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All Journal Desimal: Jurnal Matematika Takuana: Jurnal Pendidikan, Sains, dan Humaniora
Putri, Lutfia Aisyah
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Arts Humanities Education Mathematics Social Sciences

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Kiprah Kerajaan Islam Dalam Penyebaran Islam di Indonesia Yahya, Iffatussabrina; Putri, Lutfia Aisyah; Hidayat, M. Zikri; Riadi, Muhammad Akbar; Agung, Muhammad Ariiq Alhafizh; Gusmawarni, Mutia; Domo, Arrasyidin Akmal
Takuana: Jurnal Pendidikan, Sains, dan Humaniora Vol 2, No 1 (2023): Takuana, April 2023
Publisher : MAN 4 Kota Pekanbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56113/takuana.v2i1.41

Abstract

The role of Islamic kingdoms before the colonial era significantly influenced the spread of Islam in Indonesia. They succeeded in forming and developing an Islamic education system different from the Dutch colonial education system. This research conducted a qualitative analysis of various historical and literary sources to explore the contribution of Islamic kingdoms to the spread of Islam in Indonesia. The results showed that Islamic kingdoms played a crucial role in Dakwah activities and in forming Islamic educational institutions such as pesantren. They also contributed to the development of Islamic culture through arts, literature, architecture, and so on. Therefore, understanding the history of Islamic kingdoms in Indonesia is crucial in comprehending the country's development of religion, culture, and education.
Enhancing multiclass SVM classification using a hybrid directed acyclic graph and rest-vs-rest strategy Nadeak, Christyan Tamaro; Farid, Fajri; Rassiyanti, Linda; Siahaan, Arielva Simon; Putri, Lutfia Aisyah
Desimal: Jurnal Matematika Vol. 8 No. 3 (2025): Desimal: Jurnal Matematika
Publisher : Universitas Islam Negeri Raden Intan Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24042/djm.v8i3.202529469

Abstract

This study proposes a modified Directed Acyclic Graph Support Vector Machine (DAG-SVM) using a Rest-vs-Rest (RvR) strategy to address the multiclass classification problem in the Hepatitis C dataset from Kaggle, which contains four diagnostic categories with a highly imbalanced class distribution, with class sample sizes of 540, 24, 21, and 30, respectively. The aim of this study is to examine how hierarchical decision structures interact with extreme class imbalance in SVM-based multiclass classification. The method is implemented through three fixed hierarchical decision schemes {0,1} vs. {2,3}, {0,2} vs. {1,3}, and {0,3} vs. {1,2} which restructure the decision flow of conventional DAG-SVM. Experimental evaluation shows that although the proposed schemes achieve relatively high overall accuracy (0.91–0.93), the precision, recall, and F1-scores for minority classes remain extremely low. These findings offer a new empirical insight into how class imbalance propagates through the DAG hierarchy, leading to early elimination of minority classes, and highlight the need for imbalance-handling techniques such as resampling, cost-sensitive learning, or synthetic data generation. The contribution of this work lies in demonstrating the limitations of DAG-RvR under severe imbalance and providing a structured evaluation that can guide future improvements for more reliable multiclass recognition.
Co-Authors Agung, Muhammad Ariiq Alhafizh Christyan Tamaro Nadeak Domo, Arrasyidin Akmal Farid, Fajri Gusmawarni, Mutia Hidayat, M. Zikri Rassiyanti, Linda Riadi, Muhammad Akbar Siahaan, Arielva Simon Yahya, Iffatussabrina