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Intechno Journal : Information Technology Journal
Published by Universitas AMIKOM Yogyakarta
ISSN : 26551632     EISSN : 26551438     DOI : https://doi.org/10.24076/intechnojournal.2024v6i1
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering
Intechno Journal (e-ISSN 2655-1438 | p-ISSN 2655-1632) published by Universitas Amikom Yogyakarta in collaboration with Indonesian Computer, Electronics and Instrumentation Support Society (IndoCEISS) to promote high-quality Information Technology (IT) research among academics and practitioners alike, including computer scientists, Software Engineering & Big Data, Multimedia, Networking, IT professionals, and other stakeholders in the IT industry.
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 6 Documents
 1 
Search results for , issue "Vol. 6 No. 2 (2024): December" : 6 Documents clear
Testing of Usability and Correctness Aspects in Marketing and Ordering Information Systems Masyora, Nabilah Faqita; Hartono, Nahrun; Rahman, Rahman
Intechno Journal : Information Technology Journal Vol. 6 No. 2 (2024): December
Publisher : Universitas Amikom Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24076/intechnojournal.2024v6i2.1807

Abstract

Purpose: This research systematically evaluates the quality of the web-based marketing and ordering information systems at UD. Percetakan Citra Satria by focusing on the aspects of usability and correctness, aiming to determine the effectiveness of the system in meeting user needs. Methods/Study design/approach: Data collection was conducted through observation and questionnaires. The observation aimed to identify issues during the use of the marketing and ordering information systems at UD. Percetakan Citra Satria, while the questionnaire, using a Likert scale, involved 8 employees and 70 customers to assess usability and correctness. The system was implemented and tested by allowing users to interact with it, with evaluations analyzed using the McCall model. The testing focused on usability, reflecting ease of use, and correctness, ensuring information accuracy, both critical for enhancing user experience and operational effectiveness. Result/Findings: The test results indicate that the overall quality of the web-based marketing and ordering information systems at UD. Citra Satria Printing achieved a score of 85% for the usability aspect and 85.3% for correctness. These scores are classified as very good according to the quality factor percentage scale. Novelty/Originality/Value: This study introduces novelty in applying quality testing for a web-based marketing and ordering information systems using the McCall method, focusing on two main aspects: usability and correctness. The novelty lies in its specific application context, namely in the printing industry (UD. Percetakan Citra Satria), as well as the use of questionnaires to directly incorporate the perspectives of employees and customers.
Quality Evaluation of Ticketing Management System Using ISO/IEC25010:2023 Standards and AHP Method Ariningsih, Puji; Muhammad, Alva Hendi
Intechno Journal : Information Technology Journal Vol. 6 No. 2 (2024): December
Publisher : Universitas Amikom Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24076/intechnojournal.2024v6i2.1870

Abstract

Purpose: Information Technology plays a crucial role in supporting education service systems. When system-related issues arise, a Ticket Management System (TMS) becomes essential to address various software and hardware problems. Evaluating the performance and quality of TMS applications is necessary to ensure their effectiveness. This study assesses the quality of a TMS application developed by University of Amikom Yogyakarta, using ISO/IEC 25010:2023. Methods/Study design/approach: The Analytic Hierarchy Process method is employed to prioritize three key ISO/IEC 25010 characteristics by engaging TMS application users. Following the ranking, the study conducts quality measurements using questionnaires and black box testing. The questionnaire results are assessed using a Likert scale to determine scores for the TMS application based on the sub-characteristics of the three selected ISO/IEC 25010:2023 characteristics and the AHP-derived rankings. Result/Findings: The findings indicate that the TMS application achieved a quality score of 4.354. This shows that the TMS application is in the good category. Novelty/Originality/Value: The study highlights the need for performance efficiency improvements, specifically in the Time Behavior sub-characteristic, to enhance the overall quality of the TMS application.
Forecasting A Major Banking Corporation Stock Prices Using LSTM Neural Networks Rauf, Budi Wijaya
Intechno Journal : Information Technology Journal Vol. 6 No. 2 (2024): December
Publisher : Universitas Amikom Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24076/intechnojournal.2024v6i2.1888

Abstract

The increasing complexity of stock market predictions necessitates advanced computational techniques to address the unique challenges posed by financial data's non-linear and volatile nature. This study aims to leverage Long Short-Term Memory (LSTM) neural networks to accurately forecast stock prices, using historical data collected from a major banking corporation as a primary source. The LSTM model excels at processing sequential time-series data, allowing it to predict monthly stock closing prices over a one-year horizon with a high degree of precision. Our findings indicate a Root Mean Squared Error (RMSE) of 3.2, underscoring the model's efficiency and reliability in financial forecasting tasks. The novelty of this research lies in the systematic incorporation of preprocessing techniques and fine-tuned hyperparameters to optimize model performance. Furthermore, this study explores the practical implications of implementing LSTM models in real-world trading scenarios, analyzing their adaptability to dynamic market conditions and their potential integration into automated trading systems. These findings contribute to the growing body of knowledge in financial analytics and demonstrate the viability of machine learning-based solutions for accurate and robust market predictions.
Reverse Engineering GitHub CoPilot: Creating an OpenAI-Compatible Endpoint for Enhanced Developer Integration Akbar, Nur Arifin; Krida, Ardian Webi; Setiawan, Akbar
Intechno Journal : Information Technology Journal Vol. 6 No. 2 (2024): December
Publisher : Universitas Amikom Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24076/intechnojournal.2024v6i2.1895

Abstract

This paper presents the reverse engineering of GitHub CoPilot to develop an OpenAI-compatible endpoint, enabling broader access and integration possibilities for AI-assisted code completion. By analyzing CoPilot's communication protocols and creating a proxy server that translates OpenAI API requests to CoPilot's internal API, we bridge the gap between proprietary tools and open standards. The implementation, allows developers to utilize CoPilot's capabilities within their preferred environments using the familiar OpenAI API interface. We detail the system architecture, authentication mechanisms, request processing pipeline, and performance optimization techniques. Our results demonstrate successful integration, with robust performance metrics, including low response times and high compatibility rates. This work opens avenues for enhanced developer productivity and flexibility in AI-assisted coding tools.
Prototype for Implementing Data Exchange with The FHIR-HL7 Standard in The Personal Health Record Application Prakosa, Hendri Kurniawan; Ibad, Hasan Nurul; Anwar, Saiful
Intechno Journal : Information Technology Journal Vol. 6 No. 2 (2024): December
Publisher : Universitas Amikom Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24076/intechnojournal.2024v6i2.1902

Abstract

Monitoring health is essential for early disease detection, prevention, and managing chronic conditions. Active tracking empowers patients to make informed decisions and adhere to treatments, improving outcomes. However, fragmented medical records across facilities can lead to incomplete information. Integrating records through interoperable systems provides patients and providers with a comprehensive health overview, ensuring continuity, reducing redundancies, and enhancing collaboration. Centralized health data enables better monitoring for patients and more personalized, efficient care from healthcare professionals. This study focuses on developing a prototype for data exchange implementation using the FHIR-HL7 standard in a Personal Health Record (PHR) application. The prototype is tested with selected resources: Patient, Encounter, Observation, and Condition. The data flow involves obtaining patient medical records from an Electronic Health Record (EHR) and displaying them in a Personal Health Record application, ensuring secure access via National Identification Number (NIK) matching as Personal Identification Number. The approach includes mapping FHIR resources to relevant data structures, modifying both EHR and PHR applications to support the data exchange process. The results demonstrate that patient medical records stored in EHR can be accessed by patients through the PHR apps. Specific FHIR resources enable the exchange of various data types: patient demographics using the Patient resource, diagnoses using the Condition resource, and vital signs (e.g., systolic/diastolic blood pressure, weight, height) using the Observation resource. This prototype highlights the feasibility of integrating FHIR-HL7 standards for interoperable health data exchange, enhancing patient engagement and data accessibility.
EEG Emotion Recognition using Deep Neural Network (DNN) in Virtual Reality Environments Agastya, I Made Artha; Marco, Robert; Handayani, Dini Oktarina Dwi
Intechno Journal : Information Technology Journal Vol. 6 No. 2 (2024): December
Publisher : Universitas Amikom Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24076/intechnojournal.2024v6i2.1903

Abstract

Purpose: The purpose of this study is to explore the integration of EEG technology with virtual reality (VR) systems to enhance therapeutic interventions, improve cognitive state recognition, and develop personalized immersive experiences. Specifically, it investigates the classification of EEG signals in a VR environment using machine learning models and identifies the most effective methods for individual-level analysis.Methods: The study utilized EEG data collected from 31 participants using the Muse 2016 headset, with electrodes positioned according to the 10-20 international system. EEG signals were analyzed for features such as statistical metrics (mean, median, standard deviation, skewness, and kurtosis) and Hjorth parameters (activity, mobility, complexity). Machine learning models, including K-Nearest Neighbors (KNN), Random Forest (RF), and Support Vector Machine (SVM), were evaluated for their performance in classifying emotional and cognitive states in a VR environment. Result: The results indicate that the Deep Neural Network (DNN) outperformed SVM and KNN models, achieving the highest average classification accuracy. SVM demonstrated consistent performance, with accuracy values consistently above 0.8 across subjects, while KNN showed greater variability and lower overall performance. DNN's architecture, incorporating two hidden layers with ReLU activation and a softmax output layer, demonstrated superior capability in modeling complex EEG patterns. The findings emphasize the effectiveness of DNN in handling high-dimensional and non-linear data, particularly for multi-class classification tasks.Novelty: This study is novel in its focus on personalized machine learning model performance in a VR-EEG setup. Instead of a one-size-fits-all approach, it emphasizes individualized analysis, identifying the most effective model for each participant.

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