Claim Missing Document
Check
Articles

Found 12 Documents
Search

Enhancing Digital Literacy Through the Introduction of Robotics and Scratch for Primary School Students in Kedah, Malaysia Syafie, Lukman; Herman, Herman; As'ad, Ihwana; Badriah, Anita
Mattawang: Jurnal Pengabdian Masyarakat Vol. 6 No. 4 (2025)
Publisher : Yayasan Ahmar Cendekia Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/454RI.mattawang4519

Abstract

This community service program aims to improve digital literacy among elementary school students in Kedah, Malaysia through introducing robotics and Scratch programming. The main issue faced was students' limited exposure to computational thinking and STEM-based learning. Through intensive training provided by Universitas Muslim Indonesia, students from 13 schools were equipped with fundamental skills in visual programming using Scratch and basic robotics concepts. The training was officially opened by the Special Officer to the Minister of Education Malaysia and covered introduction to computational thinking, hands-on Scratch programming, and educational robotics demonstrations. Results showed significant improvement in students' understanding of programming concepts and increased enthusiasm towards technology. Additionally, an educational module was created for continued learning. The program successfully enhanced students' digital literacy and received strong appreciation from school principals who expressed interest in ongoing collaboration for similar activities.
Evaluating the Effectiveness of TBaWI for Imputation of Missing Rainfall Data Syafie, Lukman; Awangga, Narendra; Salim, Yulita
ILKOM Jurnal Ilmiah Vol 18, No 1 (2026)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v18i1.3273.97-108

Abstract

Daily rainfall data plays an important role in hydrological and climatological analysis, especially in tropical regions characterised by high rainfall variability and sharp seasonal changes. However, observational data often has gaps, which can reduce model accuracy and obscure relevant climatological signals. This study addresses these issues by applying the Trend-Based Adaptive Window Imputation (TBaWI) method, an adaptive imputation approach that considers local temporal trends and seasonal dynamics in estimating missing rainfall values. This method was tested using CHIRPS data for the Makassar region for the period 2014–2023 with synthetic data loss scenarios of 10%, 15%, 20%, and 25%. The results show that TBaWI consistently provides a lower Mean Absolute Error (MAE) value, namely 6.14–7.65 mm, compared to linear interpolation, which produces 6.46–7.75 mm. The SMAPE value of TBaWI is also lower, for example 33.16% in the 15% data loss scenario, compared to interpolation at 35.06%. In addition, this method showed an improvement in the ability to identify dry days through the Zero Hit Rate (ZHR), which reached 60.08% in the 20% data loss scenario, higher than the interpolation of 58.32%, while the Rainy Hit Rate (RHR) remained in a stable range of 79–88%. These findings indicate that TBaWI is more effective in maintaining climatological consistency and numerical accuracy of tropical rainfall data. Further research is expected to integrate spatial aspects and optimise machine learning-based parameters to improve the generalisation of the method under various climatic conditions.