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Enhanced Self-Esteem Classification: Leveraging Data Augmentation and Transformer-Based Sentence Embeddings Reza Ahmadiansah; Mukti Ali; Rasimin; Achmad Maimun; Kastolani; Imam Subqi; Embun Bening Di Moravia; Andi Bahtiar Semma
Advance Sustainable Science Engineering and Technology Vol. 8 No. 4 (2026): August-October
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v8i4.2741

Abstract

This study investigates automated self-esteem assessment from self-descriptive text using transformer-based sentence embeddings. Although prior research has explored text, behavioral, and multimodal signals, the combined effects of data augmentation, embedding choice, and classifier complexity in text-only self-esteem classification remain insufficiently understood.Accordingly, this study aims to systematically evaluate embedding–classifier combinations under both low-resource and augmented data conditions. Textual self-descriptions were collected from 298 undergraduate students at UIN Salatiga and labeled using the Indonesian version of the Rosenberg Self-Esteem Scale, yielding three self-esteem categories. To address data scarcity, a controlled translation-based augmentation pipeline with expert psychological validation was applied exclusively to the training set. Seven multilingual sentence embedding models were paired with eight classification algorithms, and performance was evaluated using macro-averaged metrics, along with training and inference time. Results reveal a two-regime pattern: (1) in limited-data settings, strong embeddings with simple classifiers perform best, (2) whereas in augmented settings, representation quality dominates and classifier choice has a marginal effect. The findings suggest that prioritizing high-quality embeddings and carefully validated data augmentation enables accurate, scalable, and cost-effective text-based self-esteem assessment for real-world psychological applications.
The Sorogan Method as an Individualized Pedagogical Approach to Enhancing Qur’anic Reading Competence in Public Schools Ulya Makwa Arrahma; Chusna Nur Rosyida; Syaikhu Abdillah Faqih; Achmad Maimun
AL-ADABIYAH: Jurnal Pendidikan Agama Islam Vol. 7 No. 1 (2026): AL-ADABIYAH: Jurnal Pendidikan Agama Islam
Publisher : Published by the Islamic Religious Education Study Program Faculty of Tarbiyah and Teacher Training Kiai Haji Achmad Siddiq University, Jember, East Java, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35719/adabiyah.v7i1.1259

Abstract

This study examines the sorogan method as an individualized pedagogical approach for enhancing Qur’anic reading competence in public secondary schools. Situated within a Qur’anic Reading and Writing (BTQ) program, the study explores how a traditional Islamic instructional practice is adapted to a formal school context, identifies factors that support and constrain its implementation, and analyses its pedagogical impact on students’ Qur’anic reading competence and religious engagement. Using a qualitative case study design, data were collected through classroom observations, semi structured interviews, and document analysis involving Islamic education teachers, student mentors from Islamic student organizations, teaching practicum students, and learners. The findings indicate that the individualized nature of the sorogan method, supported by talaqqi, musyafahah, and peer assisted learning, facilitated improvements in reading fluency, pronunciation accuracy, and the application of basic tajwid principles. In addition to technical reading skills, the approach contributed to the development of students’ religious attitudes, learning motivation, and collaborative responsibility. This study argues that the sorogan method, when implemented in public school settings, provides a pedagogically viable and culturally grounded model for strengthening Qur’anic literacy within contemporary formal education.