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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 77 Documents
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An Advanced Deep Learning Approach for Automatic Disease Recognition and Classification in paddy leaf disease detection Robert Marco; Alva Hendi Muhammad; Nur Aini; Yana Hendriana
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 Yoseph Satria Praka; Rifki Setiawan; Syasya Amalia; Annisa Eka Danti; Candi Rahayu Tri Prameswari; Desna Romarta Tambun; Indah Sari Mukkaramah
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 Rahmat Hidayat; Farhan Aminulhaq
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 Ferdinand Murni Hamundu; Muhammad Dimas Rusdarianto; Putri Eka Wulandari Alam; Selin Rahmadani; Deswita Maharani; Khusnul Qhatimah Khamaisyah
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.
A Hybrid Rule-Based and Multinomial Naïve Bayes System for Sentiment and Intent Classification of Indonesian Public Reports with Sarcasm Detection Suhendri Suhendri; Sahal Ubaidillah Gunardo; Kartika Dwi Mulyana; Ruli Susanti; Dika Alfaizal Akbar; Amelia Putri
Intechno Journal : Information Technology Journal Vol. 8 No. 1 (2026): July
Publisher : Universitas Amikom Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24076/intechnojournal.2026v8i1.2765

Abstract

Public service reporting systems in Indonesia face significant challenges in processing large volumes of unstructured citizen feedback efficiently. This study proposes Aspiralytica, a mobile-based citizen report classification system that integrates TF-IDF feature extraction with a Multinomial Naive Bayes (MNB) classifier within a hybrid rule-based and machine learning architecture. The system simultaneously performs three-class sentiment classification (positive, negative, neutral) and five-class intent classification (complaint, appreciation, request, emergency, suggestion), with automated priority level determination and a rule-based sarcasm detection module achieving F1 of 0.8980. Evaluated on an augmented dataset of 1,137 sentiment-labeled and 2,187 intent-labeled Indonesian-language citizen report texts using Stratified 10-Fold Cross-Validation, the proposed MNB model achieved sentiment classification accuracy of 96.59% (F1: 96.59%) and intent classification accuracy of 96.97% (F1: 96.96%). An ablation study confirmed TF-IDF with MNB as the dominant performance driver, and a computational efficiency benchmark empirically justified MNB selection with mean inference latency of 0.3955 ms and throughput of 52,312 requests per second. The system is deployed as a FastAPI backend integrated with a React Native mobile frontend, delivering real-time classification through a citizen-facing interface.
An Empirical Benchmarking Framework for IoT Traffic Anomaly Detection Using Elastic Stack SIEM Ferdiansyah Ferdiansyah; Reynaldi Rizki Billanivo; M Ardiansyah; Tegar Putra; M. Habibullah Amin
Intechno Journal : Information Technology Journal Vol. 8 No. 1 (2026): July
Publisher : Universitas Amikom Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24076/intechnojournal.2026v8i1.2808

Abstract

Purpose: This study aims to construct a realistic IoT-MQTT benchmark dataset and evaluate supervised machine learning classifiers for detecting network traffic anomalies, specifically Distributed Denial of Service (DDoS) and spoofing attacks, within a live Security Information and Event Management (SIEM) environment. Methods: An empirical benchmarking framework based on a live Elastic (ELK) Stack SIEM environment was developed, and supervised machine learning classifiers were evaluated for IoT network traffic anomaly detection. Result: KNN and SVM achieved the highest accuracy (0.99), whereas Naive Bayes achieved 0.96. Further analysis revealed that the superior performance of KNN and SVM was largely influenced by data leakage caused by the _attacker_ip feature, while Naive Bayes demonstrated better generalization without relying on identity-based features. Conclusion: The findings highlight the importance of rigorous feature engineering and data leakage analysis when developing machine learning models for IoT traffic anomaly detection, particularly in live SIEM environments. Moreover, the proposed framework contributes to the achievement of Sustainable Development Goals (SDGs) 9 by supporting resilient digital infrastructure and secure IoT-based innovation.
Robust Deep Learning Approach for Automatic Age and Gender Recognition Based on Voice M. Nasyid Yunitian Rizal; Theopilus Bayu Sasongko; Arifiyanto Hadinegoro; Kumara Ari Yuana; Wahid Miftahul Ashari
Intechno Journal : Information Technology Journal Vol. 8 No. 1 (2026): July
Publisher : Universitas Amikom Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24076/intechnojournal.2026v8i1.2827

Abstract

Voice-based age and gender recognition plays an important role in biometric authentication, personalized human–computer interaction, and digital forensic applications. However, existing single deep learning architectures often struggle to simultaneously capture local acoustic patterns, temporal dependencies, and long-range contextual information from speech signals. This study proposes a hybrid CNN–BiLSTM–Transformer framework to improve the accuracy and robustness of multi-class age and gender classification. The proposed approach employs Mel-spectrogram representations generated from the Mozilla Common Voice dataset, followed by audio standardization and feature extraction. A Convolutional Neural Network (CNN) enhanced with Squeeze-and-Excitation blocks extracts discriminative spectral features, a Bidirectional Long Short-Term Memory (Bi-LSTM) network models bidirectional temporal dependencies, and a Transformer Encoder captures global contextual relationships through a multi-head self-attention mechanism. The model was evaluated on 40,392 speech samples across 12 age–gender categories. Experimental results achieved an overall classification accuracy of 91%, outperforming standalone CNN and Bi-LSTM models, which obtained accuracies of 84% and 74%, respectively. In addition, the proposed model demonstrated balanced performance with macro-average precision, recall, and F1-scores of 0.91, 0.92, and 0.91. The novelty of this research lies in the integration of complementary spatial, temporal, and global attention mechanisms within a unified architecture for large-scale multi-class voice-based demographic classification, providing an effective and scalable solution for intelligent biometric and speech analysis systems.
Aspect-Level User Sentiment Patterns in Shopee Mobile App Reviews Using TF-IDF and Logistic Regression Itishom Al Khoiry
Intechno Journal : Information Technology Journal Vol. 8 No. 1 (2026): July
Publisher : Universitas Amikom Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24076/intechnojournal.2026v8i1.2871

Abstract

Purpose: This study examines recent Shopee mobile application reviews from a service diagnostic perspective by linking sentiment classification with aspect-level interpretation. The aim is to identify not only whether user feedback is positive or negative, but also which service dimensions are associated with satisfaction and dissatisfaction. Methods: A total of 160,492 Google Play Store reviews of the Shopee Android application were collected from the 2026 review period. After cleaning, rating-based sentiment labeling, and preprocessing, a stratified random sample of 50,000 reviews was used for modeling. TF-IDF unigram and bigram features were applied with a majority class baseline, Naive Bayes, Logistic Regression, and Linear SVM. Performance was evaluated using accuracy, Macro F1-score, confusion matrix, classification report, and 5-fold stratified cross-validation. A keyword-based aspect mapping was then used to interpret sentiment across six service dimensions. Result: The TF-IDF and Logistic Regression model achieved 0.9194 accuracy, 0.9096 Macro F1-score, and 0.899 cross-validated Macro F1-score. Aspect-level findings show that positive sentiment was concentrated in Promotion and Voucher, Product and Seller, and Customer Service, while negative sentiment was concentrated in Payment, Delivery, and Application Performance. Advertising and delivery-related expressions emerged as key negative signals. Novelty: This study proposes a service diagnostic task with aspect-level analysis, which includes sentiment classification, term-weight interpretation, and service-aspect mapping, to position Shopee review analysis. The strategy transforms bulk app reviews into actionable data to track platform service quality.
Web Based Smartphone Recommendation System Using C4.5 Decision Tree Algorithm for User Need Classification Resca Amelina; Usman Usman; Bayu Rianto
Intechno Journal : Information Technology Journal Vol. 8 No. 1 (2026): July
Publisher : Universitas Amikom Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24076/intechnojournal.2026v8i1.2875

Abstract

The rapid growth of the smartphone market has created significant difficulty for retail staff in providing consistent, objective purchase recommendations, particularly for customers with limited technical knowledge. This study presents SmartPick, a recommendation system that applies the C4.5 Decision Tree algorithm to address the inefficiency of manual recommendation at Sahabat Ponsel Tembilahan retail store. Purpose: To classify users into smartphone usage categories Gaming, Photography/Videography, Student, and General User based on budget, usage intent, and technical preferences, replacing subjective salesperson judgment with a consistent, rule-based decision process. Methods: A balanced dataset of 500 smartphones was used to train and evaluate the C4.5 model, chosen for its interpretability and ability to represent decision logic as human-readable rules. Results: The model achieved 96.00% testing accuracy, outperforming CART, Random Forest, Naive Bayes, and K-NN. Novelty: Unlike prior recommendation approaches relying on collaborative or content-based filtering, which require extensive historical transaction data, SmartPick integrates technical specifications and budget constraints directly into a single interpretable Decision Tree model, offering a transparent and scalable alternative for retail-based smartphone recommendation that remains explainable to non-technical users.
Sugarcane Plant Disease Classification Based on Leaf Image Using ConvNeXt V2 Deep Learning Model Agung Indra; Fitri Yunita; Usman Usman
Intechno Journal : Information Technology Journal Vol. 8 No. 1 (2026): July
Publisher : Universitas Amikom Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24076/intechnojournal.2026v8i1.2876

Abstract

Sugarcane plant diseases pose a significant threat to agricultural productivity, yet early and accurate identification remains challenging for farmers due to the limitations of manual inspection. This study proposes a sugarcane leaf disease classification system using ConvNeXt V2 Tiny, a modern convolutional architecture with a Global Response Normalization (GRN) mechanism, combined with an ensemble Stratified K-Fold Cross Validation strategy (K=6) to improve generalization on real-world field data. A dataset of 2,948 leaf images spanning five classes (Red Rot, Mosaic, Rust, Yellow Leaf, and Healthy) was used, with field-collected images held out as a fixed test set. The ensemble model achieved a mean validation accuracy of 98.49% ± 0.58% across six folds and a test accuracy of 98.39% on 427 unseen field images, with macro-average precision, recall, and F1-score each reaching 98%. ConvNeXt V2 Tiny substantially outperformed ResNet-50 (87.35%) and EfficientNetV2-S (83.37%) under identical experimental settings, demonstrating superior generalization across the domain gap between curated and field data. The primary contribution of this study is the first application of ConvNeXt V2 Tiny with ensemble K-Fold strategy for sugarcane disease classification, offering high accuracy with moderate computational complexity (28.6M parameters) and practical deployability, as demonstrated through the SugarScan web application.

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