JTIM : Jurnal Teknologi Informasi dan Multimedia
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Game Virtual Tour Pengenalan Sistem Tata Surya Berbasis VR Google Cardboard dan Voice Command
No full textThe solar system is an essential topic in elementary school science education, playing a role in developing students\u27 critical thinking skills and scientific understanding. However, conventional teaching methods often make students passive recipients of information, discouraging interaction and independent exploration. This can decrease interest and engagement in learning. Therefore, a more interactive and engaging approach is necessary to teach students about the solar system ef-fectively. This game uses VR technology and voice commands to provide an immersive experience exploring the solar system. Players can interact with objects in the solar system. It also features voice control, which allows students to interact with objects in the solar system and access addi-tional information. The average score on the questionnaire survey of the application was 86.30%. Integrating VR and voice commands has proven effective in increasing students\u27 interest in learn-ing and facilitating navigation in immersive learning environments
Evaluasi Teknik Prompting pada Large Language Model untuk Otomatisasi Penyusunan Skenario Unit Testing Smart Contract
No full textAutomated unit testing is essential for ensuring the security and reliability of smart contracts, particularly because their immutable nature prevents post-deployment modifications. However, manually creating test scenarios remains time-consuming, costly, and highly dependent on expert knowledge. A potential solution is to utilize AI technology, particularly Large Language Models (LLMs), to automatically generate test scenarios. This study fills the research gap in leveraging LLM technology in the software testing space by proposing a workflow for automatically gener-ating unit test scenarios for blockchain smart contract code using Large Language Models (LLMs). The proposed workflow consists of two stages: converting Solidity smart contracts into structured Gherkin scenarios and translating those scenarios into executable Hardhat unit test scripts. This study proposes an automated workflow using Large Language Models (LLMs) to address these challenges. The workflow consists of two stages: con-verting Solidity smart con-tracts into structured Gherkin scenarios and trans-lating those scenarios into executable Hardhat unit test scripts. Using the Gemini 2.5 Pro model, the research evaluates three prompting tech-niques such as Chain-of-Thought, Few-Shot, and Role-Based through quantitative analysis based on code coverage metrics, including Statements, Branches, Functions, and Lines. The experimental results show that Role-Based Prompting achieves the highest average coverage (92.02%), fol-lowed by Few-Shot Prompting (89.52%), while Chain-of-Thought produces the lowest coverage (78.79%). Role-Based Prompting also attains the highest Branch coverage, demonstrating superi-or capability in capturing conditional logic within smart contracts
Penerapan Web Content Accessibility Guidelines dan Design Thinking dalam Perancangan UI/UX Aplikasi Ujian Online
No full textThe most significant change in education due to the pandemic is the implementation of online evaluation methods. Online exams provide students with the flexibility to take tests from any lo-cation. Although online examinations offer flexibility for learners to participate from any loca-tion, their implementation still faces various challenges, such as the potential for cheating, tech-nical disruptions, and limited accessibility for users with diverse physical conditions and digital competencies. These issues highlight the need for designing online examination interfaces that are not only easy to use but also inclusive. This study aims to design the user interface and user expe-rience by employing the design thinking approach and applying the Web Content Accessibility Guidelines (WCAG) to enhance interface accessibility starting from the design phase. The WCAG 2.0 standard, developed by the W3C, is applied as a guideline for meeting accessibility require-ments. The WCAG principles implemented include perceivability, operability, understandability, and robustness. Usability testing on the prototype was conducted using the System Usability Scale (SUS), scoring 69. This score indicates that the designed prototype falls into the "marginal high" category within the acceptability range. Accessibility testing using Axe for Designers indi-cated that, overall, the prototype met accessibility requirements. The results also show that inte-grating WCAG 2.0 principles during the design stage effectively enhances the accessibility of the user interface
Analisis Kesiapan Pengguna di Indonesia dalam Adopsi Teknologi Blockchain Pada Platform Pertukaran Kripto Menggunakan TRI
No full textThe use of blockchain technology on cryptocurrency exchange platforms in Indonesia has experi-enced notable growth, yet challenges persist concerning user preparedness and preferences be-tween domestic and international platforms. This research seeks to explore the factors influencing users’ selection of crypto platforms and to evaluate the technological readiness of Indonesian us-ers by integrating the Technology Readiness Index (TRI) with an information technology adop-tion framework. The study employed purposive sampling to gather data from 156 active crypto platform users. Quantitative data analysis was performed using PLS-SEM through SmartPLS version 4.0. Findings indicate that the majority of respondents are young adults aged 17 to 25, predominantly male, and show a preference for international platforms due to their innovative capabilities, greater liquidity, and lower transaction fees. Nonetheless, local platforms remain attractive because of easier accessibility and enhanced regulatory backing. Structural model re-sults demonstrate that optimism, innovativeness, and insecurity significantly influence user readiness, whereas discomfort does not have a meaningful impact. Moreover, user readiness stands out as a critical factor strongly facilitating blockchain technology adoption on crypto ex-changes. This study validates the combined application of the TRI and IT adoption models as ef-fective tools for pinpointing key determinants of blockchain adoption readiness, offering strategic insights for developers of local platforms and serving as a foundation for future research on blockchain implementation in Indonesia
Perancangan Dan Implementasi Game Interaktif untuk Pembelajaran Digital pada Pelajaran TIK di MTsN 2 Banda Aceh Menggunakan Scracth
No full textICT learning currently demands the use of innovative learning media, to increase students\u27 moti-vation and understanding of the material being studied, but at MTsN 2 Banda Aceh, convention-al methods such as lectures still dominate, which reduce students\u27 motivation towards storage device and output device materials. This study aims to design, develop, and implement learning media in the form of scratch-based interactive games as a means of ICT (information and com-munication technology) learning to increase interactivity, learning motivation, and understand-ing of grade VIII students towards storage device and output device materials. Development re-search with a 4D model (Define, Design, Develop, Disseminate) and a mixed methods approach integrates quantitative data from questionnaires and qualitative data from observations and in-terviews. The results of media expert validation reached an average of 91% (strongly agree), while the assessment of 36 students showed an average score of 91% (very good) in the aspects of design, navigation, material understanding, and learning motivation. The Maze Tech game has been proven to be feasible, effective, and fun as an ICT digital learning media that supports inde-pendent and active learning. Furthermore, the Maze Tech game was developed with attention to the characteristics of junior high school students who tend to prefer visual and challenge-based learning. The integration of game elements, such as maze navigation, audio-visual feedback, and evaluative quizzes, provides a contextual and meaningful learning experience. This media also encourages active student involvement in the learning process, so it serves not only as a means of entertainment but also as an effective pedagogical tool. Therefore, the results of this study are ex-pected to contribute to the development of game-based digital learning media and serve as a ref-erence for educators in implementing ICT learning innovations in the madrasah environment
Implementasi Algoritma FP-Growth untuk Sistem Rekomendasi Produk Kebutuhan Pokok pada E-Commerce
No full textThe rapid development of e-commerce in Indonesia necessitates recommendation systems that can capture user purchasing patterns accurately, adaptively, and in a data-driven manner. This study implements the FP-Growth algorithm to analyze transaction data from a self-developed essential-goods e-commerce platform. The research dataset consists of 60 user accounts with a total of 600 completed transactions, processed using a Python-based analytical module and au-tomatically integrated into a Laravel backend through a dedicated execution script. The FP-Growth algorithm is applied to generate frequent itemsets and association rules using a min-imum support of 0.01, a minimum confidence of 0.1, and a minimum lift of 1.0. The results indi-cate that the most dominant associative patterns occur among kitchen staple products such as in-stant noodles, chicken eggs, and wheat flour, as well as household cleaning products such as de-tergents and fabric softeners. Several rules exhibit confidence values as high as 0.9615 and lift values up to 4.451, indicating strong and statistically significant relationships between products. System performance evaluation using a Top-4 recommendation scheme shows a Hit Rate of 54.35% and a Recall of 54.35%, demonstrating that the system is able to provide relevant recom-mendations for the majority of transactions. This implementation is shown to improve recom-mendation accuracy while strengthening personalization and cross-selling strategies on essen-tial-goods e-commerce platforms. These findings confirm that FP-Growth is an effective and effi-cient method for identifying empirical purchasing patterns and supporting the development of recommendation systems in small- to medium-scale e-commerce environments
Perancangan Enterprise Architecture Aplikasi Manage Personal Finance dengan Framework TOGAF dan PEAF
No full textThe development of information technology encourages the use of the Manage Personal Finance application as a more systematic and data-driven financial management tool. However, in prac-tice, the application still faces various problems, such as an unintegrated financial recording pro-cess, limited system architecture documentation, and the absence of an enterprise architecture framework that best suits business needs and long-term development. These problems have the potential to cause the system to be out of alignment with the organization\u27s strategic objectives and difficult to develop sustainably. This study aims to: (1) design an enterprise architecture for the Manage Personal Finance application using the TOGAF ADM 9.2 framework; (2) produce an integrated business, data, application, and technology architecture blueprint; and (3) compare the effectiveness of the TOGAF and PEAF frameworks quantitatively based on five indicators, namely alignment with business needs, completeness of documentation, implementation speed, scalability, and resource requirements. The research method used is the design of an enterprise architecture based on TOGAF ADM, followed by a comparative analysis of the PEAF framework as an alternative, more pragmatic approach. The results of the study indicate that TOGAF excels in 3 out of 5 indicators (60%), namely business needs alignment, completeness of documentation, and system scalability, while PEAF excels in 2 indicators (40%), namely speed of implementation and resource efficiency. These findings indicate that TOGAF is more suitable for the development of Manage Personal Finance applications that have high complexity and long-term orientation. Thus, this study provides an empirical contribution in selecting the optimal enterprise architec-ture framework for the development of integrated and sustainable financial systems
Implementasi Model Machine Learning untuk Deteksi Phishing dengan Pendekatan Ekstraksi Fitur yang Dioptimalkan
No full textPhishing is a common form of cybercrime used by digital criminals to steal sensitive information such as passwords, personal data, and financial details through fake websites designed to re-semble legitimate pages. However, conventional detection methods such as blacklists and manual inspection are currently considered ineffective due to their static nature, often failing to recognize new, evolving and increasingly sophisticated attack patterns. To address this issue, this study developed a machine learning-based phishing detection model focused on improving the accura-cy and efficiency of identifying malicious sites. This model applies an optimized feature extrac-tion technique to enable the system to analyze URL characteristic patterns more comprehensively and targeted. The research dataset was taken from the Kaggle platform, which provides a dataset of phishing and benign URLs with a high reputation. The data was then processed through nor-malization, cleaning, and extraction of important features such as URL structure and domain at-tributes. The classification process was carried out using an ensemble learning approach that combines four popular algorithms: Random Forest, Gradient Boosting, Logistic Regression, and AdaBoost through a soft voting mechanism. The evaluation results show that the proposed model has excellent performance with an accuracy of 98.10%, a precision of 97.81%, a recall of 93.90%, an F1-Score of 95.82%, and a ROC-AUC of 98.62%. These findings confirm that the ensemble ap-proach with optimized features has great potential for application in artificial intelligence-based cybersecurity systems capable of adaptive and real-time phishing detection
Smart Admissions: Meningkatkan Efesiensi Proses Penerimaan Mahasiswa Baru dengan Chatbot Interaktif
No full textThe new student admission (PMB) process at the Baru Universitas Bima Internasional MFH often faces challenges related to time efficiency and administrative burdens due to repetitive inquiries from prospective students. This research aims to design and build an interactive chatbot system named "Sipenmaru MFH" to enhance the efficiency and effectiveness of the PMB process. The de-velopment method used is Extreme Programming (XP), which allows for an adaptive and flexible process. Data was collected through interviews and questionnaires to build the chatbot\u27s knowledge base. The application was developed using the Flutter framework with a NoSQL da-tabase, and its functionality was tested using the Black Box testing method. The result of this re-search is a functional chatbot application with a user-friendly interface, capable of providing fast and accurate responses to common questions regarding new student admissions. The implemen-tation of this chatbot has successfully provided easier access to information for prospective stu-dents and reduced the committee\u27s workload, thus making the admission process more efficient
Pengembangan Platform Intervensi Status Gizi Ibu Hamil Berbasis Integrasi Case-Based Reasoning dan Teori Dempster–Shafer
No full textNutritional problems among toddlers and pregnant women remain a major public health issue in Indonesia, necessitating a decision-support system capable of providing rapid and accurate nu-tritional diagnosis and intervention. This study develops an expert system integrating Case-Based Reasoning (CBR) and the Dempster–Shafer theory to diagnose the nutritional status of toddlers and pregnant women. The CBR method is employed to identify solutions for new cases based on similarity to previous cases, while the Dempster–Shafer theory is utilized to handle un-certainty and combine multiple forms of evidence derived from anthropometric, clinical, and health history parameters. The system was tested using 20 cases involving variables such as body weight, height, mid-upper arm circumference (MUAC), hemoglobin level (Hb), gestational age, and dietary intake. The results indicate that the system achieved an accuracy of 90%, an average confidence level of 82.7%, and a diagnostic precision of 88% when compared to expert nutrition-ists’ assessments. Diagnostic discrepancies occurred in only two cases (10%), both of which ex-hibited parameter values near the classification thresholds. These findings demonstrate that the integration of CBR and the Dempster–Shafer theory enhances the reliability of expert systems in generating accurate and measurable nutritional diagnoses despite data uncertainty, and shows strong potential as a decision-support tool for nutritionists in providing faster, more objective, and evidence-based nutritional interventions