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Yeni Kustiyahningsih
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Informatics Department, Engineering Faculty University of Trunojoyo Madura Jl. Raya Telang - Kamal, Bangkalan 69162, Indonesia Tel: 031-3012391, Fax: 031-3012391
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INDONESIA
Jurnal Ilmiah Kursor
Published by Universitas Trunojoyo Madura
ISSN : 02160544     EISSN : 23016914     DOI : https://doi.org/10.21107/kursor
Core Subject : Science,
Computer Science & IT Decision Sciences, Operations Research & Management
Jurnal Ilmiah Kursor is published in January 2005 and has been accreditated by the Directorate General of Higher Education in 2010, 2014, 2019, and until now. Jurnal Ilmiah Kursor seeks to publish original scholarly articles related (but are not limited) to: Computer Science. Computational Intelligence. Information Science. Knowledge Management. Software Engineering. Publisher: Informatics Department, Engineering Faculty, University of Trunojoyo Madura
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 165 Documents
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Optimization EfficientNetV2 model variant using Grad-CAM for multiple MRI brain tumor classification Denisa Septalian Alhamda; Wahyu Syaifullah J; Prasetyaning Estu Pratiwi; Surjo Hadi; Wan Suryani Wan Awang; I Gede Susrama Mas Diyasa
Jurnal Ilmiah Kursor Vol. 13 No. 3 (2026)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/kursor.v13i3.428

Abstract

Fast and accurate diagnosis plays a critical role in effectively treating brain tumors. This study optimized and evaluated the EfficientNetV2 architecture through transfer learning, fine-tuning, and data augmentation, using three variants Small, Medium, and Large to classify MRI images into four categories: glioma, meningioma, pituitary tumors, and no tumor. Grad-CAM visualization was employed to enhance interpretability, providing a clear view of the critical regions in the MRI images that influenced the model’s decisions. Grad-CAM was tested across all model variants, and the best results were observed with EfficientNetV2-Large, where the model successfully highlighted the key areas associated with brain tumors. Among the variants, EfficientNetV2-Large achieved the best performance, with 99.85% accuracy, 99.60% precision, 99.65% recall, and 99.50% F1-score. However, this model required the longest computation time of 288 seconds per step, which may not be feasible in resource- constrained environments. Overall, this study underscores the potential of EfficientNetV2 models in revolutionizing brain tumor diagnosis by balancing accuracy, efficiency, and interpretability through advanced optimization techniques.Key words: Brain tumors, MRI classification, EfficientNetV2, Grad-CAM, Deep learning.
Deep learning algorithms for autism spectrum disorder detection using eye-tracking patterns Muhamad Syukron; Rangga Alif Faresta
Jurnal Ilmiah Kursor Vol. 13 No. 3 (2026)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/kursor.v13i3.468

Abstract

Early diagnosis of autism spectrum disorder (ASD) plays a crucial role in facilitating prompt interventions and assessing post-therapy progress, both of which can greatly improve developmental trajectories. The emergence of artificial intelligence—particularly deep learning—has opened new possibilities for clinicians to detect ASD with improved precision and speed. This study investigates the utilization of deep learning models to recognize ASD among 13-year-old children based on eye movement data collected as participants observed static images and short video sequences. The dataset included visual and numerical variables, such as gaze position and pupil diameter, allowing for a multimodal analytical approach. For the numerical dataset, a multilayer perceptron (MLP) neural network produced the best outcomes, yielding an accuracy of 91.7% and a recall rate of 83.3% in ASD classification. Meanwhile, the Vision Transformer (ViT) model performed best for image-based analysis, reaching an accuracy of 78.2% and a recall rate of 88.6%. Overall, the findings highlight the potential of deep learning frameworks as objective, data-driven tools for ASD detection in both clinical and research contexts.Key words: Autism, Computer Vision, Deep Learning Model, Vision Transformer.
Classification of human scabies skin disease using ConvNeXt Mochammad Zaky Zamroni; Muhammad Afif Hendrawan; Agwin Fahmi Fahanani; Dhelya Widasmara; Cahya Rahmad
Jurnal Ilmiah Kursor Vol. 13 No. 3 (2026)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/kursor.v13i3.490

Abstract

Scabies is a contagious skin disease caused by Sarcoptes scabiei and remains a health issue in many countries, including Indonesia. Clinical diagnosis is often difficult due to symptom similarities with other skin conditions. This study evaluates the ConvNeXt architecture for scabies classification from skin images to support automated diagnostic systems. The dataset consisted of 273 images (86 scabies and 187 non-scabies), expanded through cropping to 819 images, divided into 488 scabies and 331 non-scabies images. ConvNeXt was trained with fine-tuning at stage 3, implemented using PyTorch on an A100 GPU with the AdamW optimizer. Evaluation used 70:15:15, 80:10:10, and 5-Fold Cross Validation schemes with accuracy, precision, recall, and F1-score metrics. The best configuration (learning rate 0.0001, batch size 32) achieved 95.97% testing accuracy. Overall, ConvNeXt reached 97.75% average accuracy and an F1-score near 0.98, demonstrating highly competitive performance that slightly exceeds established baselines like ResNet-50 (97.69% accuracy). This shows its potential as an accurate and practical solution for automated scabies diagnosis in resource limited healthcare settings.Key words: Automated Diagnosis, ConvNeXt, Deep Learning, Image Classification, Scabies.
Modified BERTopic using IndoSBERT for topic modeling in Bahasa Nur Huda Riyantoni; Khalid Sjamsuri; Subhan Nooriansyah
Jurnal Ilmiah Kursor Vol. 13 No. 3 (2026)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/kursor.v13i3.497

Abstract

The vast amount of textual data -particularly undergraduate theses abstracts produced in the digital age- makes it difficult for readers to identify the topics contained within them. Topic modeling facilitates readers in identifying topics within a collection of textual data. One method for topic modeling is BERTopic. BERTopic is a framework for topic modeling that utilizes the BERT model in embedding stage. This study use IndoSBERT and multilingual SBERT in the BERTopic embedding stage to determine which model performs better in generating topic for a dataset of Indonesian-language undergraduate theses abstract. The topic generated using these IndoSBERT and multilingual SBERT embedding are then evaluated using the topic coherence and topic diversity metrics. The research results show that topics generated by IndoSBERT have higher topic coherence and topic diversity scores, than those generated by multilingual SBERT. These results indicate that IndoSBERT is better in generating topics with topic coherence and topic diversity than multilingual SBERT. The contribution of this research lies in the modification of the BERTopic embedding stage using IndoSBERT for topic modelling in Bahasa. This is because the use of IndoSBERT has so far been limited for text classification task.Key words: Bahasa Indonesia, BERTopic, IndoSBERT, Information System, Topic modelling.
Comparative analysis of CatBoost and LightGBM algorithms for predicting dental caries risk based on lifestyle factors Chayadi Oktomy Noto Susanto; Muhajirah Ulfah; Bimo Aditya Pangestu; Slamet Riyadi
Jurnal Ilmiah Kursor Vol. 13 No. 3 (2026)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/kursor.v13i3.504

Abstract

Dental caries is a common oral infection but significantly impacts life. This study aimsto compare the performance of the CatBoost and LightGBM algorithms in predictingdental caries risk based on health and lifestyle data from over 55,000 South KoreanNHIS participants. Imbalanced data was addressed using the SMOTE technique, whilehyperparameter tuning was performed using Optuna to maximize the F1-score. Themodels were evaluated using the confusion matrix, accuracy, logloss, ROC Curve,AUC, Precision, Recall, and F1-Score metrics. The results showed that CatBoost wasslightly superior with an accuracy of 90.4%, an F1-score of 89.7%, and an AUC of 0.95,compared to LightGBM with an accuracy of 90.1%, an F1-score of 89.2%, and an AUCof 0.94. In addition, CatBoost also produced fewer false negatives, making it moresensitive in detecting caries cases. Therefore, CatBoost is recommended as the primarymodel in a caries risk prediction system to support early detection and diseaseprevention more effectively.

Page 17 of 17 | Total Record : 165

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