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All Journal Jurnal Teknimedia: Teknologi Informasi dan Multimedia Journal of Dinda : Data Science, Information Technology, and Data Analytics
Innova, Zacky
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Computer Science & IT Control & Systems Engineering Engineering

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Classification of Indonesian Disasters with Decision Trees Based on Spatial and Text Data Ramadhan, Ridwan; Saputra, Ragil Raditya; Innova, Zacky; Prabowo, Ary
Indonesian Journal of Data Science, IoT, Machine Learning and Informatics Vol 5 No 2 (2025): August
Publisher : Research Group of Data Engineering, Faculty of Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/dinda.v5i2.2006

Abstract

Indonesia is one of the countries with a very high level of natural disaster vulnerability. The types of disasters that frequently occur include earthquakes, floods, landslides, volcanic eruptions, and others. This is because Indonesia is located at a geographical position where three world tectonic plates meet and has tropical climate conditions that make it prone to disasters. Therefore, Indonesia needs a system that can classify disaster types automatically and accurately to help the decision-making process quickly and accurately. This research aims to develop a natural disaster classification model based on information such as location (regency and province), time of occurrence (date), and causes that lead to disasters. The method used for classification in this research is the Decision Tree algorithm, because this algorithm can handle both numerical and categorical data and has high interpretability. Classification processing is also performed using textual cause data using Term Frequency-Inverse Document Frequency (TF-IDF) technique to convert text format into numerical form that can be processed by machine learning algorithms. The dataset obtained from the National Disaster Management Agency (BNPB) is open source. Test results show that the trained Decision Tree model can classify disaster types with an accuracy of 87%. This model also shows good precision, recall, and f1-score values in each disaster category. It is hoped that the results of this research can help in developing historical data-based disaster detection systems and assist government and society in responding to disasters more effectively and efficiently.
SISTEM OTOMATISASI RENDERING VIDEO TEMPLATE AFTER EFFECTS BERBASIS WEB DENGAN EXTENDSCRIPT DAN FLASK Ragil Raditya Saputra; Innova, Zacky; Katiga Capricorna, Epsilona; Bambang Irawan; Ramadhan, Ridwan; Hafizh Rahman Hakim, Muhammad
TEKNIMEDIA: Teknologi Informasi dan Multimedia Vol. 6 No. 2 (2025): Desember 2025
Publisher : Badan Penelitian dan Pengabdian Masyarakat (BP2M) STMIK Syaikh Zainuddin NW Anjani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46764/teknimedia.v6i2.299

Abstract

The process of creating animations or videos in Adobe After Effects typically requires repetitive and time-consuming manual interaction, making it less efficient in digital multimedia workflows. To address this challenge, this study designed a web-based rendering automation system by integrating ExtendScript, After Effects' internal scripting language, and the Flask framework as the backend. The HTML-based interface allows users to input parameters such as text, color, or formatting options, which are then processed by Flask and passed to After Effects via a local subprocess. Beyond integration, we also compared this automation system with manual methods. This system increased work efficiency by up to 67.78% compared to traditional manual methods and supported automated processing based on user input. This system provides a practical and affordable solution for users who want to accelerate the production of visual content from After Effects templates with customizations to text and color, without compromising the flexibility and quality of the final result. This technology integration demonstrates that automating the creative process can be done effectively, stably, and easily accessible via the web if further developed.
Co-Authors Ary Prabowo Bambang Irawan Hafizh Rahman Hakim, Muhammad Katiga Capricorna, Epsilona Ragil Raditya Saputra Ramadhan, Ridwan Saputra, Ragil Raditya