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All Journal Jurnal Edukasi dan Penelitian Informatika (JEPIN)
Sajid, Syahmi
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Computer Science & IT Education

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Development of an Image Captioning Model to Assist The Activities of Visually Impaired Pedestrians in Urban Environments Sajid, Syahmi; Harjoko, Agus
JEPIN (Jurnal Edukasi dan Penelitian Informatika) Vol 11, No 2 (2025): Volume 11 No 2
Publisher : Program Studi Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/jp.v11i2.95754

Abstract

Visual impairment is a global issue with significant impacts on the mobility and safety of individuals, especially in urban environments. Artificial intelligence solutions, such as image captioning, promise assistance for people with visual impairments to aid their daily activities. However, the field of image captioning in this context still has performance limitations. To address this, this study proposes a hybrid method combining image feature extraction from VGG16, ResNet50, and YOLO on the encoder side with LSTM and BiGRU on the decoder side to generate descriptions that have proven to enhance model performance on the Flickr8k dataset in previous research. By adapting this method to the Visual Assistance dataset, incorporating image augmentation through a combination of rotation and zoom, and applying transfer learning to address the dataset size limitation, this study successfully improved the model"™s performance in supporting the activities of visually impaired pedestrians in urban environments. Evaluation results showed significant improvements in several evaluation metrics. Overall, this model shows improvement compared to previous research, where Sharma et al. (2022) reported that the InceptionV3-BiLSTM model with Adaptive Attention achieved a BLEU-4 score of only 0.266 on the Visual Assistance dataset. This study achieved a 60.53% increase in BLEU-4 score compared to previous research on the Visual Assistance dataset. Overall, this study provides a positive contribution to developing more effective and accurate solutions for visually impaired navigation users in urban environments.
Hybrid TF-IDF and Sentence-BERT for Academic Advisor Recommendation Enhancing Topic Matching in Undergraduate Theses Sajid, Syahmi; Fadil, Nurdana Ahmad; Mtd, Aidizzacky Harizulfadly; Ajiwinata, Habib Gili
JEPIN (Jurnal Edukasi dan Penelitian Informatika) Vol. 12 No. 2 (2026): Volume 12 No 2
Publisher : Program Studi Informatika

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Abstract

The selection of thesis supervisors is a crucial stage that affects the smooth completion of students’ research. The manual process traditionally used often creates difficulties in finding supervisors whose expertise aligns with the research topic, leading to academic inefficiencies. This study aims to design and develop a thesis supervisor recommendation system based on a hybrid TF-IDF and Sentence-BERT (SBERT) approach to improve the accuracy of matching students’ thesis titles with supervisors’ areas of expertise. The dataset used consists of 60 publications from four areas of expertise and 36 thesis titles from DIKE UGM students. The research stages include data collection, aggregation of supervisor publications, text preprocessing, feature extraction, and evaluation using Accuracy@K and Mean Reciprocal Rank (MRR). Experimental results indicate that the combination of stemming and stopword removal provides the best performance in placing relevant supervisors within the top-3 recommendations. The hybrid TF-IDF + SBERT method demonstrates superior performance compared to single methods, achieving Acc@3 of 0.8056, Acc@5 of 0.8611, and MRR of 0.6607, due to its ability to combine lexical information with semantic context. This study shows that a text-based recommendation system can speed up supervisor assignment and improve the match between research topics and supervisors’ expertise.
Co-Authors Agus Harjoko Ajiwinata, Habib Gili Fadil, Nurdana Ahmad Mtd, Aidizzacky Harizulfadly