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All Journal Heart Science Journal Jurnal Inovasi Pendidikan dan Teknologi Informasi (JIPTI)
Haryati, Lina
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Computer Science & IT Education Health Professions Immunology & microbiology Medicine & Pharmacology Other

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Validation of Self-Assessment-Based Chest Pain Algorithm (DETAK) as An Early Identification Tool for Acute Coronary Syndrome Nugraha, Krishna Ari; Rohman, Mohammad Saifur; Rahimah, Anna Fuji; Anjarwani, Setyasih; Rizal, Ardian; Astiawati, Tri; Adi, Andi Wahjono; Haryati, Lina
Heart Science Journal Vol. 4 No. 4 (2023): The Science and Art of Caring for Critically III Patients in Intensive Cardiac
Publisher : Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/ub.hsj.2023.004.04.5

Abstract

BackgroundThe most common reason of prehospital delay in ACS patients is inability to pay attention to symptoms in order to act fast and effectively. Patient oriented machine learning algorithms has the opportunity to reduce the total ischemic time, that determines the clinical outcome of ACS patients.AimAssessing the accuracy of the chest pain self-assessment algorithm (DETAK) in identifying ACS.MethodThis study included seven hospitals, five PCI capable hospitals and two of non-PCI capable hospitals. The study was conducted from August 2021 to June 2022. The study included all patients with chest pain who visited the hospital and used the DETAK algorithm. Patients were interviewed after being confirmed hemodynamically stable. Patients with UAP, as well as those who died or declined to participate in this study were excluded. The area under the curve receiver operating characteristic (AUROC) was used to verify DETAK's performance in identifying SKA. We compare the DETAK algorithm's diagnosis with the definitive diagnostic based on ECG and/or troponin results.ResultsA total of 539 patients (mean age 58 years) with a higher proportion of male patients (n=424). An AUC value of 0.854 was obtained, where the cut of point accuracy of DETAK in identifying ACS for the entire sample had a sensitivity of 89.5% and a specificity of 81.2%. The algorithm's specificity decreased in certain subgroups, including type 2 diabetes (79.4%), women (77.3%), and hypertensive patients (80.9%). Algorithm reliability test obtained moderate to strong level of agreement values.ConclusionDETAK's self-assessment-based chest pain algorithm offers an excellent diagnostic performance in early identification of ACS.
PERANCANGAN MEDIA PEMBELAJARAN INFORMATIKA KELAS VII MENGGUNAKAN ADOBE CAPTIVATE Haryati, Lina; Derta, Sarwo; Musril, Hari Antoni; Okra, Riri
Jurnal Inovasi Pendidikan dan Teknologi Informasi (JIPTI) Vol. 5 No. 2 (2024): Jurnal Inovasi Pendidikan dan Teknologi Informasi (JIPTI)
Publisher : Information Technology Education Department

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52060/jipti.v5i2.2371

Abstract

This research is focused on designing Informatics learning media for class VII Semester I at Al-Ishlah Bukittinggi Islamic Junior High School using Adobe Captivate. This research is limited to the development of valid, practical, and effective learning media, using the research and development (R&D) method and the Hannafin and Peck model. The research sample involved 4 experts for the validity test and students and teachers at Al-Ishlah Bukittinggi Islamic Junior High School for the practicality and effectiveness test. The results showed that the media developed had a high level of validity with an average value of 0.76 %, as well as practicality and effectiveness with values of 0.83 % and 0.77 % respectively. Thus, this learning media not only meets the criteria of practicality and effectiveness, but also makes a positive contribution to improving the quality of Informatics learning at the school. The implications of the results of this study indicate that technology-based learning media such as Adobe Captivate can be an effective solution to the learning problems faced, by increasing student engagement and understanding.
Co-Authors Adi, Andi Wahjono Anjarwani, Setyasih Astiawati, Tri Hari Antoni Musril Mohammad Saifur Rohman Nugraha, Krishna Ari Rahimah, Anna Fuji Riri Okra Rizal, Ardian Sarwo Derta