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Gene Expression-Based Lung Cancer Prediction in Smokers Using SVM and Moth-Flame Optimization Algorithm
Purpose: Lung cancer remains one of the leading causes of death worldwide, especially among active smokers, yet early detection is still difficult because traditional imaging methods have limited sensitivity for identifying early-stage abnormalities. This study was conducted to address the need for a more accurate computational approach capable of detecting lung cancer at a molecular level using gene expression data. The goal is to build a model that can reliably distinguish cancerous from non-cancerous samples based on genomic features. Methods: This study uses the GSE4115 gene-expression dataset consisting of 187 bronchial epithelial samples and 22,215 gene features. The Moth-Flame Optimization (MFO) algorithm was implemented to select the most informative subset of genes from this high-dimensional dataset. A Support Vector Machine (SVM) classifier was then trained using multiple kernels, with hyperparameter tuning performed to identify the optimal configuration for each kernel. Results: Experimental results show that the Polynomial kernel achieved the highest performance using 286 MFO-selected features, reaching an accuracy of 0.84 and an F1-score of 0.85. These results confirm that combining MFO with SVM improves classification performance compared to using raw gene data without feature selection. Novelty: This study provides the first application of MFO-based feature selection for lung cancer prediction in smokers using the GSE4115 dataset. The findings demonstrate the value of nature-inspired optimization for handling high-dimensional genomic data and offer a promising direction for developing early computational detection methods.
Copyright (c) 2026 Scientific Journal of Informatics
Development of VR-Based Virtual Tours for South Sorong Regency Using the Analysis, Design, Development, Implementation, and Evaluation Model
Purpose: The limited availability of digital documentation and interactive promotional media has reduced public awareness of tourism destinations in South Sorong Regency. This study aims to develop and evaluate a Virtual Reality (VR)-based virtual tour as a solution to enhance user understanding, engagement, and visitation intention. Methods: This study employed a Research and Development approach using the Analysis, Design, Development, Implementation, and Evaluation (ADDIE) model. Three tourism destinations were captured using 360° panoramic video and integrated into an interactive VR-based virtual tour. The virtual tour was evaluated with 97 respondents. Respondents completed a pre-test before using the virtual tour and a post-test after the experience to measure changes in understanding and interest, analyzed using the N-Gain method. Usability and acceptance were assessed through Likert-scale user questionnaires and percentage-based feasibility analysis. Media and material experts evaluated technical quality and content accuracy; their feedback was used for virtual tour media revision before final validation. Results: The results show improved user understanding and engagement, indicated by an N-Gain score of 0.64, categorized as moderate to high effectiveness. User evaluation yielded a feasibility score of 93%. Expert validation confirmed readiness, with feasibility scores of 89% from media experts and 94% from material experts. Novelty: This study introduces a systematic ADDIE-based engineering framework for multi-destination VR virtual tour development, integrating effectiveness analysis, usability evaluation, and expert validation as a scalable informatics solution for digital tourism promotion in underdeveloped regions.
Optimization of Mineral Fuel Export Forecasting Using Attention-based Long Short-Term Memory
Purpose: This study aims to optimize the forecasting of the Net Value of Indonesia's mineral fuel exports using the Attention-based Long Short-Term Memory (LSTM) model, supported by Dropout and Recurrent Dropout techniques that are combined to produce an optimal model. Methods: Modeling uses an LSTM architecture equipped with an Attention mechanism, as well as Dropout and Recurrent Dropout. The research procedure uses the CRISP-DM (Cross-Industry Standard Process for Data Mining) methodology. The research material used is the Indonesian mineral fuel export dataset with HS code 27 from 2014 to 2025. Model was built using the Random Search method to optimize hyperparameters such as the number of neurons (units), activation functions (Tanh, ReLu), and optimizers (Adam, Nadam, RMSprop). Result: The Attention-based LSTM model with Dropout and Recurrent Dropout techniques achieved a MAPE of 7.76%, which was better than the other models tested. Attention analysis shows that lag 12 has the greatest dominance, while lags 11 to 10 also contribute significantly, indicating an annual seasonal pattern. Projections for the next 12 months show a moderate decline in Net Value, in line with seasonal trends and historical data. Novelty: The main contribution of this research is the optimization of an Attention-based LSTM model using a combination of Dropout and Recurrent Dropout techniques, which is effective in forecasting Indonesia's mineral fuel export values because it is able to capture annual seasonal patterns, thereby improving the accuracy and stability of the forecast results.
Optimized LSTM Model Using Simulated Annealing for Autoignition Temperature (AIT) Prediction as a Hazard Indicator
Purpose: Autoignition Temperature (AIT) is the lowest temperature at which a substance will spontaneously ignite in normal air without any external ignition source. AIT is an important safety parameter in industries that handles flammable materials. Measuring AIT with conventional method is unfortunately slow, costly, and dangerous. As an alternative, an AIT prediction model can be developed using in silico approaches, specifically based on machine learning. Methods: One of the methods that can be used is Long Short-Term Memory (LSTM) since it is good at modeling the complex relationships that is involved, but unfortunately it is difficult to tune manually due to their numerous hyperparameters. Therefore, an automated strategy can be used to find the best hyperparameters for the architecture. This study aims to develop an AIT prediction model as a hazard indicator using an LSTM model optimized with Simulated Annealing (SA). Result: The experiment showed that the SA-LSTM model which uses a cooling schedule of Delta T = 0.7 outperformed the unoptimized baseline model. Novelty: The optimization raised the R2 on test data from 0.5682 to 0.5939 while also lowering the RMSE from 74.35 K to 72.10 K and the MAPE from 9.29% to 8.87%. These results confirmed that optimizing LSTM with SA gave a more robust tool for hazard indicator.
Copyright (c) 2025 Scientific Journal of Informatics
Ultra-Low-Cost Hybrid OCR–LLM Architecture for Production Grade E-KTP Extraction
Purpose: The purpose of this study is to be able to avoid limitations of inexpensive ID card data extraction services and preserve privacy, which can simultaneously achieve reliable operation even under an environment with minimum infrastructure, in particular if no dependency on GPU-based servers are required. Method: The proposed approach is a microservice pipeline with three stages: (1) local lightweight pre-processing on devices, (2) Tesseract CPU-based OCR. js, (3) fast text tokenization through a small premature external LLM. The system is developed as TypeScript backend utilizing the Hono framework with all image processing taking place locally in order to keeping user data private. Result: The result of the experimental evaluations with real ID card samples is that the system can run stably in low-performance VPS (1 vCPU, 1 GB RAM) with operation cost approximately IDR 2.5047 per extraction process and its accuracy level is acceptable for use in a production environment. Moreover, the results indicate that system latency is dominated by LLM inference at the cloud. Novelty: The main contribution and novelty of this study is that we demonstrate, for the first time, a cost-effective (privacy-preserving) OCR-LLM hybrid pipeline without demanding expensive GPU models at large scale which makes our system suitable under limited storage and resource constraints on-premises or edge environments in small organizations including micro-SaaS services.
Robust Human Gait Recognition with Convolutional Neural Network based on Gait Energy Image
Purpose: Human gait recognition is one of the developments in artificial intelligence technology. Gait recognition is a biometric recognition technique that uses no direct interaction with an object, allowing for identification of individuals based on their gait. However, this recognition faces challenges, including varying camera angles (00 - 1800), so this requires a more in-depth introduction. Methods: Therefore, based on the references, this study proposes using the Gait Energy Image (GEI) and Convolutional Neural Network (CNN) features for in-depth extraction and recognition of each image in the Casia B Dataset, which is then compared with the results of previous studies. Result: The results of this study, with the division of the Casia B Dataset 80% as training data and 20% as testing data and 11 camera angles between 00 - 1800 produced an accuracy rate of 99.48%. Novelty: So the accuracy achieved with this deep learning technique exceeds that of previous research using conventional methods and this gait pattern recognition technique can be used to be implemented in a biometric recognition system based on human gait patterns.
Optimizing Early Breast Cancer Classification Using Hybrid SVM-ANN with Ridge Embedded Feature Selection
Purpose: This study aims to enhance early breast cancer detection by systematically evaluating multiple machine learning (ML) algorithms and feature selection strategies. The goal is to identify the most effective combination of classifiers and feature selection methods for accurately distinguishing malignant from benign breast tumors, thereby improving diagnostic reliability and clinical decision support. Method: The Wisconsin Breast Cancer Dataset containing 699 samples described by nine diagnostic features was used. Tumor classes were encoded as 0 (malignant) and 1 (benign). The analysis was conducted in two stages. First, five ML algorithms—K-Nearest Neighbors (KNN), Logistic Regression, Support Vector Machine (SVM), Artificial Neural Network (ANN), and a hybrid SVM–ANN—were evaluated to establish baseline performance. Second, two feature selection approaches (wrapper and embedded) were applied to four ML models and the optimized hybrid classifier. The embedded approach employed Ridge-based feature selection to identify the most discriminative attributes and improve model generalization. Results: The hybrid SVM–ANN combined with Ridge Embedded feature selection achieved the best performance, with an accuracy of 97.86%, precision of 96.5%, recall of 96.5%, and an F1-score of 96%. This configuration outperformed all other algorithms and feature selection techniques, affirming the effectiveness of hybrid integration and embedded feature optimization. Novelty: The novelty lies in the integration of an SVM–ANN hybrid model with Ridge-based embedded feature selection for breast cancer classification. Unlike prior works that rely primarily on conventional filter or wrapper techniques, this approach demonstrates superior accuracy and robustness. The proposed framework provides a promising pathway for developing more reliable ML-based diagnostic tools in oncology.
Evaluating the Implementation of the FinOps Framework for Cloud Infrastructure Cost Management: A Case Study of Technology Companies in Indonesia
Purpose: The management of cloud infrastructure costs has become increasingly relevant in light of the rapid rate of cloud service adoption, especially in Indonesia's technology space, where there is tremendous economic pressure. Cloud infrastructure cost management solutions such as the FinOps Framework are clearly intended to provide a greater degree of accountability for financial governance and to enable cloud spending optimization when implemented holistically. In this research, we want to evaluate how FinOps adoption maturity is in several technology companies, and to find the gap between FinOps Framework principles and their real-world implementation. Methods: A mixed-methods case study was employed. To gather primary data, the first step was to interview six FinOps practitioners using semi-structured interviewing methods. Thematic analysis was used to identify thematic similarities across responses. Following the development of a simplified three-point maturity score system based on the FinOps framework, we compared each participant's maturity state in reference to the variations outlined in the FinOps literature. Result: From the interviews, there was an emphasis on key processes, such as cost ownership, budgeting, and governance, which were quite centralized, reactive, and somewhat informal, although everyone was using technical optimization approaches such as rightsizing. Our quantitative analysis corroborated this, with a cohort average maturity score of only 1.53 out of 3. This indicates a general maturity score in the "Crawl" or first stage, indicating shortcomings related to the process element of the basic practices not being integrated in a systematic way. Novelty: This research provides meaningful and helpful insights into the real challenges of tech companies in Indonesia implementing FinOps. Our research provides a clear baseline for companies working locally by measuring maturity levels and making recommendations at the end. More importantly, beyond technical optimization, it clearly identifies the need to formalise policies and to improve cooperation across functional areas. This mixed methods approach should be extended to other research by increasing sample size and allowing for a deeper exploration of the cultural context.
Optimizing Fair and Efficient Group Formation in Community Service Program Using Particle Swarm Optimization
This study aims to evaluate the performance of the Particle Swarm Optimization (PSO) algorithm in solving the problem of group formation for community service program (Kuliah Kerja Nyata). The motivation for this research stems from the need for an efficient and effective method to optimally form KKN groups based on specific criteria. The research problem is how PSO performs in terms of computational time, convergence speed, and the quality of solutions produced. The method employed involves testing the PSO algorithm on 27 groups with 10 independent runs, measuring computational time, optimal iterations, initial scores, and best final scores. The results show that PSO is highly computationally efficient, achieving exceptionally fast average execution times and demonstrating remarkable convergence, with most groups reaching optimal solutions in the first iteration. However, two distinct patterns emerged: several groups achieved low optimal scores (95–97), while most other groups (16–26) stagnated at extremely high initial scores (100000.0) without improvement. These findings suggest that although PSO is a fast and effective method, its performance is highly dependent on problem characteristics and initialization. As a recommendation, further studies are needed to identify the factors causing stagnation at high scores and to test parameter adjustment or alternative initialization strategies to enhance the algorithm’s ability to escape local minimal. The contribution of this research is to provide a deeper understanding of the potential and limitations of PSO in the context of group formation, which may serve as a foundation for developing more robust optimization algorithms in the future.
Improving Sentiment Analysis with a Context-Aware RoBERTa–BiLSTM and Word2Vec Branch
Purpose: We improve the accuracy of Twitter/X sentiment analysis with a hybrid model combining Word2Vec and the Robustly Optimized BERT Pretraining Approach (RoBERTa). However, Twitter/X text is noisy (slang/OOV) and ambiguous, so the performance of the pre-trained transformer decreases. Word2Vec is also limited to local contexts. Integrative studies of both are still limited. The idea is that Word2Vec is strong for slang/novel vocabulary (distributional semantics), while RoBERTa excels in contextual meaning; combining the two mitigates each other's weaknesses. Methods: The Sentiment140 dataset contains 1.6 million balanced tweets. The split is stratified; Word2Vec is trained solely on the training data. RoBERTa is pretrained (frozen in the first stage, then fine-tuned with some layers in the second stage). The Word2Vec and RoBERTa vectors are concatenated and processed using Bidirectional Long Short-Term Memory (BiLSTM) with sigmoid activation. Training utilizes TensorFlow and the Adam optimizer, incorporating dropout and early stopping. The decision threshold is optimized during the validation process. Result: The hybrid model achieved an accuracy of 88.09%, an F1-score of 88.09%, and an Area Under the Curve (AUC) ≈ 95.19% on the Receiver Operating Characteristic (ROC). No overfitting was observed, and the hybrid model outperformed both single baselines. The confusion matrix and ROC curve corroborate the findings. Novelty: The novelty lies in the fusion of distributional and contextual representations with a structured fusion mechanism. Limitations: Computational requirements and hyperparameter tuning are not yet extensive. Further directions: Systematic hyperparameter search and cross-validation across other large sentiment datasets to assess generalization.
Unveiling IT Governance Effectiveness in e-Puskesmas Using COBIT 2019
Purpose: This study aims to evaluate the effectiveness of IT governance implementation in the e-Puskesmas system at Pengasinan Community Health Center, Bekasi City, using the COBIT 2019 framework. The research focuses on identifying the capability level and governance gaps to ensure risk management and IT service delivery align with institutional objectives. Methods: The study employs a mixed-method approach combining qualitative interviews and quantitative questionnaires. Two COBIT 2019 domains—APO12 (Managed Risk) and DSS02 (Managed Service Requests and Incidents)—were analyzed using capability level measurements and GAP analysis to determine performance alignment with governance standards. Result: The findings reveal that e-Puskesmas achieved a high capability level, with 96.88% for APO12 and 90.47% for DSS02. Despite this success, several weaknesses were identified, including delays in patient registration, data confidentiality issues, and service disruptions. The study provides strategic recommendations to enhance user training, strengthen data security, and implement continuous risk management. Novelty: This research presents one of the first applications of COBIT 2019 in evaluating IT governance for public healthcare systems in Indonesia. It demonstrates how COBIT can serve as a practical tool for assessing digital health governance maturity, ensuring data integrity, and supporting the sustainable transformation of healthcare services.
Comparative Performance Analysis of Deep Learning Models for Cryptocurrency Price Forecasting
Purpose: This research aims to find an accurate cryptocurrency price prediction model to mitigate financial risks caused by high price volatility. This research compares the predictive capabilities of five Deep Learning model, namely LSTM, GRU, BiLSTM, Transformer, and Performer, for predicting cryptocurrency prices with the highest accuracy in the digital financial market. Methods: The methods applied in this research are dataset, preprocessing data, model training, and model evaluation. The dataset used in this study, namely the price per minute data for BTC, ETH, BNB, and XRP, was obtained from Kaggle. Data processing includes normalization using MinMaxScaler and sequence generation through the Sliding Window technique. To validate each deep learning model, and four metrics consisting of MAE, MSE, RMSE, and MAPE are used for evaluation. Result: The Transformer model created the best results for the lowest MAPE value across all datasets, the smallest being BTC and ETH at 0.20%, BNB at 0.29%, and XRP at 0.36% demonstrating high accuracy and generalization. The BiLSTM was ranking second since it captured effectively the bidirectional temporal dependencies; the GRU was moderate but stable in its performance. The data showed that the accuracy of LSTM and Performer varied. Novelty: This study offers a comprehensive comparison of various Deep Learning models in detail, enabling it to find the best model for predicting cryptocurrency prices with high accuracy. This study provides valuable insights for the development of advanced deep learning-based price forecasting systems in the field of digital financial analysis.
Mapping the Contribution of Kasepuhan Ciptagelar Tourism to Regional Income and Community Economy using the K-Means Clustering Method
Purpose: This study aims to map the contribution of Kasepuhan Ciptagelar tourism to regional income and the community economy as part of the UNESCO Global Geopark Ciletuh–Pelabuhanratu, Sukabumi. The research addresses the problem of limited infrastructure, poor accessibility, and low optimization of local tourism potential that hinders its contribution to regional income. The objective is to provide a data-driven understanding of tourism’s economic and socio-cultural impact. This study aims to map the contribution of Kasepuhan Ciptagelar tourism to regional income and the community economy as part of the UNESCO Global Geopark Ciletuh–Pelabuhanratu, Sukabumi. The research addresses the problem of limited infrastructure, poor accessibility, and low optimization of local tourism potential that hinders its contribution to regional income. The objective is to provide a data-driven understanding of tourism’s economic and socio-cultural impact. Methods: A mixed-method approach was used, integrating quantitative and qualitative analyses. Data were collected through questionnaires and interviews from three main respondent groups: local community (58%), tourists (31%), and government/MSMEs (11%). The K-Means Clustering algorithm was applied to classify perceptions into three contribution levels-high, medium, and low-while an ANOVA test was used to examine statistical differences among clusters. Results: The clustering results revealed three contribution categories: C1 (low) with 186 data points, C2 (medium) with 195 data points, and C3 (high) with 216 data points. The high cluster demonstrated a strong positive contribution to regional income, local economy, and infrastructure development, although challenges remain in socio-cultural sustainability. The ANOVA test confirmed significant differences in economic and infrastructure variables, while destination attractiveness was relatively uniform across clusters. Novelty: This study provides a multidimensional mapping model that integrates socio-economic and participatory data with K-Means clustering to analyze cultural tourism contribution. It introduces a data visualization information system prototype for policymakers to evaluate tourism performance interactively. The research offers new insights into evidence-based strategies for sustainable and community-centered cultural tourism development.
A Hybrid YOLOv8-ResNet50 Architecture for Enhanced Cardiomegaly Prediction from Chest X-rays
Abstract. Objective: This study aims to develop a reliable deep learning architecture for predicting cardiomegaly by integrating the ResNet-50 backbone into the YOLOv8 object detection framework, overcoming the challenges of detecting subtle anatomical variations and low-contrast features often found in chest radiographs. Methods: This study used a publicly available chest X-ray dataset, with rigorous data annotation to establish ground truth for the heart and thoracic cavity regions. Preprocessing included resizing input images to 640×640 pixels, automatic orientation correction, and an 80:20 data split between training and testing. Real-time data augmentation was applied to the training set. The ResNet-YOLOv8 hybrid model was trained for 150 epochs with optimized hyperparameters (learning rate, momentum, weight decay, loss weight), and performance was evaluated using metrics such as mAP, precision, recall, and confusion matrix results. Results: The experimental results show that the proposed architecture achieves high accuracy in detecting cardiomegaly, with mAP50-95 of 0.7578, precision of 0.9955, recall of 0.9962, F1 score of 0.9959, and inference latency of only 4.5 ms/img. This model is more optimal than the standard YOLOv8 variant in both accuracy and computational efficiency. Innovation: The integration of ResNet-50 into YOLOv8 significantly improves feature extraction capabilities for chest X-ray images, enabling the recognition of fine anatomical details with high precision. This innovative hybrid approach advances automated cardiomegaly detection, offering potential for large-scale, real-time implementation in clinical settings and contributing to the development of advanced AI-powered diagnostic tools.
Cardiomegaly, Deep Learning, YOLOv8, ResNet-50, Chest X-ray, Medical Image Analysis, Hyperparameter Tuning.
A Systematic Review and Bibliometric Study of Climate Change Sentiment Analysis: Trends and Approaches
Purpose: This study aims to map research trends in sentiment analysis on the climate change topic from the beginning of 2020 to the middle of 2025 by utilizing a Systematic Literature Review (SLR) method, along with bibliometric analysis. Climate change represents a worldwide challenge that profoundly affects both the environment and human social interactions, making it essential to comprehend public perceptions of this issue thoroughly. The escalating use of social media is driving an increase in research related to sentiment analysis, which is utilized to gain insights into public opinions and emotions. Methods: Data was collected from six leading databases such as Scopus, ScienceDirect, Taylor and Francis, IEEE Xplore, Sage Journals, and ProQuest, resulting in 3,326 articles. After a screening process using the PRISMA 2020 framework, 42 articles were selected for further analysis. Result: The findings suggest that Twitter is the predominant platform for climate change sentiment analysis, referenced in 32 articles, while Sina Weibo is mentioned in nine articles, Reddit in two articles, and both Facebook and YouTube in one article each. Of the four approaches assessed, the leading approaches identified in this research are Machine Learning and Deep Learning. In the Machine Learning category, Naïve Bayes is the predominant approach, appearing in 18 articles, followed by Naïve Bayes, cited in 17 articles. Furthermore, Logistic Regression and Random Forest are each mentioned in 13 articles. In the field of Deep Learning methodologies, 10 articles used Convolutional Neural Networks (CNNs), nine articles featured Bi-LSTMs, six articles featured LSTMs, and 13 articles referenced Transformer-based models, particularly BERT. Furthermore, model validation primarily used cross-validation techniques, and the most referenced evaluation metrics were accuracy, recall, and F1-score in 33 articles and precision in 32 articles. Novelty: The novelty of this research lies in the time of information collection for research on climate change sentiment analysis, spanning 2020 to the middle of 2025. The latest research on a related issue was conducted from 2008 to 2022. Furthermore, this study provides insights into research trends and includes the distribution of articles by country, separating them into Single-Country Publications (SCPs) and Multi-Country Publications (MCPs). This research also presents information on social media platforms, classification approaches, and commonly employed validation and evaluation tools, which differentiate it from prior studies. This analysis is conducted on six leading databases, producing valuable findings for researchers and policymakers.
Contact, Fulfillment, and Privacy as Key Drivers of Mobile Commerce Success: A SOR-Extended M-S-QUAL Analysis
Purpose: M-commerce has become crucial for facilitating grocery shopping through delivery services, yet challenges like delayed orders and systems failures continue to hinder user satisfaction and loyalty. There are still lack of research that have investigated in depth both mobile apps service quality and paid attention to the perspective of user behavior in a structured way. This study addresses a gap by uniquely integrating the Mobile Service Quality (M-S-QUAL) and Stimulus Organism Response (SOR) to find out the key drivers for enhancing m-commerce grocery shopping services quality, and analyzing the influence of the m-commerce services quality factors as Stimulus in m-commerce apps to satisfaction felt by users as Organism, and their relationship with loyalty and E-WOM as user Response. Methods: Using a quantitative approach, 362 Indonesian m-commerce user responses from online survey were analyzed through Partial Least Squares Structural Equation Modeling (PLS-SEM). Outer model and inner model was carried out to test the significance between the construct and the strength of the model. Result: Results show that Contact ( =0.229), Fulfillment ( =0.192), and Privacy ( =0.166) are the most influential factors driving perceived m-commerce service quality that has strong predictive power (R2=0.804). These findings, which repositions these specific dimensions as primary stimulus within the SOR framework significantly impacts user satisfaction as organism and positively drive both loyalty and E-WOM as response. Novelty: This study provides valuable insights and a structured perspective to explain post-adoption user behavior in m-commerce delivery. The study offers novel academic insights and practical strategies for enhancing customer service, delivery reliability, and data protection through user-centered design with more attention to factors such as Contact, Fulfillment, and Privacy (Stimulus) to drive user satisfaction (Organism), loyalty and E-WOM (Response).
Strategy Improvement for IT Governance in Library Services Using an Adaptation of ITIL 4 and FitSM : A COBIT 2019 Evaluation
Purpose: Effective management of IT services in public libraries is critical to maintaining service continuity, optimizing resources, and ensuring user satisfaction. However, current practices face persistent challenges in workforce capability, operational consistency, and problem resolution. This study evaluates IT service governance and develops improvements across four objectives: APO07 (Managed Human Resources), DSS01 (Managed Operations), DSS02 (Managed Service Requests and Incidents), and DSS03 (Managed Problems). Methods/Study design/approach: An integrated approach was applied by combining COBIT 2019 and ITIL 4 with FitSM to fit the scale of public libraries. A capability assessment established the baseline, followed by a gap analysis to identify weaknesses. Adaptive procedures were then formulated by retaining the comprehensive guidance of ITIL 4 and integrating the lightweight, practical principles of FitSM, while excluding elements unsuited for small-scale organizations. Result/Findings: The assessment showed that all four objectives are at capability level 1 and have not reached the “fully achieved” benchmark. Gaps were identified in human resource development, operational standardization, and incident and problem handling. The proposed improvements introduce standardized processes to strengthen workforce capability, stabilize daily operations, and accelerate service request and problem resolution. Novelty/Originality/Value: This study offers a practical governance improvement model tailored for public libraries and similar small-scale public sector organizations. The integration of ITIL 4 and FitSM provides a structured yet simplified framework that supports process standardization and service quality enhancement, addressing the limitations of applying COBIT 2019 in isolation.
FitSM, ITIL 4, COBIT 2019, IT Governance
Which Features Matter Most? Evaluating Numerical and Textual Features for Helpfulness Classification in Imbalance Dataset using XGBoost
Purpose: This study aims to develop and realistically evaluate a reliable model for identifying helpful online reviews, particularly in the context of Indonesian-language texts, which are often informal and challenging. Methods: This study addresses several key challenges in predicting review helpfulness: the relative effectiveness of numerical features from metadata compared with traditional text representations (TF-IDF, FastText) on noisy data; the impact of severe class imbalance; and the limitations of standard validation compared with time-based validation. To address these challenges, we built an XGBoost model and evaluated various feature combinations. A hybrid approach combining SMOTE and scale_pos_weight was applied to handle class imbalance, and the best configuration was further assessed using time-based validation to better simulate real-world conditions. Result: The results show that the model based on numerical features consistently outperformed the text-based model, achieving a peak macro F1-score of 0.7214. Compared to the IndoBERT baseline (F1-score = 0.6400) and the RCNN FastText baseline (F1-score = 0.5317), this indicates that simpler feature-driven models can provide more reliable predictions under noisy review data. Time-based validation further revealed a performance decline of up to 8.06%, confirming the presence of concept drift and highlighting that standard validation tends to yield overly optimistic estimates. Novelty: The main contribution of this research lies in offering a robust methodology while demonstrating the superiority of metadata-based approaches in this context. By quantifying performance degradation through temporal validation, this study provides a more realistic benchmark for real-world applications and highlights the critical importance of regular model retraining.
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.
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 and Bayesian Model Averaging (BMA). Model validation was conducted using walk-forward validation with a sliding window approach to evaluate each model’s generalization performance on out-of-sample validation data. 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.
Review: 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. Result: 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.
Improved Stroke Classification Accuracy by Using Hybrid Inception and Xception Models
Purpose: Stroke is one of the leading causes of death and disability in the world that requires a fast and accurate diagnosis system. A major challenge in classifying strokes using deep learning is data imbalances, where the number of stroke patients is much less than that of non-stroke patients. Methods/Study design/approach: This research proposes a Hybrid model approach that combines Inception and Xception architectures, and applies Synthetic Minority Over-sampling Technique (SMOTE) to balance the data distribution. The dataset used consisted of 5,110 entries with 12 stroke risk features, and evaluation was performed using accuracy, precision, recall, and F1-score metrics. Result/Findings: The results show that the Hybrid model provides the best performance with an accuracy of 92.2%, outperforming the Inception (86.28%) and Xception (89.26%) models. In addition, the Hybrid model showed high and balanced precision and recall values, reflecting its reliability in detecting stroke cases. Novelty/Originality/Value: The novelty of this research lies in combining the multi-scale feature extraction power of the Inception architecture and the depthwise separable convolution efficiency of the Xception architecture in a hybrid model. This approach is proven to excel in tabular data-based stroke classification and has the potential to be applied in automated medical diagnosis systems.
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.