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All Journal E-Dimas: Jurnal Pengabdian kepada Masyarakat ILKOM Jurnal Ilmiah
Awangga, Narendra
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Computer Science & IT Social Sciences

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Peningkatan Literasi Digital di Kalangan Siswa Internasional Melalui Pelatihan Microsoft Office Syafie, Lukman; Purnawansyah, Purnawansyah; Herman, Herman; Awangga, Narendra; Wahyudi, Ifan
E-Dimas: Jurnal Pengabdian kepada Masyarakat Vol 16, No 2 (2025): E-DIMAS
Publisher : Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/e-dimas.v16i2.20721

Abstract

Program pengabdian ini bertujuan meningkatkan keterampilan digital siswa di Sekolah Kebangsaan Syeikh Mohd Idris Al-Marbawi, Malaysia. Masalah utama yang dihadapi sekolah ini adalah kurangnya akses siswa terhadap komputer dan aplikasi dasar seperti Microsoft Word. Melalui pelatihan intensif yang diberikan oleh Universitas Muslim Indonesia, siswa dibekali dengan keterampilan dasar penggunaan komputer dan aplikasi Microsoft Word. Pelatihan mencakup pengenalan perangkat keras dan perangkat lunak, serta praktik penggunaan fitur Microsoft Word, mulai dari dasar hingga fitur lanjutan seperti Word Art dan pengaturan kolom.Hasil dari pelatihan ini menunjukkan peningkatan signifikan pada keterampilan digital siswa sesudah pelatihan. Selain itu, sebagai luaran dari program ini, panduan pengantar komputer yang dapat digunakan oleh siswa secara berkelanjutan. Program ini berhasil meningkatkan literasi digital siswa dan diharapkan dapat menjadi model bagi sekolah-sekolah lain. Tantangan yang dihadapi adalah tingkat pemahaman siswa yang beragam, namun hal ini diatasi dengan sesi pendampingan dan konsultasi intensif.
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.
Co-Authors Ifan Wahyudi Purnawansyah Purnawansyah Salim, Yulita Syafie, Lukman Syamsiar, Syamsiar