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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.

Stunting occurs due to malnutrition which inhibits growth in toddlers. Stunting can also be caused by problems during pregnancy. This study aims to identify the risk of stunting during pregnancy and determine pregnant women who are at risk of this condition. By identifying and prioritizing critical factors that contribute to stunting in children under five, this research is expected to assist policy makers in developing effective solutions to reduce stunting rates. Handling the problem of stunting is important for the Government because it relates to the future generation of Golden Indonesia 2045. This study evaluates appropriate actions or therapies to reduce the risk of having children born with the potential to experience stunting. In the process of selecting pregnant women who are at risk of giving birth to children with the risk of stunting, a selection procedure is carried out that considers several factors such as the mother's age, mother's nutritional intake, arm circumference, hemoglobin level, parity, birth spacing, height, and mother's body mass index (BMI). The analytic network process (ANP) approach is used to determine the outcome of the selection process. The ranking is determined based on the calculation of the weighting of the criteria and sub-criteria in the ANP method. Based on the results of calculations using the ANP approach, PM 1 pregnant women get the highest score and are ranked first. These pregnant women are considered to have the highest risk of giving birth to babies with stunting risk.

Academic data increases every year in line with the increase of students. Abundant data store is alsoan abundance of information. Data mining technology is a tool for extracting information on largedatabases and has been widely used in many domains. Predicting student performance (study evaluation) isan activity to determine a future state based on existing data. Data in the field of academic research hasbeen done with various methods and algorithms, but the use of algorithm SLIQ (Supervised Learning InQuest) has not been done.SLIQ is an algorithm developed by the IBM's Quest project team in 1996 for mining large datasets.SLIQ algorithm classify and predict the students performance, beginning with the data cleaning, conductedelection training and testing data. By calculating gini index of each attribute and then selecting thesmallest gini index data table is split according to the criteria until find the same class. From the results ofthe calculation process can produce a set of rules that can be used to predict student performance.From the experiment it can be concluded that the algorithm SLIQ with decision tree technique canbe used as an alternative in designing a system datamining applications. Tests conducted system showedthat the constructed model can be used to predict the performance of new students. The resulting accuracyof the model system in fact has a lower score than the accuracy of other applications that are used as acomparison of Tanagra. Advantages of the proposed system is in its design does not need complexcalculations in obtaining the gini index attributes.

This article discuss research progress and future opportunities for modeling consumer choice on the Internet using clickstream data and also to compare the nature of Internet choice (as captured by clickstream data) with supermarket choice (as captured by UPC scanner panel data). Though the application of choice models to clickstream data is relatively new, and review existing early work and provide a two-by-two categorization of the applications studied to date (delineating search versus purchase on the one hand and within-site versus across-site choices on the other). The article discusses additional opportunities afforded by clickstream information, including personalization, data mining, automation, and customer valuation and also offers directions for further research in these areas. Notwithstanding the numerous challenges associated with clickstream data research.

Study for the effect of share trading days to the daily share returns in Stock Exchange has been carried out most invarious countries. Overseas studies produce consistent findings that the daily share returns results in negative impact on Monday (Monday Effect) and resulting inpositive impact on Friday (Week End Effect). Those effects are called as day of the week effect. The similar studies carried out Indonesia have not obtained consistent findings as similar as overseas including overseas studies make use of Ordinary Least Squares (OLS). Whereas this study does not utilize this approach, as most of the assumptions could not be fulfilled. Technical Approaches dealt with the share returns calculation dominantly affect to the amount of daily share return. These are due to the facts that the irrational factors also used by the investors to do the transactions. Consequently, the demand and supply of the share are also influenced by the behaviour of the investors them elves. Statistical approach used for examining the day effects is Autoregressive Moving Average (ARIMA) Approach. This approach is chosen as there is no hetero scedasticity or there is an existence of stationary data. To examine the difference of share trading day effects to the share returns this study utilizes Analysis of Variance (ANOVA), 244 daily aggregate share price indexes (IHSG) and LQ45 Indexes are used for this study. The findings of this study suggest that (1) all of the share trading days give a positive impact on the share returns, (2) daily aggregate share price indexes (IHSG) do significantly affect the share returns on Thursday and (3) LQ45 Indexes do significantly affect the share returns on Thursday and Friday. These findings imply that the investors should consider the day effects in deciding to be involved in share trading so that they could maximize their future returns.