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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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An Advanced Deep Learning Approach for Automatic Disease Recognition and Classification in paddy leaf disease detection Marco, Robert; Muhammad, Alva Hendi; Aini, Nur; Hendriana, Yana
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.2025v7i2.2482

Abstract

Purpose: Accurate detection of paddy leaf diseases is essential to ensure optimal crop yield and effective disease management. Methods/Study design/approach: In this study, we propose a hybrid deep learning model combining Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and an Attention mechanism for paddy leaf disease classification using the Paddy Doctor dataset. The CNN layers extract spatial features from leaf images, the LSTM captures contextual relationships between these features, and the Attention mechanism emphasizes the most relevant patterns for accurate classification. Result/Findings: Experimental results show that the proposed CNN+LSTM+Attention model achieves 95.5% accuracy, 98.12% precision, 98.3% recall, and 0.994 macro AUC, outperforming a simple CNN-3 layer while offering competitive performance compared to state-of-the-art architectures such as ResNet34 and Xception. Novelty/Originality/Value: These results demonstrate that the proposed model is highly effective in detecting paddy leaf diseases with minimal false negatives, providing a reliable and practical solution for automated paddy disease monitoring systems
Implementation of a 3D Animation Production Pipeline for a Psychological Well-Being Trailer : Visualizing Self-Acceptance Using Autodesk Maya Praka, Yoseph Satria; Setiawan, Rifki; Amalia, Syasya; Danti, Annisa Eka; Prameswari, Candi Rahayu Tri; Tambun, Desna Romarta; Mukkaramah, Indah Sari
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.2025v7i2.2485

Abstract

Purpose: This study aims to investigate the implementation of a structured 3D animation production pipeline in the creation of a 3D animation trailer themed around psychological well-being with a self-acceptance dimension. The research addresses the lack of academic studies that specifically discuss the technical and procedural aspects of 3D animation trailer production, as most previous works emphasize narrative messages or educational outcomes rather than the production workflow itself.Methods: A practice-based research approach was applied by implementing a three-stage animation production pipeline consisting of pre-production, production, and post-production. Pre-production included concept development, scriptwriting, storyboard and concept art creation, and dubbing. The production stage involved material collecting, studio setup, and keyframe-based animation using Autodesk Maya. Post-production comprised frame-by-frame rendering using Arnold, compositing and editing with Adobe After Effects, internal evaluation, expert-based beta testing, and publishing via YouTube. All stages were systematically documented to ensure clarity and reproducibility.Result: The results show that the implemented pipeline enabled efficient task distribution, consistent visual quality, and accurate synchronization between animation and audio across 15 scenes with a total duration of 97 seconds. Internal evaluation confirmed that all scenes met predefined technical, visual, and narrative standards. Furthermore, expert-based evaluation by media professionals yielded a feasibility score of 92% across five aspects: animation quality, camera movement, lighting, sound design, and narrative timing. These findings indicate that the proposed pipeline effectively supports short 3D animation trailer production by reducing workflow ambiguity and improving coordination between visual and audio components.Novelty: The novelty of this research lies in its detailed and reproducible documentation of a 3D animation trailer production pipeline that integrates a fantasy-based narrative as a conceptual foundation for self-acceptance. This study provides a practical workflow reference that can be adapted for future academic, educational, and creative animation projects.
A Comparative Analysis of Decision Tree, Logistic Regression, and Support Vector Machine Algorithms in Sentiment Analysis of Threads App Reviews Hidayat, Rahmat; Aminulhaq, Farhan
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.2025v7i2.2497

Abstract

Purpose: This study aims to analyze user sentiment regarding the Threads application by comparing the performance of different machine learning models. As a relatively new social media platform, understanding user feedback is crucial for identifying service gaps and improving user retention. The research seeks to determine which algorithm provides the highest precision in classifying user reviews into positive and negative sentiments. Methods: The research utilized a dataset of 3,000 user reviews scraped fromthe Google Play Store. The methodology followed a systematic text mining workflow, including preprocessing stages such as noise removal, tokenization, stopword removal, and stemming. Feature extraction was performed using the Term Frequency-Inverse Document Frequency (TF IDF) method. Three machine learning algorithms—Support Vector Machine (SVM), Decision Tree, and Logistic Regression—were implemented and evaluated using K-Fold Cross Validation to ensure statistical reliability. Result: The experimental results indicate that the Support Vector Machine (SVM) consistently outperformed the other two models. SVM achieved a superior average accuracy of 88.18%, with a peak performance reaching 92.69% during K-Fold testing. Logistic Regression and Decision Tree showed lower accuracy and less stability in handling the high-dimensional text data. These figures confirm that SVM is the most effective model for analyzing the linguistic nuances found in Threads app reviews. Novelty/Originality/Value: This research contributes to the field of software evaluation by providing an empirical comparison of classification algorithms specifically for newly launched social media platforms like Threads. The findings offer practical value for developers to automate the monitoring of user satisfaction. The study demonstrates that integrating rigorous TF-IDF weighting with SVM significantly enhances the accuracy of sentiment detection in short-form mobile application reviews.
User Satisfaction with E-BRAY Digital Library: An Integrated EUCS and TAM Analysis Using PLS-SEM Hamundu, Ferdinand Murni; Rusdarianto, Muhammad Dimas; Alam, Putri Eka Wulandari; Rahmadani, Selin; Maharani, Deswita; Khamaisyah, Khusnul Qhatimah
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.2025v7i2.2499

Abstract

Purpose: This study aims to evaluate user satisfaction with the E-BRAY digital library by integrating the End-User Computing Satisfaction (EUCS) model and the Technology Acceptance Model (TAM), with a particular focus on identifying key determinants of end-user satisfaction in a regional digital library context. Methods/Study design/approach: Data were collected through an online survey involving 106 active users of the E-BRAY digital library system. The relationships between system quality dimensions (accuracy, content, security), perceived usefulness, perceived ease of use, and user satisfaction were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). Result/Findings: The results indicate that perceived usefulness has the strongest and statistically significant effect on user satisfaction (B = 0.712, p < 0.01), supporting the central assumption of the TAM framework. Perceived ease of use shows a positive but weaker and statistically insignificant influence on satisfaction, while other system quality dimensions—accuracy, content, and security—exhibit negligible direct effects. These findings confirm that perceived usefulness plays a critical mediating role between system quality and end-user satisfaction in the digital library environment. Novelty/Originality/Value: This study contributes theoretically by empirically validating perceived usefulness as a key mediating construct linking EUCS system quality dimensions to user satisfaction within a regional digital library setting—an area that remains underexplored in prior research. Practically, the findings provide actionable insights for digital library managers, emphasizing the importance of enhancing system usefulness through improved search functionality, metadata completeness, and research-support features, alongside strengthening security mechanisms to foster long-term user trust.

Page 7 of 7 | Total Record : 64

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