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Vol. 11, No. 2, May 2026 (Article in Progress)

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Analysis and Classification of Capital Assistance Recipients Kediri Trade and Industry Department Using Random Forest

https://doi.org/10.22219/kinetik.v11i2.2352
Arika Norma Wahyu Dorroty
Universitas Dian Nuswantoro
Ardiawan Bagus Harisa
Universitas Dian Nuswantoro

Corresponding Author(s) : Arika Norma Wahyu Dorroty

612202100037@mhs.dinus.ac.id

Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, Vol. 11, No. 2, May 2026 (Article in Progress)

Abstract

Capital assistance provided by the Kediri City Department of Trade and Industry often faces challenges related to the uncertainty of fund distribution, making it difficult to ensure the effectiveness of the assistance itself in improving business revenue. To address this, a prediction-based model is applied to evaluate the factors influencing the success of capital assistance in increasing recipients’ income. This study aims to classify recipients based on business revenue outcomes using the Random Forest algorithm. Furthermore, the model identifies key factors affecting the success of assistance and offers recommendations for optimizing future distribution through feature importance analysis. The results demonstrate that the Random Forest model achieves an accuracy of 75%, highlighting its potential as a reliable tool for predicting the success of capital assistance. The feature importance analysis further reveals that training contributes 49% and business type 43%, emphasizing their crucial role in enhancing the effectiveness of future assistance programs.

Keywords

Prediction Capital Assistance Random Forest Data Mining Classification
Dorroty, A. N. W., & Harisa, A. B. (2026). Analysis and Classification of Capital Assistance Recipients Kediri Trade and Industry Department Using Random Forest. Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, 11(2). https://doi.org/10.22219/kinetik.v11i2.2352
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