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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 168 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
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
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
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
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.
Pengembangan Sistem Informasi CSIRT Berbasis Web Menggunakan Framework Laravel dengan Metode Prototyping pada Diskominfo Purwakarta Akbar, Muhammad Rifky; Fadhilah, Aidah Nur; Primajaya, Aji
Jurnal Informatika Terpadu Vol 12 No 1 (2026): Maret, 2026
Publisher : LPPM STT Terpadu Nurul Fikri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54914/jit.v12i1.2770

Abstract

The increasing threat of cybersecurity attacks on regional government infrastructure requires a structured incident handling mechanism. However, cybersecurity incident handling at the Diskominfo of Purwakarta Regency is still conducted manually, resulting in slow responses and unstructured documentation. Previous studies discuss policies for CSIRT formation and Laravel-based system security, but none have developed a web-based CSIRT information system for the operational needs of incident response teams at the regional government level. This study aims to develop a web-based CSIRT (Computer Security Incident Response Team) information system using the prototyping method at the Diskominfo of Purwakarta Regency. The development process includes the stages of requirements analysis, design, prototyping, customer evaluation, review and refinement, development, testing, and release. Data collection is conducted through observation, interviews, and literature study. Testing uses Black Box Testing across 12 scenarios covering authentication, incident ticket management, incident reporting, security vulnerability notification letters, and report recapitulation, achieving a 100% success rate. It is concluded that this system can improve the effectiveness of cybersecurity incident handling through structured documentation and integrated report management. It is recommended that the system be integrated with the BSSN reporting system and developed as a mobile application.
Sistem Pakar untuk Diagnosis Acne Vulgaris dan Tingkat Keparahannya Berbasis Web Menggunakan Metode Certainty Factor Guntara, Azwa; Ayu, Jesika Citra
Jurnal Informatika Terpadu Vol 12 No 1 (2026): Maret, 2026
Publisher : LPPM STT Terpadu Nurul Fikri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54914/jit.v12i1.2772

Abstract

Acne vulgaris is a polymorphic skin disease with various clinical manifestations that can affect both the physical and psychological conditions of sufferers. Limited public knowledge and access to dermatological healthcare services have increased the need for an easily accessible early diagnostic support system. Previous studies have generally focused on identifying types of acne without integrating severity classification as part of the system output, resulting in limited information provided. This study aims to develop a web-based expert system using the Certainty Factor (CF) method that not only identifies types of acne vulgaris but also determines their severity levels. The system’s knowledge base consists of six types of acne, three levels of severity, and 24 symptoms obtained through literature review and expert consultation. The inference mechanism is carried out by calculating the CF value for each symptom based on the multiplication of expert confidence values (MB–MD) and user confidence levels, followed by combining multiple CF values using the CF Combine formula to obtain the final CF value for each type of acne. The diagnosis is determined based on the highest CF value obtained. The results of Black Box Testing indicate that all system functions operate as expected. Usability evaluation using the System Usability Scale (SUS) yielded an average score of 75.25, which falls into the acceptable category with a grade B (Good), indicating that the system is well accepted by users. This system is expected to serve as a supporting tool in obtaining preliminary information about acne conditions in a fast, accessible, and accurate manner.
Komparasi Model ARIMA, Regresi Linear, Random Forest, dan LSTM untuk Peramalan Harga Beras Jawa Barat Ormanda, Marsello; Ardiansah, Irfan
Jurnal Informatika Terpadu Vol 12 No 1 (2026): Maret, 2026
Publisher : LPPM STT Terpadu Nurul Fikri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54914/jit.v12i1.2789

Abstract

Rice price fluctuations in West Java significantly impact regional inflation and national food security. The main challenge in forecasting this commodity price is the high volatility of the data, which conventional statistical models often fail to capture. This study aims to compare the accuracy of four forecasting methods: ARIMA, Linear Regression, Random Forest, and Long Short-Term Memory (LSTM), to identify the best-performing model. The data used are daily medium rice prices in West Java from January 2022 to October 2024, obtained from Kaggle. The methodology includes data pre-processing, model parameter optimization, and performance evaluation using RMSE, MAE, and MAPE metrics. The results show that non-linear models significantly outperform linear models. LSTM recorded superior performance with the lowest error rates (MAPE 0.43% and RMSE 91.95), followed by Random Forest (MAPE 0.67%). In contrast, ARIMA and Linear Regression produced errors above 10%. In conclusion, Deep Learning and Machine Learning approaches are more robust at handling volatile food price data than classical econometric methods, making them highly recommended as a basis for policymakers' early warning systems.
Sistem Pendukung Keputusan untuk Deteksi Dini Faktor Risiko Stunting pada Ibu Hamil dengan Metode SAW Poernareksa, Dwidya; Wardhina, Faizah; Hasanah, Uswatun; Ramadhaningsih, Ledy
Jurnal Informatika Terpadu Vol 12 No 1 (2026): Maret, 2026
Publisher : LPPM STT Terpadu Nurul Fikri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54914/jit.v12i1.2774

Abstract

Stunting remains a critical public health challenge in Indonesia, with a prevalence of 21.5% according to the 2023 Indonesian Health Survey. The nutritional status of pregnant women is a pivotal factor in the prevention. This study aims to implement a Decision Support System (DSS) using the Simple Additive Weighting (SAW) method to assess the risk of Chronic Energy Deficiency (CED) in pregnant women as a proactive measure to prevent stunting. The research analyzed data from 70 pregnant women at a public health center (Puskesmas) using four clinical criteria: height (10% weight), body weight (20%), Mid-Upper Arm Circumference/MUAC (45%), and hemoglobin levels (25%). Data were collected through observation, interviews with healthcare professionals, and ANC medical record documentation, subsequently processed using the SAW algorithm for risk ranking. The implementation results identified 54 women (77.14%) at low risk, 13 (18.57%) at moderate risk, and 3 (4.29%) at high risk. The system was evaluated using a confusion matrix, which yielded a high accuracy of 96%. This system can be integrated into primary healthcare centers (Puskesmas) and integrated healthcare posts (Posyandu) to facilitate precise and rapid interventions for CED risks, effectively replacing traditional manual processes.

Page 17 of 17 | Total Record : 168

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