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All Journal Jurnal Ilmiah Ilmu Terapan Universitas Jambi
Ihsandi, Muharoma Fahnur
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Education Engineering Industrial & Manufacturing Engineering

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STATISTICAL LEARNING FOR ISLAMIC HERITAGE IDENTIFICATION: CLASSIFYING SUMATRAN SULTANATE AFFILIATION THROUGH GRAVESTONE CHARACTERISTICS Nengsih, Titin Agustin; Ihsandi, Muharoma Fahnur; Huda, Imam Arifa’illah Syaiful; Husni, Amir; Andhifani, Wahyu Rizky; Putra, Benny Agusti
Jurnal Ilmiah Ilmu Terapan Universitas Jambi Vol. 10 No. 5 (2026): Volume 10, Nomor 5, October 2026
Publisher : LPPM Universitas Jambi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22437/jiituj.v10i5.59887

Abstract

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.
Co-Authors Amir Husni Andhifani, Wahyu Rizky Benny Agusti Putra Huda, Imam Arifa’illah Syaiful Titin Agustin Nengsih