Journal of Innovation and Technology Polbeng Series on Informatics (INOVTEK Polbeng - Seri Informatika)
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: Jaroji, M. 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.
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. The objective of this study is to design a layered architecture for the web-based single-farm fish farming management system using the prototype method. 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 and maintainable basis for integrating cultivation and business processes in a single-farm fish farming environment. Future development may extend the architecture with multi-farm management, Internet of Things integration, and quantitative evaluation of software quality attributes.
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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 organizational 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 Leave-One-Out Cross-Validation (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 generalizability.
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 (Empathize, Define, Ideate, Prototype, Test). The contribution is context-specific rather than methodological: it presents a user-centered 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 artifact 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 utilize information systems to improve public services. Kerobokan Regional Office has utilized the Complaint Management Information System (SIPANDU) to manage citizen complaints digitally. However, a comprehensive evaluation aligning operational technical performance, organizational 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 neighborhood 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, optimizing server performance, and implementing real-time notification features for complaint status updates.
POS System Success Evaluation at a Civil Servants Cooperative: DeLone-McLean Evidence and Green Accounting as an Extended Net Benefit
Information system success models define net benefits in economic and operational terms, while the environmental implications of digitalization remain 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 Information Systems Success Model, extended by incorporating green accounting transformation as an environmental benefit alongside operational efficiency. A mixed-methods evaluative design involved customers (N = 78), tenants (N = 12), managers (N = 7). Data were analyzed using validity and reliability tests, Pearson correlation, path analysis, the Wilcoxon signed-rank test, and thematic analysis. All 18 constructs were reliable, with Cronbach’s alpha values ranging from 0.757 to 0.976, and all 17 customer items were valid. Queue management and comfort significantly predicted customer satisfaction (β = 0.683, p < 0.001; R² = 0.715), while satisfaction significantly predicted green accounting transformation (β = 0.773, p < 0.001; R² = 0.597). Ease of use and speed were not significant when tested jointly. Although average monthly sales declined after implementation, the difference reflected changes in measurement basis rather than system performance. The model offers a replicable framework for cooperative evaluation.
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-vectorizer 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
Forecasting XAU/USD prices requires a model capable of learning temporal dependencies and an appropriate hyperparameter configuration. This study compares grid search, random search, and Bayesian optimization for tuning a long short-term memory model to predict the next trading-day XAU/USD closing price. The dataset comprises 1,705 daily observations from January 2020 to July 2026, using Open, High, Low, Close, and year-on-year United States inflation as input features. Inflation data were aligned according to official Consumer Price Index release dates to prevent look-ahead bias. The models used a 30-observation look-back window and were evaluated using validation data, independent testing data, five-fold expanding-window walk-forward validation, MAE, RMSE, MAPE, search time, and total computational time. Grid Search achieved the best testing MAPE of 2.134620%. Random Search recorded the shortest search time of 660.23 seconds and the lowest total computational time of 705.27 seconds among the optimized models. It also produced the lowest mean walk-forward MAPE of 6.053219% and standard deviation of 4.235567%. The forecasting procedures were implemented in a Streamlit application and verified through black-box testing. Grid Search is preferable for final-period accuracy, whereas Random Search offers a better balance of accuracy, stability, and computational efficiency.
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_neighbors=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 program has generated a variety of public responses on social media. This study aims to analyze 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 and then processed through preprocessing, lexicon-based sentiment labeling validated using manually labeled 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 they 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 normalization. The recommendation process utilizes historical user ratings to identify similarities among products, while min–max normalization is applied to standardize product attributes, including price, category, material, color, 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 normalization, 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 behavior. This study proposes a CLV prediction model using the LSTM algorithm with Recency, Frequency, Monetary, and Tenure (RFM-T) 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 normalization using min-max scaling, LSTM model training, denormalization, and performance evaluation using MAE and RMSE. The LSTM model incorporates stacked LSTM layers, batch normalization, dropout, and L2 regularization to improve learning stability and generalization. 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.
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 analyzed (minimum required by the Slovin formula: 372). The instrument's 30 retained items (15 LMS-usage, 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 utilized 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 Naïve 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 labeling, 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 standardized 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 IoT (Internet of Things) Using LoRa (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.
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
Implementation of YOLO26 for Mold Detection on White Bread Based on Digital Imagery
White bread is highly susceptible to visible mold 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 mold contamination detection on white bread based on digital imagery. A primary dataset of 300 images (150 fresh bread and 150 moldy breads) 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 optimizer 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 Bangrajanmuaythai 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 a low-sugar food diet. 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 program through food recommendations tailored to their individual behaviors 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 personalized 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 utilizes 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.
Weather Prediction Using CNN-LSTM-Based AI for Weather Pattern Analysis in Banten Province
Weather variability in Banten Province poses challenges across various sectors, including community activities, agriculture, and disaster preparedness, necessitating accurate weather prediction methods. This study proposes a hybrid Convolutional Neural Network–Long Short-Term Memory (CNN-LSTM) model to predict air temperature and rainfall based on historical weather time-series data. The dataset was obtained from the Open-Meteo API and BMKG for the observation period from January 2019 to May 2024. Input variables include air temperature, rainfall, wind speed, sea surface temperature anomaly, and the El Niño–Southern Oscillation (ENSO) index. The data preprocessing stages involve data cleaning, normalization using Robust Scaler, and the construction of data sequences using the sliding window method prior to the model training process. Model performance was evaluated using Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) and compared against baseline models. The experimental results demonstrate that the CNN-LSTM model achieves an MAE of 0.60°C and an RMSE of 0.73°C for air temperature prediction and an MAE of 6.15 mm and an RMSE of 8.31 mm for rainfall prediction. The prediction outcomes were subsequently integrated into a web-based dashboard to facilitate information visualization. Initial validation against BMKG observation data in South Tangerang showed a relatively low temperature deviation during the testing period. These findings confirm that the proposed approach has adequate potential to support short-term weather prediction systems in Banten Province.
Web-Based Job Recommendation Based on LinkedIn Profiles Using Domain-Aware SBERT Retrieval and TF-IDF Reranking
Online job search often relies on keyword matching, while the semantic relationship between candidate profiles and job descriptions may not be captured adequately. This study develops a web-based job recommendation system based on LinkedIn-style candidate profiles using SBERT retrieval and domain-aware TF-IDF reranking. Candidate profiles are constructed from target role, headline, skills, experience, education, preferred location, and work preference, with non-English input translated into English when needed. The job corpus consists of approximately 1.3 million job postings represented by precomputed 384-dimensional SBERT embeddings. The system retrieves initial candidates using cosine similarity and reranks them using TF-IDF similarity with domain, experience, and location constraints. Manual evaluation on 1,012 judged profile-job pairs shows that the proposed method achieves Precision@5 of 0.428, Precision@10 of 0.368, NDCG@10 of 0.531, and MRR of 0.605. An additional validated pseudo-label evaluation achieves Precision@5 of 0.840, with 83.33% agreement and a Cohen’s Kappa of 0.75 against human-checked samples. These results indicate that semantic retrieval combined with explainable domain-aware reranking can improve the relevance of web-based job recommendations.
Automated Fish Feeding System and Real-Time Water Quality Monitoring Based on Internet of Things for Milkfish Aquaculture
Milkfish (Chanos chanos) aquaculture in Indonesia still relies heavily on manual feeding and periodic water quality monitoring, which increases the risk of operational inefficiency and human error. This study designs and implements an integrated Internet of Things (IoT)-based prototype to automate feeding and monitor real-time water quality parameters, including temperature, turbidity, and water level, in milkfish ponds. The system uses an ESP32 microcontroller connected to a DS18B20 temperature sensor, HC-SR04 ultrasonic sensors for water level and feed volume detection, an FC-51 infrared sensor for feed presence detection, and a turbidity sensor. Data transmission is carried out using the MQTT protocol over WiFi and displayed through web and smartphone-based interfaces. The novelty of this study lies in the integration of automated feeding with multi-parameter real-time water quality monitoring specifically designed for milkfish pond conditions in Indonesia, incorporating MQTT-based communication architecture and a dual-platform monitoring interface. Prototype validation showed that scheduled feeding was executed successfully across three daily feeding sessions. Sensor testing produced small measurement deviations, with a temperature deviation of 0.1°C, a water level deviation of 0.2 cm, and a turbidity deviation of 0.2 NTU. The MQTT communication latency ranged from 120 to 180 ms under normal conditions. These results confirm that the proposed system effectively supports automated feeding, real-time pond monitoring, and remote management with low-cost IoT infrastructure.
Knowledge Management System Development Using Tiwana Roadmap and Agile Scrum at HP Service Center Denpasar
This study aims to design and develop a Knowledge Management System (KMS) at HP Service Center Denpasar by integrating the Tiwana Roadmap framework and the Agile Scrum development method. HP Service Center Denpasar faces challenges in knowledge retention and dissemination, where valuable knowledge assets, both tacit and explicit knowledge, are still stored individually. Therefore, this study focuses on documenting all knowledge possessed by various operational units, ranging from Senior PC/Laptop Technicians and Printer Technicians to logistics staff, in order to prevent knowledge loss and accelerate the knowledge transfer process. In the development process, the Tiwana Roadmap was selected as the main framework because of its ability to provide structured, systematic, and gradual guidance for identifying, managing, auditing, and optimally utilizing organizational knowledge. Meanwhile, to execute the blueprint into software form, the Agile Scrum method was implemented to support a system development process that is flexible, adaptive, and responsive to changing requirements and dynamic field conditions through iterative sprint cycles. After the system has been developed, the testing phase will be conducted using two comparative approaches: the User Experience Questionnaire (UEQ) to quantitatively measure user experience aspects, and Cognitive Walkthrough / Cognitive Task Analysis (CTA) to evaluate cognitive efficiency and system usability when used by staff. The expected final result of this study is a KMS platform that is not only technically valid but also interactive and capable of improving operational efficiency at HP Service Center Denpasar.
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Convolutional Block Attention Module Integration into YOLO11 Architecture for MRI Image-based Brain Tumor Detection
Brain tumor is one of the deadly diseases in the world that can affect anyone, this disease is characterized by the growth of abnormal cells or tissues in the brain, medically it can be life-threatening if not treated properly. Most tumor detection tasks are done by manual assessment by radiologists or pathologists where this work is time-consuming, so accurate and reliable detection is needed in the medical field in diagnosing brain tumors. The purpose of this study is to integrate CBAM on the YOLO11 architecture in detecting brain tumors and determine the performance of the brain tumor detection model using the YOLO11 architecture with CBAM integration. The method used to detect brain tumors is the YOLO11 architecture with CBAM integration. The dataset used is an image in the form of brain MRI. The results of this study indicate that the precision is 86.9%, recall is 86.2%, mAP50 is 91%, and mAP50-95 is 64% in the validation data and precision is 89.1%, recall is 92%, mAP50 is 79%, mAP50-95 is 51.6%, and F1 score is 90.5% in the test data which can be used to help medical personnel in detecting and treating brain tumors considering that this model has outstanding results, especially in the recall metric section which reaches 92% in the test data.
SIAMIK Success Evaluation at Bali Tourism Polytechnic: DeLone-McLean Evidence and Development Recommendations
Academic information systems are essential for supporting integrated academic services in higher education. However, SIAMIK at Bali Tourism Polytechnic still faces issues related to system integration, interface interactivity, manual grade processing, and limited digital service integration. This study evaluates SIAMIK success using the DeLone and McLean Information System Success Model and formulates system development recommendations based on empirical findings. A quantitative survey was conducted involving 102 users consisting of management, lecturers, administrative staff, and students. Data were collected through questionnaires and analyzed using descriptive statistics and Partial Least Squares Structural Equation Modeling. The results show that system quality significantly affected user satisfaction (β = 0.324, p < 0.001), information quality had the strongest effect (β = 0.433, p < 0.001), and service quality also had a significant effect (β = 0.170, p = 0.039). User satisfaction significantly influenced net benefits (β = 0.724, p < 0.001). The model explained 67.2% of user satisfaction and 52.3% of net benefits, with strong predictive relevance (Q² = 0.843). Findings suggest that SIAMIK development should prioritize information completeness, responsive access, technical support assurance, and cross-system integration.
Comparative Evaluation of Hand-Engineered Features for CRF-Based Named Entity Recognition in Indonesian Clinical Research Articles
Extracting clinical entities such as signs, symptoms, diagnoses, and treatments from Indonesian clinical research makes clinical information more accessible to readers with varying levels of medical knowledge. Conditional Random Fields (CRFs) combined with hand-engineered features have proved effective for health domain named entity recognition (NER). However, studies comparing hand-engineered feature combinations for clinical NER in Indonesian clinical research articles remain limited. This study compares five hand-engineered feature combinations using a CRF model to identify the most effective combination for recognizing seven clinical entity types, using a newly annotated corpus of 29 Indonesian internal medicine research articles. The dataset was split into 80% training and 20% testing data. Model performance was evaluated using weighted-average and macro-average precision, recall, and F1-score, reported as mean ± SD across five random states (5, 10, 15, 20, 25) and training epochs (50, 100, 150). The combination of contextual, prefix and suffix, and orthographic features achieved the best weighted F1-score of 78.90% at Epoch 50, though macro-F1 was considerably lower at 64.32%, reflecting weaker performance on minority entity types. This gap indicates a need for further corpus expansion and data balancing.