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All Journal JOIV : International Journal on Informatics Visualization Syntax Literate: Jurnal Ilmiah Indonesia
Krisnadhi, Adila Alfa
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Humanities Computer Science & IT Education Environmental Science Law, Crime, Criminology & Criminal Justice Social Sciences Other

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Toponym Extraction and Disambiguation from Text: A Survey Windiastuti, Rizka; Krisnadhi, Adila Alfa; Budi, Indra
JOIV : International Journal on Informatics Visualization Vol 9, No 1 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.1.2763

Abstract

Toponym is an essential element of geospatial information. Traditionally, toponyms are collected in a gazetteer through field surveys that require significant resources, including labor, time, and money. Nowadays, we can utilize social media and online news portals to collect event locations or toponyms from the text. This article presents a survey of studies that focus on the extraction and disambiguation of toponyms from textual documents. While toponym extraction aims to identify toponyms from the text, toponym disambiguation determines their specific locations on the earth. The survey covered articles published between January 2015 and April 2023, presented in English, and gathered from five major journal databases. The survey was conducted by adopting the Kitchenham guidelines, consisting of an initial article search, article selection, and annotation process to facilitate the reporting phase. We employed Mendeley as a reference management tool and NVivo to categorize certain parts of the articles that are the focal points of interest in this survey. The primary focus of the survey was on the methods or approaches performed in the research articles to extract and disambiguate toponyms. Additionally, we also discuss some general challenges in toponym research, different applications for toponym extraction and disambiguation, data sources, and the use of languages other than English in the studies. The survey confirms that each approach has its limitations. Extracting and disambiguating toponyms from text is complex and challenging, especially for low-resource languages. We also suggest some research directions related to toponym extraction and disambiguation that could enrich the gazetteer.
Astronomical Image Denoising Using AttentionGAN Naufal, Faishal Zaka; Rachmadi, Muhammad Febrian; Krisnadhi, Adila Alfa
Syntax Literate Jurnal Ilmiah Indonesia
Publisher : Syntax Corporation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36418/syntax-literate.v10i1.13154

Abstract

Denoising astronomical images is a significant challenge in the field of astronomical data processing. Image data acquired from astronomical sources typically contains noise from various sources. The study aims to investigate the denoising of astronomical images using an image-to-image translation approach with AttentionGAN method. This method combines attention-guided techniques with a Generative Adversarial Network (GAN) model to improve the quality of noisy astronomical images. Attention-guided technique allows the model to learn the most important features of the image and guide the image generation process. This approach has been tested on several images in different domains, each with varying levels of noise. The results shows that AttentionGAN method produces denoised images with better and sharper quality than several other denoising methods. Two databases, The Panoramic Survey Telescope and Rapid Response System (PAN-STARRS) and the Sloan Digital Sky Survey (SDSS), were used in this research. Images acquired from PAN-STARRS contain noise, while images acquired from SDSS are clean. Overall, this research contributes to improving the quality of astronomical images by demonstrating the effectiveness of the AttentionGAN method in denoising noisy astronomical images. We employed denoising techniques using CycleGAN and AttentionGAN and evaluated them using metrics such as PSNR, SSIM, and FID. The analysis showed that the AttentionGAN model outperformed CycleGAN. We also conducted ablation studies to further investigate the components of the AttentionGAN model. This study provides a foundation for future research in the field of astronomical data processing, which has the potential to enhance image quality.
Model Klasifikasi Lightweight Untuk Deteksi Hama Pertanian Dengan Efficient NET, Spinal Net FC, dan Sharpness-Aware Minimization Pratama, Naufal Ihsan; Azizi, Fityan; Krisnadhi, Adila Alfa
Syntax Literate Jurnal Ilmiah Indonesia
Publisher : Syntax Corporation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36418/syntax-literate.v7i9.15119

Abstract

Petani Indonesia menghadapi tantangan besar yang disebabkan oleh hama, yang menyebabkan gagal panen, busuk batang, kerusakan daun, dan busuk buah. Mengembangkan model pendeteksian hama yang lightweight menjadi penting untuk membantu petani dalam peningkatan program pengendalian hama. Tujuan utama dari model ini adalah untuk mengklasifikasikan hama secara akurat dengan memanfaatkan kumpulan data berskala besar. Kumpulan data ini mencakup berbagai spesies dengan beragam skala, bentuk, latar belakang kompleks, dan tingkat kesamaan visual yang tinggi di antara spesies serangga. Penelitian ini menggunakan model klasifikasi lightweight berbasis Efficient Net. Model ini menggabungkan Spinal Net FC sebagai classifier dan mengadopsi Sharpness-Aware Minimization sebagai optimizer untuk meningkatkan kinerjanya. Model yang dikembangkan mencapai akurasi sebesar 68,2%. Selain itu, dengan mengimplementasikan metode yang diusulkan, performa model mengalami peningkatan yang signifikan, menghasilkan peningkatan akurasi tambahan sebesar 4%.
Co-Authors Azizi, Fityan Budi, Indra Muhammad Febrian Rachmadi, Muhammad Febrian Naufal, Faishal Zaka Pratama, Naufal Ihsan Rizka Windiastuti, Rizka