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All Journal Jurnal Teknologi Informasi dan Pendidikan JOURNAL OF ICT APLICATIONS AND SYSTEM Jurnal Penelitian Rumpun Ilmu Teknik Innovative: Journal Of Social Science Research Venus: Jurnal Publikasi Rumpun Ilmu Teknik
Yogi Pratama
Institut Kesehatan dan Teknologi Al Insyirah
Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering Law, Crime, Criminology & Criminal Justice Public Health Other

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Analisis Dan Implementasi Perban Analisis Dan Implementasi Perbandingan Protokol VRRP Dan HSRP Pada Jaringan Topologi Star Ramdhani Syahputra; Romi Mulyadi; Muhamad Yusuf; Yogi Pratama; Adri Yanto
Jurnal Penelitian Rumpun Ilmu Teknik Vol. 3 No. 1 (2024): Februari : Jurnal Penelitian Rumpun Ilmu Teknik
Publisher : Pusat Riset dan Inovasi Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/juprit.v3i1.3397

Abstract

The development of information and communication technologies forms more stringent requirements for indicators of reliability and availability of modern communication network services, which are expected to be available 24 hours a day throughout the year so that it is possible to use applications and services running on them at any time. To support network reliability and availability, routing protocols and redundancy are needed to handle failures in a network. A solution to increase network availability and reliability is using the First Hop Redundancy Protocol (FHRP), which consists of the Hot Standby Router Protocol (HSRP) and Virtual Router Redundancy Protocol (VRRP). The results of the tests show that the delay difference between VRRP and HSRP is 0.16ms with a combination of EIGRP routing. In terms of packet loss parameters, when the primary network route is disconnected, there is an increase in packet loss of 1.01% on VRRP, 3.05% on HSRP combined with EIGRP routing, and 0.2% on VRRP, 0.4% on HSRP. Although delay and packet loss increased, the results obtained in this study met the standards set by ITU-T. The difference between VRRP and HSRP is 0.305 bit/ms in the throughput parameter. Meanwhile, the convergence time in VRRP is 5.14 seconds, HSRP 5.07 seconds, and the downtime parameter in VRRP is 12.6 S, 17.1 in HSRP.
Analysis of Mobile Banking Acceptance in Indonesia using Extended TAM (Technology Acceptance Model) Andhika, Imam; Pratama, Ahmad R; Pratama, Yogi
Jurnal Teknologi Informasi dan Pendidikan Vol. 16 No. 2 (2023): Jurnal Teknologi Informasi dan Pendidikan
Publisher : Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/jtip.v16i2.626

Abstract

The use of Communication and Information Technology which is developed through the years is one of the key of organization’s success in business rivalry through pandemic era nowadays. In line with the development of technology and information, bank authorities also offer the facility of banking through mobile banking (m-banking) application that can be accessed by using smartphone. This research aims to analyze factors that can influence the acceptance of m-banking application in Indonesia. The data was gathered through survey of 412 m-banking users in Indonesia and it was analyzed by using Structural Equation Modeling (SEM) with Extended Technology Acceptance Model (TAM). The findings of the research showed positive attitudes, perceived usefulness and perceived ease of use felt by the m-banking users and become the main reasons in adopting this technology besides social influence and perceived risk of m-banking technology. Meanwhile, the fear of using technology in using m-banking technology has a potential to obstruct the technology adoption. The result of this research can help the bankers and stakeholder in formalizing strategical steps in improving the adaptation of m-banking technology and application, especially in Indonesia.
Analisis Model Digital Forensic Readiness Index (Difri) Terhadap Serangan Malware Yogi Pratama; Ramdhani Syahputra
Venus: Jurnal Publikasi Rumpun Ilmu Teknik  Vol. 2 No. 3 (2024): Juni : Jurnal Publikasi Rumpun Ilmu Teknik
Publisher : Asosiasi Riset Ilmu Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/venus.v2i3.305

Abstract

The increasing number of malware spread in the world today, then there will be more opportunities to commit crime, so readiness is needed for every internet user in dealing with these crimes. The readiness to handle crime is called digital forensic readiness. Therefore, we need a specific digital forensic readiness model to measure the level of readiness of internet users or institutions in achieving malware attacks. This model has the main components used to determine or calculate the level of readiness of internet users or institutions, the main components are the strategy component, the policy & procedure component, the technology & security component, the digital forensic response component, the control & legality component. The calculation method used in this study is a Likert Scale, with this method the results will be obtained that are closer to the real situation. The value / index of readiness level obtained will provide recommendations to internet users and these recommendations can be used to make improvements properly and on target.
Evaluasi Penggunaan Sistem Informasi Akademik Al Insyirah (SINAI) menggunakan Pendekatan TAM pada Fakultas Teknologi Kesehatan IKTA Pratama, Yogi; Andhika, Imam
Innovative: Journal Of Social Science Research Vol. 5 No. 3 (2025): Innovative: Journal Of Social Science Research
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/innovative.v5i3.20077

Abstract

Digital transformation in the field of education has encouraged higher education institutions to adopt academic information systems as tools to support administrative activities. This study aims to assess the level of user acceptance and satisfaction regarding the use of the Al Insyirah Academic Information System (SINAI) at the Faculty of Health Technology, Al Insyirah Institute of Health and Technology (IKTA). The study applies the Technology Acceptance Model (TAM) framework, which emphasizes four key variables: perceived usefulness, perceived ease of use, behavioral intention to use, and user satisfaction. This research employs a quantitative approach with a descriptive design. A total of 60 respondents—comprising active students and lecturers—were selected using purposive sampling. Data were collected through questionnaires and analyzed using descriptive statistics. The results indicate that all TAM constructs fall within the high to very high categories. These findings suggest that the SINAI application has been positively received and effectively supports academic processes within the faculty. The study recommends further feature development and continuous evaluation to enhance the system’s performance and adaptability to user needs.
Retinal Disease Classification Using Deep CNN on Fundus Images Yanto, Adri; Pratama, Yogi; Ridwan
Journal of ICT Applications System Vol 4 No 2 (2025): Journal of ICT Aplications and System
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56313/jictas.v4i2.451

Abstract

Diabetic retinopathy (DR) is one of the primary causes of preventable blindness, highlighting the necessity for accurate and automated retinal screening systems. Manual diagnosis through fundus image inspection is time-consuming and prone to subjective interpretation, particularly in regions with limited access to ophthalmic specialists. This study presents a deep convolutional neural network (CNN) approach based on ResNet50 architecture with fine-tuning for multi-class classification of retinal diseases. The proposed model was developed using the APTOS 2019 Blindness Detection dataset, consisting of 3,662 fundus images categorized into five levels of DR severity. A robust preprocessing pipeline, including illumination correction, contrast enhancement, normalization, and extensive data augmentation, was implemented to improve image quality and balance the dataset. The network was trained using the Adam optimizer with a learning rate of 1×10?? and categorical cross-entropy loss for 30 epochs under an 80:20 train–validation split. Experimental evaluation demonstrated high performance with 92.4% accuracy, 0.91 precision, 0.92 recall, 0.91 F1-score, and an AUC of 0.95, outperforming baseline CNN and VGG16 models. Furthermore, Grad-CAM visualization confirmed that the model accurately localized critical retinal regions associated with microaneurysms, hemorrhages, and exudates, enhancing interpretability and clinical trust. The proposed ResNet50-based framework provides an explainable, efficient, and reliable solution for automated diabetic retinopathy detection, supporting large-scale tele-ophthalmology and early diagnosis applications in medical imaging
Co-Authors Adri Yanto Andhika, Imam Muhamad Yusuf Pratama, Ahmad R Ridwan Romi Mulyadi Syahputra, Ramdhani Yanto, Adri