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The global economic recession is a global economic downturn that affects the domestic economies of countries in the world. The stronger the economic dependence of one country on the global economy, the faster a recession will occur in that country. In 2020 the country of Indonesia and even the world are exposed to the COVID-19 virus which has an impact on the country's economic growth, even the world economy. This is the trigger for an economic recession. This has led to many different public perspectives on the occurrence of a global economic recession whose opinions or reactions are expressed on social media Youtube. The data was obtained by crawling techniques from social media Youtube with a total of 500 comments used. The data is then labeled (class) with a lexicon-based method with an Indonesian language dictionary. From the labeling results, it was obtained 185 positive labeled data (37%) and 315 negative opinions (63%). The data preprocessing stage is carried out in preparation for the data to be processed for sentiment analysis. Of the many opinions obtained, an analysis of public sentiment regarding the 2023 global economic recession will be carried out using the Naïve Bayes classification algorithm. This study also applied the TF-IDF word weighting method with the n-gram feature used, namely bigram (n=1). The system will be evaluated using a confusion matrix. The implementation results show a prediction model with a total of 500 opinion data with a comparison of training data and test data of 9:1, producing an accuracy value of 84.00%, a precision value of 75.00%, a recall of 30.00%, and an f1-score of 42.86%. The performance of the system model built in this study can be said to be good.

This article reviews a syntax-based analysis of predication in language, delving into its underlying linguistic structure. The research conducted employs analytical methods sourced from literature to comprehend sentence construction and the syntactic relationships forming predication. The findings of the analysis present a profound understanding of the framework of predication in language and its implications in human communication. In the exploration of syntax and predication in linguistics, the syntactic approach highlights the essential relationship between subject and predicate in a sentence. Predication maps what is stated about the subject, and syntax-based analysis reveals its basic structure. Syntax, with its central role, is key to understanding sentence structure and the meaning conveyed in communication. Research on this concept shows how the arrangement of words, phrases, and clauses forms predication.

Indonesia is entering a critical period for mental health. Research results from the The Indonesia National Adolescent Mental Health Survey (2022), around 15,5 million Indonesian teenagers experience mental health disorders. Students are part of late adolescence and are vulnerable to mental disorders. The binary logistic regression model is used to examine in more depth what variables have a significant effect. So, this research aims to predict mental health of students in the Faculty of Health Sciences, Pekalongan University. This type of research is observasional with a cross-sectinal design. Data were collected using the SRQ-20 via the Google Form platform using simple random sampling of 186 students. There were 130 students who indicated mental health disordes (69,9%). Simultaneously age, gender, major, semester level, mother’s educational level, father’s educational level, social support and dependence on using smartphone influence student’s mental health status (P Value<0,05). Even though only a few variables were partially significant, the precision percentage of the model that could be predicted correctly was 71,5%. The accuracy of the predicted model is quite good, namely student mental health status (y) = -3,720 + 2,403 (Major) – 1,980 (Mother’s Educational Level) + 1,444 (Father’s Educational Level) + 0,888 (Dependence on using Smartphone). Promotive and preventive interventions such as further screening and education to support student’s healthy mental health.  

This research aims to evaluate the effectiveness of linear regression as a forecasting tool to estimate the Provincial Minimum Wage (UMP) in Indonesia. Utilizing UMP data from various provinces during the period 2002-2022, this study employs linear regression to analyze the factors influencing UMP determination. The predicted UMP for North Sumatra in 2023 demonstrates a high level of accuracy (R-squared = 0.9678), affirming the potential of linear regression as an effective tool to understand regional economic dynamics. The research provides a crucial foundation for policymakers in regional economic planning and suggests avenues for further investigation, including exploring alternative prediction methods and analyzing the impact of UMP regulation policies.

This research aims to increase knowledge and insight in the field of industrial economics, both in terms of concept, application and development of industrial economics in Indonesia. In order to increase knowledge and insight specifically in the field of industrial organization and business competition, both in terms of concept and implementation. Determine estimates and predictions and what is most likely regarding the company's condition and performance in the future. The research method uses qualitative methods and library research. The data collection technique is to record important information in carrying out data analysis by means of data reduction, data display and drawing conclusions to obtain conclusions. The results of this research show several developments in the industrial sector in Indonesia. The industrial sector is the largest contributor to Indonesia's GDP, especially the manufacturing sector which contributes around 73% of Indonesia's total industrial production. However, the industrial sector in Indonesia is still hampered by several factors, such as poor infrastructure and limited human resources..  

Data mining is a technology that plays an important role in supporting data-driven decision making, especially in complex and dynamic higher education environments. In the context of education management, the ability to predict student graduation is an essential aspect because it can help institutions plan strategic steps, intervene earlier, and optimize academic resources. This study aims to apply the C4.5 decision tree algorithm to build a student graduation prediction model based on academic data. The research dataset includes key variables such as Grade Point Average (GPA), total Semester Credit Units (SKS) taken, and student attendance rates during lectures. The analysis was conducted using the C4.5 algorithm, which is known for its high level of interpretability, making the model results easy to understand by policy makers. The test results showed an accuracy of 84.6%, indicating that this method has the potential to support data-based academic management systems. These findings are expected to serve as a basis for educational institutions to improve the effectiveness of monitoring and evaluating the student learning process.

Diabetes Mellitus is a hazardous disease, and according to the World Health Organization (WHO), diabetes will be one of the main causes of death by 2030. One of the most popular diabetes datasets is PIMA Indians, and this dataset has been widely tested on various machine learning (ML) methods, even deep learning (DL). But on average, ML methods are not able to produce good accuracy. The quality of the dataset and features is the most influential thing in this case, so deeper investment is needed to examine this dataset. This research will analyze and compare the PIMA Indians and Abelvikas datasets using the Random Forest (RF) method. The two datasets are imbalanced, in fact, the Abelvikas dataset is more imbalanced and has a larger number of classes so it is be more complex. The RF was chosen because it is one of the ML methods that has the best results on various diabetes datasets. Based on the test results, very contrasting results were obtained on the two datasets. Abelvikas had accuracy, precision, and recall, reaching 100%, and PIMA Indians only achieved 75% for accuracy, 87% for precision, and 80% for the best recall. Testing was done with 3, 5, 7, 10, and 15 tree number parameters. Apart from that, it was also tested with k-fold validation to get valid results. This determines that the features in the Abelvikas dataset are much better because more complete glucose features support them.

Tabular-based clinical risk prediction models are extensively applied in medical decision support systems; however, two major challenges often reduce their reliability: predictions that contradict basic clinical logic and poorly calibrated probability outputs that weaken threshold-based decision making. This study investigates explainable binary risk prediction using the processed Cleveland subset of the UCI Heart Disease dataset as a public clinical benchmark. A lightweight and CPU-efficient pipeline is proposed by employing an XGBoost classifier integrated with monotonic constraints on clinically relevant features, followed by probability calibration through post-hoc methods, including Platt scaling, temperature scaling, and isotonic regression on a separate validation set. Model performance is assessed in terms of discrimination capability using AUROC, AUPRC, F1-score, sensitivity, and specificity, while probability reliability is evaluated using ECE and Brier score metrics. A monotonicity audit is also conducted through counterfactual feature sweeps to measure violation rates. In addition, the model is applied for risk stratification into low-, medium-, and high-risk categories with corresponding event-rate reporting. The findings demonstrate that isotonic regression improves probability reliability without degrading discrimination performance. Furthermore, the monotonicity audit reveals no observed violations for constrained features. Overall, the integration of monotonic constraints and probability calibration produces more decision-ready risk estimates for threshold-based clinical decision support while maintaining transparency through SHAP-based analysis.

Property price determination is a complex challenge influenced by various factors, thus requiring an effective method for accurate prediction to support investment decision-making. In the current digital era, conventional approaches are being replaced by data-driven and artificial intelligence methods, where Linear Regression remains a popular choice due to its simplicity and effectiveness in modeling linear relationships. This study aims to analyze the relationship between the physical characteristics of a house and its selling price, and to build an accurate predictive model using the Linear Regression algorithm. A quantitative method was used, focusing on Building Area , Number of Rooms, and Building Age  against the House Selling Price. Correlation analysis results show that Building Area has the strongest correlation (0.81) with price, while Building Age shows a negative correlation (-0.52). The Linear Regression model demonstrated very strong and stable performance. The model achieved an R² Score of 0.9396 on the testing data, meaning 93.96% of house price variability can be explained by the model. Furthermore, the low MAE of only 11.31 million rupiah indicates a small prediction error, and the consistency of R² scores confirms that the model does not suffer from overfitting. This study concludes that the Linear Regression model provides excellent, stable, and reliable prediction performance for projecting house selling prices

Perum Bulog as a State-Owned Enterprise has the main task, which is to conduct a quality and adequate basic food logistics business for the survival of the people. The problem that occurred in Perum Bulog Sub Divre Medan is that the rice supply in Bulog does not consider the demand in the market. Forecasting is an important tool in effective and efficient planning. Therefore, prediction is indispensable for predicting future events. This method essentially uses past data initiated by performing an exponentially decreasing weighting of older observational values or newer values. Brown's double exponential smoothing is a linear model proposed by Brown. This double exponential smoothing method is used when the data indicate a trend. In this study, the terbaik best parameter for forecasting the Number of Rice Sales in Perum Bulog Sub Divre Medan was α = 0.2 with MAPE of 0.27%. And the results of the forecast for Rice Sales at Perum Bulog Sub Divre Medan in 2022 are decreasing every month.