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This Author published in this journals
All Journal ELINVO (Electronics, Informatics, and Vocational Education) JOIV : International Journal on Informatics Visualization KOMPUTIKA - Jurnal Sistem Komputer JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Chemistry Education Review Online Learning in Educational Research Journal of Embedded Systems, Security and Intelligent Systems Journal of Deep Learning, Computer Vision and Digital Image Processing Jurnal MediaTIK Journal of Computers, Informatics, and Vocational Education
Fajar B, Muhammad
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Chemistry Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Social Sciences Other

Published : 13 Documents Claim Missing Document
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Journal : Journal of Embedded Systems, Security and Intelligent Systems

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Comparative Analysis of the Performance of Hadith Text Classification Methods: A Case Study with ANN and SVM Surianto, Dewi Fatmarani; Fajar B, Muhammad; Mulia, Musda Rida; Indanasufya, Indanasufya
Journal of Embedded Systems, Security and Intelligent Systems Vol 5, No 1 (2024): March 2024
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v5i1.2942

Abstract

Hadith is the second holy book for Muslims after the Quran, containing instructions from the Prophet Muhammad SAW, and narrated by Ulama / Mufti. As one of the main sources of Islamic teachings, hadith is used to explain and illustrate the teachings of the Quran. This study aims to compare the performance of hadith text classification using Artificial Neural Network (ANN) and Support Vector Machine (SVM) with Hadith Bukhari dataset. The stages include preprocessing, feature extraction with TF-IDF, classification, and evaluation. The evaluation results show different performance between ANN and SVM in two scenarios: with and without stemming. The use of stemming has a significant impact on model performance, reducing word variation and can result in a decrease in accuracy. The SVM model consistently showed higher accuracy than ANN in both scenarios, with the highest accuracy reaching 85% for classification without stemming. This study provides insight into the application of ANN and SVM in hadith text classification, emphasizing the importance of selecting a method that suits the characteristics of the data.
A Hybrid Framework for Plagiarism Detection: Integrating Token-Based Similarity with Density-Based Clustering Fajar B, Muhammad; Lestary, Fitriyanty Dwi; Surianto, Dewi Fatmarani
Journal of Embedded Systems, Security and Intelligent Systems Vol 6, No 1 (2025): March 2025
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v6i1.7664

Abstract

Plagiarism detection in academic assignments remains a critical challenge in maintaining academic integrity in higher education. This study proposes an automated method to detect content similarity between student assignment documents by combining Jaccard Similarity and DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithms. The process begins with the collection of student assignment files in digital format, followed by text extraction to form a set-based representation of each document. Jaccard Similarity is then used to compute the degree of similarity between every document pair, and the resulting similarity matrix is transformed into a distance matrix as input for DBSCAN. Experiments conducted on 23 documents yielded 253 unique document pairs. The results demonstrate that the method successfully identified pairs with high similarity scores—such as 0.9114 and 0.7226—which were visually confirmed through a heatmap and effectively grouped into clusters by DBSCAN. Parameter settings of eps = 0.3 and min_samples = 1 proved optimal for distinguishing original documents from those exhibiting substantial content overlap. This approach is not only accurate and efficient, but also eliminates the need for predefined cluster numbers, making it suitable for deployment in automated plagiarism detection systems for academic texts.
Co-Authors AA Sudharmawan, AA Abdul Muis Mappalotteng Abdul Wahid Arifiyanti, Fitria Bakri, Muh. Fajrin Bakri Dary Mochamad Rifqie Della Fadhilatunisa Dewi Fatmarani Surianto Edy Sabara Fajrin, Farid Fathahillah Fathahillah Hardy M, Galang Imran, Al Indanasufya, Indanasufya Indriani, Gebby Jumadi Mabe Parenreng Lestary, Fitriyanty Dwi M. Miftach Fakhri Makmur, Haerunnisya Mangesa, Riana T Muhammad Agung Muhammad Yahya Mulia, Musda Rida Nur, Muh Irfan Nurjannah, Elma Perdana, Am Akbar Mabrur Rahman, Ahmad Fadhli Rahmania , Suci Rosidah Rosidah Sanatang Satnur, Muh. Alham Sudarmanto Jayanegara Syam, Abd. Azis Wahid, M. Syahid Nur Wahyudi Wahyudi Wulandari Wulandari