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All Journal Jurnal Ilmu Alam dan Lingkungan Advances in Food Science, Sustainable Agriculture and Agroindustrial Engineering (AFSSAAE)
Mursidah, Sri
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Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemistry Engineering Environmental Science Immunology & microbiology Industrial & Manufacturing Engineering Mechanical Engineering Veterinary

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Application of Adaptive Neuro-Fuzzy Inference System Model for Predicting CO2 Emission Based on Production Rate in Sugarcane Agroindustry Mursidah, Sri; Djatna, Taufik
Jurnal Ilmu Alam dan Lingkungan Vol. 17 No. 1 (2026): Jurnal Ilmu Alam dan Lingkungan
Publisher : Universitas Hasanuddin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70561/jal.v17i1.50604

Abstract

Carbon dioxide (CO₂) emissions from industrial processes, particularly in sugar production, have become an important environmental concern that requires accurate prediction and monitoring. This study aims to predict carbon dioxide (CO₂) emissions using the Adaptive Neuro-Fuzzy Inference System (ANFIS) model based on production rate and the fuel used for operating the boiler machine. The model was developed using hypothetical data obtained from the literature, which were divided into 80% training data and 20% testing data. A trial-and-error approach was employed to determine the optimal parameters of the ANFIS model. Various membership function (MF) types and numbers were evaluated, and the optimal configuration was found to be three MFs with a triangular MF type. Model performance was assessed using Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and the coefficient of determination (R²). The results show that the ANFIS model provides good predictive performance, with an RMSE value of 17.083, a MAPE of 14.338, and an R² of 0.8686. These findings indicate that the proposed ANFIS model has a high capability in estimating CO₂ emissions based on production rates and fuel usage.
An intelligent model for sustainability assessment and improvement of sugarcane agroindustry supply chain Mursidah, Sri; Marimin, Marimin; Fauzi, Anas Miftah
Advances in Food Science, Sustainable Agriculture and Agroindustrial Engineering (AFSSAAE) Vol 9, No 3 (2026)
Publisher : Advances in Food Science, Sustainable Agriculture and Agroindustrial Engineering (AFSSAAE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/ub.afssaae.2026.009.03.1

Abstract

The sugarcane agroindustry supply chain plays a crucial role in ensuring the sustainability of Indonesia’s sugar sector. However, suboptimal supply chain management has threatened its long-term viability. This study aimed to identify the structural configuration of the sugarcane supply chain, define sustainability dimensions and indicators, and develop an adaptive assessment model to evaluate and enhance sustainability performance. were analyzed through an intelligent framework integrating Artificial Neural Networks (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) techniques. ANN was applied to model complex relationships among sustainability indicators, while ANFIS was used for sustainability index aggregation. The results show that at the supplier level, economic, social, and environmental sustainability indices are 31.39%, 57.95%, and 59.26%, respectively. At the industry level, the indices reach 48.14%, 71.08%, and 61.25%. The aggregated sustainability index using ANFIS achieved a Root Mean Square Error (RMSE) of 1.95 × 10⁴, with overall sustainability scores of 41.50% (supplier) and 55.90% (industry), categorized as moderately sustainable. The proposed model provided an adaptive and intelligent framework for improving decision-making and advancing sustainable development in the sugarcane agroindustry.
Co-Authors Anas Miftah Fauzi Erliza Hambali Marimin Marimin