Journal Title: Journal of Innovation and Technology Polbeng Series on Informatics (INOVTEK Polbeng - Seri Informatika) Initials: ISI Frequency: 4 issues per year (February, May, August, and November) ISSN: 2527-9866 Accreditation: Sinta 3 Editor in Chief: Agus Tedyyana, Ph.D. Managing Editor: Afis Julianto, M.Kom Publisher: P3M Politeknik Negeri Bengkalis Citation Analysis: DOAJ, Google Scholar, Crossref, Dimensions, Sinta, Garuda The Journal of Innovation and Technology Polbeng Series on Informatics (INOVTEK Polbeng - Seri Informatika) Politeknik Negeri Bengkalis is an informatics-based scientific research journal. This journal aims to serve as a forum for academics, researchers, and practitioners to disseminate their research findings. INOVTEK Polbeng—Seri Informatika focuses on a broad spectrum of topics within informatics, including but not limited to web and mobile computing, image processing, machine learning, artificial intelligence (AI), intelligent systems, information systems, databases, decision support systems (DSS), IT project management, geographic information systems, information technology, computer networks and security, and wireless sensor networks. By covering such a wide range of subjects, the journal ensures its relevance to a diverse readership interested in both the practical and theoretical aspects of informatics.
Performance Evaluation of a 344-Egg Automatic Incubator with Temperature-Humidity Regulation and Periodic Rack Turning
This study evaluates an automatic poultry egg incubator designed for 344 chicken eggs. The prototype combines temperature regulation, humidity monitoring, and periodic rack turning to reduce manual handling during a 21-day incubation cycle. The operating temperature is maintained within 38-39°C, while the humidity setting is 55-70%. A digital temperature sensor, hygrometer, Arduino Uno controller, relay module, heating lamp, ventilation fan, and DC rack-turning motor form the sensing-control-actuation chain. Hatching performance was assessed by classifying the 344 incubated eggs into normal hatch, defective hatch, dead embryo, and no-embryo outcomes. The test produced 327 normal chicks, 10 defective chicks, 5 dead embryos, and 2 eggs without embryos. The proportion of normal hatches was 95.06%, indicating that the incubator can sustain the environmental and mechanical conditions required for high-capacity chicken egg incubation. The study emphasises integrated operation and outcome-based performance rather than only controller implementation.
Copyright (c) 2025 Journal of Innovation and Technology Polbeng Series on Informatics (INOVTEK Polbeng - Seri Informatika) https://creativecommons.org/licenses/by-nc-sa/4.0
Production and Evaluation of 360° 3D Assets Using a Mini Booth and CR-Scan Ferret
This study documents and evaluates an end-to-end workflow for producing 30 textured three-dimensional (3D) assets from small-scale objects (maximum dimension 50 cm) using the active near-infrared scanner Creality CR-Scan Ferret inside a mini booth with diffuse lighting. Raw data were processed in Creality Scan and Blender through artefact elimination, remesh/retopology, mesh repairing, texture baking, and export. Digital dimensions were extracted from the model bounding box using Python and Open3D, then compared with reference physical measurements. The mean per-asset RMSE was 2.00 cm (SD = 1.61; median = 1.80; 95% CI = 1.40–2.60; range = 0.02–6.18 cm). Under the internal thresholds of the study, 12 assets were rated Very Good, 11 Good, 5 Fair, and 2 Poor. The mean RMSE for easy, medium, and hard objects was 1.32, 1.31, and 3.36 cm, respectively; an exploratory Kruskal-Wallis test indicated a difference between categories (H = 9.76; p = 0.0076). Mesh analysis was applied to six representative samples only. Two regular bottle models had Non-manifold Edges = 0 and Shells = 1, whereas the Hashmal model had 14,945 non-manifold edges and 7 shells. The workflow is usable as an initial pipeline for digital visualisation, but the results do not prove universal metric accuracy or watertightness because repeated scans, measurement uncertainty, independent scale calibration, and mesh-metric normalisation are not yet available.
Copyright (c) 2026 Journal of Innovation and Technology Polbeng Series on Informatics (INOVTEK Polbeng - Seri Informatika) https://creativecommons.org/licenses/by-nc-sa/4.0
Development of a Layered Architecture for a Single-Farm Fish Farming Management System Using Prototype Method
Small-scale fish-farming businesses often experience challenges in managing the fragmented operational data, which includes cultivation activities, inventory, transactions and reporting processes. Lack of an integrated information management structure restricts the traceability of data and reduces the efficiency of operations. Unlike prior digital aquaculture systems, which mainly target environmental monitoring, this study designs a layered architecture for the web-based single-farm fish farming management system using the prototype method, with novelty in the architecture-level integration of five system layers with four business domains rather than in either method individually. The research methodology consists of requirement analysis, software requirements specification development, layered architecture design, prototype development, system implementation, and functionality verification. The proposed architecture separates system components into five logical layers: presentation, application, data access, data and cache layers and decomposes business functions into production, inventory, financial and reporting domains. The developed system comprises eight functional modules that enable the fish farming management processes from end to end. The functional verification was done by black-box testing with nine test scenarios, which had a 100% success rate. The results indicate that the proposed layered architecture provides a structured basis for integrating cultivation and business processes in a single-farm fish farming environment; maintainability and scalability follow from the design but were not empirically measured. Future development may extend the architecture with multi-farm management, Internet of Things integration and quantitative evaluation of software quality attributes.
Implementation of Handwritten Digit Recognition Using CNN in an Augmented Reality-Based Geometry Learning Application
Instruction on 3D shapes in elementary schools is still dominated by the use of two-dimensional media, making it difficult for students to visualise three-dimensional objects. Furthermore, most Augmented Reality (AR)-based learning applications focus primarily on presenting learning materials without providing an assessment mechanism that supports handwritten student answers. This study aims to implement Handwritten Digit Recognition (HDR) based on a Convolutional Neural Network (CNN) in an AR-based 3D-shape learning application. The proposed approach integrates CNN-based handwritten digit recognition as an evaluation mechanism within the AR-based learning application, allowing students' handwritten answers to be automatically recognised and evaluated. The CNN model utilises the LeNet-5 architecture and was trained using a combined dataset consisting of 70,000 MNIST images and 10,000 handwritten images independently collected by the researcher. The testing results show that the model achieved an accuracy of 98.32%, while HDR testing within the application using 250 samples of student handwriting achieved an accuracy of 96.80%. The results indicate that the implementation of CNN-based HDR in an AR-based 3D shape learning application can recognise handwritten digits with high accuracy and provide an automated learning evaluation mechanism based on handwritten answers.
Implementation of the C4.5 Decision Tree Algorithm in a Decision Support System for Employee Payroll at PT Hesed
Payroll management is a critical business process because it directly affects employee compensation and organisational administration. PT Hesed still encounters challenges in payroll verification, including calculation errors, limited visibility of payroll components, and sequential verification procedures. This study implements the C4.5 decision tree algorithm in a web-based payroll decision support system and evaluates both software functionality and classification performance. The study employed a design and creation strategy using qualitative analysis for system requirement identification and quantitative analysis for model evaluation. Historical payroll data consisted of 12 verified records from January to April 2026, with payroll period, attendance, years of service, employment status, and tardiness as predictor attributes and payroll recommendation as the class label. C4.5 attribute selection used entropy, information gain, split information, and gain ratio, while LOOCV evaluated predictive performance. Tardiness achieved the highest gain ratio (1.0000) and became the root node in the full dataset and in every LOOCV fold. LOOCV produced 7 correctly classified Appropriate records and 5 correctly classified Inappropriate records, yielding 100% accuracy, macro-precision, macro-recall, and macro-F1, compared with a 58.33% majority-class baseline accuracy. Black-box testing recorded outputs that matched the specified functions, while logic-level white-box verification reproduced the reference C4.5 calculations. Because the dataset is limited and no processing time, usability, payroll-error reduction, or field-impact study was conducted, these results should be interpreted as evidence of functional conformity and internal classification consistency rather than demonstrated operational efficiency or broad generalisability.
Comparing LSTM, Bi-LSTM, and Transformer Across Feature Scenarios for Rupiah Exchange Rate Forecasting
This study compares LSTM, Bi-LSTM, and Transformer models for one-step-ahead JISDOR forecasting across eight feature scenarios using historical JISDOR, IHSG, Brent oil prices, and inflation. The dataset comprised 1,300 chronological observations from January 2021 to May 2026. Evaluation used chronology-safe preprocessing, four expanding-window walk-forward folds, validation-based model selection, five stochastic seeds, a naive persistence benchmark, statistical testing, and Integrated Gradients. Among the deep-learning models, LSTM under S1 achieved the lowest mean MAPE of 0.6662%, followed by Bi-LSTM at 0.6899% and Transformer at 1.8135%. However, naïve persistence achieved a lower mean MAPE of 0.2618%, and all model–scenario combinations were significantly worse after Holm-adjusted Diebold–Mariano testing. External variables did not improve forecasting accuracy over historical JISDOR alone. Integrated Gradients showed that historical JISDOR received the largest attribution in all three architectures. These results highlight the importance of temporal validation and simple benchmarks in exchange-rate forecasting.
UI/UX Optimization of an Android-Based Waste Management Application Using Design Thinking and the System Usability Scale
Community-based waste management increasingly relies on mobile applications, whose success depends heavily on the quality of the user interface (UI) and user experience (UX). This study designed and evaluated the UI/UX of a high-fidelity prototype of the Resikel Android application for the Batam City context using the design thinking methodology (empathise, define, ideate, prototype, test). The contribution is context-specific rather than methodological: it presents a user-centred interface design and its perceived-usability evaluation for a community-based waste-management prototype situated in Batam. Usability was measured through unmoderated, cross-sectional testing on the Maze platform involving 100 respondents, followed by the System Usability Scale (SUS). Task-based metrics (success rate, completion time, and misclicks) were recorded, and the mean SUS score was 78.2 (SD = 6.70), indicating a good level of perceived usability within the Acceptable range. Because the artefact evaluated is a prototype rather than a functioning Android application, technical feasibility, performance, accessibility, and real-world task completion were not established. The findings provide an empirical usability reference for the future development of community-based waste-management applications and should be interpreted within the stated design and sampling limitations.
Performance and User Satisfaction Analysis of Kerobokan Village’s SIPANDU Using IT Balanced Scorecard and EUCS
Information technology advancements push government agencies to utilise information systems to improve public services. Kerobokan Regional Office has utilised the Complaint Management Information System (SIPANDU) to manage citizen complaints digitally. However, a comprehensive evaluation aligning operational technical performance, organisational contribution, future readiness, and the psychological satisfaction of users has not been conducted. This study evaluates SIPANDU's overall performance and user satisfaction using the integration of the IT Balanced Scorecard (IT BSC) as a macro evaluation framework and End User Computing Satisfaction (EUCS) as a micro instrument focused on the user orientation perspective. This mixed-methods study involved 15 internal end users (village head, secretary, section heads, and neighbourhood heads) selected through saturated sampling. EUCS measures satisfaction based on content, accuracy, format, ease of use, and timeliness. The evaluation results show SIPANDU's performance is highly satisfactory across all IT BSC perspectives with an overall score of 0.918 (91.8%). Corporate Contribution scored 0.900, User Orientation scored 0.964, Operational Excellence scored 0.892, and Future Orientation scored 0.908. User satisfaction measured by EUCS is also very high, averaging 4.82 out of 5. Despite the excellent results, the integration of IT BSC and EUCS revealed gaps primarily in operational excellence and timeliness. Priorities for improvement and the strategic roadmap developed include enhancing system reliability, optimising server performance, and implementing real-time notification features for complaint status updates.
POS System Success Evaluation at a Civil Servant Cooperative: DeLone-McLean Evidence and Perceived Paperless Environmental Benefits
Information system success models define net benefits mainly in economic and operational terms, leaving the environmental implications of digitalization insufficiently addressed. This study evaluated a centralized Point of Sale (POS) system at the Civil Servants’ Cooperative of Politeknik Negeri Bali using the DeLone and McLean model and examined whether perceived paperless environmental benefit could qualify as an additional net-benefit dimension. A mixed-methods evaluative design involved customers (N = 78), tenants (N = 12), and managers (N = 7). Analyses included validity and reliability testing, correlation analysis, robust path analysis, mediation testing, and the Wilcoxon signed-rank test. Of eighteen stakeholder-specific scales, reliability was adequate for customer scales (α = 0.888–0.961) but unstable for tenant and manager scales, with confidence intervals extending to [0.271, 0.952]. Queue management and comfort were significantly associated with customer satisfaction (β = 0.683, p < 0.001; R² = 0.715), while satisfaction was associated with PPEB (β = 0.773, p < 0.001). Mediation evidence was inconclusive: the indirect path was significant under classical standard errors (Sobel z = 2.31) but not under heteroscedasticity-consistent errors (z = 1.59, p = 0.111), while the direct association remained strong. Sales declined after implementation, but differing measurement bases prevented causal interpretation.
Multi-Platform Healthcare Sentiment Analysis Using Kappa-Validated Lexicon Labelling and SMOTE-Enhanced Naive Bayes
This study develops an automated sentiment analysis system for classifying reviews of the RSI Sunan Kudus mobile application, collected from Google Play Store, Google Maps, and YouTube (N = 1,428). Sentiment labels were automatically assigned using a domain-adapted Indonesian lexicon (87 positive / 100 negative terms), with label reliability validated against a human-annotated gold-standard subset (n = 100, two annotators; κ = 0.900, almost perfect agreement), yielding 85.71% classifier accuracy against adjudicated labels. After excluding neutral reviews, a binary multinomial Naive Bayes classifier was evaluated via 5-fold stratified cross-validation, achieving 91.01 ± 1.13% accuracy and 89.20 ± 1.48% macro F1-score. Class imbalance (ratio = 2.39:1) was addressed using SMOTE within each training fold in feature-vector space. An ablation study across six model-vectoriser combinations identified LinearSVC with Bag-of-Words as best-performing (Macro F1 = 96.03 ± 1.34%); a preprocessing ablation showed the six-stage normalisation pipeline did not meaningfully improve Macro F1 over simpler variants. Cross-platform transfer experiments revealed substantial generalisation gaps (e.g., Macro F1 dropping from 81.1% to 26.8% for a Google-Maps-trained model applied to YouTube), underscoring the value of multi-platform data collection. Findings indicate three primary service pain points: OTP/login failures, long waiting times, and application connectivity issues.
Comparison of Hyperparameter Optimization Methods for LSTM-Based XAU/USD Forecasting and Web-Based System Implementation
Unequal search budgets and single-seed experiments can confound comparisons of hyperparameter optimisation methods in financial forecasting. This study compares grid search, random search, and Bayesian optimisation for tuning a long short-term memory model to forecast the XAU/USD closing price for the next trading day. The dataset comprised 1,705 daily observations from January 2020 to July 2026 using open, high, low, close, and release-date-aligned United States inflation. Each method evaluated the same 32 configurations using three random seeds, resulting in 96 candidate-model evaluations per method. Performance was assessed on 37 independent testing dates and descriptively examined using a five-fold post-selection walk-forward diagnostic without repeating hyperparameter optimisation within each fold. All methods selected the same configuration and produced a mean testing MAPE of 2.785052% and an ensemble MAPE of 2.696161%. Grid Search reached the final-best configuration earlier, but naïve persistence achieved the lowest MAPE of 1.358325%. Thus, optimisation improved the LSTM relative to the predefined baseline but did not outperform persistence. The procedures were also implemented in a Streamlit application. The findings are limited to the examined dataset, search space, seeds, testing period, and computational environment.
Random Forest and LightGBM Comparison for Acute Pain Diagnosis Using SMOTE on an Expert-Labeled Dataset
Limited healthcare personnel may delay early pain assessment and encourage self-medication, increasing medication error risk. However, evidence remains limited regarding whether bagging or boosting is more suitable for multiclass acute pain classification using imbalanced, expert-system-derived symptom data, and whether SMOTE improves performance. This study compared Random Forest as a bagging approach and LightGBM as a boosting approach for classifying nine acute pain diagnostic classes without SMOTE and with SMOTE using k_neighbours=1 and 5. The dataset comprised 2,722 records and 36 discrete symptom features. Of 125 representative symptom combinations reviewed by a medical expert, 115 were considered appropriate; the remaining records were synthetically generated using the same expert-system knowledge base and inference mechanism. Data were divided using stratified 80:20 sampling, while model configuration was evaluated using five-fold cross-validation. SMOTE was applied only to training data within each fold. LightGBM without SMOTE achieved the best performance, with 83.49% accuracy, a macro F1-score of 0.81, and a weighted F1-score of 0.83, compared with 80.18%, 0.77, and 0.80 for Random Forest. With SMOTE, Random Forest achieved 78.35% and 77.61% accuracy, while LightGBM achieved 81.10% and 82.39%. Thus, LightGBM without SMOTE performed best for this dataset. Validation using real clinical data and multiple experts is required.
Implementation Of The Naïve Bayes Method For Skincare Product Recommendations According To Skin Type At Dermakila Clinic
Advances in information technology have encouraged the implementation of recommendation systems in various fields, including beauty and skincare. Selecting skincare products that are not suitable for an individual's skin type and condition may reduce treatment effectiveness and potentially lead to skin problems. Dermakila Clinic offers a wide range of skincare products with diverse characteristics, creating a need for a system that can assist users in selecting products that best suit their skin needs. This study aims to implement the Naïve Bayes method in developing a skincare product recommendation system based on user characteristics, including age range, gender, skin type, and skin concerns. The research applies a data mining approach using the Naïve Bayes classification algorithm. The dataset consists of 960 skincare product records that have undergone preprocessing and data transformation stages. The system was developed as a web-based application to provide users with fast and accurate product recommendations. The experimental results demonstrate that the Naïve Bayes method achieved an accuracy of 88%, with a precision of 89%, a recall of 88%, and an F1-score of 88%. These findings indicate that the Naïve Bayes method is effective for implementing a skincare product recommendation system at Dermakila Clinic.
Sentiment Analysis of Indonesia's Economic Acceleration Program 2025 Using Support Vector Machine
Indonesia's Economic Acceleration Program 2025 is a government policy to accelerate national economic growth in response to the global economic slowdown. The implementation of this programme has generated a variety of public responses on social media. This study aims to analyse public sentiment towards the Indonesian Economic Acceleration Program in the launch and implementation phases and identify differences in sentiment distribution in both phases. Data in the form of TikTok comments was collected through web scraping, then processed through preprocessing, lexicon-based sentiment labelling validated using manually labelled samples, TF-IDF feature representation, and classification using the Support Vector Machine. Model evaluation was carried out using 10-fold cross-validation. The results of the study showed that SVM provided superior performance to the comparison model. In the launch phase, SVM achieved an accuracy of 81%, while in the implementation phase it achieved an accuracy of 79.5% with superior performance in all evaluation metrics. The distribution of sentiment in the launch phase was dominated by neutral sentiment by 67.1%, while in the implementation phase the proportion of negative sentiment increased to 46.38%. These results show that there is a difference in the distribution of sentiment between the launch and implementation phases, so that it can be an input in understanding the public's response to the Indonesian Economic Acceleration Program
Decision Support System for Furniture Product Recommendation Using Item-Based Collaborative Filtering and Min-Max Normalization
The increasing variety of furniture products available in the market makes it difficult for consumers to identify products that best match their preferences. Conventional product selection is often carried out manually, requiring consumers to compare product specifications one by one, which is time-consuming and may lead to less appropriate purchasing decisions. This study proposes a web-based decision support system for furniture product recommendation by integrating the Item-Based Collaborative Filtering (IBCF) method with min–max normalisation. The recommendation process utilises historical user ratings to identify similarities among products, while min–max normalisation is applied to standardise product attributes, including price, category, material, colour, and size, into a comparable scale. To address the cold-start problem for newly added products with insufficient rating data, a content-based similarity mechanism is incorporated into the recommendation process. The system was developed using a dataset consisting of 322 furniture products, 50 consumers, and 1,099 rating transactions. System functionality was evaluated using black-box testing, while user acceptance was assessed through user acceptance testing (UAT) involving ten respondents. The evaluation results show that all primary system functions operated as expected and achieved an overall UAT score of 88.0%, indicating that the proposed system is acceptable and capable of assisting consumers in selecting furniture products based on their preferences. This study contributes by integrating collaborative filtering, multi-attribute normalisation, and a content-based cold-start mechanism into a single recommendation framework for furniture product recommendation.
Customer Lifetime Value Prediction Using Long Short-Term Memory with RFM-T Features Based on E-Commerce Customer Data
CLV prediction plays an important role in e-commerce by helping companies identify valuable customers and develop effective retention strategies. However, predicting CLV remains challenging due to the sequential nature of customer purchasing behaviour. This study proposes a CLV prediction model using the LSTM algorithm with Recency, Frequency, Monetary, and Tenure features extracted from the Online Retail II dataset. The CLV target is calculated as the accumulated monetary value generated during the three months following the historical observation period. The proposed approach consists of data preprocessing, monthly RFM-T feature engineering, feature normalisation using min-max scaling, LSTM model training, denormalisation, and performance evaluation using MAE and RMSE. The LSTM model incorporates stacked LSTM layers, batch normalisation, dropout, and L2 regularisation to improve learning stability and generalisation. Experimental results indicate that the model was able to capture customer purchasing patterns based on sequential RFM-T features, with training and validation loss trends showing stable convergence. The proposed model achieved an MAE of 43.16 and an RMSE of 125.39 on the original monetary scale, reflecting the prediction performance obtained on the evaluated dataset. These findings suggest that the proposed LSTM model with RFM-T features provides an approach for CLV prediction in e-commerce.
Evaluation Of CNN-LSTM with Attention for Forest Fire Prediction in Indonesia: Challenges of Imbalanced Data
Forest and land fires are annual disasters in Indonesia that are influenced by the temporal and nonlinear dynamics of surface weather conditions. This study evaluated a hybrid Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) architecture with an attention mechanism for predicting forest fire risk based on multivariate surface weather data from BMKG stations for the 2015–2024 period. The dataset comprised 4,931 daily observations with eight weather features and four fire risk levels. The models were evaluated using temporal cross-validation and compared with the baselines (Random Forest, XGBoost, LightGBM, SVM, and standalone LSTM). The results indicate that the CNN-LSTM-attention model achieved 71.47% accuracy and 62.36% F1-score in the initial configuration. However, optimization attempts using SMOTE substantially degraded the performance to 50.95% accuracy (F1=0.55). The critical findings are as follows: (1) SMOTE is unsuitable for time-series data as it disrupts temporal patterns; (2) simpler architectures outperform excessive complexity; (3) class weighting is preferable to oversampling for handling class imbalance; and (4) basic weather features (without extensive engineering) yield optimal results. This study concludes that deep learning approaches for forest fire prediction in tropical regions face significant challenges related to class imbalance and temporal stability, necessitating the development of more adaptive modeling strategies
The Relationship Between Moodle-Based Learning Management System Usage and Employee Knowledge Retention in the Individual Development Program at BPJS Ketenagakerjaan
This study examines the relationship between Moodle-based learning management system (LMS) usage within the Individual Development Program (IDP) and employee knowledge retention at BPJS Ketenagakerjaan. A cross-sectional survey was distributed to IDP participants nationwide using a purposive, voluntary-response technique; after removing 73 duplicate submissions, 417 valid responses were analysed (minimum required by the Slovin formula: 372). The instrument's 30 retained items (15 LMS-usage and 15 knowledge-retention) were all valid (r = 0.715–0.886 > r-table = 0.096) and reliable (Cronbach's Alpha 0.957–0.979). Regression residuals departed from normality, homoscedasticity, and strict linearity, so heteroscedasticity-robust (HC3) errors were used to confirm inference. The model (Y = 3.113 + 0.901X) showed a significant positive association between LMS usage and knowledge retention (robust t = 29.643, p < .001; F = 1008.233, p < .001), with usage associated with 70.84% of the variance in retention (R² = 0.7084). Greater LMS engagement is linked to stronger self-reported retention within the IDP, informing digital training design at BPJS Ketenagakerjaan. Given the cross-sectional, single-source design, findings should be read as associations rather than causal effects.
Sentiment Analysis of User Reviews for the Locket Widget Application on Google Play Store Using the Naive Bayes Classifier
User reviews on the Google Play Store contain valuable opinions that can be utilised to evaluate application quality, including the Locket Widget application. This study aims to classify user review sentiment into positive, neutral, and negative categories using the Naive Bayes Classifier algorithm with Term Frequency–Inverse Document Frequency (TF-IDF) weighting. The data were collected through web scraping, resulting in 589 user reviews. The dataset then underwent preprocessing, sentiment labelling, TF-IDF weighting, and sentiment classification. The model was evaluated using an 80:20 stratified train–test split. The results showed that the proposed model achieved an accuracy of 76.27%, with a weighted precision of 0.69, weighted recall of 0.76, and weighted F1-score of 0.72. The findings indicate that the combination of TF-IDF and the Naïve Bayes classifier is effective in classifying positive and negative user review sentiments. However, the model was unable to effectively classify the neutral class, resulting in an F1-score of 0.00 for this category. This finding indicates that further improvements are needed to enhance the model's ability to distinguish neutral sentiment from positive and negative classes.
Application of RCA and AHP to Identify Functional Testing Failures in Loan Origination Systems by QA Engineers
Recurring functional testing failures in DJOIN’s digital banking systems during 2023–2025 created operational risks, particularly in the Balance Validation Dashboard and financial transaction modules. This study aimed to identify the principal causes of recurring failures and prioritize appropriate improvement solutions by integrating Root Cause Analysis (RCA) and the Analytical Hierarchy Process (AHP). The analysis used 1,759 production bug tickets, comprising 1,724 tickets from the Coopmax system and 35 tickets from the KOCEK loan origination system. RCA was conducted using a Fishbone Diagram based on the 6M framework and 5 Whys Analysis, supported by bug-tracking records, system logs, testing documentation, and stakeholder interviews. The analysis identified 12 root causes. The three highest-severity causes were hardcoded product-type assumptions in the Balance Validation Dashboard query, with a severity score of 4.67; the absence of a regression test suite after hotfix implementation, with a score of 4.33; and distributed system logs that complicated failure tracing, with a score of 4.33. AHP evaluation assigned the highest weight to solution effectiveness at 42.44%, followed by business impact at 30.65%, implementation cost at 13.07%, implementation time at 8.36%, and technical complexity at 5.48%. The comparison matrix achieved a consistency ratio of 0.024, indicating acceptable judgment consistency. Among 12 alternatives, Automated Testing Tools ranked first with a priority score of 0.1872, followed by Test-Driven Development at 0.1412 and CI/CD Implementation at 0.1372. These findings provide an evidence-based prioritization of proposed corrective and preventive actions; however, their actual effectiveness requires post-implementation evaluation.
Expert System for Detecting Academic Burnout Levels among University Students Using the Certainty Factor Method
Academic burnout is a condition of physical, emotional, and mental exhaustion resulting from prolonged academic demands and may negatively affect students' learning and well-being. This study aimed to develop a web-based expert system to support the preliminary identification of academic burnout among university students using the Certainty Factor method. The system knowledge was constructed through a literature review and a structured knowledge acquisition process involving a psychology expert before being represented in the knowledge base. The system was developed using the Waterfall model with PHP, the CodeIgniter framework, and MySQL. Functional evaluation using black-box testing showed that all system features operated as expected. In addition, User Acceptance Testing (UAT) involving 102 students of Universitas Muria Kudus achieved an overall acceptance score of 83.92%, indicating a Very Good level of user acceptance. The developed system was able to classify consultation results into mild, moderate, and severe academic burnout categories based on the implemented Certainty Factor inference process. Therefore, the proposed expert system may serve as a supporting tool for the preliminary identification of academic burnout. Further validation using standardised psychological instruments or professional psychological assessments is recommended in future studies.
A Business Intelligence Approach to Visualizing and Mapping Families at Risk of Stunting Using Microsoft Power BI: Evidence from Bengkulu
Stunting is a chronic nutritional problem that affects children's physical growth and cognitive development and remains a national priority in Indonesia. Bengkulu Province continues to face challenges in managing Families at Risk of Stunting (KRS) data, which are complex and difficult to analyze efficiently using conventional reporting methods. This study aims to design an interactive dashboard for visualizing and mapping Families at Risk of Stunting (KRS) using Microsoft Power BI to support monitoring, evaluation, and evidence-based decision-making at the Bengkulu Provincial BKKBN. The study adopts the Business Intelligence Roadmap, consisting of the justification, planning, business analysis, design, and construction stages. Official secondary KRS data for the 2022–2025 period were preprocessed in Python through data integration, cleaning, validation, and transformation, followed by Exploratory Data Analysis (EDA) to identify data characteristics and regional patterns before dashboard development. The results indicate that the number of families at risk of stunting reached 302,667, with an average risk percentage of 23.39%. Trend analysis revealed a decline in 2024 followed by an increase in 2025, while Bengkulu City, North Bengkulu, and Rejang Lebong consistently recorded relatively higher numbers of families at risk of stunting. The developed dashboard provides interactive visualizations that improve data analysis and support evidence-based decision-making for accelerating stunting reduction in Bengkulu Province.
Waste Level Monitoring System Based on Internet of Things Using Long-Range Technology
The increasing need for efficient waste management encourages the development of monitoring systems that can provide timely information on the condition of waste bins. This study proposes an Internet of Things (IoT)-based waste level monitoring prototype that employs LoRa communication for wireless data transmission. The system was developed using Arduino Uno, Arduino Mega, LoRa modules, Raspberry Pi, and an ultrasonic sensor to detect the waste level and transmit the collected data to a monitoring dashboard. A quantitative experimental method was applied, supported by Quality of Service (QoS) analysis, to evaluate LoRa network performance. The testing was conducted in a non-line-of-sight (NLOS) environment using a 433 MHz LoRa frequency. The experiment involved 30 testing scenarios, consisting of five transmission distances, namely 20 m, 40 m, 60 m, 80 m, and 100 m, combined with six spreading factor settings from SF7 to SF12. The evaluation focused on packet loss, throughput, and time on air. The results indicate that transmission performance was influenced by environmental obstruction and spreading factor configuration. Packet loss was mainly observed at 80 m, with the highest loss reaching 9% at SF7, while higher spreading factor values tended to reduce packet loss. In terms of throughput, increasing the spreading factor decreased the transmission rate, whereas the time on air became longer. These findings show that LoRa communication can be implemented for waste bin-level monitoring, although signal barriers in NLOS conditions may affect data transmission quality.
DRASTIC: Big Data Quality in Big Data Integration
In today’s digital era, organisations are increasingly relying on data-driven decision-making to enhance operational efficiency and strategic planning. Data from multiple sources should be integrated to support this movement. However, this process is complex due to the emergence of big data. Consequently, it significantly increases the challenges of managing and integrating data, which can degrade data quality and lead to poor decision outcomes. In fact, existing data quality characteristics are no longer adequate in the big data era. Therefore, this paper conducted a comprehensive literature review of peer-reviewed articles retrieved from electronic databases published between 2010 and 2025 to examine existing data quality characteristics and identify gaps related to the 5V's big data characteristics. Moreover, this paper compares and evaluates existing data quality characteristics and their sufficiency for assessing the quality of data in big data integration. Based on these evaluations, this paper proposes DRASTIC, a set of 14 data quality characteristics, with dependency and scalability introduced as new characteristics in the context of big data integration because these characteristics are underexplored in existing literature. The findings contribute to the literature by extending current data quality characteristics and addressing the challenges posed by big data's unique characteristics in data integration.
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Optimization of MobileNet Architecture with Ghost Module for Dental Enamel Caries Classification
Dental and oral diseases, particularly dental caries, represent a global health issue that requires early identification at the enamel stage to prevent further demineralization and damage. The utilization of artificial intelligence technology through Convolutional Neural Networks (CNNs) has been widely applied for medical image analysis. However, complex conventional models often incur high computational loads. Therefore, this study aims to implement and evaluate MobileNet variants (V1, V2, V3, and V4) optimized using the Ghost Module to classify dental caries images. The integration of the Ghost Module aims to mitigate feature redundancy and enhance feature representation without compromising image extraction quality. A dataset of 2,000 clinical dental images from the public "Caries-Spectra," categorized as advanced enamel caries, early-stage enamel caries, and no enamel caries, was curated and expanded to 12,000 images using preprocessing and augmentation. The dataset splitting was executed with a final learning proportion of 80% training data, 10% validation data, and 10% testing data. The image preprocessing utilized histogram equalization, CLAHE, and adaptive thresholding methods. The overall hybrid architecture was then evaluated based on performance metrics (such as accuracy, precision, recall, and F1 score). The experimental results demonstrate that the integration of the Ghost Module consistently enhances the classification accuracy across all MobileNet variants. The proposed Hybrid MobileNetV1 with Ghost Module achieved the highest performance, securing an overall accuracy of 96.83%, with well-balanced precision and recall across all enamel caries stages
Navigating Deceptive Realities: Public Perceptions and Cybersecurity Threats of Deepfake Technology
The rapid advancement of deepfake technology presents a profound cybersecurity threat by seamlessly fabricating synthetic media, which severely erodes digital trust. This study aims to evaluate the community's awareness of deepfake threats and assess the necessity of multifaceted mitigation strategies. Employing a quantitative methodology, an online survey was conducted with 67 respondents, predominantly young adults, to measure their exposure, psychological vulnerability, and perspectives on cybersecurity countermeasures. The findings reveal that while the public is generally aware of deepfakes, 75 per cent struggle to visually differentiate manipulated content from authentic media. Consequently, the proliferation of deepfakes has diminished perceived societal trust in online information. Notably, there is a unanimous consensus among respondents demanding strict legal frameworks and comprehensive public education to combat this menace. The study concludes that relying exclusively on technical detection algorithms is insufficient. Instead, preserving information integrity requires a multidisciplinary approach combining robust technological defences, proactive policymaking, and widespread digital literacy trainings.
Implementation of YOLO26 for Mold Detection on White Bread Based on Digital Imagery
White bread is highly susceptible to visible mould contamination, which causes physical deterioration and potential health risks. Conventional manual visual inspection is slow, subjective, and inconsistent, necessitating an automated detection system. This study implemented the YOLO26n algorithm for mould contamination detection on white bread based on digital imagery. A primary dataset of 300 images (150 fresh bread and 150 mouldy bread) was collected independently, annotated via Roboflow, and split into 70% training, 20% validation, and 10% testing. The model was trained on Google Colab using the MuSGD optimiser with 200 epochs. The YOLO26n model achieved an overall precision of 0.827, recall of 0.734, and mAP50 of 0.711, with an inference speed of 8.1 ms per image, demonstrating its potential as a fast and lightweight solution for automated mold inspection, though further improvement in moldy bread detection performance is required before reliable deployment in bakery production lines
Web-Based Customer Loyalty Point System Using QR Code with Whatsapp Notification and Reward Management at Bismole Elektrik Store
The development of information technology encourages retail business operators to implement digital systems to improve service quality and operational efficiency. At Bismole Elektrik Store, the processes of recording customers, purchase transactions, and calculating loyalty points were previously done manually, leading to data-recording errors and slowing down the service process. Based on the observation of 120 customer transaction data and interviews with 2 store owners and cashiers, several issues were found, such as difficulties in searching for customer data and discrepancies in point calculations. This research aims to develop a web-based customer loyalty point system using QR codes as a digital customer identity integrated with reward management, sales reports, and real-time WhatsApp notifications. The system development uses the Waterfall method, which consists of the stages of requirements analysis, design, implementation, testing, and maintenance. The system is developed using the programming languages PHP, HTML, CSS, JavaScript, and the MySQL database. The system evaluation was conducted using the black-box testing method with 9 testing scenarios and user acceptance testing involving 5 users consisting of the store owner, cashier, and customers. The results of the black box testing showed that all system features operated with a success rate of 100%, while the user acceptance testing results indicated a user satisfaction level of 92%, demonstrating that the system is easy to use and capable of supporting store operational activities. The research results show that the implementation of QR codes can accelerate the customer identification process, automate point calculations, manage the reward redemption process, and provide transaction information through WhatsApp notifications. Thus, the developed system can enhance the efficiency and accuracy of managing the customer loyalty program at Toko Bismole Elektrik.
Low-Sugar Diet Recommendations for Bangrajan Muay Thai Boxing Athletes Using Collaborative Filtering
Adjusting diet patterns according to nutritional requirements, training intensity and an athlete's physical condition is often a challenge in implementing a healthy diet, particularly one low in sugar foods. This study aims to develop an artificial intelligence (AI)-based recommendation system that can help boxing and muay thai athletes in implementing a more targeted diet programme through food recommendations tailored to their individual behaviours and nutritional needs. The methods used are collaborative filtering with a nutrition scoring approach, athlete preference analysis, and dynamic nutrition planning. The results show that the developed system, namely the Smart Nutrition System, is able to provide recommendations based on similarities among athletes’ preferences and nutritional requirements, thus supporting more effective decision-making in managing athlete diet patterns. Furthermore, the Smart Nutrition System also has the potential to evolve into an “athlete intelligence nutrition platform" that supports the implementation of personalised nutrition for combat sports athletes to support athlete performance.
Implementation of a Convolutional Neural Network Using VGG19 for Ogan Malay Script Recognition
Regional languages and scripts, including the Melayu Ogan script, face the threat of extinction due to declining usage and limited digital documentation in the modern era. While current Indonesian script research primarily focuses on popular scripts, research addressing the Ogan Malay script remains severely limited. To address this gap, this study provides one of the earliest implementations of the Convolutional Neural Network (CNN) VGG-19 architecture specifically designed for Ogan Malay script classification. This research utilises a primary dataset provided by the Language Center of South Sumatra Province, consisting of 185 distinct character classes, with each class initially containing one original image. The VGG-19 architecture is applied and supported by data augmentation techniques to enrich spatial variability, followed by evaluation using k-fold cross validation. Evaluation results demonstrate excellent classification performance. The model achieved maximum convergence without any indications of overfitting at an optimal configuration of 30 epochs with a learning rate of 0.0001. This configuration successfully resulted in an accuracy of 99.14%, a precision of 0.9870, a recall of 0.9914, and an F1-score of 0.9885. The success of this classification model provides a strong foundation for future real-time regional script recognition applications to support cultural preservation.