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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Ilmiah Kursor Indonesian of Health Information Management Journal (INOHIM) Nusantara Hasana Journal Proceeding of International Conference Health, Science And Technology (ICOHETECH) GEMASSIKA: Jurnal Pengabdian kepada Masyarakat
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 Public Health

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Lung diseases detection caused by smoking using support vector machine Sri Widodo; Ratnasari Nur Rohmah; Bana Handaga; Liss Dyah Dewi Arini
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 17, No 3: June 2019
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v17i3.9799

Abstract

Type of lung disease is very much manifold, but type of lung disease caused by smoking there are only 4, namely Bronchitis, Pneumonia, Emphysema and Lung Cancer. Doctors usually diagnose lung disease from CT scans using the naked eye, then interpret data one by one.This procedure is not effective. The aim of this research is improvement accuracy of lung diseases detection caused by smoking using support vector machine on computed tomography scan (CT scan) images. This study includes 4 (four) main points. First is the development of software for segmentation of lung organ automatically using Active Shape Model (ASM) method. Second is the segmentation of candidates who are considered illness by using Morphology Mathematics. The third process of lung disease detection using Support Vector Machine (SVM). Fourth is visualization of disease or lung disorder using Volume Rendering.
LUNG FIELD SEGMENTATION ON COMPUTED TOMOGRAPHY IMAGE USING ACTIVE SHAPE MODEL Sri Widodo; Wijiyanto Wijayanto
Jurnal Ilmiah Kursor Vol 7 No 2 (2013)
Publisher : Universitas Trunojoyo Madura

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Abstract

LUNG FIELD SEGMENTATION ON COMPUTED TOMOGRAPHY IMAGE USING ACTIVE SHAPE MODEL a Sri Widodo, bWijiyanto aMedical Record and Health Informatics Academic of Citra Medika Surakarta Samanhudi, Surakarta a Sekolah Tinggi Manajemen Informatika dan Komputer Duta Bangsa Surakarta Indonesia E-mail: papa_lucky01@yahoo.com Abstrak Metode saat ini yang banyak digunakan untuk segmentasi bidang paru dari citra CT scan adalah thresholding dan Active Contour, yang mengandalkan kontras dari nilai keabuan antara parenkim paru dan jaringan sekitarnya. Kelemahan dari kedua metode tersebut adalah jika nodul tersebut besar dan terletak pada batas tepi paru, maka nodul tersebut tidak akan masuk dalam citra paru (bagian paru yang terdapat kelainan atau nodul akan hilang). Hal ini berarti proses segmentasi bidang paru dianggap gagal, karena citra nodul yang menjadi pokok perhatian akan hilang. Tujuan dari penelitian adalah segmentasi bidang paru yang mengandung kelainan pada citra CT scan dengan menggunakan Active Shape Model. Kontribusi dalam penelitian ini adalah jenis baru dari Active Shape Model dengan variasi tingkat keabuan dari dua batas objek, yaitu, bidang paru kanan dan kiri. Pada penelitian ini, kami mempertimbangkan struktur gambar lokal, yaitu momen dari histogram lokal yang berasal dari pengurangan antara citra asli dengan citra hasil negasi. Kami juga melakukan segmentasi bidang paru dengan thresholding dan Active Contour, sebagai bahan perbandingan dengan metode yang diusulkan. Hasil dari penelitian kami menunjukkan bahwa pendekatan segmentasi dengan Active Shape Model mempunyai akurasi 94.6%, sensitifitas 90.2%, dan spesifisitas 95.9%. Kata kunci: Active Shape Model, Active Contour, Thresholding. Abstract Current method is widely used for lung field segmentation of CT scan image is Thresholding and Active Contour, which relies on contrast of gray values between lung parenchyma and surrounding tissues. Drawback of both methods is that if nodule is large and located on borders of lung, then nodules will not be included in the image of lung (nodules contained will be lost). This means that segmentation of lung field was considered a failure, because image of nodule is object of attention will be lost. Purpose of this research is lung field segmentation that contain abnormalities in CT scan image using Active Shape Model. Contribution in this paper is a new type of Active Shape Model with gray level variation of two object boundaries, namely right lung field and left lung field. In this study, we consider the local image structure, which moments of a local histogram derived from the subtraction of the original image with the negation image. We also perform lung fields segmentation using thresholding and Active Contour, as a comparison with method we proposed. Results of our study show that segmentation with ASM approach has accuracy 94.6%, sensitivity 90.2%, and specificity of 95.9%. Key words: Active Shape Model, Active Contour, Thresholding
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

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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

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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%.
The Development of Intelligent Software for Early Detection of Stunting in Toddlers Based on Anthropometry Sri Widodo Widodo; Anik Sulistiyanti -
Proceeding of International Conference on Science, Health, And Technology Proceeding of the 3rd 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 (361.169 KB) | DOI: 10.47701/icohetech.v3i1.2279

Abstract

Stunting describes chronic undernutrition status during growth and development since early life. This situation is represented by the z-score of height for age (TB/U) less than -2 standard deviations (SD). The current method used to determine the presence of stunting in toddlers is to use Health Card. The way to do this is by weighing toddlers every month, the weighing results are recorded in the Health Card, and between the weight points from last month's weighing results and this month's weighing results are connected by a line. The series of children's growth lines form a child's growth graph. This procedure is of course less effective. In addition, midwives can differ from one another in determining stunting status in toddlers. The research aims to conduct early detection of stunting in toddlers based on anthropometry using an intelligent system. This research includes 3 (three) main things. The first is the development of software for managing children's medical record data. The second is the development of software for early detection of stunting using the Support Vector Machine (SVM). The third is the development of graph software for toddler anthropometric data development. The results of this study are expected to provide a tool for posyandu cadres and midwives in early detection of stunting in toddlers.
PENERAPAN APLIKASI CERDAS PENCEGAHAN STUNTING DI POSYANDU ARUMDALU DESA SANGGRAHAN GROGOL SUKOHARJO Sari, Devi Pramita; Widodo, Sri; Mustofa, Khoirul; Januar Isnaini, Vina; Muhammad Darmawan, Diki
GEMASSIKA: Jurnal Pengabdian Kepada Masyarakat Vol. 9 No. 2 (2025): NOPEMBER
Publisher : P3M Universitas Aisyiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30787/gemassika.v9i2.2182

Abstract

Stunting is a condition of failure to achieve normal growth due to poor nutritional status over a long period of time. The purpose of this Community Service Program is to increase the knowledge of stunting cadres through smart applications for stunting prevention. This is the basis for community service with the title "PKM Implementation of Smart Applications for Stunting Prevention at the Arumdalu Integrated Health Post (Posyandu) in Sanggrahan Village, Grogol, Sukoharjo". The activity stages begin with coordination and communication activities with the Village Head, Village Midwife, and the Chairperson of the Arumdalu Integrated Health Post (Posyandu) in Sanggrahan Village. The implementation of Community Service (PKM) is carried out offline through digital-based education with a brainstorming method. The outputs of this PkM activity are the publication of scientific articles in the PkM journal, proceedings, posters, mass media publications, application guides, 5-minute PkM activity videos, and the integration of PkM activity results into courses. The outputs of this PkM are expected to have an impact on increasing knowledge, improving the quality of abilities, and real practices of the Arumdalu Integrated Health Post (Posyandu) cadres in Sanggrahan Village in stunting prevention.
ARTIFICIAL INTELLIGENCE-BASED MODEL FOR STUNTING RISK PREDICTION AND GROWTH MONITORING USING CHILD ANTHROPOMETRIC DATA Devi Pramita Sari; Sri Widodo; Khoirul Mustofa
Proceeding of the International Conference Health, Science And Technology (ICOHETECH) 2026: Proceeding of the 7th International Conference Health, Science And Technology (ICOHETECH)
Publisher : LPPM Universitas Duta Bangsa Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47701/2k8vdk50

Abstract

Stunting remains a major public health problem thatrequires early identification and continuous growth monitoring. This study developed an artificial intelligence-based model for stunting risk prediction and child growth monitoring using anthropometric data. The proposedframework integrates stunting detection, growthmonitoring, risk prediction, nutrition analysis, and healtheducation through an AI Engine consisting of data input,data processing, feature extraction, AI modeling, and riskoutput. A total of 60 child records were evaluated using four anthropometric configurations: Weight-for-Age, Height-for-Age, Head Circumference-for-Age, and a combination of Weight, Height, and Head Circumference according to age.The results showed accuracies of 85.1%, 86.0%, 90.6%, and82.3%, respectively. Head Circumference-for-Age achievedthe highest accuracy of 90.6% in the evaluated dataset.These findings demonstrate the potential ofanthropometric data and artificial intelligence forsupporting stunting risk prediction and child growthmonitoring. Further validation using larger and longitudinaldatasets is required to establish model robustness andgeneralizability.
Co-Authors Anik Sulistiyanti Antonius Suhartanto Bana Handaga Devi Pramita Sari Indra Agung Yudistira Januar Isnaini, Vina Khofifah Nurul Sa’adah Khoirul Mustofa Liss Dyah Dewi Arini Maryatun Muhammad Darmawan, Diki Nurhayati Ratnasari Nur Rohmah Wijiyanto