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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Syntax Idea Jurnal Ekonomi Indonesia
Monika, Anugerah Karta
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Aerospace Engineering Agriculture, Biological Sciences & Forestry Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Economics, Econometrics & Finance Engineering

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The Utility of ‘Covid-19 Mobility Report’ and ‘Google Trend’ for Analysing Economic Activities Monika, Anugerah Karta
Syntax Idea Vol 3 No 6 (2021): Syntax Idea
Publisher : Ridwan Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36418/syntax-idea.v3i6.1224

Abstract

WHO urged the world population to practice social distancing to prevent transmission of the COVID-19. Social distancing leads the policy to limit mobility of people to work, school and other social activities. Restricting population mobility makes people start to look for other alternatives to do economic activities. The use of the internet is optimized in carrying out economic activities. This research aims to describe the economic activity using internet during COVID-19 pandemic. The research also aimed to show the relationship between the people mobility during COVID-19 with economic activity using internet. Descriptive analysis and inference analysis are applied to analyse the economic activities. Data source are provided by google trend and google COVID-19 community mobility report. Google trend data are used to represent the economic activities by deriving the queries of keywords related to economic activities. The People mobility to retail, grocery, parks, transit station, workplace and residential are obtained from Google COVID-19 mobility report. Data from COVID-19 mobility report treated as independent variables. As for dependent variables, data from google trend are applied with the keyword ‘jual’ and ‘beli’. Two multiple linear regression models are applied to show the relationship between independent variables and dependent variables. The result showed that mobility to workplace and residential are significantly affecting economic activity during pandemic time for both models
Peran Ekonomi Kreatif terhadap Perekonomian Indonesia Selama Pandemi dengan Analisis Tabel Input-Output Bernika, Levina; Monika, Anugerah Karta
Jurnal Ikatan Sarjana Ekonomi Indonesia Vol 13 No 3 (2024): December
Publisher : Jurnal Ekonomi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52813/jei.v13i3.344

Abstract

Ekonomi kreatif (ekraf) merupakan sektor yang berpotensi menjadi sumber pertumbuhan di tengah perlambatan ekonomi akibat mewabahnya Covid-19 di Indonesia sehingga dijadikan sebagai sasaran dalam program prioritas nasional dalam rangka percepatan pemulihan ekonomi. Namun, sumber data ekraf yang terbatas mengakibatkan kurang beragamnya informasi untuk menunjang pengembangan sektor ini. Penelitian ini bertujuan untuk memperkaya ketersediaan data ekraf dan menganalisis peran ekraf terhadap perekonomian Indonesia dengan menggunakan analisis deskriptif, analisis input-output, serta analisis uji peringkat bertanda Wilcoxon. Hasil analisis menunjukkan kontribusi sektor ekraf terhadap perekonomian Indonesia selama Covid-19 terus meningkat signifikan dilihat dari sisi permintaan. Subsektor kuliner, aplikasi dan game developer, serta televisi dan radio merupakan subsektor unggulan sehingga dapat menjadi fokus pembangunan pemerintah untuk mencapai target yang telah ditetapkan.
Automatic Classification of Multilanguage Scientific Papers to the Sustainable Development Goals Using Transfer Learning Suadaa, Lya Hulliyyatus; Monika, Anugerah Karta; Putri, Berliana Sugiarti; Rimawati, Yeni
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 3 (2025): June 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i3.6560

Abstract

The classification of scientific papers according to their relevance to Sustainable Development Goals (SDGs) is a critical task in identifying the research development status of goals. However, with the growing volume of scientific literature published worldwide in multiple languages, manual categorization of these papers has become increasingly complex and time-consuming. Furthermore, the need for a comprehensive multilingual dataset to train effective models complicates the task, as obtaining such datasets for various languages is resource intensive. This study proposes a solution to this problem by leveraging transfer learning techniques to automatically classify scientific papers into SDG labels. By fine-tuning pretrained multilingual models mBERT on SDG publication datasets in a multilabel approach, we demonstrate that transfer learning can significantly improve classification performance, even with limited labelled data, compared to SVM. Our approach enables the effective processing of scientific papers in different languages and facilitates the seamless mapping of research to the relevance of SDGs, the four pillars of SDGs, and the 17 goals of SDGs. The proposed method addresses the scalability issue in SDG classification and lays the groundwork for more efficient systems that can handle the multilingual nature of modern scientific publications.
Co-Authors Bernika, Levina Putri, Berliana Sugiarti Rimawati, Yeni Suadaa, Lya Hulliyyatus