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Iwan Iskandar
Department of Informatics Engineering, UIN Sultan Syarif Kasim Riau, Pekanbaru 28293, Indonesia
Computer Science & IT Electrical & Electronics Engineering Engineering Materials Science & Nanotechnology

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A classification of Quran translations using K-nearest neighbors, support vector machine and random forest method Nur Delifah; Nazruddin Safaat Harahap; Surya Agustian; Muhammad Irsyad; Iwan Iskandar
Science, Technology, and Communication Journal Vol. 6 No. 1 (2025): SINTECHCOM Journal (October 2025)
Publisher : Lembaga Studi Pendidikan dan Rekayasa Alam Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59190/stc.v6i1.337

Abstract

A Classification of Quranic verses based on topics is one of the efforts to facilitate understanding and searching for information in the holy book, especially for non-Arabic readers. This study aims to test and compare the performance of three text classification methods, namely K-nearest neighbors (KNN), support vector machine (SVM), and random forest (RF), in grouping translated Quranic verses into 15 topic classes, such as Islamic arkanul, faith, the Quran, science and its branches, charity, da'wah, jihad, human and social relations, and others. The dataset used is the English translation of the Quran with full preprocessing and an 80:20 data split for training and testing. The evaluation was carried out using accuracy, precision, recall, and F1-score metrics. The results show that RF achieved the best performance with an average F1-score of 58.48% and testing accuracy of 90.81%. KNN followed with an F1-score of 54.07% and the highest testing accuracy of 92.05%, while SVM produced the lowest F1-score at 50.76% and accuracy of 88.20%. The RF demonstrates a more balanced ability in recognizing all classes, KNN excels in overall accuracy, and SVM performs less optimally in this classification task. This research is expected to serve as a foundation for developing a more intelligent and contextual topic-based verse classification system.
Application of recursive feature elimination for sex classification of skull bones using random forest Zam Afryan; Iwan Iskandar; Iis Afrianty; Benny Sukma Negara; Fadhilah Syafria
Science, Technology, and Communication Journal Vol. 6 No. 3 (2026): SINTECHCOM Journal (June 2026)
Publisher : Lembaga Studi Pendidikan dan Rekayasa Alam Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59190/stc.v6i3.390

Abstract

In forensic anthropology, sex estimation from the skull was a crucial initial step when visual identification of a body was not possible. Conventional methods relied on morphological assessment by experts, which was inherently subjective and dependent on the observer's experience. To address this limitation, this study implemented a computational approach using the random forest algorithm combined with the Recursive Feature Elimination feature-selection technique. The approach was evaluated using craniometric measurements from 2,524 individuals, comprising 1,368 males and 1,156 females, sourced from the Howell's craniometric dataset. The main challenge was the high dimensionality of the data, comprising 85 measurement features after non-biological attributes were removed. Using an excessive number of variables simultaneously introduced irrelevant information that lowered the model's ability to recognize true patterns, so the feature-selection technique was used to iteratively select the most informative measurements. The results showed that the model using all 82 features achieved an accuracy of 86.49 percent, while the optimized model using only 20 selected features achieved a higher accuracy of 86.85 percent. This indicated that by reducing the feature set by 75 percent, the model became lighter while remaining more accurate. The selection process further identified that cheekbone width and the height of the posterior ear protrusion were the most discriminative measurements between male and female crania, consistent with established biological evidence. In conclusion, the combination of random forest and recursive feature elimination produced an efficient and accurate sex-identification model, opening opportunities for its development as an objective forensic identification tool in the future.
Co-Authors Benny Sukma Negara Fadhilah Syafria Iis Afrianty Muhammad Irsyad Nazruddin Safaat Harahap Nur Delifah Surya Agustian Zam Afryan