Article Per Year (5 Year)
p-Index From 2020 - 2025
0.778
P-Index
This Author published in this journals
All Journal Indonesian of Health Information Management Journal (INOHIM) Nusantara Hasana Journal Proceeding of International Conference Health, Science And Technology (ICOHETECH)
Sri Widodo
Universitas Duta Bangsa Surakarta
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Health Professions Industrial & Manufacturing Engineering Medicine & Pharmacology Nursing

Published : 4 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 4 Documents
Search

 1 
EVALUASI SISTEM INFORMASI REKAM MEDIS DI RUMAH SAKIT BRAYAT MINULYA Nurhayati Nurhayati; Sri Widodo; Antonius Suhartanto
Nusantara Hasana Journal Vol. 1 No. 4 (2021): Vol. 1 No. 4 (2021): Nusantara Hasana Journal, September 2021
Publisher : Nusantara Hasana Berdikari

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Motivation/Background: Brayat Minulya Hospital already uses a medical record information system, but no evaluation has been done. Constraints were found including the reports generated by the system were invalid and real time, there was duplication of patient data. This study aims to evaluate the system using the PIECES method. Method: This research was conducted in March-June 2018. This type of research is descriptive with a cross sectional approach. The research variables are performance, information, economy, control, efficiency, service. Data collection techniques are interviews and observation. Processing of data collecting, editing, coding, classification and tabulating. Research instruments include interview guidelines, observation guidelines and questionnaires. Results: The Performance aspect is considered quite good by the registration section, service staff, but not good enough in the reporting section. The information aspect produced by the system is not good in all units because it does not meet the needs of hospital statistical data. The economy aspect of the system is considered poor because the resulting data still has to be crosschecked. The control aspect of the system is considered very good because it meets the data security aspect. The efficiency aspect of the system is good enough to increase time efficiency, but in the reporting section it is not good. The service aspect of the system for accuracy and speed is still quite good, the system is easy to learn. Conclusions: The medical record information system is good in terms of performance and data security but is lacking in terms of the accuracy of the information and reports generated.
TINJAUAN PENILAIAN SISTEM INFORMASI REKAM MEDIS RAWAT JALAN DI RUMAH SAKIT JIWA DAERAH Dr. ARIF ZAINUDIN SURAKARTA TAHUN 2022 Nurhayati Nurhayati; Sri Widodo; Khofifah Nurul Sa’adah
Nusantara Hasana Journal Vol. 2 No. 3 (2022): Nusantara Hasana Journal, August 2022
Publisher : Nusantara Hasana Berdikari

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Regional Mental Hospital Dr Arif Zainudin Surakarta has an outpatient medical record information system that was implemented in 2019, the system is operated by the registrar, polyclinic, and pharmacy staff. There are still obstacles in managing outpatient medical record data, but the management has never conducted an assessment/evaluation of the system. This study aims to assess the outpatient information system to see the extent to which the user's assessment of the performance of the system. This research is a descriptive study, the object of this research is the Transmedic outpatient medical record information system, the research subjects include registration officers, polyclinic officers, and pharmacy officers. Research instruments in the form of observation guidelines, interview guidelines, and questionnaires. The results of this study are in the aspect of performance that is of good value with an interpretation of the assessment of 69.97%; the information aspect is of good value with 68.91% interpretation of the assessment; the economic aspect is of good value with an interpretation of the assessment of 71.21%; the control aspect has good value with the interpretation of the assessment 71.86%; the efficiency aspect is of good value with an interpretation of the assessment of 67.25%; service aspect with the interpretation of the assessment of 67.03%. The outpatient electronic medical record system is categorized as good and acceptable to respondents
Klasifikasi Kanker dan Artery pada Citra Computed Tomography Menggunakan Deep Learning Convolution Neural Network Sri Widodo
Indonesian of Health Information Management Journal (INOHIM) Vol 10, No 2 (2022): INOHIM
Publisher : Lembaga Penerbitan Universitas Esa Unggul

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47007/inohim.v10i2.444

Abstract

AbstractDetection of lung cancer can significantly reduce the average death rate from lung cancer. Research on detection of lung cancer has been done. Most research on lung cancer detection always begins with image preprocessing, lung segmentation, lung candidate segmentation and lung cancer detection. These steps can cause the detection process to take a long time. The proposed research is to classify cancer and arterial images on CT-Scan using   Convolution Neural Network (CNN). This research consists of two main points. Starting with the process of determining the region of interest (ROI) from the image of cancer and artery. The second is cancer classification and artery using CNN deep learning. The accuracy obtained from testing is 95%.Keyword: CNN, CtScan, Deep Learning, Lung Cancer, ROI AbstrakDeteksi awal kanker paru dapat menurunkan rata-rata angka kematian akibat kanker paru secara signifikan. Penelitian tentang deteksi awal kanker paru sudah banyak dilakukan. Sebagian besar studi mengenai deteksi kanker paru pada CT-Scan selalu diawali dengan preprosesing citra, segmentasi paru, segmentasi kandidat paru dan deteksi kanker paru. Langkah-langkah tersebut dapat menyebabkan proses deteksi membutuhkan waktu yang lama. Penelitian yang dilakukan adalah melakukan klasifikasi kanker dan arteri pada gambar Computed Tomography menggunakan Convolution Neural Network (CNN). Penelitian ini terdiri dari dua hal pokok. Pertama adalah preprosesing  dari citra kanker dan artery.  Kedua adalah klasifikasi  kanker dan artery  menggunakan deep learning CNN. Akurasi tertinggi yang didapatkan dari ujicoba adalah 95%.Kata Kunci: CNN, CtScan, Deep Learning, Lung Cancer, ROI
Detection of Covid-19 on X-Ray Images Using a Deep Learning Convolution Neural Network Sri Widodo; Anik Sulistiyanti; Indra Agung Yudistira; Maryatun
Proceeding of International Conference on Science, Health, And Technology 2021: Proceeding of the 2nd International Conference Health, Science And Technology (ICOHETECH)
Publisher : LPPM Universitas Duta Bangsa Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1887.08 KB) | DOI: 10.47701/icohetech.v1i1.1136

Abstract

Pneumonia Coronavirus Disease 2019 (COVID-19) is an inflammation of the lung parenchyma caused by Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Supporting examinations carried out to establish a diagnosis of Covid-19 is through radiological examinations, one of which is a X-Ray The current method used to diagnose COVID-19 from X-Ray images is by studying the 2-D X-Ray image data set using the naked eye, then interpreting the data one by one. This procedure is ineffective. Proposed research aims to develop a Covid-19 detection application on localized X-Ray images using a Deep Learning Convolution Neural Network. This research includes four main points. The first is taking a X-Ray image from the internet. The second is X-Ray image preprocessing. The third is the determination of Region of Interest (ROI) from X-Ray imagery containing Covid-19 and normal X-Ray. The fourth is to detect COVID-19 automatically by classifying image suspected of being COVID-19 on X-Ray using the Deep Learning Convolution Neural Network method. The accuracy obtained is an accuracy of 95%.
Co-Authors Anik Sulistiyanti Antonius Suhartanto Indra Agung Yudistira Khofifah Nurul Sa’adah Maryatun Nurhayati