Klasifikasi Tingkat Kekakuan Dinding Beton Terhadap Getaran Dengan Metode K-Nearest Neighbor


Mochammad Shidqi Taufiqurrahman(1*), Lukman Awaludin(2)

(1) Program Studi Elektronika dan Instrumentasi, FMIPA UGM, Yogyakarta
(2) Departemen Ilmu Komputer dan Elektronika, FMIPA UGM, Yogyakarta
(*) Corresponding Author


The low level of wall stiffness can cause damage to buildings during large-scale earthquakes. There are many systems for measuring the level of stiffness in buildings, but they have not yet reached the classification stage. Therefore, a system that can classify stiffness is needed to determine the impact of vibrations on the wall to minimize the losses incurred.

This study creates a system that can classify the level of wall stiffness using the K-Nearest Neighbor (KNN) method into several categories (safe, vulnerable, dangerous, and destroyed). The data taken at the acquisition stage are ground acceleration, inclination angle, displacement, drift ratio, and peak value. The KNN input is a peak ground acceleration value, which causes a drift ratio of 1%. The resulting output is a category of wall stiffness based on the Earthquake Intensity Scale by BMKG.

Functionally, the system designed can classify wall stiffness with non-linear data input using the K-Nearest Neighbor (KNN) method. The success rate of KNN reaches a value of 100%. Based on the PGA drift ratio reading, it is assumed that the wall can withstand the maximum vibration with a PGA drift ratio value of 0.34 g without causing damage to the wall even though it has a low level of stiffness. Testing on the walls has a less high degree of precision. That may be due to factors other than PGA. That can affect the drift ratio on the walls, which have not been considered in this study.


KNN; IMU Sensor; Wall Stiffness; PGA

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DOI: https://doi.org/10.22146/ijeis.59588

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