Improving the efficiency and accuracy of classification of recipients of the free nutrition food program through the application of knowledge distillation in a convolutional neural network algorithm

Authors

DOI:

https://doi.org/10.15587/1729-4061.2026.357493

Keywords:

Food Program, CNN, student distillation model, classification system

Abstract

The object of this study is a deep learning-based classification system applied to recipients of the Free Nutritional Food Program, using recipient data as a representation of the eligibility determination process in providing social assistance. The problem to be solved is the high computational complexity of large-capacity convolutional neural networks (CNN) models which, despite their high accuracy, require significant computational resources and are therefore less than optimal for large-scale implementation. To overcome this, this study applies the Knowledge Distillation method, utilizing a large-capacity CNN as the teacher model and a lightweight architecture CNN as the student model through soft label-based knowledge transfer.  According to this study, it is shown that the student model generated by distillation is about 90–93% accurate. Also, this figure is very close to that of the teacher model (from 92–95%) and much better than that of a CNN without distillation model (85–88%). This is an improvement since this distillation method can transfer information in the form of richer probabilities than simply hard labels as done do in traditional training. The model proposed in this work has many advantages such as higher accuracy, more compact size, and faster inference times. These features help in making the classification process computationally less intensive. Furthermore, this leads to more efficient memory use and lower energy consumption. These results could be applied in many deep learning classification systems, particularly resource limits devices. Such application is observable in the implementation of Free Nutritious Food Program under real life conditions which requires better accuracy with no loss on efficiency

Author Biographies

Relita Buaton, STMIK Kaputama

Doctor of Computer

Department of Information Technology

Mesra Betty Yel, STIKOM Cipta Karya Informatika

Doctor of Computer

Department of Information System

Novriyenni Novriyenni, STMIK Kaputama

Master of Computer

Department of Information System

Anton Sihombing, STMIK Kaputama

Master of Management

Department of Management Information System

Ida Ria Royentina Sidabukke, Universitas Sari Mutiara Indonesia

Master of Health

Department of Midwifery

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Improving the efficiency and accuracy of classification of recipients of the free nutrition food program through the application of knowledge distillation in a convolutional neural network algorithm

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Published

2026-04-30

How to Cite

Buaton, R., Yel, M. B., Novriyenni, N., Sihombing, A., & Sidabukke, I. R. R. (2026). Improving the efficiency and accuracy of classification of recipients of the free nutrition food program through the application of knowledge distillation in a convolutional neural network algorithm. Eastern-European Journal of Enterprise Technologies, 2(3 (140), 37–45. https://doi.org/10.15587/1729-4061.2026.357493

Issue

Vol. 2 No. 3 (140) (2026): Control processes

Section

Control processes

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Copyright (c) 2026 Relita Buaton, Mesra Betty Yel, Novriyenni Novriyenni, Anton Sihombing, Ida Ria Royentina Sidabukke

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