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All Journal International Journal of Electrical and Computer Engineering Indonesian Journal of Information System Journal of Maternal and Child Health Proceeding of International Conference Health, Science And Technology (ICOHETECH)
Husin, Husna Sarirah
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Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Health Professions Industrial & Manufacturing Engineering Medicine & Pharmacology Public Health

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Named entity recognition on Indonesian legal documents: a dataset and study using transformer-based models Yulianti, Evi; Bhary, Naradhipa; Abdurrohman, Jafar; Dwitilas, Fariz Wahyuzan; Nuranti, Eka Qadri; Husin, Husna Sarirah
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i5.pp5489-5501

Abstract

The large volume of court decision documents in Indonesia poses a challenge for researchers to assist legal practitioners in extracting useful information from the documents. This information can also benefit the general public by improving legal transparency, law enforcement, and people's understanding of the law implementation in Indonesia. A natural language processing task that extracts important information from a document is called named entity recognition (NER). In this study, the NER task is applied to legal domains, which is then referred to as legal entity recognition (LER) task. In this task, some important legal entities, such as judges, prosecutors, and advocates, are extracted from the decision documents. A new Indonesian LER dataset is built, called IndoLER data, consisting of approximately 1K decision documents with 20 types of fine-grained legal entities. Then, the transformer-based models, such as multilingual bidirectional encoder representations from transformers (BERT) or M-BERT, Indonesian BERT or IndoBERT, Indonesian robustly optimized BERT pretraining approach (RoBERTa) or IndoRoBERTa, XLM (cross lingual language model)-RoBERTa or XLMR, are proposed to solve the Indonesian LER task using this dataset. Our experimental results show that the RoBERTa-based models, such as XLM-R and IndoRoBERTa, can outperform the state-of-the-art deep-learning baselines using BiLSTM (bidirectional long short-term memory) and BiLSTM-conditional random field (BiLSTM-CRF) approaches by 7.2% to 7.9% and 2.1% to 2.6%, respectively. XLM-RoBERTa is shown to be the best-performing model, achieving the F1-score of 0.9295.
Coral Detection based on Optimised Lightweight YOLO Model Saragih, Raymond Erz; Husin, Husna Sarirah; Mursalim, Muhammad Khairul Naim; Yodi
Indonesian Journal of Information Systems Vol. 8 No. 1 (2025): August 2025
Publisher : Program Studi Sistem Informasi Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/ijis.v8i1.11628

Abstract

Coral reefs are essential marine ecosystems that face significant threats due to climate change, pollution, and overfishing. Effective monitoring is crucial for conservation efforts, but traditional methods are labor-intensive and inefficient. This study proposes a deep learning-based coral detection model built based on the YOLOv8 architecture, specifically for nano and small. In addition, the Ghost modules and Ghost bottlenecks were utilized to modify the original YOLOv8 small. The proposed model was trained on an underwater coral dataset and evaluated in terms of precision, recall, and mean Average Precision (mAP) metrics. Experimental results demonstrate that the YOLOv8 small model and YOLOv8 small model with Ghost modules achieved a mAP of 53.675% and 55.88%, respectively, while maintaining a compact model size. This work contributes to developing efficient and lightweight coral detection systems to support conservation efforts.
The Role of Local Wisdom in Driving Innovation and Green Economic Growth through Digital Platforms Oktaviani, Intan; Widyaningsih, Pipin; Triana; Husin, Husna Sarirah
Proceeding of the International Conference Health, Science And Technology (ICOHETECH) 2025: Proceeding of the 6th International Conference Health, Science And Technology (ICOHETECH)
Publisher : LPPM Universitas Duta Bangsa Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47701/99zprq92

Abstract

This study investigates the transformative potential of integrating local wisdom encompassing indigenous knowledge, cultural values, and traditional ecological practices into digital platforms to drive innovation and foster green economic growth, aligning with global sustainability agendas. It highlights how local wisdom, often manifested in heritage crafts, regenerative agriculture, and community-based resource management, serves as a foundation for eco-friendly production systems, ethical trade, and inclusive entrepreneurship when amplified through digital technologies such as e-commerce, traceability systems, and financial technology. Employing a mixed-method approach involving case studies from Asia, Africa, and Latin America alongside quantitative analyses of export growth, job creation, and supply chain resilience, the research reveals that embedding cultural identity and sustainable practices into digital commerce enhances product authenticity, attracts premium markets, reduces carbon footprints through localized production, and empowers marginalized groups, including women and indigenous communities. The findings underscore that such integration not only contributes to fair trade and decent work but also strengthens biodiversity conservation, promotes circular economy principles, and mitigates socio-economic inequalities. The study recommends implementing policy incentives, digital literacy programs, and multi-stakeholder collaborations between governments, platform developers, and cultural institutions to scale these initiatives, ultimately positioning local wisdom as a strategic driver for environmentally responsible, socially inclusive, and innovation-driven economic transformation in the digital era.
Developing Electronic-Based Maternal and Child Health Monitoring Riska Rosita; Tominanto, Tominanto; Farida, Siti; Yulianto, Andi; Husin, Husna Sarirah
Journal of Maternal and Child Health Vol. 9 No. 6 (2024)
Publisher : Masters Program in Public Health, Universitas Sebelas Maret, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26911/thejmch.2024.09.06.02

Abstract

Background: The first 1,000 days of life, encompassing fetal development during pregnancy (approximately 270 days) through the child's second year (approximately 730 days), represent a critical window for the development of vital organs, as well as cognitive and motor functions. Optimal monitoring during this period is essential for ensuring healthy growth and development. This study aimed to develop an electronic-based monitoring tool for maternal and child health, designed to deliver comprehensive, accurate, and timely information to facilitate early detection of health risks and support evidence-based interventions. Subjects and Method: The study was conducted using the Rapid Application Development (RAD) approach, which includes the stages of planning, design workshops, and implementation. The tool is intended to be used by health cadres at integrated health posts (posyandu) to support maternal and child health monitoring. Results: A simple and user-friendly electronic-based program has been developed to record and monitor maternal and child health status over time. Users can input the results of maternal and child health assessments, which are then presented in graphical form and can be printed as needed. The application received a feasibility score of 89.8% based on the PIECES framework, indicating strong potential for practical implementation. Conclusion: Graphical representations in maternal and child health applications allow for easier monitoring of examination results. These visual tools enable early identification of potential malnutrition-related risks, such as stunted growth in children, thereby supporting timely intervention and prevention efforts.
Co-Authors Abdurrohman, Jafar Andi Yulianto Bhary, Naradhipa Dwitilas, Fariz Wahyuzan Eka Qadri Nuranti Evi Yulianti MUHAMMAD KHAERUL NAIM MURSALIM Oktaviani, Intan Pipin Widyaningsih Riska Rosita Saragih, Raymond Erz Siti Farida Tominanto, Tominanto Triana Yodi