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Jurnal Informatika Terpadu
Published by STT Terpadu Nurul Fikri
ISSN : -     EISSN : 24608998     DOI : -
Core Subject : Science, Education,
Computer Science & IT Decision Sciences, Operations Research & Management Education
Jurnal Informatika Terpadu memuat jurnal ilmiah di bidang Ilmu Komputer, Sistem Informasi dan Teknik Informatika. Jurnal Informatika Terpadu diterbitkan oleh LPPM STT Nurul Fikri dengan periode dua kali dalam setahun, yakni pada bulan Maret dan September.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 164 Documents
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Penyusunan Roadmap Strategis Tata Kelola TI LMS Sinau Berdasarkan Maturity Level COBIT 2019 Santikarama, Irma; Renaldi, Faiza; Destiani, Dea
Jurnal Informatika Terpadu Vol 12 No 1 (2026): Maret, 2026 (On Going)
Publisher : LPPM STT Terpadu Nurul Fikri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54914/jit.v12i1.2737

Abstract

This study aims to assess the maturity level of information technology (IT) governance and to develop a strategic improvement roadmap for the internally developed SINAU Learning Management System at Universitas Jenderal Achmad Yani using the COBIT 2019 framework. A mixed-method approach with a convergent design was employed, combining quantitative data from questionnaire-based maturity assessments with qualitative insights from in-depth interviews. The results indicate that the overall IT governance maturity is at Level 2 (Managed), where processes are operational but not yet fully documented or standardized across organizational units. The largest governance gaps were identified in service agreement management, change and release management, and performance monitoring and evaluation. Based on the gap analysis and qualitative findings, a phased strategic roadmap was formulated to guide governance improvements toward Level 3 (Established Process). The study concludes that strengthening formal policies, process documentation, and monitoring mechanisms is essential to ensure sustainable LMS governance and alignment with institutional objectives in higher education.
Pengembangan Sistem Terintegrasi Klinik dan Aplikasi Manajemen Obat Menggunakan Application Programming Interface (API) Telaumbanua, Elvi Ningsih; Hura, Fasrian Mauren Niella; Telaumbanua, Juang Anjes Putra; Waruwu, Carolina Sayangi Cahaya; Hura, Syukur Famta Febriman
Jurnal Informatika Terpadu Vol 12 No 1 (2026): Maret, 2026 (On Going)
Publisher : LPPM STT Terpadu Nurul Fikri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54914/jit.v12i1.2751

Abstract

The purpose of this study is to create a clinical information system that can be integrated with an Application Programming Interface (API) for drug management. Unintegrated management of patient and drug data is a major problem facing clinics. This can lead to duplicate data and recording errors. Needs analysis, system design, implementation, and testing are the stages of development for a software engineering approach. Observations, interviews, and literature studies were used to collect data. APIs and NIKs connect applications to the system. The results of the study show that the developed system can automatically and consistently integrate patient data and drug prescriptions into clinical applications and drug management. The results of the system test using the Black-Box Testing method show that all the system's main functions run as expected under the predetermined scenario, with a 100% test success rate. Based on these findings, this integrated system is recommended for small- to medium-scale clinics to improve service efficiency and data management accuracy. Overall, the study shows that APIs help integrate clinical information systems and medication management.
Aplikasi WebAR Berbasis Marker untuk Visualisasi Tengkorak Manusia Tiga Dimensi pada Pembelajaran IPA Wicaksono, Muhammad Azrul; Dijaya, Rohman
Jurnal Informatika Terpadu Vol 12 No 1 (2026): Maret, 2026 (On Going)
Publisher : LPPM STT Terpadu Nurul Fikri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54914/jit.v12i1.2761

Abstract

Science learning requires visual media capable of explaining abstract concepts clearly, particularly in the topic of the human skeletal system. One subject that students often find difficult to understand is the structure of the human skull due to its level of complexity. This study aims to develop an Augmented Reality (AR)-based science learning medium using marker-based AR integrated into an interactive Flipbook utilizing WebAR technology. The research method employed is Research and Development (R&D) with the ADDIE development model, which encompasses the stages of analysis, design, development, implementation, and evaluation. The results indicate that the developed learning medium is capable of displaying interactive three-dimensional visualizations of the human skull through a web browser without requiring the installation of any additional applications. Functional testing and feasibility evaluation demonstrate that this learning medium is suitable for use, with a feasibility percentage of 75.94%, indicating that the medium received a positive response from users. Therefore, this WebAR-based learning medium can serve as an innovative alternative to support conceptual understanding and enhance students' learning interest in science education.
Deteksi Perokok Menggunakan Algoritma You Only Look Once (YOLO) dan Convolutional Neural Network (CNN) Gevindo, Aprilian; Arlis, Syafri
Jurnal Informatika Terpadu Vol 12 No 1 (2026): Maret, 2026 (On Going)
Publisher : LPPM STT Terpadu Nurul Fikri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54914/jit.v12i1.2783

Abstract

Image processing technology continues to advance and is widely used for visual identification of human activities, including monitoring smoking behavior in no-smoking areas. This study develops an automated smoking activity detection and recognition system based on digital image processing, combining YOLO (You Only Look Once) for object detection and a CNN (Convolutional Neural Network) as an image classifier. YOLO detects and crops human objects, while the CNN classifies smoking and non-smoking activities based on visual features. The preprocessed dataset contains 560 valid images per class (smoking and not smoking). Training results show 96.09% accuracy on the training set and 94.44% on the validation set, with stable loss, while model evaluation yields 94.44% accuracy, 92.55% precision, 96.67% recall, 94.57% F1-score, and Average Precision (AP), indicating excellent classification performance. The model can also detect smoking activities in real-time images and camera feeds, demonstrating the effectiveness of combining YOLO and a CNN for automated detection, with potential applications in no-smoking areas.

Page 17 of 17 | Total Record : 164

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