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MENTORA: Inovasi Digital untuk Pemberdayaan Masyarakat Berbasis Data Fiddin Yusfida; Hartatik, Hartatik; Firdaus, Nurul; Kusuma Riasti, Berliana; Supriyadi, Andy
KOMUNITA: Jurnal Pengabdian dan Pemberdayaan Masyarakat Vol 4 No 3 (2025): Agustus
Publisher : PELITA NUSA TENGGARA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60004/komunita.v4i3.225

Abstract

Kegiatan Pelatihan dan Serah Terima Aplikasi MENTORA dilaksanakan oleh Grup Riset Applied Data Science and AI (DSAI) Universitas Sebelas Maret (UNS) Surakarta melalui skema Pengabdian Kepada Masyarakat Hibah Grup Riset (PKM HGR-UNS) pada 10 Juli 2025 di D3 Teknik Informatika, Sekolah Vokasi UNS. Kegiatan ini bertujuan meningkatkan efektivitas pengelolaan data pendampingan komunitas dengan memanfaatkan teknologi informasi. MENTORA adalah aplikasi digital inovatif yang dirancang untuk mendukung pemberdayaan masyarakat berbasis wilayah dengan fitur unggulan seperti Admin Center, Fasilitator Hub, Group Management, Community Management, Activity Management, Activity Insights Dashboard, dan Data Exporter. Pelatihan diikuti oleh admin dan fasilitator yang akan mengoperasikan aplikasi di lapangan untuk memastikan implementasi optimal. Acara ini juga menjadi momentum inisiasi kerja sama tridharma perguruan tinggi antara UNS dan Majelis Pemberdayaan Masyarakat PP Muhammadiyah. Diharapkan dengan hadirnya MENTORA, pengelolaan data pendampingan masyarakat menjadi lebih terstruktur, transparan, dan mendukung transformasi digital di komunitas.
COMPARATIVE STUDY OF TRANSFORMER-BASED MODELS FOR AUTOMATED RESUME CLASSIFICATION Firdaus, Nurul; Kusuma Riasti, Berliana; Asri Safi'ie, Muhammad
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 2 (2025): JITK Issue November 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i2.7453

Abstract

This study presents a comparative evaluation of transformer-based models and traditional machine learning approaches for automated resume classification—a key task in optimizing recruitment workflows. While traditional approaches like Support Vector Machines (SVM) with TF-IDF demonstrated the highest performance (93.26% accuracy and 95% F1-score), transformer models such as DistilBERT and RoBERTa showed competitive results with 93.27% and 91.34% accuracy, respectively, and fine-tuned BERT achieved 84.35% accuracy and an F1-score of 81.54%, indicating strong semantic understanding. In contrast, Word2Vec + LSTM performed poorly across all metrics, highlighting limitations in sequential modelling for resume data. The models were evaluated on a curated resume dataset available in both text and PDF formats using accuracy, precision, recall, and F1-score, with preprocessing steps including tokenization, stop-word removal, and lemmatization. To address class imbalance, we applied stratified sampling, macro-averaged evaluation metrics, early stopping, and simple data augmentation for underrepresented categories. Model training was conducted in a PyTorch environment using Hugging Face’s Transformers library. These findings highlight the continued relevance of traditional models in specific NLP tasks and underscore the importance of model selection based on task complexity and data characteristics