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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Khazanah Informatika : Jurnal Ilmu Komputer dan Informatika
Rickman Roedavan
Telkom University
Computer Science & IT Control & Systems Engineering Engineering

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Detecting Language Anxiety in Indonesian Students: Deep Learning and Traditional Classification Methods for English Learning Anxiety Kemas Muslim Lhaksmana; Muhammad Sya’bani Falif; Iis Kurnia Nurhayati; Muhammad Yudhi Rezaldi; Esa Prakasa; Rickman Roedavan
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 1 (2026): February 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i1.7109

Abstract

Mastery of the English language represents a fundamental determinant of professional achievement, particularly for individuals seeking to develop their careers and participate in international contexts. However, when learning a foreign language such as English, Indonesian students may experience language anxiety that cause them to hesitate to practicing English in the class, both in orally and in writing. For English teachers, it is crucial to identify students experiencing language anxiety early on, so that they may provide appropriate teaching strategies and interventions from the first class meeting. To address this issue, this study compares machine learning methods to provide a solution for early detection of students experiencing language anxiety. Furthermore, these methods are classification models, including LSTM, GRU, decision tree, naïve Bayes, logistic regression, and SVM. The implementation of each of these models is combined with different text representation techniques, such as Word2Vec, BERT, FastText, Glove, and TF-IDF. The advantage of our model is that despite the imbalance, limited, and smaller than baseline dataset size, this research finds that GRU with focal loss achieves the highest F1 score of 0.89. This result outperforms our baseline and thus suggests that this method is effective in detecting students who experience language anxiety.
Developing a Dyscalculia Identification Game Using The Rapid Game Development (RGD) Model Ady Purna Kurniawan; Rickman Roedavan; Bambang Pudjoatmodjo; Sazilah Salam
Khazanah Informatika : Jurnal Ilmu Komputer dan Informatika Vol. 10 No. 2 (2024): Oktober 2024
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/khif.v10i2.2113

Abstract

Dyscalculia, a common learning difficulty impacting arithmetic comprehension, poses challenges across diverse intelligence levels. Often misconstrued as a lack of intelligence or effort in mathematics, dyscalculia's accurate identification remains elusive. This study focuses on developing a 3D digital game incorporating basic arithmetic instruments for dyscalculia identification. Leveraging the Rapid Game Development (RGD) method and Unity Game Engine, the game integrates interactive features to assess arithmetic skills. Through standardized assessments and game participation, children both with and without dyscalculia were evaluated, revealing a significant correlation between the game and standard assessments. This innovative, interactive game holds promise for early dyscalculia detection and intervention, enhancing educators' and professionals' capacity to address this learning difficulty effectively. Future research should validate its efficacy across varied populations and explore its integration into educational settings. In conclusion, this study presents a compelling tool in the form of a 3D digital game utilizing basic arithmetic instruments, fostering timely support and improved outcomes in dyscalculia identification and intervention.
Co-Authors Ady Purna Kurniawan Bambang Pudjoatmodjo Esa Prakasa Iis Kurnia Nurhayati Kemas Muslim Lhaksmana Muhammad Sya’bani Falif Muhammad Yudhi Rezaldi Sazilah Salam