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All Journal Techno.Com: Jurnal Teknologi Informasi Jurnal Teknologi dan Sistem Komputer Narra J Jurnal Instruksional
Guarddin, Gladhi
Fakultas Ilmu Komputer, Universitas Indonesia
Biochemistry, Genetics & Molecular Biology Computer Science & IT Education Electrical & Electronics Engineering Engineering Health Professions Immunology & microbiology Medicine & Pharmacology Public Health

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Implementasi Sistem Registration Authority dan Personal Security Environment menggunakan Smart Card Gladhi Guarddin; Jundi Ahmad Alwan
Techno.Com Vol 20, No 4 (2021): November 2021
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/tc.v20i4.5284

Abstract

Penelitian ini bertujuan untuk menghasilkan analisis, rancangan, dan prototipe Personal Security Environment (PSE) menggunakan smart card. Analisis dan rancangan dilakukan dengan berdasarkan peraturan perundangan tentang infrastruktur kunci publik (IKP) dan sertifikat elektronik, dokumen peraturan PSrE Induk, spesifikasi Konsorsium Smart Card Indonesia (KSCI). Analisis dan rancangan tersebut kemudian diimplementasikan menjadi sebuah prototipe sistem Registration Authority (RA) yang bertugas untuk melakukan pendaftaran sertifikat elektronik bagi pengguna dan melakukan penyimpanan ke dalam PSE berupa smart card berbasis chipset Xirka yang merupakan Kartu Tanda Mahasiswa (KTM) Universitas Indonesia. Hasil implementasi menunjukkan bahwa seluruh skenario pengujian berhasil dilaksanakan dengan catatan kunci privat dan sertifikat elektronik masih diimplementasikan di dalam field chipset Xirka yang diperuntukkan sebagai penyimpanan foto dan fingerprint.
PENGEMBANGAN STANDAR DESAIN LAYANAN CLOUD BERDASARKAN ISO/IEC 20000: STUDI KASUS PT. XYZ Ahmad Fikri Adriansyah; Bob Hardian; Gladhi Guarddin
Instruksional Vol 1, No 2 (2020): Jurnal Instruksional
Publisher : Universitas Muhammadiyah Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1540.585 KB) | DOI: 10.24853/instruksional.1.2.111-121

Abstract

PT XYZ is now transforming its business from traditional telecommunication provider to an ICT (Information & Communication Technology) provider in order to deal with current stringent business competition. One of its strategic initiatives is to improve Cloud service market penetration based on the fact from market size analysis result which has reported that Cloud service would appear as an IT service with highest growth throughout Asia Pacific region from 2013 to 2018 (i.e., 49%). However, PT XYZ has reported its Cloud service sales only achieved 52% of its total revenue target in 2014 fiscal year. One of the root causes found is the lack of standard in performing design on Cloud service. As ISO/IEC 20000 certification holder, PT XYZ has to improve its IT service management practices continuously including its service design stage. This research goal is to build design standard on Cloud service in PT XYZ using two internationally recognized Information Technology Service Management (ITSM) frameworks, i.e. Information Technology Infrastructure Library (ITIL) version 3 and Microsoft Operation Framework (MOF) version 4, as well as Cloud Computing Reference Architecture (CCRA) and guidance on the application of ISO/IEC 20000-1 to Cloud Services (ISO/IEC TR 20000-9:2015). As the result, a design process flow and design documentation templates are proposed to be used as Cloud service design standard in PT XYZ.
Large-scale integrated infrastructure for asynchronous microservices architecture Ramadhan, Insan; Guarddin, Gladhi
Jurnal Teknologi dan Sistem Komputer Volume 10, Issue 2, Year 2022 (April 2022)
Publisher : Department of Computer Engineering, Engineering Faculty, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jtsiskom.2022.14120

Abstract

Integrated large-scale business activities increasingly rely on the use of remote resources and services across multi-platform applications. Microservice in previous research has become a solution, but this approach still leaves a data loss problem. This research methodology proposed an architecture of data transmission managed by messaging service to prevent data loss in handling many requests to deliver a multiplatform architecture, handling the plugin services, and enabling escalation based on the requirement. As a result, this research successfully implements large-scale multiplatform Single Sign-On (SSO) infrastructure for asynchronous microservices architecture. The system test results show that the developed system can handle up to 2000 requests with 20 concurrent requests.
Designing the CORI score for COVID-19 diagnosis in parallel with deep learning-based imaging models Kamelia, Telly; Zulkarnaien, Benny; Septiyanti, Wita; Afifi, Rahmi; Krisnadhi, Adila; Rumende, Cleopas M.; Wibisono, Ari; Guarddin, Gladhi; Chahyati, Dina; Yunus, Reyhan E.; Pratama, Dhita P.; Rahmawati, Irda N.; Nareswari, Dewi; Falerisya, Maharani; Salsabila, Raissa; Baruna, Bagus DI.; Iriani, Anggraini; Nandipinto, Finny; Wicaksono, Ceva; Sini, Ivan R.
Narra J Vol. 5 No. 2 (2025): August 2025
Publisher : Narra Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52225/narra.v5i2.1606

Abstract

The coronavirus disease 2019 (COVID-19) pandemic has triggered a global health crisis and placed unprecedented strain on healthcare systems, particularly in resource-limited settings where access to RT-PCR testing is often restricted. Alternative diagnostic strategies are therefore critical. Chest X-rays, when integrated with artificial intelligence (AI), offers a promising approach for COVID-19 detection. The aim of this study was to develop an AI-assisted diagnostic model that combines chest X-ray images and clinical data to generate a COVID-19 Risk Index (CORI) Score and to implement a deep learning model based on ResNet architecture. Between April 2020 and July 2021, a multicenter cohort study was conducted across three hospitals in Jakarta, Indonesia, involving 367 participants categorized into three groups: 100 COVID-19 positive, 100 with non-COVID-19 pneumonia, and 100 healthy individuals. Clinical parameters (e.g., fever, cough, oxygen saturation) and laboratory findings (e.g., D-dimer and C-reactive protein levels) were collected alongside chest X-ray images. Both the CORI Score and the ResNet model were trained using this integrated dataset. During internal validation, the ResNet model achieved 91% accuracy, 94% sensitivity, and 92% specificity. In external validation, it correctly identified 82 of 100 COVID-19 cases. The combined use of imaging, clinical, and laboratory data yielded an area under the ROC curve of 0.98 and a sensitivity exceeding 95%. The CORI Score demonstrated strong diagnostic performance, with 96.6% accuracy, 98% sensitivity, 95.4% specificity, a 99.5% negative predictive value, and a 91.1% positive predictive value. Despite limitations—including retrospective data collection, inter-hospital variability, and limited external validation—the ResNet-based AI model and the CORI Score show substantial promise as diagnostic tools for COVID-19, with performance comparable to that of experienced thoracic radiologists in Indonesia.
Co-Authors Afifi, Rahmi Ahmad Fikri Adriansyah Anggraini Iriani Ari Wibisono Ari Wibisono Baruna, Bagus DI. Bob Hardian Chahyati, Dina Falerisya, Maharani Jundi Ahmad Alwan Krisnadhi, Adila Nandipinto, Finny Nareswari, Dewi Pratama, Dhita P. Rahmawati, Irda N. Ramadhan, Insan Rumende, Cleopas M. Salsabila, Raissa Septiyanti, Wita Sini, Ivan R. Telly Kamelia Wicaksono, Ceva Yunus, Reyhan E. Zulkarnaien, Benny