Publication Search

This study aims to develop a predictive model using linear regression to identify potential arrhythmias in the elderly based on electrocardiogram (ECG) data. Data were collected through observations at healthcare facilities from elderly patients with indications of arrhythmia, then preprocessed such as cleaning, normalization, feature selection, and outlier checking were carried out. The features used include PR interval, QRS duration, QT interval, and heart rate. The dataset was divided into training data (80%) and test data (20%) to build and evaluate the model. The training results showed that the model was able to predict the risk of arrhythmia with a Mean Squared Error (MSE) value of 0.15 and a coefficient of determination (R²) close to 1. Evaluation using a confusion matrix showed an accuracy of 76.19%, precision of 82.80%, recall of 76.19%, and F1 score of 72.70%. These results prove that linear regression can be used as an initial approach in the early detection of arrhythmias non-invasively in the elderly. This study provides a foundation for the development of ECG data-based clinical decision support systems and suggests future exploration of more complex models and integration with real-time monitoring technologies.

Business ventures are activities carried out by individuals or organizations involving the production, sale, purchase, or exchange of goods and services, with the aim of generating profit. A basic necessities store (commonly known as a “sembako” store in Indonesia) sells daily staple needs, especially the nine essential commodities (sembako), which include items such as rice, sugar, cooking oil, eggs, salt, and other key food ingredients. UD. Putri 2, located in Dusun 1A, Sumber Harapan Village (21261), Tinggi Raja Subdistrict, Asahan Regency, was established in 2018 and has since become an essential part of the local community. This has required UD. Putri 2 to constantly monitor their stock inventory. However, the company still faces inefficiencies in managing sales data processing, which often leads to inventory shortages. When the supply of goods is insufficient to meet customer demand, customers may turn to other stores. If this occurs repeatedly, the store risks losing profit due to the unavailability of goods. Supply Chain Management (SCM) refers to the integrated processes and production activities starting from the acquisition of raw materials from suppliers, the value-adding processes that turn raw materials into finished products, the inventory storage process, and the distribution of finished goods to retailers and consumers. The implementation of SCM can optimize inventory management of staple goods, minimize inventory costs, and improve supply chain efficiency at UD. Putri 2.

Behind the success of the Free Nutritious Meal Program (MBG), there are several problems related to the health factors of the program targets, namely, there are several cases of allergies that occur in schools, inadequate understanding of allergen management owned by food processing vendors, and the high cost of laboratory tests and the process that takes a long time. So, to overcome these problems, an application is proposed that can help detect allergens in food products using data mining and machine learning approaches. SVM and AdaBoost algorithms each have advantages that can be used to help build an optimal allergen detection model. This research uses a cross-validation model validation method with a value of K = 10 to help improve the performance of the model built. In this study, from the entire fold, an average accuracy value of 98.74% was obtained. To evaluate the model built, this research has also conducted several new data inputs, and in each new data input, the accuracy value is obtained above 99%. This indicates that the model built, namely the combination of SVM and AdaBoost algorithms with the cross-validation model validation method, produces high accuracy, so this model can greatly assist the allergen detection process in food products.

This study aims to analyze the factors that influence students' interest in continuing their education to college using a machine learning approach. Data was collected through an online questionnaire completed by 727 students between July 27 and August 22, 2025, covering 23 variables consisting of respondent identity (gender, grade level, major) as well as internal and external factors such as parental support, learning motivation, and preferred type of college. The data preparation stage was carried out through column cleaning, deletion of empty data, encoding of categorical variables, and division of the dataset into 80% training data and 20% test data. The Naive Bayes algorithm of the CategoricalNB type was used because it was suitable for the categorical nature of the data. The evaluation results showed that the model was able to predict student interest with 96% accuracy. For the class of students interested in continuing their studies, the precision, recall, and F1-score values were above 0.95, while the performance in the class of students who were not interested was slightly lower due to the smaller amount of data. These findings show that Naive Bayes is proven to be effective and reliable in classifying students' interest in continuing their studies and can be the basis for decision-making in designing more targeted educational strategies.

Selecting the right supplier is a critical aspect of supply chain management, especially in a retail business like Parfume Corner, which relies on product quality, availability, and on-time delivery. This study aims to implement the VIKOR (VlseKriterijumska Optimizacija I Kompromisno Resenje) method as a multi-criteria decision-making approach to determine the best perfume supplier. The VIKOR method was chosen because of its ability to handle conflicts between criteria and produce optimal compromise solutions. The evaluation criteria used include product quality, price, on-time delivery, after-sales service, and flexibility in negotiations. Data were collected from five potential suppliers through observation, interviews, and historical transaction documents. The analysis results showed that one supplier obtained the lowest VIKOR index score, thus being determined as the best compromise solution. The implementation of the VIKOR method proved effective in providing objective and transparent recommendations, which can support Parfume Corner's strategic decisions in building long-term partnerships with reliable suppliers. This approach can also be adapted by similar businesses to improve procurement efficiency and quality. The test results obtained were that in the expert test a Good value was obtained, namely 80%, while in the system test a Very Good conclusion was obtained, namely 100%.

This study compares the effectiveness of MAUT (Multi-Attribute Utility Theory), SMART (Simple Multi-Attribute Rating Technique), and WASPAS (Weighted Aggregated Sum Product Assessment) methods in a decision support system for determining the best employees at Sisilia Boutique. The quality of human resources is crucial in the retail business, but performance evaluation is often influenced by subjectivity. To address this, a multi-criteria-based decision support system is needed. MAUT translates preferences into a numerical scale, SMART calculates the average value of attributes based on weights, while WASPAS combines weighted summation (WSM) and weighted multiplication (WPM) for more balanced results. Employee performance data from Sisilia Boutique in June 2025, including attendance, store layout, customer service, and discipline, were used as the research object. The comparison results show consistency in the highest (K3) and lowest (K7) ratings across the three methods, with differences in the middle ratings. WASPAS offers a more balanced distribution of final scores, making it a comprehensive alternative for performance evaluation.

This study aims to analyze and compare the performance of the Naive Bayes, K-Nearest Neighbors (KNN), and Decision Tree algorithms in predicting the purchase intention of e-commerce visitors using the Online Shoppers Purchasing Intention Dataset, which consists of 12,330 records and 18 variables, with the Revenue variable serving as the classification target. The preprocessing stage involved transforming categorical and boolean variables into numerical form, standardizing features using StandardScaler, and splitting the dataset into 80 percent training data and 20 percent testing data. Model evaluation was conducted using accuracy, precision, recall, F1-score, and ROC-AUC metrics, and was further strengthened by 10-fold cross-validation to obtain more stable results. The findings indicate that KNN achieved the highest accuracy of 0.866180, while Naive Bayes produced the highest recall value of 0.690998 and the highest ROC-AUC value of 0.821696. Meanwhile, Decision Tree demonstrated relatively balanced performance with an accuracy of 0.857259 and an F1-score of 0.571776, whereas the cross-validation results identified KNN as the model with the highest average accuracy of 0.8770. These findings suggest that the selection of a classification model for purchase intention prediction cannot rely solely on a single evaluation metric, as each algorithm possesses different strengths. Therefore, a comparative approach among algorithms can help determine the most suitable model for supporting consumer behavior analysis on e-commerce platforms.

This study evaluates the performance of three community detection algorithms—Leiden, Infomap, and Label Propagation—on the legal network of the Republic of Indonesia spanning the period 2014–2024. The network consists of 679 nodes and 2,295 edges, constructed based on citation relationships among regulations. The evaluation employs four network topology metrics: modularity, coverage, conductance, and inter-cluster density. Results show that the Leiden algorithm achieves the highest modularity score (0.522991), indicating the formation of communities with strong internal density. Additionally, it yields the lowest conductance value (0.302455), suggesting relatively well-isolated communities. In contrast, the Label Propagation algorithm produces the highest coverage (0.835294) and inter-cluster density (0.542331), but with a lower modularity (0.431583), reflecting the formation of large communities with less distinct boundaries. Infomap exhibits moderate performance, with a modularity score of 0.508406 and inter-cluster density of 0.420803, yet records a relatively high conductance (0.410409). Network visualizations reveal three major communities for each algorithm, representing thematic clusters such as institutional governance, constitutional law, and public finance. Overall, the Leiden algorithm is considered the most optimal for detecting modular, stable, and thematically coherent community structures within the complex and interrelated network of Indonesian laws.

The bodywork industry in Indonesia is under high competitive pressure, requiring companies to be more adaptive in understanding customer behavior in order to maintain business continuity. PT. Bengawan Karya Sakti as one of the national bodywork companies, has not optimally utilized historical transaction data to assess customer loyalty. This study aims to identify customer loyalty segmentation through the application of the RFM (Recency, Frequency, Monetary) method, which is used to analyze sales transaction data in 2022 and 2023. The study uses the CRISP-DM approach which includes the stages of business understanding, data exploration, data cleaning and processing, modeling, evaluation, and implementation of results. The transaction data analyzed includes attributes of transaction date, customer, number of transactions, and transaction value, which are then processed into RFM scores based on the transaction year and classified into categories such as Very Loyal, Loyal, At Risk, and others. The segmentation results show an increase in the number of very loyal customers from 2022 to 2023, as well as a significant decrease in inactive and at-risk customers. The chi-square statistical test shows that the difference in customer distribution between years is statistically significant (p-value <0.05), indicating a real influence from the company's strategy or external factors. The main conclusion of this study is that the RFM method is effective in the bodywork industry to support data-based marketing decision making and more targeted customer retention strategies.

Supply Chain Management is a set of forms for the effective integration of suppliers, manufacturers, warehouses and warehouses, so that goods are produced and distributed in the right quantity, in the right position, at the right time, to minimize costs and provide services to client satisfaction. Currently, many companies are implementing Supply Chain Management to increase the competitiveness of companies with one another. Supply Chain Management is a strategic competitive tool for companies that make competing logical problems a strategy to win the competition. The purpose of making this paper is to explore the budget chain management section in terms of systems and operations within the company that enable it to provide value to consumers in terms of vacuum and speed of service. So that consumers will feel the superiority of the product even though it is physically somewhat similar to other products

Monitoring realizes efforts to improve the quality of health services. To obtain information on patient condition data during treatment. The monitoring process is done manually. So that it has an impact on the service and condition of the patient during treatment. The design of monitoring the patient's body temperature and heart during treatment with IoT can be controlled through the NodeMCU sensor ESP8266, MLX9014, MAX30100 sensor and Arduino IDE software program. Furthermore, it can detect the patient's temperature and the patient's heart rate during treatment. And processed by the NodeMCU ESP8266, the data from the two sensors is displayed on the SSD1306 OLED LCD and also to the smartphone of the medical officer on duty via Blynk. In order for the tool to work properly and optimally, it is necessary to adjust the pin placement so that it can work optimally. The problem of internet connection interference causes delays, resulting in a mismatch between the measurement of the test equipment and the standard tool. Data values are taken with an accuracy of 70%-93% for the MAX30100 sensor, for the temperature sensor it is close to optimal with a value that is read on a standard tool with an accuracy of 97%-99%.

BLDC motor applications used in various forms in instrumentation, robotics, household, and transportation. One application of transportation equipment used as a propeller of electric bicycle vehicles. The value of the bicycle vehicle adjusted to the speed set, the amount that has determined. The purpose used in this study is to improve the efficiency of the regulation of BLDC motors on electric bicycles. Indicators of increasing performance are increasingly steady-state errors, and transient response required. The method used in this research is to do mathematical modeling in the form of transfer and optimization function equations. The model used is the model with the structure of the transfer function, while the optimization method used in this study is the Ziegler-Nichols method and firefly algorithm. The firefly algorithm is used in this study to obtain optimal Kp, Ki, and Kd values. The results showed that the firefly algorithm achieved better performance compared to the Ziegler-Nichols method.

When creating databases for GIS-applications often existing maps are scanned and vectorised for used. However, vectorisation becomes obsolete when GIS-objects can be referred to both in theme and geometry in a raster environment. This article shows to use model spatial data raster and vector for GIS - applications in both the graphical and image structure. Geographical data must first be converted into a computer- readable format before it can be used in a GIS. Spatial data are "elements that can be stored in map form." These elements correspond to a uniquely defined location on the Earth's surface. Spatial data have also been describe as “any data concerning phenomenon a really distributed” in two or more dimensions. (Peuquet and Marble, I990.) Data model is the rules to convert real geographical variation into discrete objects. There are two main GIS data models - vector and raster. Each of the two data models has specific types of data, analysis and displays that can handle better than the other system. The vector model represents geographical reality as a series of discrete objects or features, classified as points, line's or areas (polygons). The geographical co-ordinates describing the locations of these features are stored in the computer database which lies at the heart of the GIS. In the raster model a regular grid of cells, or pixels, is used to encode the features found on the earth's surface. Each pixel has a number associated with it representing; the value of a geographical phenomenon, such as terrain elevation, soil type or biomass. Layers of raster grids covering the same region can be built up to represent further variables.