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All Journal Indonesian Journal of Humanities and Social Sciences Advance Sustainable Science, Engineering and Technology (ASSET)
Rasimin
Universitas Islam Negeri Salatiga
Religion Arts Humanities Chemistry Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice

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Efektifitas Pembelajaran Di Kelas 1 Madrasah Ibtidaiyah: Integrasi Animasi Stop Motion dan Pembelajaran Berbasis Masalah (PBL) Sebagai Inovasi Pendidikan yang Efektif Miftahul Rohmah; Rasimin
Indonesian Journal of Humanities and Social Sciences Vol. 4 No. 3 (2023): Indonesian Journal of Humanities and Social Sciences, November, 2023
Publisher : Universitas Islam Tribakti Lirboyo Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33367/ijhass.v4i3.4779

Abstract

This article aims to examine learning media innovation by utilizing stop motion animation integrated with the Problem-Based Learning (PBL) learning model to be applied in the context of grade 1 of Madrasah Ibtidaiyah. The research method applied is Research and Development (R & D). The research subjects involved low grade teachers in Central Java, and the product trial subjects were grade 1 students of MI Muhammadiyah PK Blimbing Gatak Sukoharjo. The results of the study revealed that, although PBL is considered indispensable at the low-grade level, its implementation becomes difficult at grade 1. In contrast, stop motion animation media proved to be very suitable for low-grade students, especially at the grade 1 level. The trial process of this innovative product showed that the integration of stop motion animation with PBL was feasible and effective in improving learning outcomes of grade 1 students. The implications of this study can provide practical guidance for grade 1 teachers in facing the challenges of PBL and offer innovative solutions through the use of stop motion animation media.
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.
Co-Authors Achmad Maimun Andi Bahtiar Semma Embun Bening Di Moravia Imam Subqi Kastolani Miftahul Rohmah Mukti Ali Reza Ahmadiansah