Article Per Year (5 Year)
p-Index From 2021 - 2026
0.61
P-Index
This Author published in this journals
All Journal Al-Hijr: Journal of Adulearn World Journal of Selvicoltura Asean Techno Agriculturae Studium of Research
Cedric Butler
University of The Bahamas
Agriculture, Biological Sciences & Forestry Computer Science & IT Education Languange, Linguistic, Communication & Media Other

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
TROPICAL PEATLAND FOREST RESTORATION AS A CARBON EMISSION MITIGATION STRATEGY IN INDONESIA Cedric Butler; Veronica Smith; Wayne Miller
Journal of Selvicoltura Asean Vol. 3 No. 2 (2026)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jsa.v3i2.3755

Abstract

Tropical peatland forests in Indonesia have been significantly impacted by deforestation, draining, and conversion to agricultural land, leading to large-scale carbon emissions. As one of the world’s largest peatland areas, Indonesia’s tropical peatlands are crucial in regulating global carbon cycles. Restoration of these ecosystems presents a significant opportunity to mitigate carbon emissions, which are exacerbating climate change. This research aims to evaluate the effectiveness of tropical peatland forest restoration as a strategy for carbon emission reduction in Indonesia. The study employs a mixed-methods approach, combining field observations, remote sensing data, and carbon modeling to assess the carbon sequestration potential of restored peatland forests. Findings show that successful restoration of peatlands can result in the sequestration of up to 15 million tons of CO2 annually, with significant increases in both above-ground and below-ground biomass. Additionally, the research identifies key factors influencing restoration success, including water table management and native species replanting. The study concludes that tropical peatland forest restoration is a viable and effective strategy for carbon emission mitigation in Indonesia. The research emphasizes the need for policy support and long-term monitoring to ensure the sustainability of restoration efforts and their contribution to global climate change mitigation.
THE ASSESSMENT REVOLUTION: THEORIES AND METHODOLOGIES OF AUTOMATED ASSESSMENT USING MACHINE LEARNING FOR EVALUATING LEARNING PROGRESS Misbahul Khairani; Cedric Butler; Markus Rolle
Al-Hijr: Journal of Adulearn World Vol. 4 No. 4 (2025)
Publisher : Sekolah Tinggi Agama Islam Al-Hikmah Pariangan Batusangkar, West Sumatra, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55849/alhijr.v4i4.1153

Abstract

Rapid advances in artificial intelligence and machine learning have fundamentally transformed educational assessment practices, shifting evaluation from episodic, human-centered measurement toward continuous, data-driven monitoring of learning progress. This study aims to examine the theoretical foundations and methodological approaches underlying automated assessment systems that employ machine learning to evaluate learning progress in diverse educational contexts. A qualitative systematic review with an integrative analytical framework was employed, drawing on peer-reviewed studies from international journals across education, learning analytics, and computer science. The selected literature was analyzed to identify dominant assessment purposes, theoretical alignments, data sources, modeling techniques, and validation strategies. The results indicate that most automated assessment systems prioritize predictive accuracy and efficiency, frequently conceptualizing learning progress through performance-oriented metrics while offering limited alignment with established assessment theories such as formative assessment and construct validity. Theory-informed and interpretable models remain underrepresented despite their pedagogical relevance. The findings reveal a persistent gap between technological innovation and educational meaning-making in automated assessment research. This study concludes that the assessment revolution driven by machine learning will remain incomplete without stronger integration of educational assessment theory, methodological transparency, and interpretability. Aligning machine learning methodologies with robust assessment principles is essential to ensure that automated systems support meaningful evaluation of learning progress, instructional decision-making, and educational equity.
THE ECONOMIC IMPACT AND ADOPTION RATE OF DIGITAL FARMING ADVISORY PLATFORMS AMONG SMALLHOLDER FARMERS IN INDONESIA A SURVEY STUDY Nahri Idris; Cedric Butler; Sarah Al-Jabri
Techno Agriculturae Studium of Research Vol. 2 No. 4 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/agriculturae.v2i4.2673

Abstract

Digital Farming Advisory Platforms (DFAPs) are posited to help Indonesian smallholders, but their real-world adoption and economic efficacy are unverified. A significant gap exists between the technology’s promise and its practical implementation. This study sought to: (1) empirically quantify DFAP adoption rates, (2) rigorously evaluate their economic impact on farm yield and net income, and (3) identify key drivers of adoption. A cross-sectional survey (N=1,240) was conducted in three Indonesian provinces. We employed logistic regression to identify adoption predictors and Propensity The adoption rate was low (25.0%), with a high rejection rate (33.5%). Digital literacy and education were the strongest predictors. The PSM analysis confirmed that adoption yields significant economic benefits, including a 14.2% increase in crop yield and higher net income (p < .01). The findings present a critical paradox: DFAPs are economically effective, but benefits are captured only by a digitally literate “farmer elite.” This “digital divide” mandates a policy shift from technology-centric investment to human-centric interventions focused on digital literacy.  
Co-Authors Markus Rolle Misbahul Khairani Nahri Idris Sarah Al-Jabri Veronica Smith Wayne Miller