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All Journal West Science Nature and Technology Journal of Selvicoltura Asean Techno Agriculturae Studium of Research
Eddy Silamat
Pat Petulai University
Agriculture, Biological Sciences & Forestry Energy

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Effectiveness of No-till Farming, Cover Cropping, and Crop Rotation in Improving the Sustainability of the Agricultural Sector in West Java Loso Judijanto; Eddy Silamat
West Science Nature and Technology Vol. 2 No. 02 (2024): West Science Nature and Technology
Publisher : Westscience Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58812/wsnt.v2i02.1015

Abstract

This study explores the effectiveness of three sustainable agriculture practices—No-till Farming, Cover Crops, and Crop Rotation—in enhancing the sustainability of the agricultural sector in West Java, Indonesia. Using structural equation modeling (SEM-PLS) to analyze survey data from 180 farmers, the study reveals positive relationships between each practice and sustainability outcomes. Cover Cropping emerges as particularly impactful, followed by No-till Farming and Crop Rotation. These findings emphasize the critical role of integrating these practices into agricultural policies and extension programs to bolster environmental resilience and economic viability across West Java.
THE ROLE OF SHADE-GROWN COFFEE AGROFORESTRY SYSTEMS IN CONSERVING BIRD DIVERSITY IN THE SUMATRAN HIGHLANDS Regi Fernandez; Eddy Silamat; Caroline Eide
Journal of Selvicoltura Asean Vol. 2 No. 6 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jsa.v2i6.2769

Abstract

Conservation in the Sumatran Highlands is critically threatened by the continuous simplification of shade-grown coffee (SGC) agroforestry systems into sun-grown monocultures, necessitating an urgent evaluation of the ecological function of complex SGC structures. This study aimed to systematically quantify avian species richness (SR) and functional diversity (FDI) across the coffee land-use gradient to establish the specific structural determinants necessary for developing an Avian-Optimized Agroforestry Protocol (AOAP). A quantitative, gradient-based comparative study utilized the Fixed-Radius Point Count method and meticulous structural measurements across 54 plots. Data were analyzed using ANOVA and multiple linear regression. Results showed that High-Diversity SGC (HD-SGC) plots retained 72\% of avian SR and maintained an FDI (4.1) statistically equivalent to natural forest fragments (4.8), proving their functional viability. Regression confirmed that Shade Tree Basal Area (BA) and Canopy Closure (CC) are the most significant positive predictors of bird diversity (R^2=0.78, p < 0.001). Simplified systems, conversely, registered a steep 40\% drop in SR, confirming their ineffectiveness. The research concludes that the AOAP is validated by confirming that conservation value is determined by structural complexity, not just 'shade.' This compels global certification schemes to adopt precise, performance-based ecological standards using quantitative metrics like BA and CC.
THE USE OF ARTIFICIAL INTELLIGENCE FOR PREDICTING COFFEE BEAN QUALITY BASED ON DIGITAL IMAGES AND SENSOR DATA Eddy Silamat; Khalil Zaman; Shazia Akhtar
Techno Agriculturae Studium of Research Vol. 2 No. 3 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/agriculturae.v2i3.2442

Abstract

The increasing global demand for high-quality coffee requires more efficient and objective methods to evaluate bean quality. Traditional sensory and manual inspection techniques are time-consuming, subjective, and prone to inconsistency. This study aims to develop and validate an Artificial Intelligence (AI)-based predictive model for assessing coffee bean quality using digital image processing and sensor data. The research employs a quantitative experimental approach by integrating convolutional neural networks (CNNs) for visual analysis and machine learning regression models to process multispectral sensor data related to moisture, color, and aroma parameters. A dataset of 5,000 labeled coffee bean samples from three regional plantations was used for training and validation. The results demonstrate that the hybrid AI model achieved an accuracy rate of 96.8% in predicting bean grades compared to expert cupping scores, outperforming traditional visual grading methods by 18%. Furthermore, the integration of digital imaging and IoT-based sensors significantly reduced evaluation time and human error. The findings highlight AI’s potential to revolutionize coffee quality control by enabling automated, consistent, and scalable assessment systems that support sustainable agricultural practices.
Co-Authors Caroline Eide Khalil Zaman Loso Judijanto Regi Fernandez Shazia Akhtar