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All Journal JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Locus Penelitian dan Pengabdian
Agustina, Ni Putu Dina
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Aerospace Engineering Automotive Engineering Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Mathematics

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Klasifikasi Subtipe Leukemia Limfoblastik Akut (LLA) pada Citra Mikroskopis Sel Darah Menggunakan Arsitektur EfficientNet-B3 dengan Dataset Seimbang Agustina, Ni Putu Dina; Wijayakusuma, I Gusti Ngurah Lanang
Jurnal Locus Penelitian dan Pengabdian Vol. 4 No. 6 (2025): JURNAL LOCUS: Penelitian & Pengabdian
Publisher : Riviera Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58344/locus.v4i6.4321

Abstract

Acute Lymphoblastic Leukemia (ALL) is one of the most common types of blood cancer that affects children and requires fast and accurate diagnosis. This study proposes a classification model for subtypes of acute lymphoblastic leukemia (ALL) based on microscopic blood cell images using the EfficientNet-B3 architecture. With a transfer learning approach and a balanced dataset, the model achieves a testing accuracy of 97.50% and an average F1-Score of 0.97. Overall, the macro average and weighted average values show consistent results, with precision and recall of 0.98 and an F1-Score of 0.97. This indicates that the model excels not only in one or two classes but demonstrates uniform performance across all classes, making it a robust classification tool for automatic leukemia diagnosis applications.
Public Sentiment Analysis on Demonstration Actions Using IndoBERT Based on Transfer Learning Tentriajaya, I Dewa Ayu Pradnya Pratiwi; Agustina, Ni Putu Dina; Wijayakusuma, I Gusti Ngurah Lanang
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.12116

Abstract

Sentiment analysis based on language modeling plays a crucial role in mapping public perception of socio-political dynamics in Indonesia. This study aims to evaluate public sentiment toward the House of Representatives of the Republic of Indonesia (DPR RI) in response to the August 2025 demonstrations using the IndoBERT model based on transfer learning. The dataset comprises 1,815 Indonesian-language opinion texts classified into positive and negative sentiments. Due to a substantial class imbalance dominated by negative opinions, a hybrid sampling strategy combining oversampling and undersampling was employed to obtain a balanced dataset of 650 samples per class. The research methodology included text preprocessing, an 80:20 training–testing split, and fine-tuning the IndoBERT-base-p1 model. Experimental results indicate that the proposed model achieves robust and balanced performance, with an overall accuracy of 85%. Precision and F1-score for both sentiment classes reached 0.85, while recall values were 0.86 for negative sentiment and 0.85 for positive sentiment, demonstrating the model’s ability to identify both classes effectively without bias toward the majority class. Despite the dominance of negative sentiment in the original dataset, the application of data balancing techniques successfully mitigated class imbalance effects, enabling fair and proportional sentiment classification. These findings confirm that the IndoBERT-based transfer learning approach is effective in capturing public sentiment related to mass demonstrations and can provide valuable, data-driven insights for policymakers in understanding societal concerns in the digital era.
Co-Authors Tentriajaya, I Dewa Ayu Pradnya Pratiwi Wijayakusuma, I Gusti Ngurah Lanang