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Service quality dealer identification: the optimization of K-Means clustering

Sinergi · Vol. 27 No. 3 (2023) · hlm. 433–442

DOI
10.22441/sinergi.2023.3.014
ISSN
2460-1217, 1410-2331
Terbit
2023-09-12
Penerbit
Universitas Mercu Buana
Bahasa
eng

Abstrak

Service quality and customer satisfaction directly influence company branding, reputation and customer loyalty. As a liaison between producers and consumers, dealers must preserve valuable consumer relationships to increase customer satisfaction and adherence. Lack of comprehensive measurement and standardization regarding service quality emerges as a consideration issue towards the company service excellence. Therefore, identifying the service quality performance and grouping develops into valuable contributions in decision-making to control and enhance the company's intention. This study applies the K-Means Algorithm by optimizing the number of clusters in identifying dealer service quality performance. Hence, the ultimate service quality formation will be performed. The analysis found three dealer identification categories, including Cluster One, with 125 dealers grouped as good performance; Cluster Two, with 30 dealers grouped as very good performance; and Cluster Three, with 38 dealers grouped as not good performance. In order to evaluate the efficacy of optimum k value, the lists of testing approaches are conducted and compared, whereby Calinski-Harabasz, Elbow, Silhouette Score, and Davies-Bouldin Index (DBI) contribute in k=3. As a result, the optimum clusters are determined through the highest performance of k values as three. These three clusters have successfully identified the service quality level of dealers effectively and administered the company guidelines for corrective actions and improvements in customer service quality instead of the standardized normal distribution grouping calculation.

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  • National University of Malaysia
  • University of Technology Malaysia
  • King Abdulaziz University
  • Riau University
  • Universitas Sembilanbelas November Kolaka
  • M. Kh Dulati Taraz State University
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Tampilkan sebagai tabel
Rekan penulisOrganisasiArtikel bersama
Saktioto SaktiotoAfiliasi tidak diketahui7
Raed AlDhubhaniAfiliasi tidak diketahui2
Fathy Elbouraey EassaKing Abdulaziz University2
Redwan A.K. NoamanNational University of Malaysia2
Mohd Alauddin Mohd AliNational University of Malaysia2
Nasharuddin ZainalNational University of Malaysia2
Zahidah ZulkifliAfiliasi tidak diketahui2
Dedi IrawanRiau University2
Romi Fadli SyahputraAfiliasi tidak diketahui2
Syamsudhuha SyamsudhuhaAfiliasi tidak diketahui2
Haryana HairiAfiliasi tidak diketahui2
Novi YantiAfiliasi tidak diketahui2
Ruri Hartika ZainAfiliasi tidak diketahui2
Adil SetiawanAfiliasi tidak diketahui2
Mahyarni MahyarniAfiliasi tidak diketahui2
Wresni AnggrainiAfiliasi tidak diketahui2
Taofeeq D MoshoodAfiliasi tidak diketahui2
Septian NugrahaAfiliasi tidak diketahui1
S.S.M. FauziAfiliasi tidak diketahui1
Wahyu CandraUniversitas Sembilanbelas November Kolaka1
Vepy AsyanaAfiliasi tidak diketahui1
Dadang Syarif Shihabudin SahidAfiliasi tidak diketahui1
Rina AmeliaAfiliasi tidak diketahui1
Defrianto DefriantoAfiliasi tidak diketahui1
Andika ThoibahAfiliasi tidak diketahui1
Yan SoerbaktiAfiliasi tidak diketahui1
Pizaini PizainiAfiliasi tidak diketahui1
Lola OktaviaAfiliasi tidak diketahui1
Rahmad AbdillahAfiliasi tidak diketahui1
Bolat TassuovM. Kh Dulati Taraz State University1
Lestari HandayaniAfiliasi tidak diketahui1
Pramulia SuliandriAfiliasi tidak diketahui1
Ola A. AlwesabiAfiliasi tidak diketahui1
Raimbayeva SauleAfiliasi tidak diketahui1
Kurdyukov VitaliyAfiliasi tidak diketahui1

Afiliasi berasal dari OpenAlex; metadata OAI-PMH jurnal tidak memuatnya. Penulis tanpa ID OpenAlex dicocokkan berdasarkan nama, sehingga dua orang bisa tergabung atau satu orang bisa terpisah.

Sumber metadata: OAI-PMH (metadata jurnal) https://publikasi.mercubuana.ac.id/index.php/sinergi/oai, pertama di-harvest 10 Nov 2026, terakhir terlihat 10 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-US">Service quality dealer identification: the optimization of K-Means clustering</dc:title>
	<dc:creator>Enza Wella, Yolanda</dc:creator>
	<dc:creator>Okfalisa, Okfalisa</dc:creator>
	<dc:creator>Insani, Fitri</dc:creator>
	<dc:creator>Saeed, Faisal</dc:creator>
	<dc:creator>Che Hussin, Ab Razak</dc:creator>
	<dc:subject xml:lang="en-US">Algorithm Optimization</dc:subject>
	<dc:subject xml:lang="en-US">Calinski-Harabasz</dc:subject>
	<dc:subject xml:lang="en-US">Davies-Bouldin Index (DBI)</dc:subject>
	<dc:subject xml:lang="en-US">Elbow Method</dc:subject>
	<dc:subject xml:lang="en-US">K-Mean Clustering</dc:subject>
	<dc:subject xml:lang="en-US">Service Quality Identification</dc:subject>
	<dc:subject xml:lang="en-US">Silhouette Score</dc:subject>
	<dc:description xml:lang="en-US">Service quality and customer satisfaction directly influence company branding, reputation and customer loyalty. As a liaison between producers and consumers, dealers must preserve valuable consumer relationships to increase customer satisfaction and adherence. Lack of comprehensive measurement and standardization regarding service quality emerges as a consideration issue towards the company service excellence. Therefore, identifying the service quality performance and grouping develops into valuable contributions in decision-making to control and enhance the company's intention. This study applies the K-Means Algorithm by optimizing the number of clusters in identifying dealer service quality performance. Hence, the ultimate service quality formation will be performed. The analysis found three dealer identification categories, including Cluster One, with 125 dealers grouped as good performance; Cluster Two, with 30 dealers grouped as very good performance; and Cluster Three, with 38 dealers grouped as not good performance. In order to evaluate the efficacy of optimum k value, the lists of testing approaches are conducted and compared, whereby Calinski-Harabasz, Elbow, Silhouette Score, and Davies-Bouldin Index (DBI) contribute in k=3. As a result, the optimum clusters are determined through the highest performance of k values as three. These three clusters have successfully identified the service quality level of dealers effectively and administered the company guidelines for corrective actions and improvements in customer service quality instead of the standardized normal distribution grouping calculation. </dc:description>
	<dc:publisher xml:lang="en-US">Universitas Mercu Buana</dc:publisher>
	<dc:date>2023-09-12</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en-US">Peer-reviewed Article</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:format>application/msword</dc:format>
	<dc:identifier>https://publikasi.mercubuana.ac.id/index.php/sinergi/article/view/20595</dc:identifier>
	<dc:identifier>10.22441/sinergi.2023.3.014</dc:identifier>
	<dc:source xml:lang="en-US">SINERGI; Vol. 27 No. 3 (2023); 433-442</dc:source>
	<dc:source>2460-1217</dc:source>
	<dc:source>1410-2331</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://publikasi.mercubuana.ac.id/index.php/sinergi/article/view/20595/7476</dc:relation>
	<dc:relation>https://publikasi.mercubuana.ac.id/index.php/sinergi/article/view/20595/11618</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2023 SINERGI</dc:rights>
</oai_dc:dc>