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All Journal IPTEK The Journal for Technology and Science JUTI: Jurnal Ilmiah Teknologi Informasi International Journal of Advances in Intelligent Informatics Scientific Journal of Informatics Malcom: Indonesian Journal of Machine Learning and Computer Science
Hilya Tsaniya
Universitas Telkom
Computer Science & IT Dentistry Education Library & Information Science

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Exposure Fusion Framework in Deep Learning-Based Radiology Report Generator Hilya Tsaniya; Chastine Fatichah; Nanik Suciati
IPTEK The Journal for Technology and Science Vol 33, No 2 (2022)
Publisher : IPTEK, LPPM, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j20882033.v33i2.13572

Abstract

Writing a radiology report is time-consuming and requires experienced radiologists. Hence a technology that could generate an automatic report would be beneficial. The key problem in developing an automated report-generating system is providing a coherent predictive text. To accomplish this, it is important to ensure the image has good quality so that the model can learn the parts of the image in interpreting, especially in medical images that tend to be noise-prone in the acquisition process. This research uses the Exposure Fusion Framework method to enhance the quality of medical images to increase the model performance in producing coherent predictive text. The model used is an encoder-decoder with visual feature extraction using a pre- trained ChexNet, Bidirectional Encoder Representation from Transformer (BERT) embedding for text feature, and Long-short Term Memory (LSTM) as a decoder. The model’s performance with EFF enhancement obtained a 7% better result than without enhancement processing using an evaluation value of Bilingual Evaluation Understudy (BLEU) with n-gram 4. It can be concluded that using the enhancement method effectively increases the model’s performance.
Aspect Based Sentiment Analysis of Product Review Using Memory Network Ismet, Hilya Tsaniya; Mustaqim, Tanzilal; Purwitasari, Diana
Scientific Journal of Informatics Vol 9, No 1 (2022): May 2022
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v9i1.34094

Abstract

Abstract. Purpose: Consumer opinion is one of the essential keys that affect the success of a product. Sentiment analysis of consumer opinion is needed to find out information about customer satisfaction for companies in the decision-making process. The traditional sentiment analysis process extracts a complete sentiment from a single sentence. However, it does not consist of only one sentiment in one sentence. The total number depends on the number of aspects that make up the sentence. Therefore, a sentiment analysis process is needed to pay attention to aspects.Methods: This research focuses on product reviews from Indonesian e-commerce on several aspects of sentiment. Uses fastText word embedding to avoid Out of Vocabulary in datasets and Gated Recurrent Units for aspect spread detection. Sentiment classification on aspects using the Memory Network method.Result: The experiment results showed that aspect-based sentiment classification predictions had an accuracy of 83% compared to 78% overall classification predictions for review texts, indicating that aspect-based sentiment analysis can improve model performance on product review classification predictions.Novelty: Most product reviews analysis use document-level classification to extract and predict sentiment reviews, aspect-based analysis can be applied to product reviews for better sentiment understanding, using Memory Network to store important information explicitly on aspects and polarity.
RadEval: A novel semantic evaluation framework for radiology report Tsaniya, Hilya; Fatichah, Chastine; Suciati, Nanik
International Journal of Advances in Intelligent Informatics Vol 11, No 4 (2025): November 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v11i4.2151

Abstract

The evaluation of automatically generated radiology reports remains a critical challenge, as conventional metrics fail to capture the semantic, clinical, and contextual correctness required for automatic medical analysis. This study proposes RadEval, a semantic-aware evaluation framework, to assess the quality of generated radiology reports. This method integrates domain-specific knowledge and contextual embeddings to evaluate the quality of generated radiology reports using a four-level scoring system. Given a reference report and a predicted report from a radiology image, RadEval performs scoring evaluation by first extracting relevant medical entities using a fine-tuned biomedical NER model. These entities are normalized through ontology mapping using RadLex concept identifiers to resolve lexical variation. Then, semantically related entities were clustered using BioBERT's contextual embeddings to capture deeper semantic similarity. In addition, predicted abnormality tags are incorporated to weight clinically significant terms during score aggregation. The final semantic score reflects a weighted combination of exact match, ontology match, and contextual similarity, modulated by tag importance. Experiments were conducted on the MIMIC-CXR dataset, which contains over 200,000 report pairs. Comparative evaluations show that RadEval outperforms traditional metrics, achieving an F1-score of 0.69, compared to 0.56 for BERTScore. Using this method, a more precise clinical interpretation of the predicted report was captured from the reference report. These findings suggest that RadEval method provides a more accurate and clinically aligned framework for evaluating the medical report generation model.
SentiBERT and Enhanced Bi-GRU for Weather-related Text Classification Using Lexical Features Mohamad Anwar Syaefudin; Arijal Ibnu Jati; Hilya Tsaniya; Chastine Fatichah; Diana Purwitasari
JUTI: Jurnal Ilmiah Teknologi Informasi Vol. 24, No. 1, January 2026
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24068535.v24i1.a1320

Abstract

The growing volume of weather related content on social media platforms, especially Twitter, has highlighted the need for robust classification models that can handle noisy, ambiguous, and emotionally subtle language. However, existing models machine learning such as Support Vector Machines (SVM) often fail to effectively capture implicit sentiment and sequential context in short, real time texts. This study addresses the challenge of weather related text classification by proposing a hybrid architecture that combines SentiBERT, a sentiment aware transformer model, with an Enhanced BiGRU network equipped with Self Attention and LeakyReLU activation. Experiments were conducted using a five class(sunny, cloudy, rainy, extreme, other) dataset of weather related tweets with stratified cross validation across multiple deep learning models and tokenizers. Results show that the proposed SentiBERT + Enhanced BiGRU model outperformed all baselines, achieving 88.03% accuracy and 88.25% macro F1 score demonstrating its ability to better interpret contextual and emotional nuances. These findings imply that integrating sentiment specific embeddings with sequential modeling and lexical features offers a promising direction for future real time applications in climate monitoring and disaster alert systems.
Handling Ambiguity in App Review-Based Software Requirement Classification Using Multi-Label BERT Transfer Learning Stefani Tasya Hallatu; Muhammad Jerino Gorter; Andrea Bemantoro J; Diana Purwitasari; Chastine Fatichah; Hilya Tsaniya
JUTI: Jurnal Ilmiah Teknologi Informasi Vol. 24, No. 1, January 2026
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24068535.v24i1.a1333

Abstract

User-generated reviews on mobile applications represent a valuable yet ambiguous resource for classifying software requirements, particularly when multiple aspects—such as bugs, feature requests, and user experiences—are embedded within a single review. Although prior studies have shown the potential of transformer-based and multi-label models in improving text classification accuracy and efficiency, explicit handling of semantic ambiguity in multi-aspect reviews has not been addressed. This study proposes a multi-label classification approach using BERT-based transfer learning to manage ambiguity in app reviews. Each review is manually annotated with one or more relevant requirement categories. Preprocessing involves text cleaning, normalization, and BERT tokenization to convert reviews into structured representations. The classification model categorizes reviews into four classes: bug reports, feature requests, user experiences, and ratings. Evaluation results demonstrate strong performance, with F1-scores of 0.96 for bug reports, 0.95 for feature requests, 0.97 for ratings, and 0.80 for user experiences, confirming the model’s capability in capturing overlapping labels in ambiguous reviews. This approach offers a scalable and automated solution for extracting software requirements, enabling developers to better identify, categorize, and prioritize user needs from unstructured review data.
Deteksi Kondisi Gigi Anak pada Radiografi Panoramik Menggunakan YOLOv8 dan Teknik Peningkatan Citra: Detection of Children's Dental Conditions in Panoramic Radiography Using YOLOv8 and Image Enhancement Techniques Khoifah Inda Maula; Chastine Fatichah; Hilya Tsaniya Ismet
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 6 No. 1 (2026): MALCOM January 2026
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v6i1.2438

Abstract

Citra panoramik gigi merupakan teknik radiografi yang memberikan gambaran menyeluruh terhadap struktur gigi, rahang, serta jaringan pendukung lainnya dalam satu citra, sehingga banyak digunakan untuk diagnosis awal dan perencanaan perawatan, khususnya pada pasien anak-anak. Namun, karakteristik gigi anak yang unik seperti keberadaan gigi campuran dan perubahan posisi gigi yang dinamis mengakibatkan interpretasi citra menjadi lebih kompleks. Selain itu, kualitas citra yang kurang optimal seperti kontras rendah dan distribusi sinar-X yang tidak merata dapat menghambat proses deteksi secara akurat. Penelitian ini bertujuan meningkatkan kualitas citra serta melakukan deteksi otomatis citra panoramik gigi anak menggunakan You Only Look Once (YOLO) yang dikenal unggul dalam kecepatan dan akurasi deteksi objek. Proses peningkatan citra dilakukan dengan tiga teknik, yaitu Histogram Equalization (HE), Contrast Limited Adaptive Histogram Equalization (CLAHE), dan gamma correction. Hasil pengujian menunjukkan bahwa penerapan CLAHE memberikan performa deteksi terbaik dibandingkan dengan metode HE maupun gamma correction. Berdasarkan analisis metrik evaluasi, penggunaan CLAHE terbukti paling optimal dalam meratakan kontras lokal dan menekan noise, sehingga YOLOv8 dapat mengekstraksi fitur gigi anak yang kompleks. Sebagai kesimpulan, kombinasi metode prapemrosesan CLAHE dan model deteksi YOLOv8 merupakan pendekatan yang paling efektif untuk mengatasi permasalahan kualitas citra dan direkomendasikan untuk pengembangan sistem diagnosis otomatis citra panoramik gigi anak.
Co-Authors Andrea Bemantoro J Arijal Ibnu Jati Chastine Fatichah Diana Purwitasari Khoifah Inda Maula Mohamad Anwar Syaefudin Muhammad Jerino Gorter Nanik Suciati Stefani Tasya Hallatu Tanzilal Mustaqim