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Optimizing K-Means Clustering Parameters for Mapping Smart Contract Transaction Characteristics: A Comparative Analysis of Evaluation Metrics in the IOTA Ecosystem

Indonesian Journal of Electical Engineering and Informatics (IJEEI) · Vol. 14 No. 1 (2026) · hlm. 306–324

DOI
10.52549/ijeei.v14i1.7741
ISSN
2089-3272, 2089-3272
Terbit
2026-03-31
Penerbit
IAES Indonesian Section
Bahasa
en

Abstrak

Smart contracts are already a major development in digital transaction automation thanks to blockchain technology, but their operational efficiency is still greatly impacted by resource consumption, transaction success rates, and gas cost dynamics. This study aims to optimize the K-Means Clustering algorithm's parameters in order to map the characteristics of smart contract transactions in the IOTA ecosystem and provide thorough insights into the efficiency of gas allocation. Using a massive dataset of 566,303 empirical transactions from the IOTA Tangle, three key metrics the Silhouette Coefficient, Davies-Bouldin Index, and Calinski-Harabasz Index were compared to verify the quality of the clustering. With a Silhouette Coefficient value of 0.9851, Davies-Bouldin Index of 0.4622, and Calinski-Harabasz Index of 741,423.92, quantitative evaluation results demonstrate that the 3- cluster structure performs better than two clusters. These results validate the 3-cluster model's ability to more accurately divide transactions into categories that are efficient, complex, and gas-inefficient. The results of this mapping can serve as the foundation for creating an automated recommendation system for optimizing transaction costs in decentralized networks. This study shows that the Gas Limit and Gas Consumed indicators are crucial predictors of transaction efficiency.

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  • Sriwijaya University
  • Universitas Dinamika Bangsa
  • Sultan Agung Islamic University
  • Udayana University
  • Universitas Ahmad Dahlan
  • Samudra University
  • Al Baha University
  • Organisasi lain
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Rekan penulisOrganisasiArtikel bersama
Rahmat BudiartoAl Baha University17
Mohd. Yazid IdrisUniversity of Technology Malaysia11
Sharipuddin SharipuddinUniversitas Dinamika Bangsa5
Benni PurnamaUniversitas Dinamika Bangsa5
Zainuddin NawawiSriwijaya University4
Suci DwijayantiSriwijaya University4
Kurniabudi KurniabudiUniversitas Dinamika Bangsa4
Darmawijoyo HanapiSriwijaya University4
Siti NurmainiSriwijaya University4
Ahmad ZarkasiSriwijaya University4
Tole SutiknoUniversitas Ahmad Dahlan3
Munawar Agus RiyadiDiponegoro University3
Lina HandayaniUniversitas Ahmad Dahlan2
Imam Much Ibnu SubrotoSultan Agung Islamic University2
Sri Arttini Dwi PrasetyowatiSultan Agung Islamic University2
Yaya Sudarya TrianaMercu Buana University2
Mohd Azam OsmanUniversiti Sains Malaysia2
Robi PrasetioSriwijaya University2
M. Agus Syamsul ArifinUniversitas Bina Insan2
Eko Arip WinantoUniversitas Dinamika Bangsa2
Darmawijoyo DarmawijoyoSriwijaya University2
Bedine KerimUniversité de Nouakchott2
Muhammad Khosyi’inSultan Agung Islamic University2
Hadipurnawan SatriaSriwijaya University2
Anggina PrimanitaSriwijaya University2
I Gede YusaUdayana University1
Dewi BungaUniversitas Hindu Negeri I Gusti Bagus Sugriwa Denpasar1
Ni Ketut Supasti DharmawanUdayana University1
Desak Putu Dewi KasihUdayana University1
Eka Nuryanto BudisusilaSultan Agung Islamic University1
Silfani Sandra MirandaSriwijaya University1
Dlulil AmriSriwijaya University1
Hadita AmilaSamudra University1
Ramaidhani RamaidhaniSamudra University1

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) http://section.iaesonline.com/index.php/IJEEI/oai, pertama di-harvest 8 Nov 2026, terakhir terlihat 8 Nov 2026.
Metadata asli (oai_dc) 
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	<dc:title xml:lang="en">Optimizing K-Means Clustering Parameters for Mapping Smart Contract Transaction Characteristics: A Comparative Analysis of Evaluation Metrics in the IOTA Ecosystem</dc:title>
	<dc:creator xml:lang="en">Ubaya, Huda</dc:creator>
	<dc:creator xml:lang="en">Stiawan, Deris</dc:creator>
	<dc:creator xml:lang="en">Suprapto, Bhakti Yudho</dc:creator>
	<dc:creator xml:lang="en">Ekaputra, Rivaldi Febrian</dc:creator>
	<dc:creator xml:lang="en">Afifah, Nurul</dc:creator>
	<dc:creator xml:lang="en">Ningrum, Septiani Kusuma</dc:creator>
	<dc:subject xml:lang="en">IOTA Tangle</dc:subject>
	<dc:subject xml:lang="en">Smart Contract</dc:subject>
	<dc:subject xml:lang="en">K-Means Clustering</dc:subject>
	<dc:subject xml:lang="en">Silhouette Coefficient</dc:subject>
	<dc:subject xml:lang="en">Davies-Bouldin Index</dc:subject>
	<dc:subject xml:lang="en">Calinski-Harabasz Index</dc:subject>
	<dc:description xml:lang="en">Smart contracts are already a major development in digital transaction automation thanks to blockchain technology, but their operational efficiency is still greatly impacted by resource consumption, transaction success rates, and gas cost dynamics. This study aims to optimize the K-Means Clustering algorithm's parameters in order to map the characteristics of smart contract transactions in the IOTA ecosystem and provide thorough insights into the efficiency of gas allocation. Using a massive dataset of 566,303 empirical transactions from the IOTA Tangle, three key metrics the Silhouette Coefficient, Davies-Bouldin Index, and Calinski-Harabasz Index were compared to verify the quality of the clustering. With a Silhouette Coefficient value of 0.9851, Davies-Bouldin Index of 0.4622, and Calinski-Harabasz Index of 741,423.92, quantitative evaluation results demonstrate that the 3- cluster structure performs better than two clusters. These results validate the 3-cluster model's ability to more accurately divide transactions into categories that are efficient, complex, and gas-inefficient. The results of this mapping can serve as the foundation for creating an automated recommendation system for optimizing transaction costs in decentralized networks. This study shows that the Gas Limit and Gas Consumed indicators are crucial predictors of transaction efficiency.</dc:description>
	<dc:publisher xml:lang="en">IAES Indonesian Section</dc:publisher>
	<dc:date>2026-03-31</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/7741</dc:identifier>
	<dc:identifier>10.52549/ijeei.v14i1.7741</dc:identifier>
	<dc:source xml:lang="en">Indonesian Journal of Electrical Engineering and Informatics (IJEEI); Vol. 14 No. 1: March 2026; 306-324</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/7741/1073</dc:relation>
</oai_dc:dc>