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All Journal Jurnal Informatika EKUIVALEN - Pendidikan Matematika Jurnal Pendidikan dan Pembelajaran Khatulistiwa (JPPK) Jurnal Ilmu Ternak Veteriner JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Keperawatan dan Fisioterapi (JKF)
Suryani .
Program Pendidikan Guru Sekolah Dasar FKIP UNTAN, Pontianak
Computer Science & IT Decision Sciences, Operations Research & Management Education Languange, Linguistic, Communication & Media Mathematics Veterinary Other

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Lung X-ray Image Similarity Analysis Using RGB Pixel Comparison Method Pariyasto, Sofyan; ., Suryani; Warongan, Vicky Arfeni; Sari, Arini Vika; Widiyanto, Wahyu Wijaya
Journal of Applied Informatics and Computing Vol. 9 No. 1 (2025): February 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i1.8776

Abstract

The high death rate caused by pneumonia and Covid-19 is still quite high. Based on data released by WHO, 14% of deaths in children under 5 years old are caused by pneumonia. One of the processes carried out to help the diagnosis process is to look at lung images using X-Ray images. To obtain information about normal lung X-Ray images, Pneumonia and Covid-19, calculations are carried out using the color difference in each pixel of the X-ray image. The calculation process will provide output in the form of numbers in units of 0 to 100. This is done to facilitate the process of identifying the similarity of each X-Ray image being compared. The research stages are carried out with stages starting from adjusting the image size, then by breaking down the pixel values of the two images being compared and the process of calculating the difference in value from each pixel with the same coordinates. After calculating a combination of 30,000 combinations using 300 x-ray images, the results obtained in the form of the level of similarity between normal x-ray images and pneumonia x-ray images are the highest with a similarity percentage of 80.06%. The combination of normal images and pneumonia images is 10,000 combinations using 100 normal x-ray images and 100 pneumonia x-ray images. Normal x-ray images and covid x-ray images have a similarity of 79.18%. The combination of normal images and covid images is 10,000 combinations. The combination uses 100 normal x-ray images and 100 covid x-ray images. Pneumonia x-ray images and covid x-ray images have the lowest similarity level of 78.87%. The combination of pneumonia x-ray images and covid x-ray images is 10,000 combinations. The data used in the combination are 100 pneumonia images and 100 covid images. From the test results, the information obtained was that Accuracy was worth 0.54, Precision was worth 0.54, Recall was worth 0.59 and F1-score was worth 0.56.
The Application of Artificial Intelligence in Predicting Catastrophic Disease Emergencies: Application of Artificial Intelligence in Predicting Catastrophic Disease Emergencies ., Suryani
JURNAL KEPERAWATAN DAN FISIOTERAPI (JKF) Vol. 6 No. 1 (2023): Jurnal Keperawatan dan Fisioterapi (JKF)
Publisher : Fakultas Keperawatan dan Fisioterapi Institut Kesehatan Medistra Lubuk Pakam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35451/jkf.v6i1.2535

Abstract

Catastrophic diseases, such as heart attacks, strokes, and acute organ failure, require rapid and accurate prediction to improve emergency response and patient survival. Artificial Intelligence (AI) has been increasingly utilized to enhance diagnostic accuracy and early warning systems in medical emergencies. However, the effectiveness and challenges of AI implementation in emergency prediction remain a critical area of study. This study employed a quantitative method with a retrospective observational analytic design. Data were collected from electronic medical records of 500 patients with catastrophic diseases at RS Grandmed Lubuk Pakam. Univariate analysis was conducted to describe patient characteristics, while bivariate and multivariate analyses were performed using logistic regression to evaluate the predictive capabilities of AI models in emergency cases. The study analyzed data from 500 patients, where AI-based prediction models demonstrated an accuracy rate of 87% in identifying high-risk patients. The Chi-Square test showed a significant relationship between AI predictions and actual emergency events (p < 0,001). Logistic regression indicated that AI-based models were 3.2 times more effective in predicting emergencies compared to traditional methods (p < 0.001). The findings align with previous studies that highlight AI’s potential in enhancing medical decision-making. However, challenges such as data quality, model interpretability, and integration with clinical workflows must be addressed. The study emphasizes the need for further research to optimize AI algorithms and ensure ethical, safe, and effective implementation in emergency medical settings.
Co-Authors . Agustinus, . . Riyanto . Rukisno, . . Saian . Surani, . . Suryana, . . Yesaya, . . Zakaria A Haziq A.P Sari Abdussamad . Agustinus AK, Agustinus Alfonsa F34211483 Aprillia, Risa Arlinah Imam Rahardjo Armani F34211002 Asmayani Salimi Ayu Kusumawati, Ayu Azami, M Nasir Azura (F37009048) Bainen F34211491 Budiman Tampubolon D Khairunisa Daeng Rizky Lasmana Deby Yuti Dwi Novianti, Dwi Dwierra Evvyernie Dwita Purnama Sari Emma (F34211008) Endang Uliyanti Eva Silvia Taher, Eva Silvia Evi Nalisa, Evi F34211300, Iskandar Fadilah F 34211011 Fauzi Ahmad Muda Fitriadi, Hilmi Fransiskus Alon Fransiskus Anwar, Fransiskus Hazminarni F34211524 Heri Pramono Hery Kresnadi I Ngomok Irma Rusmita Istianah F34211023 Iwan Prihantoro Iwin Daryani Janiar, Intan Jhoni Stormadi K.Y Margiyati Kartini F34210654 Kartono . Kaswari . Kolenius Kolai Kristina U Kufron, . L Abdullah Libriyati F33209086 Lidya Angelina Linawati (F34211031) Lira Dwi Ardika, Lira Dwi Lusia s Lusiana F 34211552 M Yasin Mahdalena (F34211032) Marfirah ., Marfirah Mariana Kunjing Maridjo AH Marina, . Martin, Fadli Martinus Pede Marzuki . Masni F37008013 Masnur Nadeak Mastar Asran Moehammad Yani Mulyati F34211570 Murdiana F34211350 Murni F34210488 N Rahayu N.S Yunitasari Nanang Heryana Nur Widya Ichsani Nurhadi . Nurin F 34211192 Pariyasto, Sofyan Pitri Lestari Prayoko, Hendar Purnama, Akhmad Putri, Merisa Jediah Rafael F34211365 Renawati, . Resvan, . Riko Sadewa Rohani MYS Rosmawarni F 34211197 Rosyadi Agung Rudi Gunawan Rupina Burat, Rupina Rustini HR Saian F. 34210543 Samsuri, M. Sari, Arini Vika Saridin Dalam Sartini F34210237 Siti Djuzairoh Siti Halidjah Siti Rohana Sosiawan F 34211623 Sri Mulyono Sri Ponti Kustiningsih SRI RAHAYU Sri Sulastri Sri Utami Sri Winayati Sudariat F 34211208 Sugiyono . Suhardi Marli Sukartini F 34211210 Sukatsiah F34211407 Sukmawati . Sumiyati F34210240 Sunarti F37011027, Sunarti Suparmi F34210241 Supriyono Keleng, Supriyono Surami, Eny Susanto, Firman Susilawaty F34210242 Syamsiati . Syamsul Arifin T Toharmat Tahmid Sabri Tanti Yoseva S Tata Veronika, Tata Thadius Salihin Theresia Septi Ratna Wulandari, Theresia Septi Tiarmin Marpaung Tika Nofita, Tika Titi Hanida, Titi Titiek Rezeki Trisawati, Arby Nurul Tukidi F34211726 Ummy Kalsum Ismail Uray Zuraida Veridiana Sartika Dewi, Veridiana Sartika Veronika Kornelia Pidan vicky arfeni warongan, vicky arfeni Waludimanto F34211729 Wareh Suprihatin Widiyanto, Wahyu Wijaya Yani Kemala Sari, Yasinta Kina Yason F34210246 Yonsius Amir Yulia . Yuliana Rubinem Yuliani, Resha Yuspita F34211666 Zahara . Zainuddin . Zainudin .