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Interactive Geographic Visualization and Unsupervised Learning for Optimal Assignment of Preachers to Appropriate Congregations

Journal of Applied Engineering and Technological Science (JAETS) · Vol. 6 No. 1 (2024) · hlm. 192–205

DOI
10.37385/jaets.v6i1.5760
ISSN
2715-6079, 2715-6087
Terbit
2024-12-15
Penerbit
Yayasan Riset dan Pengembangan Intelektual (YRPI)
Bahasa
en

Abstrak

Riau Province has a population of 6,642,874 and a diverse geography, which poses significant challenges in optimizing Islamic preaching activities. Traditional assignment methods often lead to inefficiencies due to misalignment between the preacher’s expertise and congregational needs, as well as logistical issues. This study integrates K-Means clustering and DBSCAN algorithms with interactive geographic visualization to optimize the assignment of preachers to mosques. We collected 435 data points, including 185 mosques and 250 preachers. K-Means was evaluated using the Elbow Method and Silhouette Score, identifying 10 clusters as optimal with a Silhouette Score of 0.435654. However, K-Means does not handle outliers effectively, as indicated by zero outliers in all configurations. DBSCAN was tested with various epsilon (eps) and minimum sample values. The optimal configuration with eps of 1.5 and 5 minimum samples resulted in 10 clusters with a Silhouette Score of 0.381108 and 60 outliers. DBSCAN effectively manages outliers and varying densities. Although K-Means is advantageous for its simplicity and higher Silhouette Scores, it is unable to handle outliers effectively. DBSCAN provides robust clustering for noisy data. Therefore, it can be concluded that hybridizing unsupervised learning algorithms with geographic visualization can potentially improve the effectiveness of preaching activities in Riau Province and enhance preacher assignment.

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29 penulis · 83 tautan kepenulisan bersama

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  • Universitas Merdeka Malang
  • Universitas Islam Negeri Sultan Syarif Kasim Riau
  • Afiliasi tidak diketahui
  • Organisasi berbeda
  • Organisasi sama
  • Afiliasi tidak diketahui
  • Garis lebih tebal = lebih banyak artikel bersama · lingkaran lebih besar = lebih banyak artikel
Tampilkan sebagai tabel
Rekan penulisOrganisasiArtikel bersama
Edy PrayitnoUniversitas Merdeka Malang1
Takim MulyantoUniversitas Merdeka Malang1
Muhammad Syaikhi Abdul Rahman WahidUniversitas Merdeka Malang1
Yenita RozaAfiliasi tidak diketahui1
Tuti AlawiyahAfiliasi tidak diketahui1
Atma MurniAfiliasi tidak diketahui1
Gumanti GumantiAfiliasi tidak diketahui1
Khairil AnwarAfiliasi tidak diketahui1
Asmal MayAfiliasi tidak diketahui1
Zamsiswaya ZamsiswayaAfiliasi tidak diketahui1
Alwizar AlwizarAfiliasi tidak diketahui1
Amril MansurAfiliasi tidak diketahui1
Makhfuzat MakhfuzatAfiliasi tidak diketahui1
Ismail MarzukiAfiliasi tidak diketahui1
Elin HaeraniAfiliasi tidak diketahui1
Fadhilah SyafriaAfiliasi tidak diketahui1
Novriyanto NovriyantoAfiliasi tidak diketahui1
Tengku NurainunUniversitas Islam Negeri Sultan Syarif Kasim Riau1
Nilda Tri PutriAfiliasi tidak diketahui1
Khairur Rijal JamaludinAfiliasi tidak diketahui1
Hayati Habibah Abdul TalibAfiliasi tidak diketahui1
Shari Mohd YusofAfiliasi 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://journal.yrpipku.com/index.php/jaets/oai, pertama di-harvest 9 Nov 2026, terakhir terlihat 9 Nov 2026.
Metadata asli (oai_dc)
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	<dc:title xml:lang="en">Interactive Geographic Visualization and Unsupervised Learning for Optimal Assignment of Preachers to Appropriate Congregations  </dc:title>
	<dc:creator xml:lang="en">Kurniawan, Rahmad</dc:creator>
	<dc:creator xml:lang="en">Daqiqil ID, Ibnu</dc:creator>
	<dc:creator xml:lang="en">Batubara, Abdul Somad</dc:creator>
	<dc:creator xml:lang="en">Lestari, Fitra</dc:creator>
	<dc:creator xml:lang="en">Adnan, Arisman</dc:creator>
	<dc:creator xml:lang="en">Fatayat, Fatayat</dc:creator>
	<dc:creator xml:lang="en">Husti, Ilyas</dc:creator>
	<dc:subject xml:lang="en">Preacher Assignment</dc:subject>
	<dc:subject xml:lang="en">Optimization</dc:subject>
	<dc:subject xml:lang="en">K-Means Clustering</dc:subject>
	<dc:subject xml:lang="en">DBSCAN</dc:subject>
	<dc:subject xml:lang="en">MUI Riau</dc:subject>
	<dc:description xml:lang="en">Riau Province has a population of 6,642,874 and a diverse geography, which poses significant challenges in optimizing Islamic preaching activities. Traditional assignment methods often lead to inefficiencies due to misalignment between the preacher’s expertise and congregational needs, as well as logistical issues. This study integrates K-Means clustering and DBSCAN algorithms with interactive geographic visualization to optimize the assignment of preachers to mosques. We collected 435 data points, including 185 mosques and 250 preachers. K-Means was evaluated using the Elbow Method and Silhouette Score, identifying 10 clusters as optimal with a Silhouette Score of 0.435654. However, K-Means does not handle outliers effectively, as indicated by zero outliers in all configurations. DBSCAN was tested with various epsilon (eps) and minimum sample values. The optimal configuration with eps of 1.5 and 5 minimum samples resulted in 10 clusters with a Silhouette Score of 0.381108 and 60 outliers. DBSCAN effectively manages outliers and varying densities. Although K-Means is advantageous for its simplicity and higher Silhouette Scores, it is unable to handle outliers effectively. DBSCAN provides robust clustering for noisy data. Therefore, it can be concluded that hybridizing unsupervised learning algorithms with geographic visualization can potentially improve the effectiveness of preaching activities in Riau Province and enhance preacher assignment.</dc:description>
	<dc:publisher xml:lang="en">Yayasan Riset dan Pengembangan Intelektual (YRPI)</dc:publisher>
	<dc:date>2024-12-15</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://journal.yrpipku.com/index.php/jaets/article/view/5760</dc:identifier>
	<dc:identifier>10.37385/jaets.v6i1.5760</dc:identifier>
	<dc:source xml:lang="en">Journal of Applied Engineering and Technological Science (JAETS); Vol. 6 No. 1 (2024): Journal of Applied Engineering and Technological Science (JAETS); 192-205</dc:source>
	<dc:source>2715-6079</dc:source>
	<dc:source>2715-6087</dc:source>
	<dc:source>10.37385/jaets.v6i1</dc:source>
	<dc:language>en</dc:language>
	<dc:relation>https://journal.yrpipku.com/index.php/jaets/article/view/5760/3685</dc:relation>
	<dc:rights xml:lang="en">Copyright (c) 2024 Journal of Applied Engineering and Technological Science (JAETS)</dc:rights>
</oai_dc:dc>