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All Journal Jurnal Ilmiah MITSU (Media Informasi Teknik Sipil Universitas Wiraraja) EKOMBIS REVIEW: Jurnal Ilmiah Ekonomi dan Bisnis Jurnal Inovasi dan Pembelajaran Fisika Seminar Nasional Pendidikan IPA SRIWIJAYA INTERNATIONAL JOURNAL OF DYNAMIC ECONOMICS AND BUSINESS Jurnal Lentera Pendidikan Pusat Penelitian LPPM UM Metro Pelangi: Jurnal Pemikiran dan Penelitian Pendidikan Islam Anak Usia Dini Jurnal Pendidikan dan Pengabdian Masyarakat Pelangi: Jurnal Pemikiran dan Penelitian Pendidikan Islam Anak Usia Dini BULETIN FISIKA Jurnal Ekonomi, Manajemen, Akuntansi dan Keuangan
Taufiq Taufiq
Universitas Sriwijaya
Astronomy Civil Engineering, Building, Construction & Architecture Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Energy Engineering Environmental Science Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Medicine & Pharmacology Physics Transportation Other

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Kajian Literatur Sistematis: Efektivitas Multi-Representasi terhadap Kemampuan Kognitif Siswa SMA pada Materi Fisika Putri Wijayanti; Nadya Anggraini; Taufiq Taufiq
JURNAL LENTERA [PENDIDIKAN PUSAT PENELITIAN LPPM UM METRO] Vol 11 No 1 (2026): Jurnal Lentera Pendidikan Pusat Penelitian LPPM UM Metro
Publisher : LPPM UM Metro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24127/jlpp.v11i1.5172

Abstract

Kemampuan kognitif siswa SMA dalam pembelajaran fisika, khususnya pemahaman konsep dan kemampuan pemecahan masalah, masih menjadi tantangan yang belum terselesaikan secara optimal. Pendekatan multi-representasi telah banyak diterapkan sebagai solusi, namun hasil penelitian yang ada masih menunjukkan inkonsistensi sehingga diperlukan kajian sistematis untuk mengidentifikasi kondisi di mana pendekatan ini efektif atau tidak efektif dalam konteks pembelajaran fisika SMA. Penelitian ini bertujuan untuk menganalisis secara sistematis efektivitas pendekatan multi-representasi terhadap kemampuan kognitif siswa SMA pada pembelajaran fisika, serta mengidentifikasi faktor-faktor implementasi yang memengaruhi efektivitasnya. Metode yang digunakan adalah Systematic Literature Review (SLR) dengan teknik narrative synthesis. Penelusuran literatur dilakukan pada database Google Scholar dan ScienceDirect menggunakan Publish or Perish, dengan rentang tahun publikasi 2015–2025. Dari 650 artikel yang teridentifikasi, sebanyak 10 artikel memenuhi kriteria inklusi dan dianalisis lebih lanjut. Hasil kajian menunjukkan bahwa pendekatan multi-representasi efektif dalam meningkatkan pemahaman konsep dan kemampuan pemecahan masalah siswa SMA pada berbagai materi fisika. Namun, efektivitas tersebut sangat bergantung pada kualitas implementasi, khususnya tingkat integrasi antar representasi, strategi pembelajaran yang digunakan, dan ketersediaan scaffolding yang memadai. Temuan ini menegaskan bahwa multi-representasi bukan sekadar variasi penyajian materi, melainkan strategi kognitif yang efektif apabila diterapkan secara terintegrasi dan terstruktur dalam pembelajaran fisika SMA.High school students' cognitive abilities in physics learning, particularly conceptual understanding and problem-solving skills, remain a challenge that has not been optimally resolved. The multi-representation approach has been widely applied as a solution, however existing research findings still show inconsistencies, necessitating a systematic review to identify the conditions under which this approach is effective or ineffective in the context of high school physics learning. This study aims to systematically analyze the effectiveness of the multi-representation approach on high school students' cognitive abilities in physics learning, as well as to identify implementation factors that influence its effectiveness. The method used is Systematic Literature Review (SLR) with narrative synthesis technique. Literature searches were conducted on Google Scholar and ScienceDirect databases using Publish or Perish, covering publications from 2015 to 2025. Out of 650 identified articles, 10 articles met the inclusion criteria and were further analyzed. The results show that the multi-representation approach is effective in improving conceptual understanding and problem-solving abilities of high school students across various physics topics. However, its effectiveness highly depends on the quality of implementation, particularly the level of integration between representations, the learning strategies employed, and the availability of adequate scaffolding. These findings confirm that multi-representation is not merely a variation in material presentation, but an effective cognitive strategy when implemented in an integrated and structured manner in high school physics learning.
Machine Learning to Predict Climate Change in Coastal Areas of Indonesia Huriyatul Firdausi; Melly Ariska; Sardianto Marcos Siahaan; Hamdi Akhsan; Yenny Anwar; Iin Seprina; Taufiq Taufiq
BULETIN FISIKA Vol. 27 No. 1 (2026): BULETIN FISIKA
Publisher : Departement of Physics Faculty of Mathematics and Natural Sciences, and Institute of Research and Community Services Udayana University, Kampus Bukit Jimbaran Badung Bali

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/BF.2026.v27.i01.p05

Abstract

Indonesia's coastal regions face significant threats from climate change, including rainfall uncertainty, rising temperatures, and sea level rise. This study aims to explore the potential of machine learning algorithms in predicting climate parameter changes in the coastal areas of Minangkabau, Pesawaran, and Maritim Panjang. Daily climatological data obtained from the Meteorology, Climatology, and Geophysics Agency (BMKG) were used as the basis for model training. Three primary algorithms were tested Random Forest, XGBoost, and Long Short-Term Memory (LSTM) selected for their capability to handle complex and temporal data. The research methodology included data preprocessing, model training, cross-validation, and predictive performance evaluation using metrics such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and the coefficient of determination (R²). Preliminary results show that LSTM excels in time series prediction, while XGBoost offers a good balance between speed and accuracy. These findings indicate that machine learning-based approaches have strong potential as decision-support tools for climate change mitigation and adaptation planning in Indonesia’s coastal regions.
Pelatihan Pembuatan Video Animasi Berbantuan Artificial Intelligence untuk Guru SMP Negeri 1 Indralaya Sardianto Markos Siahaan; Muhamad Yusup; Melly Ariska; Taufiq Taufiq; Wardah Amalia
Jurnal Pendidikan dan Pengabdian Masyarakat Vol. 9 No. 2 (2026): Mei
Publisher : FKIP Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppm.v9i2.10955

Abstract

Pemanfaatan teknologi Artificial Intelligence (AI) dalam pembuatan media pembelajaran menjadi kebutuhan mendesak bagi guru agar mampu menghadirkan pembelajaran yang kreatif, menarik, dan relevan dengan tuntutan Kurikulum Merdeka. Kegiatan Pengabdian kepada Masyarakat ini bertujuan meningkatkan kompetensi guru SMP Negeri 1 Indralaya dalam membuat video animasi pembelajaran berbantuan AI melalui pelatihan dan pendampingan terstruktur. Metode kegiatan meliputi sosialisasi, pelatihan teknis, workshop pembuatan video, pendampingan implementasi, serta evaluasi pre-test dan post-test. Hasil menunjukkan adanya peningkatan signifikan literasi digital guru dengan nilai N-gain sebesar 0,70 kategori sedang. Dari kegiatan ini, para guru menyatakan membuktikan bahwa integrasi AI membuat proses pembuatan media pembelajaran lebih efektif dan efisien dan meningkatkan kreativitas guru.
Co-Authors ., faridah . ., Zulmahdi Abdul Bashir Andre Jovi Saputra Andre Saputra Anna Yulianita Apit Fathurohman Deassy Apriani Hamdi Akhsan Huriyatul Firdausi Iin Seprina Izmi Dwi Maharani Poetri Jauharuddin, Ahmad maya Matofani Melly Ariska Muhamad Yusup Mukhlis Mukhlis Nadya Anggraini Nugroho, Meriana Wahyu Putri Wijayanti Relasari, Relasari Sardianto Markos Siahaan Sundari, Titin Totok Yulianto Tyas Amelia Wardah Amalia Yenny Anwar