Publication Search

Data mining is a technique of several fields of science to find previously unknown relationships in the data warehouse so that it becomes an information that can be used later. The unwise use of electricity will of course have an impact on the high use of electricity, therefore it is expected that every community understands the effort to use electricity wisely. Therefore, authors perform analysis of data mining on these electrical usage data in order to know which is a small, medium and large category. The authors use data on electrical use questionnaire as much as 200 data which is then presented into the ARFF format. In performing author analysis using WEKA Tools. The method used is Naive Bayes classification method with the greatest percentage of accuracy obtained using the Use Training Set Correctly of 80.5%, using a 5-Fold Cross Validation Correctly of 75%, and using 10-Fold Cross Validation amounted to 74%. While the result of the selection of the attributes using the algorithm classifier attribute evaluation (ClassifierAttributeEval) is stated that the most influential attribute against the electrical power usage classification is Electonic Goods.

Sudden Infant Death Syndrome (SIDS) is a sudden and unexpected death in infants that is often associated with the prone sleeping position. This study aims to develop an automated monitoring system capable of detecting SIDS risk factors using the YOLOv8 algorithm and to analyze the effect of data augmentation on model performance. The dataset consists of two classes, baby-lying-on-back (supine) and baby-lying-on-stomach (prone), which were processed through model training and evaluation using precision, recall, F1-score, and mAP metrics. The model was trained under two scenarios, without data augmentation and with data augmentation. The results show that the model without augmentation achieved a precision of 90%, recall of 85%, F1-score of 86%, and mAP50 of 93.7%. After applying augmentation, performance improved to a precision of 90%, recall of 87%, F1-score of 88%, and mAP50 of 95.1%. These findings indicate that augmentation increases detection accuracy and enhances model generalization, including robustness against variations in lighting and camera angles. Furthermore, testing with image and video inputs revealed that the non-augmented model exhibited a tendency toward overfitting, particularly in favor of the baby-lying-on-stomach, whereas the augmented model successfully classified both classes accurately. The developed system is also equipped with an alarm feature and early-warning notifications via Telegram to smartphone when a prone position is detected for a certain duration. Overall, the results demonstrate that YOLOv8 with data augmentation is effective for an automated, non-invasive monitoring system for infants, making it suitable for detecting and preventing potential SIDS risk factors.

Posyandu (Integrated Health Post) is a public health facility that plays a vital role in providing health services for toddlers and pregnant women. However, data management and reporting often face challenges, such as limited access to information and errors in data recording. Therefore, this study aims to develop a Web-Based Posyandu Payolaumbu Service Information System using the CodeIgniter Framework to improve efficiency and accuracy in data management and reporting. In the development phase, a system requirements analysis and web-based application architecture design were conducted. The system implementation uses the CodeIgniter Framework as a framework to produce a faster, more efficient, and more reliable application. Proposed features include recording health data for toddlers and pregnant women, immunization schedules, weighing, and health reports. The results show that the Web-Based Posyandu Payolaumbu Service Information System can improve efficiency in recording and reporting health data. Users, including posyandu officers, midwives, and administrators, can easily access and manipulate data in real-time. Furthermore, this system helps improve service quality by providing more accurate and complete information on toddler health. In conclusion, the implementation of the Web-Based Posyandu Payolaumbu Service Information System using the CodeIgniter Framework provides significant benefits for data management and health services at Posyandu Payolaumbu. Suggestions for further development include maximizing system utilization, developing additional features, routine maintenance, and ongoing evaluation based on user feedback. With these steps, it is hoped that this system can contribute more effectively to improving the quality of health services at Posyandu and supporting comprehensive public health efforts.

Internal control is a crucial aspect for SMEs in maintaining the reliability of revenue recording and preventing the risk of misappropriation. This study aims to analyze the implementation of internal control in the revenue cycle at Exmo Tea Café and to evaluate its effectiveness through internal audit. The research uses a qualitative descriptive approach with data collection through interviews with management personnel directly involved in transactions and financial recording. The analysis is conducted using the COSO framework, which includes five main components: control environment, risk assessment, control activities, information and communication, and monitoring. The research results indicate that Exmo Tea Café has implemented several basic elements of internal control, such as recording transactions through a cashier application, daily cash reconciliation, and reporting to the owner. However, the effectiveness of these controls is still limited because the segregation of duties between receiving and recording is not optimal, risk assessment is reactive, documentation and report archiving are not systematic, and monitoring is conducted informally. In addition, there are no formal policies regarding operational standards (SOPs) or internal audit procedures that could serve as guidelines for continuous control implementation. These conditions have the potential to lead to risks of fraud, recording errors, and delays in financial reporting. Therefore, these findings underscore the need for a comprehensive enhancement of the internal control system, including strengthening the separation of duties, conducting preventive risk assessments, providing employee training related to financial governance, as well as implementing more formal monitoring and documentation. These improvements are expected to increase reporting accuracy, operational effectiveness, and minimize the potential for errors or fraud in the company's financial activities.  

This study aims to determine the effectiveness of the Jakarta Smart Card Plus (KJP Plus) Program at the Junior High School (SMP) level in South Jakarta in 2024. The program is an initiative by the Provincial Government of DKI Jakarta to ensure access to education for underprivileged families. This research employs Sutrisno’s (2007) program effectiveness theory, which includes five key indicators: program understanding, target accuracy, timeliness, goal achievement, and real change. The research method used is a descriptive qualitative approach, with data collection techniques including interviews, observations, and documentation involving informants from the South Jakarta Region I Education Sub-Department, school principals, students, and parents of KJP Plus beneficiaries. The results of the study indicate: (1) Understanding of the program is still uneven, particularly among parents who lack knowledge about the mechanism and use of KJP Plus; (2) Target accuracy is not optimal, as some recipients are economically capable, such as those who own cars or fall into higher welfare deciles; (3) The timeliness of fund distribution is generally good, although there are still some administrative delays; (4) Goal achievement is evident through reduced school dropout rates and increased educational participation, but not evenly across all areas; and (5) Real change is felt by most beneficiaries through easier access to education and provision of school supplies, although misuse of funds for non-educational purposes is still present. Overall, the effectiveness of the KJP Plus program at the SMP level in South Jakarta is deemed suboptimal, highlighting the need for improved data accuracy, stricter fund usage monitoring, and broader program socialization.

Recommender systems play a critical role in shaping user decisions across digital platforms; however, the increasing complexity of recommendation algorithms has raised serious concerns regarding transparency, trust, and accountability. This study focuses on enhancing the transparency of recommender systems by integrating Explainable Artificial Intelligence (XAI) techniques within a MovieLens-based recommendation framework. The primary problem addressed is the opacity of conventional recommendation models, which limits user understanding of why certain items are recommended and may reduce trust, perceived fairness, and system acceptance. Accordingly, the main objective of this research is to design and evaluate a hybrid explainable recommender system that balances predictive accuracy with human-understandable explanations. The proposed approach combines Matrix Factorization, feature-importance-aware neural networks, and knowledge graph embeddings to construct a robust recommendation model. To enhance explainability, multiple XAI strategies are integrated, including model-agnostic methods (LIME, SHAP, and CLIME), argumentation-based explanations, and context-aware personalized explanations. A comprehensive evaluation framework is employed, incorporating algorithmic metrics (accuracy, fidelity, robustness, counterfactual consistency, and fairness) alongside human-centered evaluations measuring trust, transparency, cognitive load, and perceived usefulness. Experimental results demonstrate that the knowledge graph–enhanced hybrid model achieves superior recommendation accuracy compared to baseline approaches. Moreover, context-aware explanations consistently outperform other methods in terms of fidelity, robustness, and user-perceived transparency, while argumentation-based explanations are found to be the most persuasive. CLIME offers a strong balance between technical stability and interpretability. The findings indicate that no single explainability technique is universally optimal; instead, hybrid and adaptive explanation strategies are most effective. In conclusion, this study confirms that human-centered, context-adaptive XAI significantly improves transparency and user trust in recommender systems, highlighting explainability as a fundamental component rather than an optional enhancement.

This study discusses the introduction of digital signature patterns using the Backpropagation method on Artificial Neural Network (JST) to identify a person's characteristics and potential. The increasing use of digital identities demands a verification system that is more secure, accurate, and adaptive to the variations of each individual's signature. The main problem faced in the signature recognition system is the low level of accuracy when the visual features of the signature have similarities between users, both in terms of shape, size, and stroke pressure. In addition, variations of signatures made by the same individual are also a challenge in the identification process. As a solution, this study implements Principal Component Analysis (PCA) to extract important features from the signature image before the training process using JST. PCA is used to reduce the data dimension so that the learning process becomes more efficient and optimal. A total of 80 signature images were used in this study, consisting of 60 training data and 20 test data. The results showed that the system was able to achieve an accuracy level of 92.5%. These findings prove that the combination of PCA and JST methods is effective in recognizing digital signature patterns and has the potential to be applied to digital security-based biometric identification systems.

End-to-end autonomous driving has emerged as a promising paradigm in which deep neural networks directly map raw visual inputs to continuous control actions. Despite its effectiveness, this approach suffers from limited transparency, posing significant challenges for deployment in safety-critical driving scenarios. This study addresses the lack of interpretability in vision-based end-to-end autonomous driving systems and aims to analyze model decision-making behavior under critical conditions such as sharp steering maneuvers and abrupt control transitions. To this end, an explainable end-to-end autonomous driving framework is proposed, combining a convolutional neural network trained via imitation learning with gradient-based visual attribution techniques, including Grad-CAM. The model predicts continuous steering, throttle, and braking commands directly from front-facing camera images, while explainability mechanisms are applied to reveal input regions influencing each control decision. Model performance is evaluated using both prediction accuracy and safety-oriented behavioral metrics. Experimental results show that the proposed explainable model achieves lower control prediction errors compared to a baseline end-to-end CNN, reducing steering mean squared error from 0.034 to 0.031, throttle error from 0.021 to 0.019, and brake error from 0.018 to 0.016. Moreover, safety-oriented analysis indicates improved driving stability, with steering variance reduced from 0.087 to 0.072 and abrupt control changes decreased from 14.6 to 10.3 events. Visual explanations consistently highlight road surfaces and lane-related structures during complex maneuvers, indicating reliance on semantically meaningful cues. In conclusion, the results demonstrate that integrating explainability into end-to-end autonomous driving not only preserves predictive performance but also correlates with smoother and more stable driving behavior. This framework contributes to the development of transparent and trustworthy autonomous driving systems suitable for safety-critical applications

Gestational Diabetes Mellitus (GDM) is a pregnancy-related metabolic disorder that poses health risks to both mother and fetus if not detected early, requiring accurate prediction methods for early screening and clinical decision-making. This study applies the Random Forest algorithm to detect GDM risk using clinical data from the Pima Indian Dataset. Data preprocessing included handling missing values, standardization, feature engineering, and a 70:30 train–test split. Two models were developed: a baseline and an optimized model using GridSearchCV hyperparameter tuning, validated with 5-fold cross-validation. Performance was assessed using a classification report, confusion matrix, and ROC–AUC. Results show that the optimized model outperforms the baseline, achieving 88% accuracy, an AUC of  93%, and average recall of 81%–85%. Compared to previous studies, this approach demonstrates improved predictive performance. The findings indicate that combining Random Forest with comprehensive preprocessing, feature engineering, and model optimization is effective and feasible for developing a medical decision support system for early GDM risk screening.

: The rapid expansion of cybersecurity standards and threat intelligence frameworks has led to significant semantic fragmentation among security terminologies, hindering effective information retrieval and interoperability across systems. Traditional keyword-based search approaches are inadequate for capturing the contextual meaning of security terms, particularly within formal frameworks such as NIST, MITRE ATT&CK, and CWE. This study addresses this challenge by proposing CyberBERT, a transformer-based semantic search framework designed to align cybersecurity terminologies through deep contextual representation and ontology-driven reasoning. Research Objectives: The primary objective of this research is to develop a semantic retrieval model capable of understanding conceptual relationships between security terms beyond lexical similarity. Methodology: The proposed methodology fine-tunes a BERT-based model on the NIST Glossary corpus using a combination of masked language modeling and triplet loss objectives to generate discriminative semantic embeddings. These embeddings are further aligned with cybersecurity ontologies, including MITRE ATT&CK and CWE, to enhance semantic consistency and explainability. Semantic retrieval is performed using cosine similarity within a 768-dimensional embedding space and evaluated using Mean Reciprocal Rank (MRR) and Precision@K metrics. Results: Experimental results demonstrate that CyberBERT achieves an MRR of 0.832, outperforming domain-adapted baselines such as SecureBERT and CyBERT. The integration of ontology alignment improves semantic accuracy by over 6%, while robustness evaluations confirm resilience against adversarial linguistic perturbations. Visualization using t-SNE reveals coherent semantic clustering aligned with the five core NIST Cybersecurity Framework functions. Conclusions: In conclusion, CyberBERT effectively bridges semantic gaps across cybersecurity terminologies by combining transformer-based contextual learning with ontological reasoning. The framework offers a robust, interpretable, and scalable solution for semantic search, supporting improved interoperability and knowledge discovery in cybersecurity operations and standards harmonization.

Diseases in primary health services exhibit complex spatial-temporal dynamics due to urbanization and population mobility. Conventional surveillance approaches are difficult to capture these patterns adaptively. Machine learning (ML) based on spatio-temporal modeling offers a solution with the ability to detect disease clusters automatically and with high precision. Research Objectives: This research aims to develop a machine learning model to detect disease hotspots from primary service data in Indonesia, with a focus on improving prediction accuracy, interpretability, and relevance of health policies. Methodology: The primary service dataset for 2024 (5,343 entries) was analyzed using three ML models Gradient Boosting Machine (GBM), Temporal Random Forest (TRF), and Multi-EigenSpot with spatial (village) and temporal (week, month) features. Performance evaluation includes predictive (AUC, F1-score) and spatial (Moran's I, Spatio-Temporal Correlation Index) metrics. Results: The results showed that Multi-EigenSpot achieved the best performance (AUC=0.91; F1=0.86), with the detection of dominant hotspots in Sungai Asam and Beringin Villages. Moran's I value of 0.63 indicates a strong spatial autocorrelation, while STCI=0.57 indicates moderate temporal stability. Conclusions: ML-based spatio-temporal models are effective in identifying hidden disease patterns and have the potential to be integrated into national digital surveillance systems. This approach supports precision public health by providing a scientific basis for real-time location- and time-based intervention policies.

The development of video streaming services has made DramaBox widely used by Generation Z, who prioritize fast entertainment access through mobile devices. However, complaints such as long ad duration, unstable video quality, and inaccurate subtitles remain obstacles that can reduce user satisfaction. This study analyzes the factors that influence DramaBox user satisfaction in Jambi City using the End User Computing Satisfaction (EUCS) method with five main dimensions: content, accuracy, format, ease of use, and timeliness. Data were obtained through questionnaires completed by 385 respondents and processed using SEM-PLS with the help of SmartPLS 4. The results showed that accuracy and timeliness significantly influence user satisfaction, while content, format, and ease of use did not have a significant impact. This finding indicates that information reliability and system speed are the most determining aspects of user experience. Therefore, improvements in both aspects are important for the future development of the DramaBox application.

Manual quality assessment of Platelet Concentrate (TC) is highly subjective and inconsistent, necessitating an objective, automated classification system. This study aims to develop a computationally efficient, low-cost model for TC quality classification using Histogram Features extracted from grayscale images combined with the K-Nearest Neighbor (KNN) algorithm. The methodology employed critical preprocessing steps, including StandardScaler for normalization and SMOTE for balancing the training data, followed by optimization across K=1 to K=30. The optimal model achieved a maximum accuracy of 69.23% at K=6, with an F1-Score of 71.43%, confirming robust performance on the imbalanced testing set. The results validate the effectiveness of the Histogram-KNN approach as a consistent and reliable decision support system for rapid TC quality screening in resource-limited settings.

The development of social media platforms like TikTok has created new spaces for digital economic activities, including the practive of thrifting, which has now become a trend among the public. However, government policies that block these activities have sparked various public reactions. This study aims to analyze public sentiment regarding the issue of thrifting bans on the TikTok platform using the Bidirectional Long Short-Term Memory (Bi-LSTM) method. This method was chosen because it can understand text context from both directions, allowing it to capture deeper semantic meaning. The dataset consist of 4,000 TikTok user comments collected through a crawling process. The research stages include data preprocessing, sentiment labeling, splitting training and test data, training the Bi-LSTM model, and evaluating performance using accuracy, precision, recall, and F1-score metrics. The research results show that the Bi-LSTM model achieved an accuracy of 86.15%, with stable classification performance and minimal error rate. These findings indicate that Bi-LSTM is effective for sentiment analysis of public opinions on Indonesian language social media, particularly on context specific policy issues. Further development can be carried out by adding pre-trained embeddings or attention mechanisms to improve the model’s performance.

Tourism is one of the sectors that plays an important role in boosting economic growth through travel activities and destination exploration. Tourists' preferences for nature-based tourism options, such as mountain hiking or beach tourism, are influenced by various factors, ranging from personal experiences and recreational interests to social characteristics. Therefore, a technology-based approach is needed to predict destination choice tendencies more accurately. As artificial intelligence technology develops, deep learning methods have been widely used in classification processes due to their ability to process large amounts of data and recognize complex patterns. In this study, a Multilayer Perceptron (MLP) model is used to classify tourists' preferences between mountain or beach destinations based on a survey dataset. The research stages include data processing, data splitting using a train-test split, model training, and performance evaluation using accuracy, precision, recall, and F1-score. The test results show that the MLP model is capable of achieving an accuracy rate of 99%, confirming that deep learning methods are effective in automatically mapping tourism preference trends. This research is expected to serve as a basis for the development of more personalized travel destination recommendation systems, as well as to support tourism management in formulating targeted promotional strategies.

This research focused on improving fine motor development in early childhood through creative learning activities that utilize recycled materials, designed and implemented using the ADDIE instructional model. The study involved children from class B2 at TK Prawidya Darma and was conducted systematically through five stages, namely analysis, design, development, implementation, and evaluation. Throughout the learning process, children were engaged in a variety of hands-on craft activities, including cutting, folding, gluing, and assembling recycled objects, which were intentionally structured to train hand–eye coordination, finger flexibility, and concentration. The results demonstrate that learning activities based on recycled materials offer rich and meaningful experiences for young learners. Children showed noticeable improvements in accuracy, independence, and creative expression while completing tasks. In addition, the use of recycled materials helped cultivate environmental awareness from an early age, as children learned the value of reusing everyday objects. The classroom atmosphere became more interactive and enjoyable, with students actively participating and showing enthusiasm during the activities. Overall, the findings indicate that creative recycling-based activities are effective in supporting fine motor skill development through enjoyable, practical, and environmentally friendly learning experiences. This study concludes that integrating recycled materials into creative activities can be an innovative, sustainable, and pedagogically valuable approach for early childhood education programs.

In today's digital era, recommendation systems have become an integral part of supporting consumer purchasing decisions, including in the food and beverage industry. This study aims to develop a product recommendation system for snacks and beverages using the item-based collaborative filtering method. This method was chosen due to its ability to handle large-scale user and product data, as well as its efficiency in providing relevant recommendations based on user consumption patterns. In this study, the system calculates the average user rating and implements   Cosine Similarity to measure the similarity between products, resulting in more accurate recommendations. The system also evaluates the accuracy of recommendations using the Mean Absolute Error (MAE) metric. Based on the results obtained, which is 0.285403 for the average error on 17 items, the developed recommendation system can improve consumers' shopping experience, help them find products that suit their tastes, and support the sales of snacks and beverages products in the market

This research investigates the Peak Particle Velocity (PPV) resulting from blasting operations at Pit Pinang, PT Bukit Baiduri Energi, employing two prediction approaches: Non-Linear Geometric Regression and the USBM Oriard’s Formula. Ground vibration measurements were recorded over a one-month period, from October 9 to November 8, 2025. The findings indicate that the non-linear regression model achieves a higher predictive accuracy of 78.62%, outperforming the USBM Oriard’s Formula, which reaches 68.2%. Variations between the observed and estimated PPV values were affected by factors such as the location of geophones, differences in explosive charges, and alterations in borehole depths. In addition, the study evaluates optimal explosive charge recommendations in accordance with SNI 7571:2010 standards to mitigate potential structural damage in surrounding areas. By highlighting these predictive discrepancies and providing practical guidance on charge management, the research offers valuable insights for improving blasting safety and minimizing vibration impacts on nearby infrastructure. The comparison of methods emphasizes the importance of selecting appropriate prediction models to ensure both operational efficiency and environmental safety.

The National Food Assistance Program (Non-Cash Food Assistance/BPNT) is one of the social policy instruments designed by the Indonesian government to enhance food security and improve the welfare of poor households. This research aims to analyze the effectiveness of this program by assessing its accessibility, targeting accuracy, commodity quality, and its impact on household expenditure and stability of food consumption.The research methodology uses a descriptive-analytical approach, combining secondary data from official government reports and academic literature with primary data in the form of a limited survey of beneficiaries.The results of the analysis indicate that the program is quite effective in increasing poor households' access to staple foods, primarily through the more transparent and flexible non-cash distribution mechanism. However, several constraints are still found, including inaccurate targeting of beneficiaries, variation in food quality at the e-warung level, and the limited digital literacy of some households.Nevertheless, the program is proven to be able to reduce the burden of food expenditure and improve consumption quality, thereby contributing positively to the welfare of poor households. This research suggests improving the validation of beneficiary data, supervising distribution, and expanding food and digital education for the community to further optimize the program's effectiveness.

This study aims to describe the implementation and outcomes of training on making herbal elixir beverages from nine spice butterfly pea ingredients for exercise mothers as an effort to improve knowledge and skills in processing healthy drinks. The activity was motivated by the limited use of herbal beverages after exercise and the low level of public understanding regarding the benefits of butterfly pea flowers and traditional spices as natural stamina enhancers. The study applied a descriptive qualitative approach through observation and documentation conducted from November 22 to December 5, 2025. A total of 25 exercise mothers participated in the training, which consisted of demonstrations and direct practice in preparing elixirs using dried butterfly pea flowers combined with ginger, turmeric, galangal, lemongrass, cinnamon, cloves, betel leaves, and brown sugar. The results show that participants were able to follow all training stages properly and demonstrated increased understanding of herbal benefits, ingredient functions, dosage accuracy, and processing procedures. Achievement indicators reveal that 92 percent of participants were able to independently prepare the elixir according to the demonstrated steps, 84 percent were able to explain the function of each ingredient correctly, and all groups successfully produced elixir products with color, aroma, and taste that met the expected standards. Participants also responded positively and expressed interest in reapplying the recipes at home and developing flavor variations for personal consumption or small scale business opportunities effectively.