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All Journal Jurnal Ilmu Komputer dan Informasi International Journal of Advances in Intelligent Informatics Malahayati International Journal of Nursing and Health Science
Fariz Darari
Information Retrieval Laboratory, Faculty Of Computer Science, Universitas Indonesia
Computer Science & IT Health Professions Library & Information Science Nursing Public Health

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COVIWD: COVID-19 Wikidata Dashboard Fariz Darari
Jurnal Ilmu Komputer dan Informasi Vol 14, No 1 (2021): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information
Publisher : Faculty of Computer Science - Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21609/jiki.v14i1.941

Abstract

COVID-19 (short for coronavirus disease 2019) is an emerging infectious disease that has had a tremendous impact on our daily lives. Globally, there have been over 95 million cases of COVID-19 and 2 million deaths across 191 countries and regions. The rapid spread and severity of COVID-19 call for a monitoring dashboard that can be developed quickly in an adaptable manner. Wikidata is a free, collaborative knowledge graph, collecting structured data about various themes, including that of COVID-19. We present COVIWD, a COVID-19 Wikidata dashboard, which provides a one-stop information/visualization service for topics related to COVID-19, ranging from symptoms and risk factors to comparison of cases and deaths among countries. The dashboard is one of the first that leverages open knowledge graph technologies, namely, RDF (for data modeling) and SPARQL (for querying), to give a live, concise snapshot of the COVID-19 pandemic. The use of both RDF and SPARQL enables rapid and flexible application development. COVIWD is available at http://coviwd.org.
LINKEDLAB: A DATA MANAGEMENT PLATFORM FOR RESEARCH COMMUNITIES USING LINKED DATA APPROACH Fariz Darari; Ruli Manurung
Jurnal Ilmu Komputer dan Informasi Vol 5, No 1 (2012): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information)
Publisher : Faculty of Computer Science - Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1199.948 KB) | DOI: 10.21609/jiki.v5i1.181

Abstract

Data management has a key role on how we access, organize, and integrate data. Research community is one of the domain on which data is disseminated, e.g., projects, publications, and members.There is no well-established standard for doing so, and therefore the value of the data decreases, e.g. in terms of accessibility, discoverability, and reusability. LinkedLab proposes a platform to manage data for research communites using Linked Data technique. The use of Linked Data affords a more effective way to access, organize, and integrate the data. Manajemen data memilki peranan kunci dalam bagaimana kita mengakses, mengatur, dan mengintegrasikan data. Komunitas riset adalah salah satu domain dimana data disebarkan, contohnyadistribusi data dalam proyek, publikasi dan anggota. Tidak ada standar yang mengatur distribusi data selama ini.Oleh karena itu,value dari data cenderung menurun, contohnya dalam konteksaccessibility, discoverability, dan usability. LinkedLab merupakan sebuah usulanplatform untuk mengelola data untuk komunitas riset dengan menggunakan teknik Linked Data. Kegunaan Linked Data adalah sebuah cara yang efektif untuk mengakses, mengatur, dan mengitegrasikan data.
A Hybrid Virtual Assistant for Legal Domain Based on Information Retrieval and Knowledge Graphs Douglas Raevan Faisal; Fariz Darari; Muhammad Ilham Al Ghifari; Muhammad Zuhdi Zamrud; Marcellino Chris O'Vara; Berty Chrismartin Lumban Tobing; On Lee
Jurnal Ilmu Komputer dan Informasi Vol. 16 No. 2 (2023): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Informatio
Publisher : Faculty of Computer Science - Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21609/jiki.v16i2.1152

Abstract

Virtual assistants have gained popularity across various domains, including the legal field, where they serve to offer guidance and aid in the form of law retrieval. In this research, our aim is to develop a legal virtual assistant that combines knowledge graphs (KGs) and information retrieval (IR) techniques. This hybrid approach allows us to provide accurate answers extracted from structured interconnected data while simultaneously cater to a diverse range of legal inquiries. We categorize these inquiries into a few distinct use cases: definition lookup, law component lookup, sanctions, and domain knowledge. Our system encompasses a chatbot platform, knowledge graph querying, and information retrieval. Specifically, we construct a VA system over a legal knowledge graph pertaining to the Indonesian Act concerning Manpower or Labor (UU Ketenagakerjaan) and the Indonesian Act concerning the Creation of Jobs (UU Cipta Kerja). This marks the creation of the first legal virtual assistant in the Indonesian context that combines KG and IR methodologies. To evaluate the effectiveness of our prototype system, we conduct tests using a variety of labor law-related questions, ranging in difficulty. The integration of knowledge graphs and information retrieval proves to significantly improve the support provided for a wide range of potential applications in the legal field.
The role of chatbots in paediatric chronic disease management: Trends, findings, and future recommendations Amelia Arnis; Yeni Rustina; Allenidekania Allenidekania; Fariz Darari
Malahayati International Journal of Nursing and Health Science Vol. 7 No. 10 (2024): Volume 7 Number 10
Publisher : Program Studi Ilmu Keperawatan-fakultas Ilmu Kesehatan Universitas Malahayati

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33024/minh.v7i10.564

Abstract

Background: Chronic diseases in children, such as chronic kidney disease (CKD), diabetes, and asthma require complex long-term management because they can affect the quality of life physically, psychologically, socially, and educationally. One of the new innovations in supporting the management of chronic diseases in children is the use of chatbots that play a role in education, health monitoring, and psychological support. Purpose: To review the literature on the effectiveness of chatbots in supporting self-care in children with chronic diseases. Method: This literature review study examined the effectiveness of chatbots in supporting self-care in children with chronic diseases. The articles used were from ProQuest, Scopus, ScienceDirect, and Google Scholar. Inclusion criteria included publications from 2014-2024 that focused on children with chronic diseases. The search keywords used were "conversational agent", "chronic disease management", and "children". The search for articles used the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and obtained 6 articles for review. Results: Analysis of the selected articles suggests that chatbots have the potential to provide easily accessible educational resources, assist in regular monitoring of children's health, and provide emotional support to children in managing chronic diseases. However, there are several challenges, such as limitations in chatbot personalization and the importance of family involvement in chatbot use. Conclusion: Chatbots have great potential to support chronic disease management in children, although further development is needed on the aspects of personalization and data privacy to make their use more effective and safe for children. Suggestion: Long-term research is needed to assess the lasting impact of chatbots on children’s health outcomes, including mental development and quality of life. Developing more personalized and responsive chatbots and exploring their integration with other technologies such as wearables are also interesting areas for further exploration.
Granularity-aware legal question answering: a case study of Indonesian government regulations Faisal, Douglas Raevan; Darari, Fariz; Ryanda, Reynard Adha
International Journal of Advances in Intelligent Informatics Vol 10, No 3 (2024): August 2024
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v10i3.1105

Abstract

Question answering (QA) technologies are crucial for building conversational AI.  Current research related to QA for the legal domain lacks focus on the organized structure of laws, which are hierarchically segmented into components at varying levels of detail. To address this gap, we propose a new task of granularity-aware legal QA, which accounts for the underlying granularity levels of law components. Our approach encompasses task formulation, dataset creation, and model development. Under the Indonesian jurisdiction, we consider four law component granularity levels: chapters (bab), articles (pasal), sections (ayat), and letters (huruf). We include 15 government regulations (Peraturan Pemerintah) of Indonesia related to labor affairs and build a legal QA dataset with granularity information. We then design a solution for such a task—the first IR system to account for legal component granularity. We implement a customized retriever-reranker pipeline in which the retriever accepts law components of multiple granularities and the reranker is trained for granularity-aware ranking. We leverage BM25 and BERT models as retriever and reranker, respectively, yielding an end-to-end exact match accuracy of 35.68%, which offers a significant improvement (20%) over a strong baseline. The use of reranker also improves the granularity accuracy from 44.86% to 63.24%. In practical context, such a solution can help provide more precise answers, not only from legal chatbots, but also other conversational AI that deals with hierarchically-structured documents.
Co-Authors Allenidekania Allenidekania Amelia Arnis Berty Chrismartin Lumban Tobing Douglas Raevan Faisal Faisal, Douglas Raevan Marcellino Chris O'Vara Muhammad Ilham Al Ghifari Muhammad Zuhdi Zamrud On Lee Ruli Manurung Ryanda, Reynard Adha Yeni Rustina