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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 64 Documents
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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.
Comparative Analysis of Live Action Film Production Management Using Critical Path Method (CPM) Versus Conventional Production Processes Nugroho, Agung; Suyanto, Mohammad; Ariatmanto, Dhani
Intechno Journal : Information Technology Journal Vol. 7 No. 1 (2025): July
Publisher : Universitas Amikom Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24076/intechnojournal.2025v7i1.2019

Abstract

The production of the film “Kinah dan Redjo”, by Universitas Amikom and MSV Sinema, has been completed, prompting researchers to conduct an analysis and evaluation of the production management applied. The focus of this study is on time and cost, which are critical factors supporting film production. An extended production duration was identified as a challenge, as it reduces effectiveness and leads to cost overruns. Therefore, this study aims to compare project management strategies for successful planning and control, using both conventional methods and the Critical Path Method (CPM). This analysis is expected to yield faster project completion and establish efficient, productive standards for future productions. The conventional approach indicated a total production duration of 681 days, comprising 120 days for pre-production, 18 days for production, and 551 days for post-production. Upon analysis using the CPM method, the total duration was reduced to 459 days, including 113 days for pre-production, 152 days for production, and 191 days for post-production. The graphical comparison of methods shows significant cost fluctuations across each production phase with the conventional method, especially increased costs during production despite the shorter duration. Conversely, the CPM method demonstrates more controlled and measurable durations and costs. This study underscores the importance of cost optimization, standardization of the Work Breakdown Structure (WBS), and hybrid modeling to enhance efficiency in dynamic film projects. Furthermore, this analysis serves as a foundational reference for the architectural planning of future applications incorporating artificial intelligence (AI) integration. AI has the potential to accelerate scheduling, optimize resource allocation, and streamline cost management and production design, thereby improving overall project efficiency.
Decision Support System for Selecting the Best University for Vocational School Graduates Majoring in Multimedia Using the TOPSIS Method Febriyanti, Lisa; Febrinazahra, Mahdiyyah; Ramasahdan, Yoga Aditya; Niqotaini, Zatin
Intechno Journal : Information Technology Journal Vol. 7 No. 1 (2025): July
Publisher : Universitas Amikom Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24076/intechnojournal.2025v7i1.2108

Abstract

Purpose: According to Statistics Indonesia (2023), there are around 1.6 million vocational secondary school (SMK) graduates in Indonesia, over 30% of whom come from the Information and Communication Technology field, particularly the Multimedia major. However, more than 40% of them do not proceed to higher education (Tracer Study, Kemendikbud Ristek, 2023), partly due to difficulties in selecting universities that align with their practical skills. This study aims to develop a decision support system that helps Multimedia graduates choose higher education institutions that match their vocational background.Methods/Study design/approach: The study applies the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method, using six evaluation criteria: program accreditation, multimedia facilities, industry partnerships, alumni reputation, tuition fees, and internship opportunities. Data were gathered from official secondary sources such as BAN-PT and PDDikti. The TOPSIS method was then used to rank university alternatives based on weighted criteria.Result/Findings: The highest-scoring alternative obtained a preference score of 0.6968, representing an institution with superior accreditation, strong industry collaboration, and complete multimedia infrastructure. Sensitivity analysis showed consistent rankings for some alternatives, while others shifted depending on changes in criteria weights.Novelty/Originality/Value: This study offers a replicable and adaptable decision support system that enables vocational school graduates to make informed, data-driven decisions in selecting relevant higher education pathways. The framework can be customized for other vocational fields or regional applications, providing practical value for both students and education planners.
Application of the SAW Method in a Decision Support System to Determine the Best Public Transportation Mode for UPN “Veteran” Jakarta Students Nareswari, Fausta Sarah; Ramadhan, Rafa Muhammad Ghifar; Al-Ghifari, Muhammad Hanif; Niqotaini, Zatin
Intechno Journal : Information Technology Journal Vol. 7 No. 1 (2025): July
Publisher : Universitas Amikom Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24076/intechnojournal.2025v7i1.2125

Abstract

Purpose: The high use of private vehicles in Jakarta exacerbates traffic congestion, making public transportation a vital alternative, especially for students of the National Development University "Veteran" Jakarta (UPNVJ). However, with numerous options available, many students struggle to select the most suitable mode of transport. This study aims to aid this decision-making process by developing a Decision Support System (DSS) to evaluate and recommend the most optimal public transportation mode. Methods/Study design/approach: This research implements a DSS using the Simple Additive Weighting (SAW) method. Five transportation modes were analyzed: MRT, KRL, TransJakarta, Jaklingko, and Online Motorcycle Taxi. The evaluation was based on five criteria: cost, speed, comfort, accessibility, and safety. Primary data was collected through questionnaires distributed to students, which were then processed using normalization and weighting based on the perceived importance of each criterion. Result/Findings: The analysis revealed that Online Motorcycle Taxi is the top choice among students (score: 0.859), followed by MRT (score: 0.853) and TransJakarta (score: 0.837). The results indicate that each mode has distinct advantages; for instance, MRT excels in comfort and safety, while Jaklingko is perceived as the most economical option. Novelty/Originality/Value: This study demonstrates that flexibility and accessibility are the primary considerations for students when choosing transportation. The findings provide a useful, structured reference for students to select efficient transport that meets their needs, moving beyond anecdotal decision-making. The research effectively applies the SAW method to offer a practical solution to a common student mobility challenge.
Integrating CVSS, OWASP, and APPI for a Comprehensive Risk Analysis of SQL Injection Vulnerabilities in E-Commerce Adrian, Muhammad Kholilul; Ibnugraha, Prajna Deshanta; Nuha, Hilal Hudan
Intechno Journal : Information Technology Journal Vol. 7 No. 1 (2025): July
Publisher : Universitas Amikom Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24076/intechnojournal.2025v7i1.2184

Abstract

Purpose: By integrating the technical severity evaluation provided by the Common Vulnerability Scoring System (CVSS), the business risk assessment framework of the OWASP Risk Rating Methodology, and the legal compliance standards outlined in Japan’s Act on the Protection of Personal Information (APPI), this study aims to conduct a holistic risk analysis of SQL injection vulnerabilities within e-commerce platforms. The primary objective is to offer stakeholders a robust and actionable model for enhancing the security of online shopping environments. Methods/Study design/approach: This study employed a mixed-methods experimental case study approach. A custom-built, intentionally vulnerable e-commerce web application was subjected to a simulated SQL injection attack to extract fictitious user and transaction data. The technical severity of the vulnerability was quantified using CVSS v3.1, while the OWASP Risk Rating Methodology was applied to assess the associated business risks. Additionally, the legal implications were evaluated in accordance with Japan’s Act on the Protection of Personal Information (APPI). Result/Findings: The simulation confirmed that a SQL injection attack could extract sensitive personal and transactional data. The vulnerability was rated “Critical” with a CVSS v3.1 score of 9.1, and the OWASP assessment indicated a “High” business risk due to financial impact, APPI non-compliance, and privacy violations. The leaked purchase history was classified under APPI as “Personal Information Requiring Special Attention.” Novelty/Originality/Value: This study’s main contribution is its integrated methodology that links CVSS, OWASP, and APPI frameworks to assess cyber threats. It offers a multidimensional view, showing how a technical vulnerability can lead to serious legal and business consequences under specific data protection laws.
Development of a Web-Based Teacher Attendance System in Government Agencies: A Case Study at the Ministry of Religious Affairs Assyifaurrohmah, Farwah; Mubin, Ahmad Fuzul; Tabrani, Ahmad Tabrani
Intechno Journal : Information Technology Journal Vol. 7 No. 1 (2025): July
Publisher : Universitas Amikom Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24076/intechnojournal.2025v7i1.2233

Abstract

Objective: This study addresses the issues of inefficiency and lack of transparency in manual attendance recording systems in government institutions, particularly in the Ministry of Religious Affairs. The study aims to develop a web-based teacher attendance information system that addresses the inaccuracy and susceptibility to manipulation found in conventional paper-based methods, while improving administrative accountability.Method: The system was developed using an iterative software engineering approach that integrates comprehensive needs analysis, modular design architecture, and functional testing stages. The methodology combines web-based technology with user-centered interface design, accommodating the needs of administrative staff and teaching staff. The development phase includes gathering stakeholder requirements, planning the system architecture, implementation using the Django framework, and systematic validation procedures.Results: The implemented system successfully demonstrated improved accuracy in attendance recording, automatic validation of working hours, comprehensive absence history tracking, and Excel data export capabilities. Initial testing revealed increased reporting efficiency and a significant reduction in administrative processing time. The system achieved seamless integration with existing government workflows while maintaining data integrity and security standards.Contribution: This research contributes innovative solutions specifically tailored to Indonesian government institutions, bridging the integration of information technology with human resource management governance. The study presents a strategic digital transformation approach for public sector administrative services, offering a replicable model for similar government organizations seeking to modernize their attendance management systems.
The Effectiveness of Dropout Layers in LSTM Architecture for Reducing Overfitting in Sony Stock Prediction Saputra, Roni; Kurnia, Dian Ade; Wijaya, Yudhistira Arie
Intechno Journal : Information Technology Journal Vol. 7 No. 2 (2025): December
Publisher : Universitas Amikom Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24076/intechnojournal.2025v7i2.2369

Abstract

This study investigates the effectiveness of dropout layers in reducing overfitting within Long Short-Term Memory (LSTM) neural networks for Sony stock price prediction. Financial time series forecasting presents significant challenges due to market volatility and noise, often leading to models that overfit historical data while failing to generalize to unseen market conditions. We implemented two LSTM models: one without dropout layers and another with dropout layers (rate=0.2) applied after each LSTM layer. Using historical Sony stock data from 2015-2025, we evaluated both models using Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE) metrics. The model with dropout demonstrated superior performance on testing data, achieving RMSE of 0.5971, MAE of 0.4411, and MAPE of 2.1502%, compared to the model without dropout which obtained RMSE of 0.7124, MAE of 0.5636, and MAPE of 2.6684%. Furthermore, the dropout model exhibited significantly reduced overfitting, with smaller performance gaps between training and testing datasets across all metrics, particularly in MAPE where the difference approached zero (0.0509%). This research provides empirical evidence that dropout regularization effectively enhances LSTM model generalization for stock prediction, offering practical value for developing more reliable financial forecasting models. Future research could explore optimal dropout rates for different market conditions and investigate combinations of dropout with other regularization techniques.

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