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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) MAJAMATH: Jurnal Matematika dan Pendidikan Matematika JTIK (Jurnal Teknik Informatika Kaputama) JISA (Jurnal Informatika dan Sains) JPKMI (Jurnal Pengabdian Kepada Masyarakat Indonesia) Abdimas Singkerru JURNAL ABDI MASYARAKAT INDONESIA (JAMIN) Jurnal Pengabdian Masyarakat dan aplikasi Teknologi
Christina Eni Pujiastuti
Universitas Trisakti
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Mathematics Public Health Social Sciences Other

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Decision Tree for Determining Hospital Treatment for Covid-19 Patients Based on Hematology Parameters Using the C5.0 Algorithm Joko Riyono; Christina Eni Pujiastuti; Supriyadi Supriyadi; Dody Prayitno; Aina Latifa Riyana Putri
JISA(Jurnal Informatika dan Sains) Vol 7, No 2 (2024): JISA(Jurnal Informatika dan Sains)
Publisher : Universitas Trilogi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31326/jisa.v7i2.2103

Abstract

The rapid spread of the COVID-19 disease, which occurred globally from late 2019 to the early 2020s, significantly impacted communities worldwide, requires early detection of COVID-19 which is very important for patients and also the people around them to be able to fight the COVID-19 pandemic. Therefore, a classification analysis will be carried out to make decisions regarding determining COVID-19 patients who do not require hospitalization or who require Regular Ward, Semi-Intensive Care Unit, or Intensive Care Unit (ICU) in hospitals based on hematology parameters from the Machine Learning Repository. Kaggle Dataset uses the C5.0 algorithm assisted by Rstudio software. It is also known that because the data contains missing data, it is also necessary to handle missing data using the Mean Method assisted by SPSS software. Performance evaluated using the Confusion Matrix method produces an accuracy value of 78% which is considered quite good, where testing with the C5.0 Algorithm uses a training and testing data ratio of 40:60. This research simplifies and speeds up medical decision-making, improving patient management. With COVID-19 declining, the method can be applied to enhance healthcare systems' accuracy and efficiency in handling other diseases or emergencies, ensuring better preparedness for future challenges.
Literacy Review Study on the Implementation of Convolutional Neural Network Architecture in Segmentation and Classification of Lung Medical Images Joko Riyono; Supriyadi Supriyadi; Christina Eni Pujiastuti; Sofia Debi Puspa; Aina Latifa Riyana Putri
JISA(Jurnal Informatika dan Sains) Vol 8, No 1 (2025): JISA(Jurnal Informatika dan Sains)
Publisher : Universitas Trilogi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31326/jisa.v8i1.2172

Abstract

Medical image processing has become an essential aspect of healthcare, enabling accurate disease diagnosis and monitoring through advanced technologies. One of the most widely used methods in this domain is the Convolutional Neural Network (CNN), which has demonstrated high effectiveness in segmentation and classification tasks, particularly for chest X-ray images used in diagnosing lung-related diseases. This study aims to evaluate and analyze various CNN architectures implemented in lung X-ray imaging through a Systematic Literature Review (SLR) approach. The research explores the application, accuracy, challenges, and future opportunities of CNN-based models such as VGG, ResNet, AlexNet, and GoogLeNet. A total of 15 relevant studies published between 2019 and 2023 were selected after applying rigorous inclusion and exclusion criteria. The findings indicate that CNN architectures significantly enhance the accuracy of lung disease detection and support both segmentation and classification tasks. However, challenges such as dataset variability, model generalization, and ethical implications remain. This review provides comprehensive insights into CNN applications in medical imaging, emphasizing their potential and highlighting areas for further research.
Co-Authors Aina Latifa Riyana Putri Dianing Novita Nurmala Putri Dody Prayitno Fani Puspitasari Fayza Nayla Riyana Putri Fayza Nayla Riyana Putri Joko Riyono Joko Riyono Sofia Debi Puspa Sofia Debi Puspa Supriyadi Supriyadi Syaifudin