wijaya
School of Mathematical Sciences, Beijing Normal University, China

Published : 2 Documents Claim Missing Document
Claim Missing Document

Found 2 Documents

Elementary Teachers' Perceptions on Genially Learning Media Using Item Response Theory (IRT) Neni Hermita; Zetra Hainul Putra; Jesi Alexander Alim; Tommy Tanu Wijaya; Subuh Anggoro; Diniya Diniya
Indonesian Journal on Learning and Advanced Education (IJOLAE) Vol. 4, No. 1, January 2022
Publisher : Faculty of Teacher Training and Education, Universitas Muhammadiyah Surakarta, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/ijolae.v4i1.14757


Many studies have developed learning media, but few studies focus on developing learning media in ele-mentary schools using Genially and studying teachers' perceptions of Genially learning media. This study aims to determine teachers' perceptions of hybrid learning media with a more accurate and precise method, namely the item response theory (IRT) from the Rasch model. The survey was carried out by distributing a Likert scale questionnaire of 19 statements. Moreover, the subjects were 45 elementary school teachers in Riau Province, Indonesia. The results showed that they positively perceived the developed genially-based learning media. Genially learning media can support teachers in teaching. Based on these results, teachers need to develop skills in making various technology-based media, in order to support hybrid learning.
Fuzzy subtractive clustering (FSC) with exponential membership function for heart failure disease clustering Annisa Eka Haryati; sugiyarto - surono; Tommy Tanu Wijaya; Goh Khang Wen; Aris Thobirin
International Journal of Artificial Intelligence Research Vol 6, No 1 (2022): June 2022
Publisher : STMIK Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (674.315 KB) | DOI: 10.29099/ijair.v7i1.306


Objective: Fuzzy clustering algorithm is a partition method used to assign objects from a data set to a cluster by marking the average location. Furthermore, Fuzzy Subtractive Clustering (FSC) with hamming distance and exponential membership function is used to analyze the cluster center of a data point. Therefore, the purpose of this research is to determine the number of clusters with the best quality by comparing the Partition Coefficient (PC) values for each number produced. Methods: The data set which is heart failure patient data is 150 data obtained from UCI Machine Learning. The data consists of 11 variables, including age , anemia , creatinine phosphokinase , diabetes ejection fraction , high blood pressure , platelets , serum creatinine , serum sodium , gender , and smoke . It simulated and processed using Fuzzy Subtractive Clustering Algorithm, Jupyter Notebook Software with Python programming language. Result: The results showed that the most optimal number of clusters is 3, which are selected based on the largest PC value. Conclusion: Based on the results obtained, the highest P value is in cluster 3, therefore heart failure can be grouped into 3, namely low, moderate, severe.