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The Prediction of Earthquake Building Structure Strength: Modified K-Nearest Neighbour Employment

Indonesian Journal of Electical Engineering and Informatics (IJEEI) · Vol. 8 No. 4 (2020) · hlm. 733–745

DOI
10.52549/ijeei.v8i4.2403
ISSN
2089-3272, 2089-3272
Terbit
2020-11-29
Penerbit
IAES Indonesian Section
Bahasa
en

Abstrak

The earthquake damage brings significant effects. The resilience of buildings against the earthquake and the destruction’s location is not an efficient outcome from previous research. This study applied the Modified K-Nearest Neighbor (MK-NN) in predicting the concrete structures’ performance despite the earthquakes. The 2-story building prediction covered earthquake history, time, concrete quality, displacement, velocity, and acceleration. The analysis of MK-NN provided the values of Euclidean, distance calculation, validity, and weight voting towards the classification of damages as “Safe” or “Immediate Occupancy” (IO). The K values exploited were 1, 3, 5, 7, 9, and 11, and simulation data training at 10:90, 20:80, 30:70. This study revealed the highest degree of accuracy at 98.85% with K=1 and a ratio of 30:70. Simultaneously, the lowest error rate was 1.15% at a similar K value and ratio. Herein, MK-NN significantly exceeds the accuracy and error rate of KNN up to 1.02% and 0.69%, respectively. To date, the automatic calculation prototyping software was then successfully developed. Ensuring the application’s accuracy, the Confusion Matrix, the Black box, and User Acceptance Test (UAT) have been performed. In a nutshell, this study provides a significant contribution to planning and information analysis of earthquake-resistant construction.

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  • Riau University
  • University of Technology Malaysia
  • Universitas Sembilanbelas November Kolaka
  • M. Kh Dulati Taraz State University
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Syamsudhuha SyamsudhuhaAfiliasi tidak diketahui5
Dedi IrawanRiau University4
Romi Fadli SyahputraAfiliasi tidak diketahui4
Haryana HairiAfiliasi tidak diketahui4
Faisal SaeedUniversity of Technology Malaysia3
Defrianto DefriantoAfiliasi tidak diketahui3
Yan SoerbaktiAfiliasi tidak diketahui3
Fitri InsaniAfiliasi tidak diketahui2
Wahyu CandraUniversitas Sembilanbelas November Kolaka2
Vepy AsyanaAfiliasi tidak diketahui2
Dadang Syarif Shihabudin SahidAfiliasi tidak diketahui2
Mahyarni MahyarniAfiliasi tidak diketahui2
Wresni AnggrainiAfiliasi tidak diketahui2
Taofeeq D MoshoodAfiliasi tidak diketahui2
Rina AmeliaAfiliasi tidak diketahui2
Andika ThoibahAfiliasi tidak diketahui2
Pizaini PizainiAfiliasi tidak diketahui2
Lola OktaviaAfiliasi tidak diketahui2
Rahmad AbdillahAfiliasi tidak diketahui2
Bolat TassuovM. Kh Dulati Taraz State University2
Lestari HandayaniAfiliasi tidak diketahui2
Pramulia SuliandriAfiliasi tidak diketahui2
Ola A. AlwesabiAfiliasi tidak diketahui2
Raimbayeva SauleAfiliasi tidak diketahui2
Kurdyukov VitaliyAfiliasi tidak diketahui2
Dwi Utari IswavigraAfiliasi tidak diketahui2
Hidayati RusnedyAfiliasi tidak diketahui2
B. PranggonoAfiliasi tidak diketahui2
Elin Haerani HaeraniAfiliasi tidak diketahui2
Novi YantiAfiliasi tidak diketahui1
Ruri Hartika ZainAfiliasi tidak diketahui1
Adil SetiawanAfiliasi tidak diketahui1
Ari Sulistyo RiniAfiliasi tidak diketahui1
Sofia AnitaAfiliasi tidak diketahui1
Yolanda Enza WellaAfiliasi tidak diketahui1

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Sumber metadata: OAI-PMH (metadata jurnal) http://section.iaesonline.com/index.php/IJEEI/oai, pertama di-harvest 8 Nov 2026, terakhir terlihat 8 Nov 2026.
Metadata asli (oai_dc) 
<oai_dc:dc xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/  http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
	<dc:title xml:lang="en">The Prediction of Earthquake Building Structure Strength: Modified K-Nearest Neighbour Employment</dc:title>
	<dc:creator xml:lang="en">Okfalisa, Okfalisa</dc:creator>
	<dc:creator xml:lang="en">Nugraha, Septian</dc:creator>
	<dc:creator xml:lang="en">Saktioto, Saktioto</dc:creator>
	<dc:creator xml:lang="en">Zulkifli, Zahidah</dc:creator>
	<dc:creator xml:lang="en">Fauzi, S.S.M.</dc:creator>
	<dc:subject xml:lang="en">Earthquake</dc:subject>
	<dc:subject xml:lang="en">Modified K-Nearest Neighbor</dc:subject>
	<dc:subject xml:lang="en">Classification</dc:subject>
	<dc:subject xml:lang="en">Prediction</dc:subject>
	<dc:subject xml:lang="en">Performance Measurement</dc:subject>
	<dc:description xml:lang="en">The earthquake damage brings significant effects. The resilience of buildings against the earthquake and the destruction’s location is not an efficient outcome from previous research. This study applied the Modified K-Nearest Neighbor (MK-NN) in predicting the concrete structures’ performance despite the earthquakes. The 2-story building prediction covered earthquake history, time, concrete quality, displacement, velocity, and acceleration. The analysis of MK-NN provided the values of Euclidean, distance calculation, validity, and weight voting towards the classification of damages as “Safe” or “Immediate Occupancy” (IO).  The K values exploited were 1, 3, 5, 7, 9, and 11, and simulation data training at 10:90, 20:80, 30:70. This study revealed the highest degree of accuracy at 98.85% with K=1 and a ratio of 30:70. Simultaneously, the lowest error rate was 1.15% at a similar K value and ratio. Herein, MK-NN significantly exceeds the accuracy and error rate of KNN up to 1.02% and 0.69%, respectively. To date, the automatic calculation prototyping software was then successfully developed. Ensuring the application’s accuracy, the Confusion Matrix, the Black box, and User Acceptance Test (UAT) have been performed. In a nutshell, this study provides a significant contribution to planning and information analysis of earthquake-resistant construction.</dc:description>
	<dc:publisher xml:lang="en">IAES Indonesian Section</dc:publisher>
	<dc:date>2020-11-29</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://section.iaesonline.com/index.php/IJEEI/article/view/2403</dc:identifier>
	<dc:identifier>10.52549/ijeei.v8i4.2403</dc:identifier>
	<dc:source xml:lang="en">Indonesian Journal of Electrical Engineering and Informatics (IJEEI); Vol. 8 No. 4: December 2020; 733-745</dc:source>
	<dc:source>2089-3272</dc:source>
	<dc:source>2089-3272</dc:source>
	<dc:language>en</dc:language>
	<dc:relation>https://section.iaesonline.com/index.php/IJEEI/article/view/2403/577</dc:relation>
</oai_dc:dc>