Claim Missing Document
Check
Articles

Found 32 Documents
Search

ARTIFICAL INTELLIGENCE IN ENGLISH-MEDIATED COMMUNICATION: BIBLIOMETRIC ANALYSIS OF EMERGING RESEARCH PATTERN (2000-2024) Laksani, Hening; Muslihah, Isnawati
EDUCATION AND LINGUISTICS KNOWLEDGE JOURNAL Vol 8 No 1 (2026): Education and Linguistics Knowledge Journal (Edulink)
Publisher : Fakultas Keguruan dan Ilmu Pendidikan Universitas Islam Kadiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32503/edulink.v8i1.8828

Abstract

The rapid integration of Artificial Intelligence (AI) into communication practices has transformed the interaction processes by means of bridging language barriers, assisting communication from different languages and cultures. This research aims to map the research trend on AI within English-mediated communication by exploring publication patters, collaboration networks, and thematic scopes in Scopus-indexed journal. Bibliometric analysis was used to interpret thematic transition retrieved from Scopus database period 2000-2024 in order to visualize the co-occurrences and co-citation networks within the field. The findings revealed the remarkable improvement in research about the role of AI in communication processes over the past decade. It revealed significant contributing regions of having highest attention in this scope including US and China, with the leading institution was Stanford University. Meanwhile, the co-citation mapping recognized the authors in this area such as Hancock and Zhai. The findings revealed the role of AI in English-mediated communication covered a wide range of interdisciplinary paradigm integrating several subject areas.
Hyperparameter Optimization of TF-IDF and SVM via Grid Search for Sentiment Analysis of Traveloka Customer Reviews Muhammad Bayu Kurniawan; Hanafi; Riki Hikmianto; Isnawati Muslihah
Khazanah Informatika : Jurnal Ilmu Komputer dan Informatika Vol. 11 No. 2 (2025): October 2025
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Customer reviews on digital platforms are crucial for improving services and making business decisions. This study focuses on automated sentiment analysis for Traveloka, a leading Indonesian online travel application. We propose a systematic hyperparameter optimization of a combined TF-IDF and Support Vector Machine (SVM) pipeline. A dataset of 20,200 user reviews was collected from the Google Play Store. After preprocessing and a two-stage labeling process, the data was split using stratified sampling (70% training, 30% testing). We conducted a comprehensive Grid Search with stratified 5-fold cross-validation to jointly optimize TF-IDF n-gram ranges (unigram, bigram, trigram) and SVM hyperparameters across four kernel types (Linear, RBF, Polynomial, Sigmoid). The results show that the Polynomial kernel with trigram features (C=5, gamma=1, degree=5, coef0=10) performs best. It achieves a test accuracy of 87.10% and a macro F1-score of 86.9%. Error analysis revealed the model's high reliability in detecting negative feedback (precision: 90.4%) but also its difficulty with contrastive sentences and informal language. The minimal performance differences among top configurations suggest the task is robust to specific parameter choices. However, the model's bag-of-ngrams approach shows limitations in processing contrastive sentences and informal language. For future work, employing contextual embeddings (e.g., IndoBERT) and exploring alternative algorithms like Random Forest or Neural Networks could address these challenges. This research presents a thoroughly optimized traditional ML methodology that establishes a strong baseline for automated sentiment analysis of Indonesian user feedback.