Intechno Journal : Information Technology Journal
Intechno Journal : Information Technology Journal (e-ISSN 2655-1438 | p-ISSN 2655-1632) is a peer-reviewed open-access scholarly journal, published biannually in July and December by Universitas Amikom Yogyakarta in collaboration with The Indonesian Computer, Electronics and Instrumentation Support Society (IndoCEISS), a national professional organization supporting research and innovation in computing, electronics, instrumentation, and information technology. The journal publishes original research and review articles in intelligent computing and applied information technologies. It provides a forum for researchers, academics, and practitioners to disseminate research that contributes to the advancement of information technology and its applications. Since Volume 5, Issue 1 (2023), INTECHNO Journal: Information Technology Journal has been accredited SINTA Rank 4 (SINTA 4) by the Directorate General of Research and Development, Ministry of Higher Education, Science, and Technology of the Republic of Indonesia under Decree No. 156/C/C3/KPT/2026. The accreditation is valid through Volume 9, Issue 2 (2027). All submitted manuscripts undergo an initial editorial screening followed by a double-blind peer-review process involving at least two independent reviewers. Published articles are made immediately available under the Creative Commons Attribution (CC BY) license, with authors retaining the copyright to their work. Authors should prepare manuscripts in English and provide complete metadata for all authors during the online submission process. Please refer to the Scope and Focus, Author Guidelines, Publication Ethics, and Article Processing Charges (APC) pages before submitting a manuscript.
Rainfall Prediction Using Random Forest with Synthetic Minority Over-Sampling Technique (SMOTE)
Rainfall is a crucial meteorological parameter that directly affects maritime activities and port operations, particularly in coastal regions. Tanjung Perak Port, as one of the busiest ports in Indonesia, is highly vulnerable to weather disturbances due to rainfall variability. This study develops a rainfall prediction model based on machine learning using the Random Forest algorithm optimized with the Synthetic Minority Over-Sampling Technique (SMOTE) to address data imbalance. Daily meteorological data for 2013-2024 were obtained from NASA POWER, including rainfall, minimum and maximum temperature, relative and specific humidity, wind speed, and surface pressure. Model performance was evaluated using Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE). SMOTE improved the model's ability to identify high-intensity rainfall events. The combined 2024-2025 evaluation produced an MAE of 1.457 mm, an RMSE of 1.819 mm, and R² of 0.861. The model therefore captures seasonal rainfall patterns and has potential as a decision-support tool for weather-risk mitigation at Tanjung Perak Port.
Copyright (c) 2026 Intechno Journal : Information Technology Journal https://creativecommons.org/licenses/by/4.0
Mitigating COCOMO II Feature Redundancy in Software Effort Estimation via a Local Search-Enhanced Particle Swarm Optimization-SVR Framework
Purpose: Software development effort estimation (SDEE) frequently suffers from accuracy degradation due to redundant project attributes in medium-sized datasets such as NASA93. Most metaheuristic optimization studies focus solely on hyperparameter tuning without filtering input dimensions, leaving the model vulnerable to overfitting. Methods/Study design/approach: This study proposes a Hybrid Local Search-Particle Swarm Optimization framework integrated with Support Vector Regression (HPSO-SVR). Instead of relying on a sequential approach, the standard PSO algorithm is modified by embedding a Simulated Annealing (SA) mechanism as a local search operator. This allows the algorithm to evaluate the feasibility of binary feature subsets and continuous parameters (C, gamma, epsilon) concurrently within a single vector space. The model is tested using a nested 10-fold cross-validation protocol on the NASA93 dataset to prevent data leakage. Result/Findings: The HPSO-SVR model successfully identified and eliminated 7 disruptive attributes (including DOCU, RUSE, and SCED), reducing the dimensionality from 22 to 15 features. This approach yielded an MMRE of 22.15%, a Pred(25) of 63.44%, and an RMSE of 38.90 PM. Statistical validation using the Wilcoxon Signed-Rank Test proved that the addition of the local search mechanism for feature selection provided a significant improvement (p < 0.05) over standard PSO-SVR. Novelty/Originality/Value: The primary contribution lies in the application of a probability-based SA local search mechanism embedded within the PSO population structure. This successfully suppresses COCOMO II feature noise simultaneously with SVR tuning, yielding a more compact and accurate predictor configuration for early-stage project planning.
Flow-Based Encrypted Network Traffic Classification Using Random Forest for Network Access Control
Encrypted network communications reduce the effectiveness of payload based traffic identification and complicate the translation of traffic analysis into network access control decisions. This study evaluates a payload independent workflow that connects flow based multiclass classification with administrator triggered, time limited firewall enforcement. The experiment used the public ISCXVPN2016 benchmark. After removing 18,719 duplicate records, 40,987 unique flows remained; all 23 available numerical flow features were retained without feature selection or normalization. A Random Forest classifier with 300 trees was selected using five-fold cross-validated grid search on a stratified 80% training partition and evaluated on an independent 8,198 sample test set covering 14 VPN and non-VPN traffic classes. The model achieved 88.01% accuracy, 88.00% weighted precision, 88.01% weighted recall, and 87.97% weighted F1-score. It exceeded the strongest reproduced baseline, K-Nearest Neighbors, by 16.09 percentage points in accuracy and 16.29 percentage points in weighted F1-score. Supplemental five-fold evaluation produced a mean accuracy of 88.05% with a 0.36 percentage point standard deviation. The trained classifier was integrated with a web application in which administrators review predicted flows and initiate temporary MikroTik RouterOS rules. All 30 temporary blocking entries observed in the evaluation database reached the Unblocked state with recorded timestamps, demonstrating rule lifecycle traceability at the database level. The findings show that Random Forest can provide competitive flow based classification while supporting an auditable, human controlled access control workflow; however, device level reliability and cross dataset generalizability require further validation.
Deepfake Content Analysis Using Error Level Analysis and Metadata
Purpose: The spread of deepfakes on platform X threatens the integrity of digital information, but previous research has applied Metadata Analysis, Error Level Analysis (ELA), and Reality Defender separately, not yet as an integrated approach. This study applied Metadata Analysis and ELA to identify manipulation in deepfake images, as well as evaluate the effectiveness of Reality Defender in detecting deepfake content in images circulating on platform X as an application case study. Methods/Study design/approach: Using a descriptive qualitative approach with digital forensic experiment methods, seven purposive selected image samples representing seven content characteristic scenarios (original, face-swap, GAN, full generative AI, anti-forensics, conventionally edited, and platform compressed), analyzed through three layered stages with tiered final classification criteria based on the cross-validated Reality Defender score threshold with ELA. Result/Findings: Metadata is only informative before uploading, as X deletes EXIF uniformly post-upload. ELA remained effective in both conditions, showing localized intensity anomalies (close to 240-255 from 255) across deepfake samples. Reality Defender correctly classified six of the seven samples (85.7% accuracy, 100% in the deepfake category, 66.7% in the original category), with one false positive (64%) in the original, conventionally edited image. Novelty/Originality/Value: This study integrated all three methods simultaneously and layered with explicit criteria, showing Metadata lost post-upload diagnostic value while ELA and Reality Defender remained the most reliable for content sourced from platform X.
Design and Feasibility Evaluation of a Web-Based Container Damage Reporting Information System for Container Terminal Operations
Purpose: This research designs and evaluates a web-based information system for Container Damage Report (CDR) management in container terminal operations. The study emphasizes applied computing, browser-based access, cloud data storage, and human-centered operational usability. The previous CDR process relied on paper forms, verbal confirmation, and separate photo files, resulting in slow retrieval, inconsistent documentation, and limited traceability. Methods: The research applied the 4D development model consisting of define, design, develop, and disseminate stages. The define stage identified operational problems, user roles, data needs, and reporting constraints through field observation, interviews, workflow review, and document analysis. The design stage formulated the reporting flow, required fields, database structure, interface layout, and access mechanism. The development stage implemented a no-code cloud prototype by integrating Tally for online input, Google Sheets for centralized records, and Google Sites as a web portal. The dissemination stage involved a limited introduction and user-acceptance testing. Results: The prototype supports structured online input, photo evidence upload, timestamped records, centralized cloud storage, browser-based retrieval, and monitoring of report status. User validation yielded scores of 76%, 94%, and 84%, with an average of 84.67%, indicating that the prototype is feasible for initial operational use. Novelty: The study contributes a lightweight applied computing model for container damage reporting. It shows how no-code cloud tools can support an affordable transitional architecture before integration with enterprise terminal operating systems.
Sentiment Analysis and Emotion Detection of ChatGPT Users on Google Play Store Using the Naïve Bayes Algorithm
With the rapid adoption of artificial intelligence applications such as ChatGPT, understanding both users' sentiments and emotional expressions has become increasingly important for evaluating user experience and improving service quality. This study aims to analyze user sentiment and identify emotional tendencies expressed in Google Play Store reviews of the ChatGPT application. Methods: A total of 10,000 user reviews were collected through web scraping from the Google Play Store. The collected data underwent several preprocessing stages, including data cleaning, case folding, tokenization, normalization, stopword removal, and stemming. Sentiment analysis was performed using the Multinomial Naïve Bayes algorithm to classify reviews into positive, neutral, and negative categories. Emotion detection was subsequently conducted by interpreting dominant lexical patterns and frequently occurring terms within each sentiment category through Word Cloud visualization, enabling the identification of users' emotional tendencies rather than direct emotion classification. Model performance was evaluated using accuracy, precision, recall, and F1-score. Results: The sentiment distribution showed that 84.7% of the reviews were classified as positive, 3.9% as neutral, and 11.4% as negative. The best-performing model, using an 80:20 train–test split, achieved an accuracy of 86.8%, precision of 83.8%, recall of 86.8%, and an F1-score of 82.0%. The emotion detection results revealed that positive reviews were dominated by words reflecting satisfaction, usefulness, and productivity, whereas negative reviews mainly expressed frustration related to subscription costs, system errors, and application performance. Novelty: Unlike previous studies that primarily focused on sentiment classification, this research combines machine learning-based sentiment analysis with lexical interpretation of emotional tendencies derived from user-generated reviews. This integrated approach provides a more comprehensive understanding of users' perceptions and emotional responses toward ChatGPT, offering practical insights for improving AI-based application services.
Comparing ARIMA and Single Exponential Smoothing for Spare-Part Demand Forecasting: A Case Study at PT XYZ
Purpose: This study aims to compare the forecasting performance of the Autoregressive Integrated Moving Average (ARIMA) and Single Exponential Smoothing (SES) methods using Battery DYC spare-part demand data from PT XYZ as an industrial case study to identify the most appropriate forecasting approach for inventory planning. Methods: Monthly sales data from January 2020 to May 2026 were analyzed using a quantitative time-series approach. The dataset was divided into training data (65 observations) and testing data (12 observations). The ARIMA model was developed according to the Box-Jenkins procedure, while the SES model used the optimal smoothing parameter estimated by SPSS. The forecast performance was assessed in terms of Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE). Result/Findings: The results show that ARIMA (0,0,1) model has RMSE of 3.8370, MAE of 2.8925 and MAPE of 53.21%. SES (alpha = 0.052) has RMSE of 3.8336, MAE of 3.0000 and MAPE of 55.19%. SES is slightly better than ARIMA in terms of RMSE but ARIMA is better in MAE and MAPE, thus ARIMA is more robust overall. Novelty/Originality/Value: The study provides empirical evidence from an industrial case study that forecasting performance is dependent on demand characteristics, rather than on the universal superiority of one method over another. The findings offer practical guidance for spare-part inventory planning and provide a basis for future comparisons with specialized intermittent demand forecasting methods.
Aspect-Level User Sentiment Patterns in Shopee Mobile App Reviews Using TF-IDF and Logistic Regression
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.
Sugarcane Plant Disease Classification Based on Leaf Image Using ConvNeXt V2 Deep Learning Model
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.
Web Based Smartphone Recommendation System Using C4.5 Decision Tree Algorithm for User Need Classification
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.
Robust Deep Learning Approach for Automatic Age and Gender Recognition Based on Voice
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.
An Empirical Benchmarking Framework for IoT Traffic Anomaly Detection Using Elastic Stack SIEM
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.
A Hybrid Rule-Based and Multinomial Naïve Bayes System for Sentiment and Intent Classification of Indonesian Public Reports with Sarcasm Detection
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.
User Satisfaction with E-BRAY Digital Library: An Integrated EUCS and TAM Analysis Using PLS-SEM
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 Comparative Analysis of Decision Tree, Logistic Regression, and Support Vector Machine Algorithms in Sentiment Analysis of Threads App Reviews
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 from the 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.
Implementation of a 3D Animation Production Pipeline for a Psychological Well-Being Trailer : Visualizing Self-Acceptance Using Autodesk Maya
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.
An Advanced Deep Learning Approach for Automatic Disease Recognition and Classification in paddy leaf disease detection
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
Deep Learning, Convolutional Neural Network, Long Short-Term Memory, Attention mechanism, Paddy leaf disease detection
The Effectiveness of Dropout Layers in LSTM Architecture for Reducing Overfitting in Sony Stock Prediction
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.
Development of a Web-Based Teacher Attendance System in Government Agencies: A Case Study at the Ministry of Religious Affairs
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.
Sistem Absensi Digital Aplikasi Web Pengelolaan Kepegawaian Validasi Waktu Kementerian Agama Django
Copyright (c) 2025 Intechno Journal : Information Technology Journal
Integrating CVSS, OWASP, and APPI for a Comprehensive Risk Analysis of SQL Injection Vulnerabilities in E-Commerce
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.
APPI (Act on the Protection of Personal Information)
Application of the SAW Method in a Decision Support System to Determine the Best Public Transportation Mode for UPN “Veteran” Jakarta Students
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.
Decision Support System for Selecting the Best University for Vocational School Graduates Majoring in Multimedia Using the TOPSIS Method
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.
Comparative Analysis of Live Action Film Production Management Using Critical Path Method (CPM) Versus Conventional Production Processes
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.
Prototype for Implementing Data Exchange with The FHIR-HL7 Standard in The Personal Health Record Application
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.
Copyright (c) 2024 Intechno Journal : Information Technology Journal
EEG Emotion Recognition using Deep Neural Network (DNN) in Virtual Reality Environments
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.
Reverse Engineering GitHub CoPilot: Creating an OpenAI-Compatible Endpoint for Enhanced Developer Integration
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.
Forecasting A Major Banking Corporation Stock Prices Using LSTM Neural Networks
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.
Quality Evaluation of Ticketing Management System Using ISO/IEC25010:2023 Standards and AHP Method
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.
Testing of Usability and Correctness Aspects in Marketing and Ordering Information Systems
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.
EEG Signal Classification Using Low Temporal Information in Virtual Reality Environments
Purpose: This research systematically compares the performance of K-Nearest Neighbors (KNN), Random Forest (RF), and Support Vector Machine (SVM) in recognizing emotional and cognitive states from EEG data in a virtual reality (VR) environment. It aims to identify the model with the highest accuracy for each participant. Methods: EEG data were collected from four channels (TP9, AF7, AF8, TP10) with a data range of 0.0 - 1682.815 µV and a sampling rate of 2 Hz. The sampling rate is shallow compared to the standard EEG datasets. Features extracted included statistical measures (mean, standard deviation, skewness, kurtosis) and Hjorth parameters (activity, mobility, complexity), classifier (SVM, RF, KNN). Each classifier’s performance was evaluated using accuracy, indicating the proportion of correctly classified instances. Result: RF achieved the highest average accuracy but showed more significant variability. SVM demonstrated a high median accuracy with consistent performance, as indicated by a narrow interquartile range (IQR) and few outliers. KNN exhibited the lowest median accuracy and highest variability, suggesting sensitivity to data characteristics and parameters. These findings highlight RF’s potential for consistent performance with careful tuning and SVM’s reliability. Novelty: The research’s novelty lies in its personalized performance analysis, evaluating each model’s accuracy individually for participants. This tailored approach reveals the best-performing model for each person, emphasizing customized machine-learning applications in VR-EEG systems. The study’s detailed, participant-specific evaluation enhances emotion and cognitive state recognition precision, advancing individualized VR therapeutic interventions and cognitive research methodologies.
Copyright (c) 2024 Intechno Journal (Information Technology Journal)