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STATISTICAL LEARNING FOR ISLAMIC HERITAGE IDENTIFICATION: CLASSIFYING SUMATRAN SULTANATE AFFILIATION THROUGH GRAVESTONE CHARACTERISTICS

Jurnal Ilmiah Ilmu Terapan Universitas Jambi · Vol. 10 No. 5 (2026) · hlm. 2759–2770

DOI
10.22437/jiituj.v10i5.59887
ISSN
2580-2259, 2580-2240
Terbit
2026-10-31
Penerbit
LPPM Universitas Jambi
Bahasa
en
Lisensi
creativecommons.org/licenses/by/4.0

Abstrak

Classifying sultanate affiliation based on gravestone characteristics is necessary to support more objective artifact identification. Similarities in gravestone shape and decoration across regions often complicate affiliation determination through visual observation alone. This study compares the performance of Linear Discriminant Analysis (LDA) and Multinomial Logistic Regression (MLR) in classifying sultanate affiliation based on the physical characteristics and decoration of gravestones in Sumatra. This study used 319 gravestone data from Islamic sultanate burial complexes in Sumatra. Sultanate affiliations were grouped as Aceh, Pagaruyung, Siak, Deli, and Others. The predictor variables included type, shape, material, physical condition, upper, middle, and lower decorative elements, and calligraphy text. All predictors were dummy-coded, and both models were evaluated using repeated stratified 5-fold cross-validation, accompanied by an assessment of LDA assumptions and a sensitivity analysis excluding gravestone type. The cross-validated accuracy was 95.28% for LDA and 93.51% for MLR. The difference between the two methods was not statistically significant, and LDA remained stable despite violating its assumptions. Gravestone type and material were the most influential attributes. Without gravestone type, the accuracy remained at approximately 87%, with material as the strongest marker of affiliation.

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  • Universitas Islam Negeri Sulthan Thaha Saifuddin Jambi
  • Universitas Jambi
  • National Research and Innovation Agency
  • Politeknik Negeri Sriwijaya
  • 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
Siti Ari BudhiyantiAfiliasi tidak diketahui3
Masagus Muhammad Prima PutraAfiliasi tidak diketahui2
U. UstadiAfiliasi tidak diketahui2
Nurfitri EkantariAfiliasi tidak diketahui2
Anandita Perwita KurniawanAfiliasi tidak diketahui1
Indun Dewi PuspitaAfiliasi tidak diketahui1
Venny AgustinPoliteknik Negeri Sriwijaya1
ALIM ISNANSETYOAfiliasi tidak diketahui1
INDAH ISTIQOMAHAfiliasi tidak diketahui1
Toni KuswoyoAfiliasi tidak diketahui1
Muwantoko MurwantokoAfiliasi tidak diketahui1
Nungky MufarochaAfiliasi tidak diketahui1
Antarif Kusuma BrataAfiliasi tidak diketahui1
Maria MadalenaAfiliasi tidak diketahui1
Yusuf Kalingga MurdaAfiliasi tidak diketahui1
Ervika Rahayu Novita HerwatiAfiliasi tidak diketahui1
Almira Islamei PratiwiAfiliasi tidak diketahui1
Bambang Retno AjiAfiliasi tidak diketahui1
Rani LaksanawatiAfiliasi tidak diketahui1
Annisa Ajeng MaharaniAfiliasi tidak diketahui1
Ivana M. DiharningrumAfiliasi tidak diketahui1
Rizqi Wahyu HerdiantoAfiliasi tidak diketahui1
Amir MugozinAfiliasi tidak diketahui1
AsnaniAfiliasi tidak diketahui1
KasmiatiAfiliasi tidak diketahui1
SuadiAfiliasi tidak diketahui1
nurjanah nurjanahAfiliasi tidak diketahui1
Zuriat ZuriatAfiliasi tidak diketahui1
Rina SyafitriAfiliasi tidak diketahui1
Mohamad GazaliAfiliasi tidak diketahui1
Mila Novalita SuhardaniAfiliasi tidak diketahui1
May Khoirunnisa SetiadiAfiliasi tidak diketahui1
Husna Vannisa SunandarAfiliasi tidak diketahui1
Elfrida Juliana NababanAfiliasi 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://online-journal.unja.ac.id/index.php/JIITUJ/oai, pertama di-harvest 5 Nov 2026, terakhir terlihat 8 Nov 2026.
Metadata asli (oai_dc) 
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	<dc:title xml:lang="en">STATISTICAL LEARNING FOR ISLAMIC HERITAGE IDENTIFICATION: CLASSIFYING SUMATRAN SULTANATE AFFILIATION THROUGH GRAVESTONE CHARACTERISTICS</dc:title>
	<dc:creator xml:lang="en">Nengsih, Titin Agustin</dc:creator>
	<dc:creator xml:lang="en">Ihsandi, Muharoma Fahnur</dc:creator>
	<dc:creator xml:lang="en">Huda, Imam Arifa’illah Syaiful</dc:creator>
	<dc:creator xml:lang="en">Husni, Amir</dc:creator>
	<dc:creator xml:lang="en">Andhifani, Wahyu Rizky</dc:creator>
	<dc:creator xml:lang="en">Putra, Benny Agusti</dc:creator>
	<dc:subject xml:lang="en">Classification</dc:subject>
	<dc:subject xml:lang="en">Linear Discriminant Analysis</dc:subject>
	<dc:subject xml:lang="en">Multinomial Logistic Regression</dc:subject>
	<dc:subject xml:lang="en">Sumatra</dc:subject>
	<dc:subject xml:lang="en">Sultanate Headstone</dc:subject>
	<dc:description xml:lang="en">Classifying sultanate affiliation based on gravestone characteristics is necessary to support more objective artifact identification. Similarities in gravestone shape and decoration across regions often complicate affiliation determination through visual observation alone. This study compares the performance of Linear Discriminant Analysis (LDA) and Multinomial Logistic Regression (MLR) in classifying sultanate affiliation based on the physical characteristics and decoration of gravestones in Sumatra. This study used 319 gravestone data from Islamic sultanate burial complexes in Sumatra. Sultanate affiliations were grouped as Aceh, Pagaruyung, Siak, Deli, and Others. The predictor variables included type, shape, material, physical condition, upper, middle, and lower decorative elements, and calligraphy text. All predictors were dummy-coded, and both models were evaluated using repeated stratified 5-fold cross-validation, accompanied by an assessment of LDA assumptions and a sensitivity analysis excluding gravestone type. The cross-validated accuracy was 95.28% for LDA and 93.51% for MLR. The difference between the two methods was not statistically significant, and LDA remained stable despite violating its assumptions. Gravestone type and material were the most influential attributes. Without gravestone type, the accuracy remained at approximately 87%, with material as the strongest marker of affiliation.</dc:description>
	<dc:publisher xml:lang="en">LPPM Universitas Jambi</dc:publisher>
	<dc:date>2026-10-31</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://online-journal.unja.ac.id/JIITUJ/article/view/59887</dc:identifier>
	<dc:identifier>10.22437/jiituj.v10i5.59887</dc:identifier>
	<dc:source xml:lang="en">Jurnal Ilmiah Ilmu Terapan Universitas Jambi; Vol. 10 No. 5 (2026): Volume 10, Nomor 5, October 2026; 2759-2770</dc:source>
	<dc:source>2580-2259</dc:source>
	<dc:source>2580-2240</dc:source>
	<dc:source>10.22437/jiituj.v10i5</dc:source>
	<dc:language>en</dc:language>
	<dc:relation>https://online-journal.unja.ac.id/JIITUJ/article/view/59887/26003</dc:relation>
	<dc:rights xml:lang="en">Copyright (c) 2026 Titin Agustin Nengsih</dc:rights>
	<dc:rights xml:lang="en">https://creativecommons.org/licenses/by/4.0</dc:rights>
</oai_dc:dc>