The Scientific Journal of Informatics (SJI) has migrated to a new platform starting in 2024 to enhance security and protect against potential threats, including journal hacking and other malicious activities. To submit your manuscript, please visit our new journal website at: https://journal.unnes.ac.id/journals/sji Click here for the official migration statement. Scientific Journal of Informatics (p-ISSN 2407-7658 | e-ISSN 2460-0040) published by the Department of Computer Science, Universitas Negeri Semarang, a scientific journal of Information Systems and Information Technology which includes scholarly writings on pure research and applied research in the field of information systems and information technology as well as a review-general review of the development of the theory, methods, and related applied sciences. The SJI publishes 4 issues in a calendar year (February, May, August, November). One of the key advantages of this journal is its fast response time. For high-quality articles, the estimated processing times are as follows: Initial decision for review: 1–10 days after submission Decision after peer review: 6–8 weeks after submission Online publication: 1–4 weeks after acceptance If you have any inquiries, feel free to contact us through the contact information provided on this website. Indexing & Abstracting: DOAJ, SINTA, ROAD, GARUDA, DIMENSIONS
Smart Monitoring Implementation for Hosting Services Using Zabbix and Autoencoder Models
Purpose: This study answers the increasing need for intelligent server monitoring systems capable of anomaly detection for computing infrastructure performance and reliability. Conventional static threshold-based monitoring systems, such as Zabbix, have limitations in identifying complex and dynamic abnormal patterns. To overcome this limitation, in this research study, the integration of an Autoencoder-based deep learning model with the Zabbix monitoring system is presented. Methods: An experimental approach was followed, wherein important server performance metrics like CPU utilization, memory utilization, and network activity were collected every 15 minutes for 40 days. The Autoencoder was trained on normal operational data to identify frequent behavioral patterns and produce reconstruction errors as anomaly scores. Deviations were alerted as anomalies when these scores exceeded an adaptive threshold. The system was deployed in a virtualized environment, with real-time notifications sent via Telegram for rapid incident response. Results: Experimental results show that the system can effectively detect different anomaly scores. Deviations were alerted as anomalies when these scores exceeded an adaptive threshold. The system was placed in a virtualized production setup, publishing real-time notifications via Telegram for prompt incident different anomaly types with high performance anomaly detection with MAE = 0.0084 and RMSE = 0.0198. Novelty: The primary contribution of this study is the development of hybrid real-time anomaly detection framework that integrates deep learning within open-source monitoring tools to be more efficient, decrease the dependence on manual configuration, and enable computing infrastructures to be more reliable and efficient.
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Estimation Model of Nutritional Content Based on Broiler Feed Images Using Convolutional Neural Network and Random Forest
Purpose: This research aims to develop an intelligent model to estimate the nutritional content of broiler chicken feed based on feed images to assist farmers in selecting the best broiler feed and quickly verifying its quality to meet requirements. Methods: The methodology of this research includes literature study, data collection, data preprocessing, image classification, model evaluation, integration of CNN and random forest models, and estimation of nutritional content based on feed images. We collected 99 samples of broiler chicken feed from online stores in various regions of Indonesia, particularly Java. Next, we took pictures with a smartphone and analyzed the nutritional content using near-infrared spectroscopy. Preprocess the data by enhancing the dataset (color space and data augmentation). We use Convolutional Neural Network (CNN) for the classification of broiler feed images. The performance of the CNN model is evaluated using a confusion matrix. We integrate CNN and Random Forest Regressor (RFR) to estimate nutritional content from the features of broiler feed images. Result: The performance evaluation shows that the CNN (VGG-16) model is 0.9744% accurate and the RFR model has the highest R2 value of 0.8018. The benefits of this research include faster, more efficient, and automated feed quality measurement compared to traditional methods; maintaining feed quality standards; and avoiding health risks for livestock. Novelty: This research introduces an intelligent model to estimates the nutritional content of broiler feed images by integrating a CNN model with an RFR.
Performance of SARIMA, LSTM, GRU and Ensemble Methods for Forecasting Nickel Prices
Purpose: There are several forecasting methods, including SARIMA, LSTM, and GRU, which are often claimed to exhibit strong performance in capturing patterns in time series data. However, few studies have conducted direct comparisons among these methods. Therefore, it is necessary to conduct a performance evaluation using empirical data, particularly nickel prices data. This study also aims to improve forecasting performance by combining prediction outputs from deep learning-based models. Methods: This study utilized data on monthly global nickel prices from January 1990 to May 2025. The models developed include SARIMA, LSTM, GRU, and two ensemble approaches: Weighted Averaging (WA) and Bayesian Model Averaging (BMA). The performance was evaluated using MAPE, RMSE, and MAE. Result: The BMA Ensemble approach shows the best performance in forecasting nickel prices, with a MAPE value of 5.39%, RMSE of 1897.84, and MAE of 1133.96. Prediction validation produces MAPE values below 10%, which indicates that the forecasting results are accurate. The ensemble BMA approach is able to produce more accurate and stable predictions compared to other models. Novelty: This study offers a novel approach combining LSTM and GRU through ensemble methods to forecast global nickel prices using monthly historical data from 1990 to 2025. In contrast to previous studies that relied on single models, the proposed method with the ensemble BMA approach demonstrates improved forecasting accuracy and stability.
Impact of Feature Engineering on XGBoost Model for Forecasting Cayenne Pepper Prices
Purpose: Cayenne pepper represents one of Indonesia’s key horticultural commodities, widely utilized in both household culinary practices and the food processing industry. Nevertheless, its market price is subject to considerable volatility, driven by factors such as weather variability, limited supply, production costs, and inefficiencies in distribution systems. This price instability generates uncertainty that adversely impacts farmers, traders, and consumers. Consequently, the development of a reliable price forecasting model is crucial to facilitate price stabilization and enable data-driven decision-making across the supply chain. This study aims to investigate the extent to which feature engineering techniques can enhance the predictive performance of the Extreme Gradient Boosting (XGBoost) algorithm in forecasting cayenne pepper prices. Through the integration of lag features, moving averages, and seasonal indicators, the proposed model is expected to more effectively capture market dynamics and provide a robust analytical tool for relevant stakeholders. Methods: The forecasting model was constructed using the XGBoost algorithm in combination with various feature engineering methods. The dataset consists of daily price records obtained from Bank Indonesia’s PIHPS system and meteorological variables sourced from BMKG, encompassing the period between 2021 and 2024. The engineered features include lag variables identified through Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF) analyses, Simple Moving Averages (SMA), seasonal indicators, and holiday-related variables designed to capture recurring patterns and event-driven price fluctuations. To enhance predictive performance, hyperparameter tuning was conducted using a grid search optimization approach. Result: The optimal model demonstrated substantial performance improvements under the following hyperparameter configuration: alpha = 0, gamma = 0.3, lambda = 1, learning_rate = 0.05, max_depth = 3, min_child_weight = 3, n_estimators = 200, and subsample = 0.6. The application of feature engineering markedly enhanced the model’s predictive capability, increasing the R² value by 99.10% while reducing the MAE, RMSE, and MAPE by 72.63%, 71.31%, and 72.04%, respectively. These outcomes signify a notable reduction in forecasting errors and demonstrate the model’s improved accuracy. Novelty: This study integrates multi-level price data with weather and holiday-related features, employing the ACF and the PACF analyses to determine optimal lag values (techniques commonly utilized in statistical modeling). This integration enhances both the accuracy and interpretability of the XGBoost algorithm, thereby providing a practical and effective tool for agricultural price forecasting and market planning.
A Hybrid Approach of Aspect-Based Sentiment Analysis and Knowledge Extraction for Evaluating Security Perceptions in Digital Payment Applications
Purpose: The rapid expansion of digital wallets in Indonesia has heightened concerns regarding user security and trust. This study evaluates user sentiment toward the security features of the DANA digital payment application using Aspect Sentiment Classification (ASC), a subtask of Aspect-Based Sentiment Analysis (ABSA). It aims to compare multiple classification models and generate structured, machine-readable sentiment outputs to support knowledge extraction and system integration. Methods: A total of 4,846 security-related reviews were collected from the Google Play Store using keyword-based filtering, supplemented by 3,000 unfiltered reviews for robustness evaluation. Sentiment labeling was performed using a hybrid rule-based and manual annotation approach. From 300 proportionally sampled reviews (150 positive and 150 negative), the validation achieved 0.8504 accuracy and a Cohen’s κ of 0.951, indicating near-perfect agreement. Five models—Support Vector Machine (SVM), Random Forest (RF), Convolutional Neural Network (CNN), Bidirectional Long Short-Term Memory (BiLSTM), and IndoBERT—were evaluated using 5-fold stratified cross-validation with random oversampling to address class imbalance. Results: IndoBERT achieved the highest performance with 98% accuracy, an F1-score of 0.974, and an AUC-ROC of 0.996, followed by CNN and BiLSTM. Robustness testing across temporal (DANA June–October) and cross-domain (GoPay) datasets confirmed IndoBERT’s strong generalization with minimal F1-score variation. Novelty: Unlike previous ABSA studies that addressed multiple aspects, this research focuses exclusively on the security aspect, providing fine-grained insights into user trust. The integration of XML-based structured output enhances interpretability and interoperability in digital financial sentiment analysis, contributing to the development of more secure and transparent fintech ecosystems.
Performance Analysis of Machine Learning Models using RFE Feature Selection and Bayesian Optimization in Imbalanced Data Classification with Shap-Based Explanations
Purpose: This research aims to evaluates the performance of Random Forest (RF) and Light Gradient Boosting Machine (LightGBM) models integrated with Recursive Feature Elimination (RFE) for feature selection, Bayesian Optimization (BO) for hyperparameter tuning, and three imbalanced data handling techniques Random Undersampling (RUS), Random Oversampling (ROS), and SMOTENC. Identifying key determinants of household food insecurity in Papua using SHAP for transparent feature interpretation. Methods: The research used 2022 SUSENAS data from Papua Province. Exploring data composition and variable characteristics, and aggregating individual data into household data. Data were split using random sampling (80% training, 20% testing). Eighteen experimental scenarios were created by combining feature selection or no feature selection, three imbalance handling methods, and default or hyperparameter tuning. RF and LightGBM were evaluated over 50 iterations using accuracy, sensitivity, specificity, and G-Mean, with SHAP applied to the best-performing models for interpretability. Result: LightGBM achieved the highest accuracy and stability, particularly when combined with SMOTENC and RFE+BO. RF showed better performance in maintaining G-Mean when paired with RUS, with the highest G-Mean (0.756) obtained by RF + BO + RUS. Three-way ANOVA proved that model type, imbalance handling, feature selection, and their interaction significantly affected the G-Mean value. SHAP analysis shows that health, financial, and educational limitations can increase the risk of food insecurity. Novelty: This research offers a new integration between feature selection, hyperparameter tuning, and imbalanced data handling within an interpretable machine learning framework, thereby providing a robust solution for food vulnerability classification on imbalanced datasets.
UTAUT-2, HOT-Fit, and PLS-SEM for User Acceptance and Success of the Face Recognition Feature in CAT BKN Application
Purpose: Face recognition feature was implemented in the National Civil Service Agency's Computer-Assisted Test in 2021. There has been no evaluation of the system's acceptance and success. This study aims to measure user acceptance and evaluate the feature's success using the R Shiny application. Methods: The study utilized 337 respondents from a Google Form-based questionnaire distributed throughout the Regional Office VII of the National Civil Service Agency in Palembang. The hybrid model used was UTAUT-2 and HOT-Fit, with PLS-SEM statistical analysis. Acceptance analysis and feature evaluation were conducted using the developed R Shiny Dashboard. Results: The findings indicated that 15 of the 26 hypotheses were accepted. Behavioral intention and use behavior significantly influence hedonic motivation and habit. User behavior significantly influences user satisfaction, system quality, service quality, information quality, system use, and organizational structure and environment. As users become more familiar with the technology, their experience improves, and system utilization becomes more effective. Novelty: The integration of UTAUT-2 and HOT-Fit models within an R Shiny Dashboard was applied to analyze user acceptance and evaluate the face recognition feature in Computer Computer-Assisted Test selection process. The findings provide recommendations for feature development and improving participant face recognition performance. Moreover, the R Shiny Dashboard can be adapted for user experience analysis and system evaluation in other contexts.
Elementary School Accreditation Assessment Using Fuzzy Tsukamoto and SMARTER Method
Purpose: The primary objective of this study is to develop and validate an Elementary School Accreditation Evaluation Model that is both measurable and fair. The proposed model integrates the Fuzzy Tsukamoto method to calculate and consistently generate the final score of each alternative, and the SMARTER method to produce a prioritized ranking that serves as a practical guide for schools in their efforts to improve and strengthen quality. Methods: This study integrates the Fuzzy Tsukamoto method to process numerical data through a rule-based inference mechanism. Simultaneously, the SMARTER method is employed to systematically assign weights to each criterion and sub-criterion using the Rank Order Centroid (ROC) approach. The evaluation is carried out on 16 alternatives based on four main criteria. The research data are derived from the IASP 2020 instrument issued by BAN-S/M, which serves as the official accreditation standard for schools and madrasahs in Indonesia. Result: The developed structured assessment model proved effective. Through ROC weighting, Criterion K1 was identified as the main determining factor (0.611). System validation using Fuzzy Logic showed a high level of consistency (87.5% agreement) with the manual assessor's decisions, confirming the model's accuracy in replicating assessments based on data triangulation. The SMARTER ranking provides targeted recommendations, placing Alternatives A13, A2, A7, and A8 as standards to be maintained, while pointing to A3 as the priority for immediate improvement. Novelty: This study offers a novel approach by integrating the Fuzzy Tsukamoto and SMARTER methods within the context of primary school accreditation a combination that has been rarely explored in previous research. The proposed model not only generates evaluation scores but also produces a ranking system that can serve as a reference for school evaluation.
SOCA-YOLO: Smart Optic with Coordinate Attention Model for Vision System-Based Eye Disease Detection
Purpose: The purpose of this research is to identify eye diseases using a modified YOLOv9. In particular, we modified YOLOv9 with the addition of Coordinate Attention (CA) for better eye disease detection performance, the use of Programmable Gradient Information (PGI), and Generalized Efficient Layer Aggregation Network (GELAN) for higher computational efficiency and accuracy. Methods: This study consists of several stages, including the acquisition of eye disease data obtained from the Roboflow website, data annotation, image augmentation, modeling using a modified YOLOv9, and model evaluation. Result: SOCA-YOLO model achieved an F1 score of 87,2% and mAP50 of 92,9%, outperforming YOLOv9-e by 1,7%. It also surpassed YOLOv6-L6 by 11,1%, YOLOv10-X by 0,8% in mAP50, and YOLOv8-X by 1,1% in recall, showcasing its superior detection accuracy and recall performance. Novelty: This research contributes by introducing the SOCA-YOLO model in improving the performance of the YOLOv9 by modifying the addition of Coordinate Attention (CA) for better eye disease detection performance, alongside Programmable Gradient Information (PGI) and Generalized Efficient Layer Aggregation Network (GELAN) for better computational efficiency and accuracy.
MoLLe: A Hybrid Model for Classifying Diseases in Chili Plants Using Leaf Images
Purpose: Leaf diseases are often early indicators of problems in plants. More detailed image information with feature extraction on leaves can improve accuracy. However, MobileNetV2 tends to be less than optimal in capturing the fine texture characteristics of leaves. This research aims to propose a classification model for diseases in chili plants based on leaf images using MobileNetV2 with Local Binary Pattern (LBP), with three fully connected layers (220-120-60 neurons) using the ReLU activation function, referred to as MoLLe. Methods: This research consists of six stages. It begins with a dataset collected from chili farms comprising 900 images, which are then preprocessed into 3,600 images. Next, LBP feature extraction is performed. After that, a comparison between the benchmark architecture and the proposed architecture is conducted. A softmax layer is used to perform three-class classification. The MoLLe model was tested with the MobileNetV2 and MobileNetV2+LBP benchmark architectures and evaluated using a confusion matrix. Result: Based on the evaluation conducted, using batch size 32, learning rate 0.001, and 20 epochs, the MoLLe model experienced early stopping at epoch 11, achieving an accuracy of 0.97 training data, 0.84 validation data, and 0.91 testing data. The evaluation results showed consistent precision, recall, and F1-score values of 0.91, indicating the model's balanced ability to identify the three disease classes. Novelty: The novelty of this research lies in the integration of MobileNetV2 and LBP with modifications to three fully connected layers, which not only reduces the number of training parameters but also accelerates the detection process. This research makes an essential contribution to the development of more efficient and effective plant disease detection systems, with experimental results showing that MoLLe outperforms the benchmark architecture.
The Digital Escape: Examining the Impact of Role Stressors on Cyberloafing
Objective: Cyberloafing Behavior (CB) has emerged as a major concern in the workplace. Apart from causing decreased performance, cyberloafing also carries the risk of hacking or data breaches. This study seeks to explore how role stressors influence Cyberloafing Behavior among healthcare professionals. Methods: This study employed a quantitative research design, utilizing accidental sampling to collect data from 109 respondents at Sinar Kasih Hospital, Purwokerto. Data analysis was conducted using Structural Equation Modeling (SEM) with the assistance of SmartPLS software. Result: The results of this study reveal that role stressors, which include Role Ambiguity (RA), Role Conflict (RC), and Role Overload (RO), have a statistically significant and positive impact on Cyberloafing Behavior (CB) among healthcare employees. High stress from unclear roles, conflicting duties, and heavy workloads increases the likelihood of non-work internet use. Addressing these stressors can help reduce counterproductive behavior and improve focus in healthcare environments. Novelty: This study recommends that healthcare organizations provide clear work guidelines to prevent role ambiguity, monitor workloads to reduce stress, and address role conflicts among employees. These strategies can help to reduce role stressor factors to prevent cyberloafing behavior among employees in order to avoid the risks arising from such behavior. The novelty of this study lies in its application of varied research subjects and a distinct methodological approach, setting it apart from previous studies. By focusing on the healthcare sector and employing SEM analysis, it offers new insights into the relationship between role stressors and Cyberloafing Behavior.
Smart Rupiah Recognition: A Mobile Machine Learning Approach for Visually Impaired Users
Purpose: Despite advances in assistive technology, low-connectivity areas lack reliable solutions for visually impaired individuals, prompting this study to enhance financial autonomy in cash-based economies. This research addresses high fraud risks and the limitations of online tools like Be My Eyes, which fail in areas with only 40% internet access, by developing a 3MB MobileNetV2 model for offline Rupiah denomination recognition on low-end Android devices. Methods: A MobileNetV2-based Convolutional Neural Network, optimized to 3MB via TensorFlow Lite quantization, was trained on 10,855 augmented images (rotation ±30°, flipping, Gaussian noise, σ=0.1). The Kotlin-based application integrates CameraX for 720p video and Bahasa Indonesia text-to-speech, with a “no object” class. The model was tested on 4–8GB RAM devices, validated through usability evaluations with diverse stakeholders. Result: The model achieves 90% accuracy (F1-score 0.90) at 1000 lux, 85% at <50 lux, 80% at >60° angles, and 88% for “no object,” with 10ms latency. Self-supervised learning (SimCLR) on 2,000 worn notes improves accuracy by 3% (p < 0.05). Usability evaluations yield 95% session success, with TTS and UI Likert scores of 4.2 and 4.0.. Novelty: The 3MB MobileNetV2 model, with 10ms latency and 15% false positive reduction, outperforms YOLOv5 (500MB, 50ms), Vision Transformer (1GB, 200ms), and YOLOv8 (200MB, 30ms). This model shows potential for cross-currency detection throught preliminary exploration (e.g., USD and euro), which may advance edge AI and financial inclusion in developing nations.
Comparative Analysis: Accuracy of Certainty Factor and Dempster Shafer Methods in Expert Systems for Tropical Disease Diagnosis
Purpose: This study aims to diagnose Neglected Tropical Diseases early by applying the concept of an expert system as a tool that works by mimicking the thought patterns of an expert (doctor). The methods applied in this expert system are Certainty Factor and Dempster Shafer. Both methods work by combining a number of pieces of evidence (symptoms) to produce a confidence value for a disease. Methods: The study began with discussions and interviews with experts to collect information and data about Neglected Tropical Diseases. Conducting a literature review study to enrich knowledge about Neglected Tropical Diseases. Two main inference methods are used to detect diseases based on patient symptoms. The Certainty Factor method uses expert value weighting parameters and patient input value weighting as a basis for knowledge. The Dempster Shafer method only uses expert value weighting in analyzing the probability of symptoms to produce a level of diagnostic accuracy. Result: The Certainty Factor method works by integrating patient and expert weight values into its calculations. Meanwhile, the Dempster Shafer method considers expert weight values without involving patient weight values. Expert system searches using the Forward Chaining inference engine show that the Certainty Factor method has an accuracy probability value of up to 90%. Meanwhile, the Dempster Shafer method has an accuracy value of 70%. Novelty: The results of the study show that expert systems can be applied in the health sector, especially in diagnosing Neglected Tropical Diseases. Of the two methods used, the Certainty Factor method shows a high accuracy value, so it can help detect Neglected Tropical Diseases early and provide treatment solutions to improve health.
Development of Employee Management Information System UI/UX Using a User Centered Design Approach
Purpose: This study aims to develop a user interface and user experience (UI/UX) prototype for an Employee Management Information System (EMIS) at PT. Galsoft. The research focuses on addressing the manual administrative processes that are still widely used in the company's daily operations. The primary objective is to produce an interface design that aligns with user needs and improves the efficiency of work processes. Methods: This research adopts a User Centered Design (UCD) approach, consisting of four main stages: understanding the context of use, specifying user requirements, designing solutions, and evaluating the design. The prototype developed focuses solely on the UI/UX aspects without involving full system implementation. Usability evaluation was conducted using the System Usability Scale (SUS), involving 38 respondents from various divisions within the company. Result: The evaluation results show that the developed UI/UX prototype achieved an average SUS score of 89.4. This score indicates that the design has a high level of usability, is easy for users to operate, and supports the administrative workflows required by the organization. These findings demonstrate that the UCD approach effectively contributed to creating a design that is both functional and responsive to user needs. Novelty: This study contributes to the field of administrative information system prototyping in workplace environments that have yet to adopt digital solutions. The novelty of this research lies in the comprehensive application of the UCD approach, combined with SUS based usability evaluation, to produce a relevant and functional design that is ready to be further developed into a fully implemented system.
Comparative Analysis of High School Student and AI-Generated Essays Using IndoBERT and Linguistic Features
Purpose: The purpose of this study is to address the growing challenge of distinguishing between essays written by humans and essays generated by AI, particularly in the context of high school education in Indonesia. This study aims to analyze the semantic and linguistic differences between student-written and ChatGPT-generated in Indonesian language. Methods: The study employs an IndoBERT-based semantic model trained with triplet loss to generate paragraph-level embeddings, allowing the measurement of semantic similarity within and between essay classes. Additionally, linguistic features such as lexical diversity, word count, modal usage, and stopword ratio were extracted to capture stylistic and structural differences. These three key features are combined and used as input to a neural network classifier. Result: The IndoBERT-based semantic model successfully grouped student-written and ChatGPT-generated essays into distinct clusters. The similarity scores within student essays ranged from 0.7 to 0.9, while the similarity between classes was mostly negative with a few outliers, reflecting the cosine similarity metric used in this study, which has a range of -1 to 1. The classification model showed a 90.55% accuracy and an AUC of 0.9999 when evaluated on the independent test set defined in the Data Preparation stage. These results suggest that student-written and ChatGPT-generated essays form distinct semantic clusters. Students’ essays show more linguistic diversity, while ChatGPT essays show consistency in the coherence and formality aspects of the essays. Novelty: This study provides empirical insights of semantic similarities and linguistic features to differentiate between human and AI-generated essays in the Indonesian language. It contributes to supporting academic integrity efforts and highlighting the need for further research across different writing models and contexts.
Ambidextrous Blockchain Governance Approach for Advancing SmartCo's Digital Transformation Using COBIT 2019 Traditional and DevOps
Purpose: This study aims to design an ambidextrous governance model based on COBIT 2019 Traditional and DevOps Focus Areas, focusing on DSS05 (Managed Security Services), to enhance SmartCo’s security readiness for blockchain adoption. Methods: Adopting the Design Science Research (DSR) and case study methodology, data were gathered through semi-structured interviews with six key stakeholders at SmartCo and triangulated with internal documents until data saturation was achieved. Governance and Management Objectives (GMOs) were prioritized using organizational design factors, the relevance of COBIT 2019 DevOps guidance, national regulations (ICT Minister No.5/2021 and SOE Ministerial No. PER-2/MBU/03/2023), and insights from prior research. Results: The study presents an ambidextrous design of the seven governance components for DSS05, addressing people, process, and technology dimensions. Recommendations include formalizing dedicated security roles, standardizing procedures, issuing new policies, and adopting a Security Information and Event Management (SIEM) system. Implementation is projected to improve the DSS05 process capability from 3.29 to 3.86. Novelty: This research contributes to the IT governance body of knowledge by proposing a practical pre-implementation governance model for blockchain security in technology-focused enterprises. Its originality lies in the application of the ambidextrous COBIT 2019 framework to the DSS05 objective and the use of a comprehensive multi-criteria prioritization method to guide governance of emerging technologies.
Integrating UX Five Elements and Design Thinking to Design a Learning Management System
Purpose: This study aims to enhance the user experience of Learning Management Systems (LMS) by integrating two established design frameworks: the UX Five Elements and Design Thinking. The research addresses the need for a more structured yet human-centered design process to improve the usability and engagement of LMS platforms in higher education. Methods: The research adopts a design and development approach by combining the UX Five Elements, which offer a systematic structure across five user experience layers, with Design Thinking, which emphasizes empathy and iterative user involvement. This integration forms an Extended Model Design (EMD) used to guide the development of a new LMS interface. The final system was evaluated using usability testing involving students as target users. Result: Evaluation of the LMS prototype using the User Experience Questionnaire (UEQ) showed positive perceptions on all six dimensions, with the highest scores on the Efficiency (1.644) and Attractiveness (1.634) aspects, reflecting a practical and attractive system design. Although the Novelty (1.203) aspect had the lowest score, its value was still above the positive threshold, indicating that the system was functionally good but could still be improved in terms of innovation to strengthen user engagement. Novelty: This study introduces a novel design framework by integrating UX Five Elements with Design Thinking in the context of LMS development. Extended Model Design (EMD) offers a replicable model that balances structure and user empathy, contributing to user-centered e-learning system design.
Performance Evaluation of Cheng & Church (CC) and Spectral Biclustering Algorithms under Collinearity and Overlap Conditions
Purpose: This study aims to address methodological challenges in evaluating biclustering algorithms under simultaneous collinearity and overlap, which often co-occur in real world multivariate data but are rarely analyzed simultaneously. This research highlights the importance of understanding how these structural challenges affect local pattern detection in data mining applications. Methods: A simulation study was conducted using synthetic matrices embedded with two constant biclusters under 15 combinations of collinearity levels (ρ = 0.3,0.6,0.9) and overlap degrees (none, small, large). Each scenario was replicated 100 times. Performance was assessed using the Liu and Wang Index (ILW), while a three-way ANOVA tested the effects of algorithm type, collinearity, and overlap. Result: Spectral Biclustering maintained stable ILW scores despite increasing collinearity, while CC performed better in low-overlap scenarios but was more sensitive to collinearity. Under high collinearity and large overlap, both algorithms experienced notable degradation. The ANOVA confirmed all main effects and interactions were significant (p < 0.001). Novelty: This study contributes empirical evidence regarding the influence of interacting structural characteristics on biclustering performance. The results deliver practical insights for selecting suitable algorithms and emphasize the potential advantages of hybrid approaches that integrate the stability of spectral methods with the adaptability of residual-based techniques.
Implementation of IndoBERT for Sentiment Analysis of the Constitutional Court's Decision Regarding the Minimum Age of Vice Presidential Candidates
Purpose: This study aims to analyze the effectiveness of the IndoBERT model for sentiment analysis of Indonesian anguage YouTube comments related to the legal Court’s ruling on the minimum age of vice presidential candidates for 2024. While previous research applied conventional machine learning methods, this study addresses the challenge of understanding nuanced public opinion using a language-specific transformer model. Methods: A dataset of 23,796 YouTube comments was collected using the YouTube Data API in January 2025. The comments underwent extensive preprocessing including normalization, case folding, text cleansing, symbol removal, stopword elimination, and stemming. Sentiment labels (positive, negative, neutral) were assigned through a lexicon based approach. Three models IndoBERT, BERT, Support Vector Machine (SVM), and Random Forest were trained and tested using an 80% and 20% split. Model result was evaluated with accuracy, precision, recall, and F1-score metrics. Result: IndoBERT achieved the maximum result with 95% accuracy, outperforming BERT 92%, SVM 88%, and Random Forest 85%. This confirms IndoBERT’s superior ability to capture contextual nuances in Indonesian sentiment analysis compared to other models. Novelty: This research demonstrates the advantage of transformer based models, particularly IndoBERT, in analyzing complex Indonesian social media texts. The findings support the use of IndoBERT for automated sentiment monitoring to inform government and media responses. Future work could extend to broader discourse analysis across diverse public sectors.
Deep Learning-Based Eye Disorder Classification: A K-Fold Evaluation of EfficientNetB and VGG16 Models
Purpose: The study evaluates EfficientNetB3 and VGG16 deep learning architectures for image classification, focusing on stability, accuracy, and interpretability. It uses Gradient-weighted Class Activation Mapping to improve transparency and robustness. The research aims to create reliable AI-based diagnostic tools. Methods: The study used a dataset of 4,217 color retinal fundus images divided into four classes: cataract, diabetic retinopathy, glaucoma, and normal. The dataset was divided into 70% for training, 10% for validation, and 20% for testing. The researchers used a transfer learning approach with EfficientNetB3 and VGG16 models, pretrained on ImageNet. Real-time augmentation was applied to prevent overfitting and improve generalization. The models were compiled with the Adam optimizer and trained with categorical cross-entropy loss. Early stopping was implemented to allocate computational resources efficiently and reduce overfitting. A learning rate scheduler (ReduceLROnPlateau) was added to adjust the learning rate if no significant improvement was made concerning validation loss. EfficientNetB3 was more efficient in model size, possessing only 12 million parameters compared to VGG16's 138 million, making it suitable for resource-constrained mobile or embedded systems. The final evaluation was done on the held-out test set. Result: The EfficientNetB3 architecture outperforms VGG16 in classification accuracy and loss value stability, with an average accuracy of 93%. It also exhibits better transparency and predicted accuracy, making it a reliable model for medical image categorization. Novelty: This work introduces a novel framework integrating EfficientNetB3 architecture, stratified cross-valuation, L2 regularization, and Grad-CAM-based interpretability, focusing on openness and explainability in model evaluation.
Optimization of Random Forest Algorithm with SMOTE Method to Improve the Accuracy of Early Diabetes Prediction
Purpose: This research aims to examine the performance of the random forest algorithm in diabetes risk classification with data balancing using the Synthetic Minority Oversampling Technique (SMOTE) method to improve the representation of minority classes and increase the prediction accuracy value. Methods: The study used the Behavioral Risk Factor Surveillance System (BRFSS) dataset, obtained from Kaggle, which contains health-related survey data used to identify individuals at risk of diabetes. The Random Forest algorithm was applied to classify diabetes. To balance the data, the SMOTE method was used. The model’s performance was evaluated using 10-fold cross-validation by comparing result before and after SMOTE. Result: The results showed that the application of the SMOTE method improved the performance of the Random Forest classification model, especially in minority classes. Model performance in minority classes without SMOTE had poor evaluation metrics with precision of 49%, recall of 18%, and F1-score of 26%. After applying SMOTE, these values increased to precision of 96%, recall of 88%, and F1-score of 92%. Representing improvements of 47 percentage points in precision, 70 points in recall, and 66 points F1-score. The overall accuracy of the Random Forest model also increased from 86% to 92%, showing a 6 percentage point improvement. Novelty: This study use integrating the Random Forest algorithm with the SMOTE technique and validating the results using 10-fold cross-validation. The combination significantly improves minority class prediction performance in early diabetes detection, addressing the common limitations of previous studies in handling imbalanced datasets effectively.
Biclustering-Based Analysis to Identify Fruit Production Potential in Indonesia Using Plaid Model Algorithm
Purpose: The application of biclustering using the plaid model aims to simultaneously identify mapping or grouping patterns of provinces and fruit type in Indonesia. The performance evaluation of the plaid model algorithm is used to assess its capability to discover and generate optimal biclusters, thereby representing the relationship between regions and fruit types with similar production characteristics. Methods: The plaid model algorithm produces optimal biclusters by configuring parameter scenarios such as model selection, managing the number of layers, and determining threshold values for rows and columns. The Average Mean Square Residue (MSR) value and the number of biclusters that can provide the most relevant data are used to determine the optimal parameter selection. Result: The plaid model algorithm effectively grouped provinces and fruit varieties into multiple biclusters. The row-constant model was choosen based on the average MSR value of 2.0537, which formed five overlapping biclusters across provinces and fruit types. Several provinces, such as Central Java and West Java, demonstrated a high potential for rose apples, breadfruit, and salak. Other provinces showed comparatively moderate levels of production. Novelty: This study presents a novel way to apply the plaid model biclustering algorithm to data on fruit varieties in various Indonesian provinces. Rarely used in horticulture, this method offers an alternative perspective on structured commodity mapping, especially when identifying specific patterns between fruit varieties and geographic distribution.
The Empirical Best Linear Unbiased Prediction and The Emperical Best Predictor Unit-Level Approaches in Estimating Per Capita Expenditure at the Subdistrict Level
Purpose: This study aims to estimate and evaluate per capita expenditure at the subdistrict level in Garut Regency by employing unit-level Small Area Estimation (SAE) techniques, specifically utilizing the Empirical Best Linear Unbiased Predictor (EBLUP) and the Empirical Best Predictor (EBP) methods. Methods: The data used in this study are socio-economic data, specifically per capita household expenditure in Garut Regency. Socio-economic data generally skew positively rather than the normal distribution, so a method that can approximate or come close to the normal distribution is needed, for example, log-normal transformation. To improve the performance of EBLUP, which may lead to inefficient estimators because of violation of the assumption of normality, this study proposes the Empirical Best Predictor (EBP) method. It handles positively skewed data by applying log-normal transformation to sample data so that it more closely conforms to the desired distribution. Result: The EBP results are more stable than EBLUP since EBLUP is highly sensitive to outliers, and in cases where the normality assumption is violated, it produces a significant mean square error and inefficient estimators. Evaluating the estimates with both EBLUP and EBP shows Relative Root Mean Squared Error (RRMSE) values above 25%, especially in the subdistricts of Pamulihan, Sukaresmi, and Kersamanah. This is probably due to the household samples being taken in these three subdistricts being comparatively small compared to the other. Novelty: In this research, we use EBP to improve the performance of EBLUP, which produces inefficient estimators when the normality assumption is violated.
Freshwater Filling Optimization Based on Price Using XGBoost and Particle Swarm Optimization on Cargo Ship Voyage
Purpose: Efficient freshwater management is critical in cargo ship operations, yet current practices often involve fixed refilling strategies that ignore price differences across ports and fail to predict actual consumption accurately. These inefficiencies lead to unnecessary operational costs. To address this, the study introduces a combined approach using XGBoost for predict freshwater usage and Particle Swarm Optimization (PSO) to minimize refilling costs through optimal port selection. Methods: Freshwater demand was predicted using an XGBoost regression model trained on real operational data from 2024, which included historical voyage distances and freshwater consumption records from cargo ships. Based on these predictions, Particle Swarm Optimization (PSO) was applied to identify cost-efficient refilling locations along each ship’s route, minimizing total water procurement cost while satisfying operational constraints. The proposed framework was validated through simulated voyage scenarios to evaluate its impact on cost efficiency and planning effectiveness. Result: The integration of XGBoost and PSO effectively optimized freshwater refilling strategies, achieving a relative prediction error of 9.48% in freshwater consumption prediction and cost savings from 9 to 40% from across 3 ships sample through strategic port selection based on consumption patterns and price variability. Novelty: Unlike prior works focused on fuel or generic logistics optimization, aim of this study is to combine XGBoost and PSO for optimizing freshwater refilling on cargo ship voyages using actual operational data. The results demonstrate practical, scalable improvements in cost efficiency, making a novel contribution to maritime resource planning.
Optimization of Coronary Heart Disease Risk Prediction Using Extreme Learning Machine Algorithm (Case Study: Patients of Dr. Soeselo Hospital)
Purpose: Coronary heart disease (CHD) is the leading cause of death globally, with 17.8 million deaths reported by the WHO in 2021. Early detection remains a major challenge due to low public awareness and dependence on manual diagnostic procedures. These limitations necessitate the development of automated and accurate predictive models. This study aims to construct a CHD risk prediction model using the Extreme Learning Machine (ELM) algorithm. The research addresses a critical limitation in existing models, namely, poor performance on minority classes (CHD stages 2–4), caused by data imbalance. To overcome this, oversampling techniques such as Synthetic Minority Oversampling Technique (SMOTE) and Adaptive Synthetic Sampling (ADASYN) are applied. The objective is to improve classification performance, particularly in high-risk categories, and to enhance the model’s generalisation capability for real-world implementation. Methods: This research implements the Extreme Learning Machine (ELM) algorithm to achieve optimal prediction results. The data used in this study as the initial database of patients consists of gender, age, height, weight, whether they have diabetes or not, the number of cigarettes consumed daily, and blood pressure. The data will be the main component in building the heart disease prediction system. The prediction classes are: no heart disease, stage 1 heart disease, stage 2 heart disease, stage 3 heart disease, and stage 4 heart disease. The total number of dataset are 521 data points, with 70% of the training data amounting to 364 patients, and 30% of the test data amounting to 157 patients. The data collection process uses patient data from RSUD Dr. Soeselo, Tegal Regency, Central Java, for the years 2023 and 2024. Result: The research successfully developed and evaluated an Extreme Learning Machine (ELM) algorithm for Coronary Heart Disease (CHD) risk prediction using patient data from Dr. Soeselo Hospital. The model achieved an overall accuracy of 82% on the dataset of 157 patients, demonstrating a promising capability for automated risk assessment. Novelty: This predictive model can be utilised in the medical field to facilitate the early detection of heart disease or other risks. This model will soon be introduced in hospitals in the Tegal Regency and City area, Central Java.
Sensor Integration and ARIMA-Based Forecasting in WAQMS for Environmental Monitoring in Riau Province, Indonesia
Purpose: This study aims to develop an integrated solution for real-time environmental monitoring in Riau Province, Indonesia, where air and water quality are increasingly impacted by industrial, agricultural, and climatic factors. Existing monitoring systems are often limited by their lack of real-time capabilities and predictive analytics. Methods: To address this, we designed the Water and Air Quality Monitoring System (WAQMS), which integrates sensor-based data acquisition with the Autoregressive Integrated Moving Average (ARIMA) model for forecasting. Sensor units were deployed across three pilot locations—Kampar, Siak, and Pekanbaru—to continuously collect environmental data. The ARIMA model was applied to historical datasets to predict future trends in air and water quality, while a web-based dashboard was developed to visualize real-time data and forecasts. Result: Calibration results showed a system accuracy of 85%, surpassing the national threshold of 75% set by the Indonesian Ministry of Environment and Forestry. This validates the use of WAQMS for Air Pollution Standard Index (ISPU) classification. Novelty: The novelty of this study lies in the seamless integration of AQMS and WQMS within a unified predictive monitoring system, combined with a user-friendly interface for stakeholders. The results demonstrate the system's potential as a decision-support tool for local governments, offering timely insights and enabling more effective and sustainable environmental management.
ATM Network Infrastructure Migration Strategy from Bank to PT XYZ
Purpose: This study addresses PT XYZ’s technical challenges in optimizing the Automatic Teller Machine (ATM) network infrastructure, which caused service disruptions and customer complaints during the initial pilot migration. The research aims to solve the network availability problem by designing a reliable migration strategy, thereby improving service quality and strengthening partnerships with banks. Methods: A qualitative case study analysis was conducted, combining stakeholder interviews with technical evaluations. The Data Center WAN zone deployment followed the waterfall methodology for structured network and security appliance implementation, while remote ATM migrations used an incremental approach. Service Level Agreement (SLA) metrics were monitored to measure performance. Result: The solution improved the ATM network average SLA from 98.31% to 99.53%. Stakeholder feedback confirmed the effectiveness of the phased migration and enhanced operational reliability. This demonstrates the benefit of direct ATM WAN network re-engineering in eliminating legacy infrastructure complexity. Novelty: The study provides actionable benchmarks for ATM network implementation, including phased migration steps combined with high availability WAN topology. The implementation and migration method presents a replicable model for similar financial institutions in emerging markets.
Evaluation of Ridge Classifier and Logistic Regression for Customer Churn Prediction on Imbalanced Telecommunication Data
Purpose: Customer churn is a crucial issue for companies, especially those in the telecommunications sector, as it has a direct impact on revenue and new customer acquisition costs. The purpose of this research is to create a customer churn prediction model through performance comparison between the Logistic Regression algorithm and Ridge Classifier, considering the effect of data balancing. Methods: This study developed a churn classification model by comparing the Logistic Regression and Ridge Classifier algorithms in three scenarios: without data balancing, balancing using SMOTE, and balancing using GAN. The dataset used was Telco Customer Churn from Kaggle. Model evaluation was performed using a confusion matrix with accuracy, precision, recall, and F1-score metrics, with a primary focus on the accuracy metric. Result: The results show that data balancing using SMOTE and GAN does not improve model accuracy. The highest accuracy was achieved by the Ridge Classifier without data balancing, at 82.47%, followed by Logistic Regression at 82.25%. However, the recall and F1-score metrics improved when using SMOTE. The highest recall was achieved by Ridge Classifier at 75.34% and Logistic Regression at 75.07% in the SMOTE 50:50 scenario. The highest F1-score was also achieved by Ridge Classifier at 64.76% and Logistic Regression at 64.68% followed by the SMOTE 50:30 scenario. Meanwhile, the precision metric tends to decrease after data balancing. Novelty: The uniqueness of this study lies in the comparison of the performance of the Ridge Classifier and Logistic Regression in data balancing scenarios using SMOTE and GAN, which has not been widely discussed in previous studies. The main findings show that the highest accuracy is achieved when the Ridge Classifier model uses original data or without applying SMOTE or GAN data balancing. However, data balancing using SMOTE has been proven to significantly improve the recall and F1-score metrics.
Optimizing LSTM-CNN for Lightweight and Accurate DDoS Detection in SDN Environments
Purpose: This study optimizes the LSTM-CNN model to detect Distributed Denial of Service (DDoS) attacks in Software-Defined Networking (SDN)-based networks and improves accuracy, computational efficiency, and class imbalance handling. Methods: We developed an Improved LSTM-CNN by removing the Conv1D layer, reducing LSTM units to 64, and using 21 features with a 5-timestep approach. The InSDN dataset (50,000 samples) was preprocessed with one-hot encoding, MinMaxScaler normalization, and sequence formation. Class imbalance was managed using class weights (0:2.0, 1:0.5) instead of SMOTE, with performance compared against Baseline LSTM-CNN and Dense-only models optimized with the Sine Cosine Algorithm (SCA). Result: The Improved LSTM-CNN achieved 0.99 accuracy, 0.93 F1-score for Benign traffic, and 1.00 for Malicious traffic, with ~25,000 parameters and 125 ms inference time on Google Colab. It outperformed Baseline LSTM-CNN (0.08 accuracy) and was more efficient than Dense-only (46,000 parameters), with a false positive rate of ~1%. Novelty: This research presents a lightweight, efficient DDoS detection solution for SDN, leveraging temporal modeling and class weights, suitable for resource-constrained controllers like OpenDaylight or ONOS. However, its generalization is limited by dataset diversity, necessitating broader validation.
From Interaction to Intention: Investigating Key Drivers of User Retention in Digital Marketplaces
Purpose: The rapid evolution of digital marketplaces has heightened the importance of understanding the drivers of user engagement. While prior research has focused on perceived ease of use and perceived usefulness, limited attention has been given to the impact of emerging interactive features such as affordances (metavoicing, guidance shopping, visibility, and trading). This study aims to bridge that gap by examining the combined effects of metavoicing, guidance shopping, visibility, trading, perceived ease of use, and perceived usefulness on users' perceptions and their continued intention to use a digital marketplace platform. These factors interact synergistically to create a cohesive and engaging user experience. Methods: A quantitative research design was adopted, utilizing structured questionnaires distributed to 200 active users of a leading Indonesian marketplace platform. Structural Equation Modeling (SEM) was employed to test the hypothesized relationships among the study variables. Result: The results reveal that affordances (metavoicing, guidance shopping, visibility, and trading) significantly influence user behavioral intention through their impact on perceived usefulness and ease of use, with trading affordance showing the strongest effect (β = 0.469) and perceived usefulness being the primary driver of behavioral intention (β = 0.753). Novelty: This study provides valuable insights for Digital marketplace developers to enhance user engagement by a comprehensive Digital Marketplace Requirements Framework that structures implementation around four core affordance categories: Trading Requirements (payment systems, checkout processes), Visibility Requirements (product display, visual search), Metavoicing Requirements (communication systems, review mechanisms), and Guidance Shopping Requirements (personalization, search functionality).