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Junadhi Junadhi
Department of Informatics Engineering, Universitas Sains dan Teknologi Indonesia, Pekanbaru 28299, Indonesia
Computer Science & IT Electrical & Electronics Engineering Engineering Materials Science & Nanotechnology

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Enhancing Indonesian hadith classification through multi-word embedding and support vector machine Mila Hastati; Junadhi Junadhi; Susi Erlinda; Agustin Agustin
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.v6i3.384

Abstract

Hadith classification plays an important role in supporting the organization and retrieval of Islamic knowledge in digital environments. However, the increasing volume of digital hadith collections presents challenges for manual classification, making automated approaches increasingly necessary. This study proposes a hadith text classification framework based on support vector machine (SVM) and a Multi-Word Embedding approach. The dataset used in this study was obtained from the Kaggle hadith dataset repository and consists of 34,441 hadith records. The textual data were preprocessed through case folding, noise removal, stopword removal, and stemming before feature extraction. Three embedding strategies were evaluated, namely Word2Vec, FastText, and the proposed multi-word embedding, which combines Word2Vec and FastText representations through vector concatenation. The generated feature vectors were subsequently classified using SVM and evaluated using accuracy, precision, recall, and F1-score. Experimental results show that the proposed multi-word embedding approach achieved the best performance, obtaining an accuracy of 75.58%, precision of 75.68%, recall of 75.58%, and F1-score of 75.46%. These results outperform Word2Vec + SVM and FastText + SVM, demonstrating that the integration of contextual semantic and subword-level information produces richer feature representations and improves classification effectiveness. The findings indicate that multi-word embedding is a promising approach for automated hadith text classification and can contribute to the development of intelligent Islamic information systems.
Implementation of U-Net as EfficientNet encoder for brain tumour type classification Rahma Aulia; Junadhi Junadhi; Lusiana Efrizoni; Rini Yanti
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.v6i3.387

Abstract

Brain tumor is one of the most dangerous diseases that requires fast and accurate diagnosis to support patient diagnose. The application of deep learning on magnetic resonance imaging (MRI) images has been widely used to assist automatic brain tumor classification. This study aims to implement a hybrid U-Net encoder-EfficientNet architecture for brain tumor classification using MRI images. In this study, the U-Net encoder was utilized to extract spatial features and generate an attention mask to highlight important regions before the classification process was performed by EfficientNet-B0. The dataset used was BRISC 2025, consisting of 6,000 MRI images divided into four classes: glioma, meningioma, pituitary, and no tumor. The experiments were conducted using three data splitting scenarios, namely 60:20:20, 70:15:15, and 80:10:10. The results showed that the proposed model achieved good classification performance across all testing scenarios. In the 60:20:20 scenario, the model achieved an accuracy of 82%, precision of 0.83, recall of 0.82, and F1-score of 0.81. In the 70:15:15 scenario, the model achieved an accuracy of 84%, precision of 0.85, recall of 0.84, and F1-score of 0.83. Meanwhile, the 80:10:10 scenario produced the best performance with an accuracy of 85%, precision of 0.86, recall of 0.85, and F1-score of 0.84. These results indicate that the use of the U-Net encoder was able to help the model focus on tumor regions, thereby improving the effectiveness of the classification process.
Nutri-score classification of snack products using word embedding and random forest Onky Wanda Darmawan; Junadhi Junadhi; Lusiana Efrizoni; Nurjayadi Nurjayadi
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.v6i3.393

Abstract

The increasing consumption of packaged snack products has raised concerns regarding their nutritional quality and potential health impacts. Although nutritional information is commonly provided on food packaging, many consumers experience difficulties in interpreting ingredient descriptions and nutritional labels, making it challenging to identify whether a product is healthy or unhealthy. Therefore, an automated classification system is needed to assist consumers in understanding nutritional information more effectively. This study proposes a text-based classification framework for categorizing snack products into healthy and unhealthy classes using Natural Language Processing (NLP), word embedding techniques, and the Random Forest algorithm. The dataset was obtained from the Open Food Facts database and filtered to include snack products only. After preprocessing and class balancing, a total of 465 samples were used for model development and evaluation. The preprocessing stage consisted of case folding, tokenization, stopword removal, and stemming. Three word embedding techniques, namely Word2Vec, GloVe, and FastText, were employed to transform textual ingredient descriptions into numerical feature representations. Subsequently, Random Forest was utilized as the classification algorithm, and its performance was evaluated using Accuracy, Balanced Accuracy, Precision, Recall, F1-score, and Macro F1-score. The experimental results show that GloVe achieved the best performance among the evaluated embedding methods, obtaining an accuracy of 86.02%, balanced accuracy of 84.72%, precision of 85.98%, recall of 86.02%, F1-score of 85.91%, and macro F1-score of 85.19%. The findings indicate that GloVe provides a more effective semantic representation of food-related textual information compared to Word2Vec and FastText. Overall, the proposed framework demonstrates the potential of NLP-based approaches for automated nutritional assessment and healthy food classification.
Comparative evaluation of PCA-based feature extraction and chi-square feature selection for student burnout classification using support vector machine Mira Salmira; Junadhi Junadhi; Rahmiati Rahmiati; Triyani Arita Fitri
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.v6i3.401

Abstract

Student depression has emerged as a critical mental health issue that adversely affects academic performance, psychological well-being, and quality of life. Early identification of depression risk is essential to enable timely intervention and effective mental health management. This study compares the effectiveness of two feature engineering techniques, principal component analysis (PCA)-based feature extraction and chi-square feature selection, for student depression classification using support vector machine (SVM). The experiments employed the student depression dataset from Kaggle, containing demographic, academic, lifestyle, and psychological attributes. Data preprocessing included data cleaning, label encoding, and feature scaling before feature engineering and classification. PCA was applied to reduce feature dimensionality while preserving the maximum data variance, whereas chi-square selected the most relevant features based on statistical significance. Model performance was evaluated using accuracy, precision, recall, and F1-score. The results demonstrate that PCA consistently outperformed chi-square feature selection. The PCA–SVM model achieved an accuracy of 84.09%, precision of 84.24%, recall of 84.09%, and F1-score of 84.14%, compared with 83.61%, 83.75%, 83.61%, and 83.65%, respectively, for the chi-square–SVM model. These findings indicate that PCA is more effective in reducing feature redundancy while preserving informative patterns, resulting in improved classification performance. Therefore, PCA-based feature extraction is a more suitable feature engineering approach for SVM-based student depression classification and offers a promising solution for intelligent early mental health screening in higher education.
Co-Authors Agustin Agustin Lusiana Efrizoni Mila Hastati Mira Salmira Nurjayadi Nurjayadi Onky Wanda Darmawan Rahma Aulia Rahmiati Rahmiati Rini Yanti Susi Erlinda Triyani Arita Fitri