Article Per Year (5 Year)
p-Index From 2019 - 2024
0.751
P-Index
This Author published in this journals
All Journal BAHASA DAN SASTRA Jurnal Kreatif Tadulako Online Jurnal Fisika FLUX Promotif: Jurnal Kesehatan Masyarakat JURNAL AKADEMIKA Dinamika Ilmu Wahana Forestra: Jurnal Kehutanan FORTECH (Journal of Information Technology) Jurnal GEOSAPTA Performance Jurnal Himasapta Majalah Kesehatan Indonesia
Riswan Riswan
STAIN Samarinda
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Medicine & Pharmacology Nursing Physics Public Health Social Sciences

Published : 19 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search
Journal : Dinamika Ilmu

 1 
Pengelompokan Prestasi Matematika Siswa Indonesia Berdasarkan Hasil Survey Timss Menggunakan Analisis Logistik Kelas Laten Riswan, Riswan
Dinamika Ilmu: Jurnal Pendidikan Dinamika Ilmu Vol 13 No 1, June 2013
Publisher : IAIN Samarinda

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1079.419 KB) | DOI: 10.21093/di.v13i1.66

Abstract

Conventional methods of clustering become weak when meet measured objects with qualitative or categorical data.Latent class logistic analysis can bean alternative method of clustering to overcome this problem. This research is aim to see the application of latent class logistic analysis to clusterthe measured objects with qualitative and quantitative variable andat once to find out backgrounds of the clusters. The objects in this research are 2171 eight grade students from 133 schools in Indonesia. There are two resultsinthis research; first in clustering and second in logistic analysis. In clustering, the students have beenclustered intofour ideal clusters,e.g.39.16 percent students were in cluster1, 32.42 percent in cluster2, 21.46 percent in cluster3, and 6.97 percent in cluster4.Each cluster representsthe students with very low, low,medium, andhigh ability in mathematics. In logistic analysis, overall, eachcluster has been explained well by covariatese.g. student’s interest, attitude, aptitudeand motivation on mathematics, parent’ssocial-economic condition, parent’s highest education level, teacher’s highest education level,teacher’s major study of mathematics and educations,teacher’s perceptions on schools,school’s facilities, etc.
Co-Authors ., Rolisman Abbas Abbas, Abbas Aditya, Dimas Agus Triantoro, Agus Akkas, Andi Muhammad Chandra Irsan Ekaningtyas Widiastuti Ferdinandus, Ferdinandus Frianto, Rudi Hadinoto Hadinoto Hardi Hardi, Hardi Kumaini, Kumaini Kurniawan, Heru Budi Mugeni Mugeni, Mugeni Muhammad Ikhwan Muliadi, Muhammad Eddy Musaidah, Musaidah Nurhakim Nurhakim Rachmatiko, Rofi Rosdiana Rosdiana Rusmawarni, Rusmawarni Saismana, Uyu Saputra, Dimas Sianipar, Flora Siska Siska Suliyanto Suliyanto Umar Harun, Umar Yunitasari, Eva