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All Journal Infotekmesin Journal of Innovation Information Technology and Application (JINITA) Journal of Informatics, Information System, Software Engineering and Applications (INISTA) Jurnal Teknik Informatika (JUTIF) Journal of English Language and Education UNNES International Conference on ELTLT ⁠International Journal of Sustainable Social Culture, Science Technology, Management, and Law Humanities Wanastra: Jurnal Bahasa dan Sastra
Sari, Laura
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Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Environmental Science Languange, Linguistic, Communication & Media Mechanical Engineering Other

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Perbandingan Pendekatan Machine Learning untuk Mendeteksi Serangan DDoS pada Jaringan Komputer Faiz, Muhammad Nur; Muhammad, Arif Wirawan; Sari, Laura
Infotekmesin Vol 16 No 1 (2025): Infotekmesin: Januari 2025
Publisher : P3M Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/infotekmesin.v16i1.2556

Abstract

Distributed Denial of Service (DDoS) attacks are a serious threat to computer network security. This study offers a comprehensive evaluation by considering accuracy, detection time, and model complexity in simulation scenarios. Using the CICDDoS2019 dataset, which includes modern attack variations and complete features, this research compares the effectiveness of Naïve Bayes (NB), Random Forest (RF), and Decision Tree (DT) algorithms in detecting DDoS attacks. The results show that RF achieves the highest accuracy (99.95%), while DT excels in recall (99.83%). These findings provide a foundation for developing hybrid ML-DL models to enhance real-time attack detection. However, limitations such as using a single dataset and offline simulations restrict the generalizability of results to real-world network conditions. This study highlights opportunities for more comprehensive future research in real-world scenarios.
Mixed-Data K-Means Clustering with Hyperparameter-Tuned Random Forest for OSS-Based MSME Investment Profiling and Policy Targeting Sari, Laura; Maharrani, Ratih Hafsarah; Hastuti, Hety Dwi; Ramadhan, Adrian Putra; Windasari, Wahyuni
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 2 (2026): JUTIF Volume 7, Number 2, April 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.2.5545

Abstract

Administrative data of Micro, Small, and Medium Enterprises collected through the Online Single Submission system are highly heterogeneous, combining numerical and categorical attributes that hinder conventional investment segmentation and early-stage policy mapping. This study aims to develop a predictive clustering framework for enterprise investment profiling using mixed-type administrative data. The proposed methodology applies robust preprocessing, including RobustScaler for numerical variables and one-hot encoding with singular value decomposition for categorical features. Mixed-type similarity is computed using Gower distance, followed by a hybrid Gower–K-Means clustering approach, where the optimal number of clusters (k = 3) is determined using Silhouette, Calinski–Harabasz, and Davies–Bouldin indices. A comparative evaluation of clustering algorithms is conducted, with K-Prototypes performing best in the initial assessment and K-Means achieving superior performance after optimization. Cluster membership is subsequently predicted using a Random Forest classifier with hyperparameters optimized through randomized search. Experiments on 20,857 enterprise records identify three distinct clusters representing low-capital micro enterprises, transitional firms, and asset-intensive corporate entities. The optimized K-Means model achieves a Silhouette score of 0.97 and a Davies–Bouldin Index of 0.54. Compared with the untuned baseline, the tuned Random Forest model improves recall from 0.25 to 0.75 (200% increase) and increases the F1-score from 0.40 to 0.86 (114% improvement), while achieving 99.89% accuracy. These gains correspond to an estimated 20–30% improvement in MSME investment mapping effectiveness compared with traditional profiling approaches, providing a scalable AI-based blueprint for targeted regional economic governance.
Politeness and Indirectness: When Sexism Hides Behind Advice in Workplace Statements Romadloni, Annisa; Wanti, Linda Perdana; Sari, Laura
Wanastra: Jurnal Bahasa dan Sastra Vol. 18 No. 1 (2026): March
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/wanastra.v18i1.12140

Abstract

Sexism in workplaces is often framed as ordinary guidance or evaluation rather than as overt hostility, which can cause sentiment and toxicity filters to miss it. A corpus analysis was conducted using the Sexist Workplace Statements dataset (1,142 statements; 627 labeled sexist). A pragmatics-informed operationalization was applied to classify sexist statements as benevolent or hostile and to label each statement’s primary speech act as advice, evaluation, insult, joke, or complaint. Benevolent sexism was estimated to constitute 73.8% of sexist statements, while hostile sexism constituted 26.2%. Benevolent sexism was concentrated in evaluation and advice, whereas hostile sexism was concentrated in insults. A sentiment-or-profanity toxicity proxy achieved high precision but low recall for sexism, capturing most hostile sexism while missing most benevolent sexism. A supervised baseline (TF–IDF plus logistic regression) performed well on the binary label but still showed false negatives dominated by benevolent evaluations. The findings were interpreted through ambivalent sexism theory, speech act theory, and politeness theory, highlighting how indirectness and face-work enable discriminatory norms to be advanced under the guise of help.  These results make explicit that sexism detection systems should incorporate pragmatics- and speech-act-aware features to reliably identify benevolent, “helpful”-framed workplace sexism that standard sentiment/toxicity signals systematically overlook.
Co-Authors Annisa Romadloni Arif Wirawan Muhammad Arif Wirawan Muhammad Davito Rasendriya Rizqullah Putra Fadlil , Abdul Faiz , Muhammad Nur Hastuti, Hety Dwi Hazrina, Fadhilla Imam Riadi Johanna, Anne Listyaningrum, Rostika Maharrani, Ratih Hafsarah Muhammad Nur Faiz, Muhammad Nur Nur Wachid Adi Prasetya Oman Somantri Perdana Wanti, Linda Purwaningrum, Santi Puspitasari, Lina Rahadi, Nur Wahyu Ramadhan, Adrian Putra Sukma Aji Supriyono, Abdul Rohman Wahyuni Windasari Wanti, Linda Perdana