JITSI : Jurnal Ilmiah Teknologi Sistem Informasi

Analisis Komprehensif Arsitektur Serverless Container dan Edge v8 Isolate pada Penerapan Industri

2026-07-05T09:26:38+01:00

This study presents a comparative performance analysis of response times between Serverless architecture (AWS Lambda container-based) and Edge Computing (V8 Isolate-based). The evaluation focused on handling CPU-intensive computational loads using the Hono.js framework via the execution of 1,000 concurrent requests per platform. Measurements were strictly centered on Client-Side Round Trip Time (RTT) to ensure a fair comparison, deliberately bypassing server-side timing due to time quantization security restrictions inherent in the V8 Isolate environment. Empirical results demonstrate that Vercel recorded an average RTT of 432.40 ms, which is 76.29% faster than Cloudflare Workers' average of 762.29 ms. The Mann-Whitney U significance test statistically confirmed this difference (p < 0.05). Furthermore, consistency analysis via the Coefficient of Variation (CV) placed Vercel ahead with a highly stable value of 0.116 compared to Cloudflare's 0.527. Cloudflare also exhibited a larger cold start latency gap during the warm-up phase (28.03 ms difference) compared to Vercel (7.16 ms difference). The conclusion indicates that for REST API applications with heavy computational loads, container-based serverless architecture provides significantly superior stability, lower tail latency, and overall efficiency compared to Edge Isolate, challenging the theoretical assumption of absolute Edge network superiority in industrial scenarios.

Transformasi Digital Pelayanan Rumah Sakit Melalui Sistem Informasi Rawat Inap dan Rawat Jalan Berbasis Web di Rumah Sakit Umum Daerah (RSUD) Kota Prabumulih

2026-07-05T09:26:23+01:00

Digital transformation has become an essential necessity in improving the quality of healthcare service, including the management of patient and outpatient services.Rumah Sakit Umum Daerah (RSUD) Kota Prabumulih faces various challenges in managing patient data, administrative record-keeping, and presenting information that is not yet optimally integrated. These conditions affect the efficiency of staff performance and the overall quality of service provided to patients. This study aims to design and implement a web-based inpatient and outpatient information system as part of the hospital’s digital transformation efforts. The system is developed using web-based technology integrated with a database to manage patient data, registration processes, and reporting. The results of study indicate that the developed information system is able to improve the speed, accuracy, and percision of data management, as well as facilitate easier acces to information of the hospital. Therefore, the implementation of web-based inpatient and outpatient information system can support digital transformation and enhance the quality of healthcare services at RSUD Kota Prabumulih.

Design and evaluation of a virtual reality architectural walkthrough system for immersive spatial learning

2026-07-05T09:26:08+01:00

This paper aims to design, implement, and empirically evaluate a modular Virtual Reality (VR) architectural walkthrough system that, through immersive spatial computing, enhances architecture students' perceived spatial understanding, engagement, and early-stage design confidence. Architectural education relies heavily on static 2D drawings and screen-based modelling tools, which impose high cognitive demands on students and limit embodied spatial understanding and experiential design reasoning. A design science research approach was adopted to develop a VR-based architectural walkthrough platform using Unity, Blender, WebXR, and VR head-mounted displays. The system was evaluated through functional testing and user testing with 40 architecture students. The study presents a scalable and extensible spatial computing platform that integrates 2D-to-3D model transformation, immersive walkthroughs, and interactive design manipulation for architectural education. The findings suggest that the system improves students' perceptions of spatial content engagement and reduces apprehensions about the early stages of design, and that most participants preferred the VR workflow to traditional 2D design representations. Architecture educators should integrate immersive VR walkthroughs into early-stage design studios to support experiential learning, spatial reasoning, and iterative design feedback. Future research should investigate collaborative multi-user VR studios, AI-assisted design feedback, and cloud-based rendering to improve accessibility and scalability. The platform supports the growth of a digitally skilled, progressive generation of architectural graduates who are strong in spatial reasoning and experiential design and are aligned with modern built-environment professions. Future developments focus on creating collaborative VR environments and integrating them into a seamless cloud-based rendering infrastructure for longitudinal studies of learning outcomes

Penerapan Augmented Reality Markerless pada E-Commerce Tanaman Hias Berbasis Web dan Mobile

2026-07-05T09:25:53+01:00

This study presents the development of an ornamental plant e-commerce system integrated with Markerless Augmented Reality (AR) technology, supporting both web and mobile platforms. The system addresses two identified problems: SME ornamental plant sales that still rely on informal social media without structured management, and the inability of online buyers to visualize products in their real environment before purchasing. The system was developed using the Waterfall method. The mobile application uses Flutter, the web admin dashboard uses Laravel with Filament, and the AR module uses Unity with ARFoundation and Google ARCore, all connected through a RESTful API with MySQL as the primary database, and integrated with Midtrans for payment and RajaOngkir for shipping. System verification was conducted through Black Box Testing and AR Performance Testing. Black Box Testing confirmed that all 15 mobile and 11 web admin test scenarios produced expected outputs, achieving a 100% success rate. AR Performance Testing on two Android devices showed stable performance on the Infinix GT 30 Pro (Dimensity 8350 Ultimate), while the Xiaomi Redmi Note 13 5G (Dimensity 6080) experienced frame rate fluctuations due to thermal throttling; neither device encountered force close or crash. These results confirm the system functionally addresses both problems: providing a structured digital sales platform for ornamental plant SMEs and enabling buyers to visualize plants in their real environment before purchasing.

Sistem Informasi Manajemen Layanan Rumah Jahit Berbasis Web Menggunakan Model Waterfall dan Framework Laravel

2026-07-05T09:25:08+01:00

The rapid growth of small and medium enterprises (SMEs) in the fashion and tailoring industry demands high operational efficiency to maintain customer satisfaction. One Tailor, a boutique tailoring service in Bukittinggi, conventionally managed its daily operations using manual paper-based logs. This approach created significant vulnerabilities, including data loss risks, tedious customer body measurement retrieval (taking 5–10 minutes per record), financial accounting discrepancies, and delayed garment completions due to disorganized production scheduling. This study aims to design and implement a web-based management information system specifically tailored to resolve these operational bottlenecks. Adopting the software development life cycle (SDLC) Waterfall model, the system was developed using PHP with the Laravel 10.x framework and MySQL as the relational database management system. Security measures such as Bcrypt hashing, Cross-Site Request Forgery (CSRF) protection, and role-based middleware were incorporated to protect customer confidentiality. The developed system features digital body measurement archiving, an automated notification scheduler for production deadlines, integrated financial auditing modules, and automated readiness alerts for customers. Functionality and validation testing confirmed that the system eliminates data redundancy, accelerates record retrieval to under three seconds, organizes production tracking, and eradicates cash-flow calculation discrepancies. This research demonstrates how the transition from manual ledger systems to localized web frameworks significantly optimizes workflows and service reliability within micro-enterprises

Digitalisasi Penjualan dan Jasa Perbaikan Elektronik Menggunakan Nuxt.Js dan Laravel Rest API

2026-07-05T09:24:53+01:00

Digital transformation has become an urgent necessity for Micro, Small, and Medium Enterprises (MSMEs) to enhance competitiveness in the global era. Berkah Laris, a business engaged in the sales and repair services of electronic goods, still relies on manual recording for transactions and inventory management, which potentially leads to inefficiencies and data errors. This study aims to design and develop a web-based information system using the Nuxt.js framework as the frontend and Laravel REST API as the backend, with MySQL as the database. The Waterfall method was applied in the stages of requirements analysis, system design, coding, testing, and implementation. The system development results show that the features for sales management, service handling, and inventory control can operate automatically, in real-time, and are well-documented. The implementation of this system is proven to improve operational efficiency, data transparency, and customer satisfaction. This research provides a tangible contribution to the application of information technology in supporting the digitalization of MSMEs, particularly in the electronic sales and service sector

Arsitektur Sistem Point of Sale (POS) Multi-Cabang Berbasis Web untuk Perusahaan Ritel Terdistribusi: Studi Kasus pada Raffi Collection

2026-07-05T09:25:38+01:00

In the modern retail ecosystem, managing distributed store operations through manual interventions presents critical vulnerabilities regarding data consistency, synchronization delays, and operational inefficiencies. This research addresses these systemic limitations by designing and implementing a web-based, centralized Multi-Branch Point of Sale (POS) system tailored for Raffi Collection, a retail enterprise managing three geographically dispersed branches in Bukittinggi. Adopting the structured Waterfall development methodology—encompassing requirements analysis, system design, implementation, testing, and maintenance—the platform was engineered utilizing the Laravel MVC framework and a centralized MySQL relational database management system. The system architecture incorporates a robust Role-Based Access Control (RBAC) mechanism defining distinct operational permissions for Super Admins and Branch Admins. Key functional workflows feature real-time transaction processing with automated cryptographic-like receipt string generation, an advanced cross-branch stock transfer approval workflow, and dynamic multi-criteria reporting engines covering sales, expenditures, inventory mutations, and net profit-loss analytics. Empirical system testing validated absolute data synchronization across all nodes, strict enforcement of security policies (demonstrated by automated 403 Access Denied responses to unauthorized routing), and precise financial auditing capability. The implementation effectively eliminates data redundancy, mitigates recording latencies, and provides business stakeholders with real-time, data-driven decision support tools

Machine Learning and Decision Support in The Exploitation of Mdx Queries: Case of Banking Products and Services

2026-07-05T09:24:37+01:00

The increasing complexity of analytical workloads in banking systems challenges traditional query scheduling mechanisms, particularly in OLAP environments where MDX queries exhibit heterogeneous computational costs and business criticality. This study proposes a semantic-aware scheduling framework based on incremental machine learning to dynamically prioritize MDX queries in real time. The approach models query prioritization as a classification problem, integrating both technical features and business-driven criticality.

A Hoeffding Tree algorithm is employed to enable continuous learning from streaming query data without requiring retraining. The model is evaluated using a simulated dataset of 10,000 MDX queries reflecting realistic banking scenarios, including risk monitoring and regulatory reporting. Experimental results show that the proposed approach achieves a classification accuracy of 94.1% and significantly reduces processing latency for high-priority queries, with improvements reaching 42.4% compared to FIFO scheduling. The inference overhead remains negligible, ensuring compatibility with real-time system constraints.

These findings demonstrate the effectiveness of integrating incremental learning into query scheduling and highlight the potential of semantic-driven optimization in decision support systems. The study contributes to bridging the gap between learned database systems and business-aware query management.

Deteksi Malware pada File Executable Menggunakan Machine Learning Random Forest

2026-07-05T09:24:22+01:00

The pervasive expansion of digital infrastructure has triggered an exponential surge in cyber threats, with malicious software (malware) posing a paramount risk to information security systems. Traditional signature-based and heuristic detection methods demonstrate severe limitations in mitigating zero-day exploits and multi-variant obfuscated malware due to their rigid dependency on existing signature repositories and susceptibility to high false-positive rates. To transcend these boundaries, this study introduces an adaptive and robust static detection framework for Portable Executable (PE) files leveraging the ensemble machine learning technique of Random Forest. Utilizing a structured dataset comprising PE files harvested from public malware repositories including Malware Bazaar alongside verified benign applications, static analysis was performed without code execution to preserve environment safety. A total of 75 distinctive structural features spanning COFF headers, section characteristics, data directories, and configuration markers were systematically extracted using the Python pefile library. The model was trained using an 80:20 data split ratio. Experimental evaluation achieved an exceptional internal generalization capability with an Out-of-Bag (OOB) score of 97.43%. Independent validation on a test suite of 332 unseen files yielded a balanced confusion matrix comprising 160 True Positives, 164 True Negatives, 5 False Positives, and 3 False Negatives, establishing a high precision, recall, and F1-score of approximately 98%. Feature importance analysis highlighted that parameters such as MajorOperatingSystemVersion, MajorSubsystemVersion, and DllCharacteristics serve as critical discriminators. Finally, the optimized predictive model was integrated into a web-accessible application architecture powered by Flask and MySQL to facilitate user-driven file uploading and real-time inference reporting, offering an scalable complementary defense layer for modern cybersecurity ecosystems

Feature Extraction of Multichannel EMG Signals for Shoulder Joint Movement Patterns

2026-07-05T09:25:23+01:00

Electromyography (EMG) signals provide information about muscle activity and can support rehabilitation and prosthetic control systems. This study aims to extract and analyze features of multichannel EMG signals recorded from seven shoulder joint movement patterns. EMG data were acquired using surface electrodes placed on eight dominant muscles associated with shoulder joint motion, namely Deltoid1, Deltoid2, Infraspinatus, Supraspinatus, Teres Major, Latissimus Dorsi, Pectoralis1, and Pectoralis2. The recorded movements included resting, shoulder flexion, shoulder extension, shoulder abduction, shoulder adduction, external rotation, and internal rotation. The proposed processing procedure consisted of signal acquisition, rectification, transformation into the frequency domain using Discrete Fourier Transform, and feature extraction using Linear Envelope, Modified Mean Frequency (MMNF), and Modified Median Frequency (MMDF). The results show that Linear Envelope can describe temporal energy changes in each movement pattern, while MMNF and MMDF can identify groups of similar signal patterns and distinguish several movements through specific muscle channels. Resting movement had very small amplitude changes, while active shoulder movements produced different dominant energy patterns across subjects. MMNF and MMDF produced two main similarity groups, although the distinguishing muscles differed among subjects. These findings indicate that multichannel EMG feature extraction is useful as an initial basis for shoulder movement pattern analysis; however, further development is required to improve online acquisition, automatic gain adjustment, and classification robustness.