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All Journal Journal of Data Insights Journal of Computing Theories and Applications Saber: Jurnal Teknik Informatika, Sains dan Ilmu Komunikasi
Wendy Sarasjati
Universitas Muhammadiyah Semarang
Computer Science & IT Decision Sciences, Operations Research & Management Mathematics

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Hate Speech Detection on X Using K-Nearest Neighbor with TF–IDF and Cosine Similarity: Deteksi Ujaran Kebencian pada X Menggunakan K-Nearest Neighbor dengan TF–IDF dan Kesamaan Kosinus Faiq Madani; Arvanida Feizal Permana; Abdul Karim; Riyagung Nuryusufa Tranggono Adi Prasetya; Wendy Sarasjati
Journal of Data Insights Vol 4 No 1 (2026): Journal of Data Insights
Publisher : Department of Sains Data UNIMUS Universitas Muhammadiyah Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jodi.v4i1.1149

Abstract

The rapid growth of social media has increased online interactions but has also accelerated the spread of hate speech content that may negatively impact individuals and communities. X (formerly Twitter), as one of the largest social networking platforms, enables users to share opinions publicly, making automatic hate speech detection increasingly important. This research proposes a hate speech classification approach using the K-Nearest Neighbor (KNN) algorithm combined with Term Frequency–Inverse Document Frequency (TF–IDF) weighting and Cosine Similarity. The dataset consists of 900 social media posts collected through the platform API and manually labeled into hate speech and non-hate speech categories, consisting of 675 training data and 225 testing data. Prior to classification, text preprocessing techniques including tokenization, stopword removal, and stemming were applied to improve text quality. Model evaluation was conducted using 10-fold cross validation to assess classification performance. Experimental results showed that the KNN algorithm with Cosine Similarity distance measurement and K=3 parameter achieved an accuracy of 78.22% in hate speech detection tasks. The findings indicate that KNN combined with TF–IDF and Cosine Similarity provides a reliable approach for social media text classification and can support automated hate speech detection systems.
Transformer-Based Support for Content-Validity Pre-Screening in Educational Materials Safuan Safuan; Dhendra Marutho; Ahmad Ilham; Muhammad Munsarif; Wendy Sarasjati; Edy Winarno; Arnold Adimabua Ojugo; De Rosal Ignatius Moses Setiadi
Journal of Computing Theories and Applications Vol. 4 No. 1 (2026): JCTA 4(1) 2026
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jcta.16829

Abstract

Content validity assessment is essential for determining whether educational materials adequately represent intended learning outcomes. However, conventional assessment procedures require substantial expert time and may produce inconsistent decisions across large item collections. This study develops a transformer-based framework to support content-validity pre-screening through two complementary tasks: predicting expert-derived Aiken’s V coefficients and classifying instructional-item essentiality. The final dataset comprised 652 Indonesian-language educational text items independently evaluated by four subject-matter experts. To reduce information leakage, identical and normalized-equivalent texts were grouped before applying a group-aware 70:15:15 training–validation–test split. Classical TF-IDF-based baselines were compared with IndoBERT, multilingual BERT, XLM-RoBERTa, and multilingual DeBERTa-v3. For Aiken’s V regression, multilingual BERT achieved the lowest MAE of 0.0501, the lowest RMSE of 0.0625, and the highest R² of 0.5239, whereas multilingual DeBERTa-v3 achieved the highest Spearman correlation of 0.7532. For essentiality classification, XLM-RoBERTa achieved the highest accuracy of 0.8557 and Macro-F1 of 0.8161, whereas multilingual BERT achieved the highest balanced accuracy of 0.8135 and ROC-AUC of 0.9111. Error analysis showed that the models captured textual patterns associated with expert-derived outcomes but remained limited when judgments depended on broader curricular context, competency hierarchies, prerequisite relationships, or relationships among instructional items. The findings support the use of transformer models as human-in-the-loop decision-support tools for prioritizing uncertain or potentially problematic educational items. However, the framework should be interpreted as a pre-screening mechanism rather than a replacement for expert judgment, and external validation across institutions and disciplines remains necessary.
Karakterisasi Fitur dan Deteksi URL Phishing pada Dataset Indonesia Menggunakan LASSO+ dan Random Forest Wendy Sarasjati; Edy Winarno; Faiq Madani
SABER : Jurnal Teknik Informatika, Sains dan Ilmu Komunikasi Vol. 4 No. 3 (2026): Juli : Jurnal Teknik Informatika, Sains dan Ilmu Komunikasi
Publisher : STIKes Ibnu Sina Ajibarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59841/saber.v4i3.3904

Abstract

Phishing URL detection using machine learning often achieves high performance, but feature relevance may vary across datasets and collection contexts. This study analyzes the characteristics of an Indonesian phishing URL dataset and evaluates a compact detection model that prioritizes phishing recall. The dataset contains 2,054 balanced phishing and legitimate URLs represented by 70 lexical, structural, domain/WHOIS, infrastructure, and reputation features. Interquartile Range (IQR) was used to identify statistically extreme values, while Pearson correlation and Mutual Information were used to examine feature-target associations. LASSO+ was applied to reduce redundant features, and Random Forest with uncertainty-weighted bootstrap sampling was evaluated using stratified 10-fold cross-validation. The results show that 92.84% of the URLs contain at least one statistically extreme feature value. Response time, IP information, domain reputation, and hyperlink-related features show prominent associations with the target. LASSO+ reduces the feature set by approximately 36.3% without reducing Random Forest performance. The complete model achieves the highest recall of 0.9942 ± 0.0050 and the lowest false-negative count of six. These findings show that feature relevance needs to be validated for the target dataset and that a compact feature representation can support recall-oriented phishing detection.
Co-Authors Abdul Karim Ahmad Ilham Arnold Adimabua Ojugo Arvanida Feizal Permana De Rosal Ignatius Moses Setiadi Dhendra Marutho Edy Winarno Faiq Madani Muhammad Munsarif Riyagung Nuryusufa Tranggono Adi Prasetya Safuan Safuan