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All Journal Scientific Journal of Informatics Building of Informatics, Technology and Science Jurnal Bina Pengabdian Kepada Masyarakat
Syabilla, Mutiara
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Implementasi LDA, TF-IDF, dan BERT dalam Sistem Rekomendasi Dosen Pembimbing untuk Mahasiswa Syabilla, Mutiara; Zeniarja, Junta; Nabila, Qotrunnada
Building of Informatics, Technology and Science (BITS) Vol 6 No 4 (2025): March 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i4.6499

Abstract

The selection of thesis supervisors is often done manually, which tends to be time-consuming in matching students' research topics with the expertise of faculty members. This study develops a thesis supervisor recommendation system based on the title and abstract of students' final projects, integrating Latent Dirichlet Allocation (LDA), Term Frequency-Inverse Document Frequency (TF-IDF), and Bidirectional Encoder Representations from Transformers (BERT). The research dataset includes 1,096 records from 71 faculty members in the Informatics Engineering Department at Universitas Dian Nuswantoro, collected through Google Scholar. The analysis process begins with text preprocessing such as case folding, tokenization, and stemming, followed by topic analysis using LDA, term-specific weighting through TF-IDF, and context-rich vector representation using BERT. The model matches students' research topics with faculty expertise using Cosine Similarity. Evaluation results show an accuracy of 80%, precision of 66%, and recall of 19%, indicating that the model can provide accurate recommendations, though some relevant items are still missed. This model proves effective in facilitating the selection of thesis supervisors. This research is expected to assist students in finding suitable supervisors and help faculty members identify students with relevant research interests.
Pemanfaatan Buku Saku Pertolongan Dan Perawatan Cedera Olahraga Bagi Masyarakat Desa Pardugul Samosir Ahmad Al Munawar; Boby Helmi; Andi Nur Abady; Atika Swandana; Nadhira Yasmine Ahmad; Rinaldi Aditya; Syabilla, Mutiara
Jurnal Bina Pengabdian Kepada Masyarakat Vol 5 No 2 (2025): Jurnal Bina Pengabdian Kepada Masyarakat
Publisher : Sekolah Tinggi Olahraga dan Kesehatan Bina Guna

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55081/jbpkm.v5i2.3977

Abstract

Kegiatan pengabdian kepada masyarakat ini bertujuan untuk meningkatkan pengetahuan dan keterampilan masyarakat desa pardugul dalam memberikan pertolongan dan perawatan cedera dalam berolahraga melalui media buku saku. Sasaran kegiatan adalah masyarakat di wilayah desa pardugul kabupaten samosir yang belum memiliki bekal penanganan cedera secara terstruktur. Metode yang digunakan meliputi penyuluhan, pelatihan, simulasi, dan pembagian buku saku sebagai panduan praktis dalam menghadapi berbagai jenis cedera olahraga ringan hingga sedang. Hasil kegiatan menunjukkan adanya peningkatan pemahaman dan kesiapan masyarakat dalam memberikan pertolongan pertama, yang ditunjukkan melalui hasil evaluasi sebelum dan sesudah kegiatan. Masyarakat juga menyatakan bahwa buku saku sangat membantu dalam memberikan rujukan cepat saat menghadapi kasus cedera saat beraktivitas maupun saat berolahraga. Respon peserta terhadap kegiatan sangat positif, meskipun terdapat keterbatasan seperti waktu pelatihan yang singkat dan belum adanya versi digital buku saku. Kesimpulan dari Kegiatan ini memberikan kontribusi nyata dalam mendukung keselamatan para masyarakat selama kegiatan olahraga di desa pardugul. Diharapkan, kegiatan serupa dapat dikembangkan lebih lanjut melalui pelatihan lanjutan, penyusunan versi digital buku saku, serta replikasi program ke wilayah lain
Capital Optical Character Recognition Using Neural Network Based on Gaussian Filter Astuti, Erna Zuni; Sari, Christy Atika; Syabilla, Mutiara; Sutrisno, Hendra; Rachmawanto, Eko Hari; Doheir, Mohamed
Scientific Journal of Informatics Vol 10, No 3 (2023): August 2023
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v10i3.43438

Abstract

Purpose: As digital technology advances, society needs to convert physical text into digital text. There are now many methods available for doing this. One of them is OCR (Optical Character Recognition), which can scan images [1]–[4] containing writing and turn them into digital text, making it easier to copy written text from an image. Text recognition in images is complex due to variations in text size, color, font, orientation, background, and lighting conditions.Methods: The technique of text recognition or optical character recognition (OCR) in images can be done using several methods, one of which is a neural network or artificial neural network. The artificial neural network method can help a computer make intelligent decisions with limited human assistance. Intelligent decisions can be made because the neural network can learn and model the relationship between nonlinear and complex input and output data. In this research, the scaled conjugated gradient is applied for optimization. SCG is very effective in finding the minimum value of a complex function, but it takes longer than some other optimization algorithms.Result/Findings: The dataset used is an image with a size of 28 x 28 which is changed in dimension to 784 x 1. This research uses 4000 epochs and obtained the best validation result at epoch 3506 with a value of 0.0087446. Results: From the statistical test results, the effect of perceived usefulness on ease of use has the highest level of influence, obtaining a test value of 3.6. Furthermore, the effect of the attitude towards using on the behavioral intention to use has the lowest level of influence, which obtained a test value of 1.2.Novelty:  In this article, Gaussian filter is used as feature extraction to improve yield. Character detection results using a Gaussian filter are known to be almost 10% higher than those using only a neural network. The result with the Neural Network alone is 82.2%, while the Neural Network-Gaussian Filter produces 92.1%.
Co-Authors Ahmad Al Munawar Andi Nur Abady Atika Swandana Boby Helmi Christy Atika Sari Doheir, Mohamed Eko Hari Rachmawanto Erna Zuni Astuti Junta Zeniarja Nabila, Qotrunnada Nadhira Yasmine Ahmad Rinaldi Aditya Sutrisno, Hendra