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The PELATIHAN DAN PEMBANGUNAN ANIMASI CONSTRUCT BERBASIS WEB DENGAN TEMATIK LINGKUNGAN SEKITAR DI TK ARROHMAH KARANGJUWET: TRAINING AND PROGRAM ANIMATION GAME EDUCATION THEMATIC ARROUND NEIGHBORHOOD AT TK AR ROHMAH KARANGJUWET MALANG Ariadi Retno Tri Hayati Ririd
Jurnal Pengabdian Masyarakat Multidisiplin Vol 9 No 1 (2025): Oktober
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/jpm.v9i1.6913

Abstract

Training for teachers in TK Ar Rohmah Karangjuwet Malang have some purpose in introducing animation game for children for education teaching, introducing how to play the application game education that built for TK Ar Rohmah Karangjuwet. Animation in game in this training for knowledge about application already build by our team with curriculum about surrounding environmental at school TK Ar Rohmah Karangjuwet in Malang. Animation in this training is displayed animation for objects in indoor or outdoor and child character try to find correct objects for get points in game application. This application including education for children and try to make help children more easy in learn about curriculum sub theme around environment. Animation in this training is built with Contruct in web based with design of objects use Inkscape application by team. In this application game there are two level, first about indoor lesson and next level for outdoor lesson as secondly level in animation game application. In this application has character as equipped for interactive game animation as child character where find objects in each level and the result is get point influenced with time and correct answer in finished animation game education point and can be access in web.
Reduksi Dimensi Metode Principal Component Analysis dan Linear Discriminant Analysis Data Gambar dengan Scilab: Dimensionality Reduction of Image Data Using Principal Component Analysis and Linear Discriminant Analysis in Scilab Ariadi Retno Tri Hayati Ririd
Jurnal Pendidikan Sains dan Komputer Vol. 6 No. 03 (2026): Call for Papers Oktober 2026
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/jpsk.v6i03.9716

Abstract

This research involves two learning stages: the first uses Principal Component Analysis (PCA) to reduce training dimensions, followed by a second stage using Linear Discriminant Analysis (LDA). Experiments were conducted on the Olivetti dataset (in grayscale format) using the Scilab programming language, with the data partitioned into training and testing sets. The results show a significant increase in classification accuracy despite using very few dimensions. In the first experiment (using 32 data points), accuracy improved by 18.75%—rising from 68.75% (using PCA with 8 dimensions) to 87.5% (using LDA with 7 dimensions). In the second experiment (using 100 data points), accuracy increased by 16%—from 84% (PCA) to 100% (LDA). This process reduced the original feature space—which spanned thousands of dimensions—to 40 dimensions via PCA, and then to 9 dimensions via LDA, using 50 training samples and 50 testing samples. In the third experiment (using 240 data points), PCA achieved 67.5% accuracy, while LDA reached 85% using 23 dimensions. Overall, the average accuracy across the test sets was 73.41% for Principal Component Analysis and 90.83% for Linear Discriminant Analysis.