Sistem Prediksi Tingkat Keamanan Objek pada Area Blind Spot Unit Forklift Menggunakan Artificial Neural Networks dengan Dua Skenario

  • Muhammad Rifqi Setiawan Gadjah Mada University
  • Tegar Prasetyo
  • Agustinus Winarno Gadjah Mada University
  • Harjono Harjono Gadjah Mada University
  • Irfan Bahiuddin Gadjah Mada University
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DOI https://doi.org/10.31544/jtera.v11.i1.2026.87-92
Published 2026-06-26
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Abstract

Forklifts are widely used in the construction and industrial sectors; however, their operation involves a high risk of accidents, particularly in blind spot areas that are not visible to the operator. Therefore, this study aims to develop a safety level prediction system for objects in forklift blind spot areas to improve operational safety. The proposed method integrates a fuzzy logic approach with machine learning based on Artificial Neural Networks (ANN). The dataset was generated using a Python program with fuzzy logic and a sensor based-system, with input parameters including distance (0–300 cm), angle (0°–180°), and steering position (0°–180°). The safety level is classified into three categories: safe, caution, and danger. The ANN model employs a feed-forward architecture with variations in training parameters. The results indicate that the optimal configuration is achieved with 4 hidden layers, 100 hidden nodes, 1500 epochs, and the Adam optimization algorithm. The developed model demonstrates strong performance based on precision, recall, and F1-score evaluation metrics. These findings indicate that the integration of fuzzy logic and ANN is effective in predicting safety levels in forklift blind spot areas. This approach has the potential to be further developed into a real-time safety monitoring system for industrial vehicles.

Keywords

Blind spot fuzzy jaringan saraf tiruan klasifikasi keamanan forklift.

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Issue

Vol. 11 No. 1: June 2026

Section

Articles

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How to Cite

[1]
“Sistem Prediksi Tingkat Keamanan Objek pada Area Blind Spot Unit Forklift Menggunakan Artificial Neural Networks dengan Dua Skenario”, JTERA, vol. 11, no. 1, pp. 87–92, Jun. 2026, doi: 10.31544/jtera.v11.i1.2026.87-92.