VARFIS: A Hybrid Neuro-Fuzzy Model for Intelligent Microclimate Control in Black Soldier Fly Farming Systems

Yunita Sartika Sari; Kusrini; Ema Utami; Ferry Wahyu Wibowo

doi:10.15408/jti.v18i2.46610

Authors

Yunita Sartika Sari Department of Informatics Doctorate, University of AMIKOM Yogyakarta, Indonesia https://orcid.org/0009-0009-4136-7275
Kusrini Department of Informatics Doctorate, University of AMIKOM Yogyakarta, Indonesia https://orcid.org/0000-0001-9573-3909
Ema Utami Department of Informatics Doctorate, University of AMIKOM Yogyakarta, Indonesia https://orcid.org/0000-0002-8237-8693
Ferry Wahyu Wibowo Department of Informatics Doctorate, University of AMIKOM Yogyakarta, Indonesia https://orcid.org/0000-0003-1913-436X

DOI:

https://doi.org/10.15408/jti.v18i2.46610

Keywords:

BSF, microclimate control, VARFIS, precision agriculture, sustainable insect farming

Abstract

Maintaining optimal microclimate conditions is essential for Black Soldier Fly (BSF) cultivation, yet traditional systems often struggle with dynamic environmental changes. This study proposes the Vector Autoregressive-Fuzzy Inference System (VARFIS), a hybrid model combining Vector Autoregression (VAR) and Adaptive Neuro-Fuzzy Inference System (ANFIS), to enhance temperature and humidity control in BSF insectariums. VARFIS adapts to uncertainty using probabilistic learning, achieving a 48% reduction in prediction error (MAPE = 1.36%) and high accuracy (R² = 0.9695), outperforming standalone VAR and ANFIS models. The model effectively captures daily climate fluctuations, improving larval growth efficiency and waste conversion. However, it remains limited in handling extreme events such as sudden heatwaves or humidity spikes, indicating the need for enhancements like adaptive fuzzy rule tuning and integration of physical constraints. VARFIS presents a scalable solution for intelligent microclimate management, supporting sustainable insect farming and circular economy goals. This work contributes to precision agriculture by offering data-driven tools for resilient environmental control.

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