Model Prediksi Kerusakan Sepeda Motor Matic Menggunakan Jaringan Saraf Tiruan dan Metode Hebb’s Rule

Revi Gusriva(1*), Yeviki Maisyah Putra(2),

(1) Universitas Putra Indonesia “YPTK” Padang, Indonesia
(2) Universitas Putra Indonesia “YPTK” Padang, Indonesia
(*) Corresponding Author

Abstract


Damage problems on automatic motorcycles often require accurate and fast diagnosis to minimize repair time and costs. This study aims to develop a damage detection system for automatic motorcycles using Artificial Neural Networks (ANN) with the Hebb's Rule learning method. This method was chosen because of its ability to strengthen associations between neurons based on the principle of activity correlation. The data used includes common symptoms of damage such as Over Heit / Overheating On the engine, Over Heit / Overheating On the engine, Braking system damage, Electrical system damage, Over Houl and lighting system, and other vehicle performance indicators. The system is trained to recognize certain damage patterns based on the input of symptoms provided. The test results show that the model is able to classify the type of damage with a satisfactory level of accuracy, and shows a higher identification process speed compared to conventional methods. Thus, this approach provides an alternative solution in the early diagnosis process of automatic motorcycle damage efficiently and effectively.

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DOI: https://doi.org/10.30645/kesatria.v6i2.601

DOI (PDF): https://doi.org/10.30645/kesatria.v6i2.601.g596

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