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Sabita, Hary
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Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Control & Systems Engineering Electrical & Electronics Engineering Engineering

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Pantauan Prediktif Covid-19 Dengan Menggunakan Metode SIR dan Model Statistik Di Indonesia Sabita, Hary; Herwanto, Riko
TEKNIKA Vol. 14 No. 2 (2020): Teknika Juli - Desember 2020
Publisher : Politeknik Negeri Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/zenodo.13358231

Abstract

During the COVID-19 pandemic, many attempts have been made to predict cases of additional patients, deaths and other medical indicators using various methods. Several forecast projects and predictions have influenced policies in several countries, including Indonesia. However, predictions and predictions for the COVID-19 pandemic are inherently uncertain. Uncertainty is rooted in a lot of the unknown. Starting from the virus itself, complexity, heterogeneity, human behavior, protocols and government intervention. In this study we explored the potential using the term "Predictive Monitoring" using the SIR method and statistical models. This method was chosen because it is one of the basic methods in epidemiological models. The purpose of this research is to capture and understand changes that occur as meaningful signals of uncertainty over changes in actual scenarios. The results of predictive monitoring obtained from this study amounted to 0.89 or 89% for the cure rate and 0.64 or 64% for the mortality rate. With this signal, it is hoped that the planning, behavior and mentality of the current community will become more forward-looking in initiating and guiding preventive actions to shape the real future. Keywords—Data Science, Forecasting, Model SIR, COVID-19
Sistem Cerdas Klasifikasi Gejala Awal COVID-19 dan Influenza Menggunakan Metode Support Vector Machine Sandra, Bella Aprilia; Sabita, Hary
TEKNIKA Vol. 18 No. 2 (2024): Teknika Juli - Desember 2024
Publisher : Politeknik Negeri Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/zenodo.12724901

Abstract

Self-diagnosis merupakan tindakan menentukan sendiri penyakit berdasarkan informasi yang dimiliki, menjadi tantangan dalam mengklasifikasikan gejala awal COVID-19 dan Influenza. Tujuan penelitian ini adalah mengembangkan sistem cerdas menggunakan metode Support Vector Machine untuk membedakan antara COVID-19 dan Influenza berdasarkan gejala awal pasien RSUD Ragab Begawe Caram Mesuji. Metode pengumpulan data meliputi studi literatur dan dokumentasi dari berbagai sumber. Evaluasi dilakukan dengan menggunakan confusion matrix untuk mengukur akurasi model SVM dalam mengklasifikasikan gejala awal COVID-19 dan Influenza. Hasil penelitian menunjukkan bahwa model SVM mencapai akurasi sebesar 62%, dengan performa yang bervariasi di setiap kelas gejala. Evaluasi memperlihatkan bahwa sistem ini dapat membedakan antara gejala awal COVID-19 dan Influenza, memberikan diagnosa berdasarkan hasil klasifikasi. Rekomendasi bagi pengembangan selanjutnya adalah peningkatan jumlah Dataset untuk meningkatkan akurasi model secara keseluruhan, serta desain antarmuka yang dapat digunakan secara efektif oleh tenaga medis dan masyarakat.
Co-Authors Herwanto, Riko Sandra, Bella Aprilia