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All Journal Informasi Interaktif Jurnal Ilmiah SIGMA: Informatics Engineering Journal of UPB
Erry Maricha Oki Nur Haryanto
Universitas Janabadra
Computer Science & IT Decision Sciences, Operations Research & Management

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PERANCANGAN APLIKASI PENGENALAN LITERASI COVID-19 MENGGUNAKAN ACTIONSCRIPT 3.0 PADA MACROMEDIA FLASH Jeffry Andhika Putra; Erry Maricha Oki Nur Haryanto
Informasi Interaktif Vol 7, No 1 (2022): Jurnal Informasi Interaktif Vol. 7 No. 1 Januari 2022
Publisher : Universitas Janabadra

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Abstract

 The Covid-19 pandemic that has hit the world is a phenomenon that has never been predicted before in modern civilization. The increasingly widespread Covid-19 pandemic requires everyone to disciplinedly apply health protocols (use masks, maintain physical distance, wash hands with soap, and stay away from crowds). In addition to maintaining cleanliness, social distancing is the most effective step to avoid contracting Covid-19. A public policy will be effective If the public supports it, Large-Scale Social Restrictions (PSBB) are one of the efforts that the Government can take to deal with the spread of the Covid-19 pandemic.The current era of information flows quickly spreads to all corners of the world. information through the media in different forms in the form of images, sound, as well as video, as well as in applications. These computer applications are generally in the form of term data processing programs, work sheet data, and media data. Personal computer software is designed to make it easier for users to do things using a personal computer. The global pandemic requires many parties, including educational institutions, to make discoveries on learning media. This study designed a learning application for knowledge literacy about Covid-19, so that it can convey knowledge literacy for the protection and prevention of the spread of the Covid-19 virus. The system will be designed using the Macromedia Flash framework or SDK (Application Development Kit) using the ActionScript 3.0 method so that it can run on computer platforms and operating systems.Keywords: Learning Application, Actionscript 3.0, Multi-Platform Application, Literacy, Covid-19.
Named Entity Recognition dan Analisa Jarak dengan Formula Haversine pada P2P Lending yogi yulianto; Erry Maricha Oki Nur Haryanto; Fajar Dwi Insani; Meita Dwi Anggraeni; Aji Anggono
Jurnal SIGMA Vol 16 No 2 (2025): September 2025
Publisher : Teknik Informatika, Universitas Pelita Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37366/sigma.v16i2.7135

Abstract

Named Entity Recognition (NER) is a core task in natural language processing for extracting structured entity information from text. However, funding descriptions on peer-to-peer lending platforms are largely unstructured. This limits reliable identification of funding categories and location-related entities required for subsequent analysis. This study addresses this problem by applying Named Entity Recognition to identify agricultural entities from peer-to-peer lending funding descriptions in Indonesia, combining it with distance analysis. The data used in this study was collected from peer-to-peer lending platforms in Indonesia using web crawling techniques with the Python Selenium library. The collected funding data was used to train and test a Named Entity Recognition model developed using the spaCy library, with entity labeling performed using the Beginning–Inside–Outside (BEIN–IND–OUTS) tagging scheme. Model performance was evaluated using a confusion matrix at the token level. The evaluation results showed that the proposed model achieved 83% accuracy, 94% precision, 82.7% recall, and a 90% F1 score, indicating its ability to detect agricultural entities from lighting descriptions. Furthermore, the collected data containing agricultural entities was processed using the Haversine formula to calculate the distance between the lender and the borrower's location. When compared to Google Maps, the average distance difference was 23.7 kilometers. These results demonstrate that Named Entity Recognition combined with distance analysis can support the preparation of peer-to-peer lending data for further decision-making.
Co-Authors Aji Anggono Fajar Dwi Insani Jeffry Andhika Putra Meita Dwi Anggraeni Yogi Yulianto