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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.

By using literature studies/literature research, this article discusses how programming algorithms can be applied to the Internet of Things (IoT). The purpose of this article is to explain the function of algorithms in Internet of Things (IoT) programming and several examples. IoT is a concept where various devices and objects can connect and communicate with each other via the internet network. After data is collected through text study, content analysis techniques are used to analyze it. Literature studies show that programming algorithms must be applied to the Internet of Things to ensure efficient collection, analysis and use of data from connected objects. Programming algorithms are widely used for data prediction and analysis, network management, data collection and processing, security, and optimization of communication between IoT objects. Internet of Things developers and researchers should pay attention to the importance of implementing appropriate programming algorithms in their systems because these algorithms enable IoT to optimize the use of resources such as bandwidth, memory, and energy. Efficient algorithms enable smarter data analysis and better data security.

By using literature studies/literature research, this article discusses how programming algorithms can be applied to the Internet of Things (IoT). The purpose of this article is to explain the function of algorithms in Internet of Things (IoT) programming and several examples. IoT is a concept where various devices and objects can connect and communicate with each other via the internet network. After data is collected through text study, content analysis techniques are used to analyze it. Literature studies show that programming algorithms must be applied to the Internet of Things to ensure efficient collection, analysis and use of data from connected objects. Programming algorithms are widely used for data prediction and analysis, network management, data collection and processing, security, and optimization of communication between IoT objects. Internet of Things developers and researchers should pay attention to the importance of implementing appropriate programming algorithms in their systems because these algorithms enable IoT to optimize the use of resources such as bandwidth, memory, and energy. Efficient algorithms enable smarter data analysis and better data security.

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.

Early childhood plays an important role in forming the basis of development, which involves stimulation of various aspects such as moral religious values, social emotional, language, cognitive, and physical motor skills. The concept of early childhood learning is focused on play, where every activity is designed to be play, so that learning becomes more effective. Parents also need to understand today's children's education to interact with children positively. This research focuses on sentiment analysis of children's education-based app reviews on the Google Play Store, using Random Forest and Logistic Regression methods. The review data is taken from three apps with the theme of child development, namely "About Kids", "PrimaKu", and "Teman Bumil", with a range of review years between 2018 and 2023. The test results show that Logistic Regression has higher accuracy compared to Random Forest, especially in the "About Kids" and "PrimaKu" applications with accuracy above 90%. The conclusion of this research highlights the importance of sentiment analysis in improving understanding of user responses to children's education applications, with suggestions for future research to increase the number of datasets and variations in testing schemes by tuning hyperparameters to improve prediction accuracy and more optimal results.

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.  

Chili is a popular vegetable in Indonesia, often used as a spice in various local dishes. The surge in demand before major celebrations, coupled with unpredictable weather, can impact chili production and lead to price fluctuations. Predicting prices becomes crucial to anticipate market changes and maintain economic stability in Indonesia. This study aims to predict the prices of curly red chili in Samarinda City in 2024 using the Linear Regression method. The data, sourced from the last three years (January 2021 to November 2023) via Lamin Etam's website, underwent processing with RapidMiner. Analysis using Root Mean Squared Error (RMSE) indicates an accuracy level of 240.487+/-, signifying a relatively large margin of error. These results underscore the importance of adding data attributes to enhance the accuracy of curly red chili price predictions in Samarinda City.  

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.

In this journal, the level of patient satisfaction with physiotherapy services is predicted using the C4.5 algorithm. The aim of this research is to create a predictive model based on patient feedback to help hospitals or healthcare facilities enhance the quality of physiotherapy services. Data collected from this study includes various variables such as age, patient name or gender, duration of physiotherapy services, health status, and patient name. This data is processed and constructed using the C4.5 algorithm. The research results indicate that the C4.5 algorithm can accurately predict the level of patient satisfaction with physiotherapy services. This model can assist healthcare providers in improving efficiency and patient satisfaction. Furthermore, the model can aid in making better decisions on how to enhance physiotherapy services and ensure patient satisfaction. The conclusions of this research can help healthcare facilities improve physiotherapy services and ensure patient satisfaction

As many as 7.3 million people worldwide die from heart disease. This indicates that heart disease is one of the diseases that cause the most deaths. As a preventive effort in handling heart disease, it is necessary to predict heart disease in patients. The classification process to predict heart disease is done using a decision tree. This decision tree is interesting because it is more flexible in providing the advantage of visualizing the advice so that the prediction can be observed. This study uses Heart Disease Prediction Dataset data with a total of 303 data. Then predictions are made using Decision tree so that the accuracy results are 83.60%, precision 89.28%, recall 78.12% and F1 score of 83.33%.

The use of the Decision Tree method in smartphone price classification is the focus of this study. By using the 10 most relevant features and data normalization to achieve scale consistency, the Decision Tree algorithm delivers an average accuracy of 81%. Although some false positives and false negatives occur, the model is able to classify smartphone prices well, especially in identifying low and high prices. These results provide important insights into the features that affect smartphone prices. While there is still room for improvement, this model provides a solid foundation for the smartphone industry to determine prices based on certain specifications. The importance of relevant feature selection and data normalization was revealed in this study. Despite the accuracy reaching 81%, improvements in the classification of medium and high price classes are still possible to reduce prediction errors. This method provides an important basis for the smartphone industry to set prices based on specifications, and data mining techniques such as Decision Tree can be improved to improve the accuracy of future price predictions.

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..  

Todays, technology is also widely used in various fields for various needs. Based on data from. Various sectors, such as education, business, tourism, to the agricultural sector, widely use websites. There are various implementations or applications of technological developments in agriculture, one of which is the application of calculating an agricultural product in Kerinci experienced ups and downs due to less stable weather conditions. Calculations in determining an agricultural product can use several calculation methods, one of which is the Tsukamoto fuzzy method. Tsukamoto's fuzzy method is an extension of monotone reasoning. In Tsukamoto's method, every consequence has a rule in the form of IF-THEN that must be represented in a fuzzy set with a monotone arrangement function. So this requires a system that can produce predictions in tea production in Kerinci tea plantations to meet market needs. The solution offered is a production prediction system in Kerinci plantations using the Tsukamoto fuzzy method. The prediction results obtained in this study were a 36% increase in tea sales production.

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.

The purpose of this study is to determine the financial condition of PT. Hero Supermarket Tbk using the Altman Z-Score, Springate, Zmijewski, and Grover methods for the 2019-2022 period. This type of research is quantitative descriptive research. The sample used in this study was PT. Hero Supermarket Tbk with simple random sampling as its sampling technique. The data used in this study is secondary data, namely financial statements obtained from the Indonesia Stock Exchange. The results of this study show that PT Hero Supermarket Tbk's Altman Z-Score, Springate, and Grover methods for four years on average have the same results, namely in 2019 the company was in good health. Meanwhile, in 2020-2022, PT Hero Supermarket Tbk went bankrupt. Unlike the three methods above, Zmijewski's method in 2019 and 2020 the company was in bankruptcy. But in 2021 and 2022 the company was in good health    

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.

This study aims to determine the bankruptcy prediction of PT Acset Indonusa tbk and PT Nusa Kontruksi Enjiniring tbk in 2021 using the Altman Z-Score method. The data source used is secondary data, namely in the form of Published Financial Reports of Companies issued by the Indonesia Stock Exchange. This study uses a quantitative descriptive method. The technique used is the Altman Z-Score bankruptcy prediction model with the calculation results that the two companies are in unfavorable results or a dangerous zone so that the two companies are predicted to experience bankruptcy.

In Indonesia, English is the first foreign language that is studied as a compulsory subject from junior high school to university. English is a very basic and important requirement in Indonesia. This is evident in the development of the current education system, English has been included as one of the subjects tested in the national exam. Based on the observations of class IX students at SMP Negeri Sukadana, in preparation for the national exam, many students still had difficulty answering English questions: reading skills. One of the causal factors of these student problems is the lack of practice by applying strategies to answer questions and do not have the skills to easily predict questions that will appear during the national exam. In addition, the lack of interest in learning English is also a major factor in learning English. Based on the results of the community service activities, it was shown that class IX students at SMP Negeri Sukadana were motivated so that they could optimize their abilities and skills in answering English questions: reading skills. Thus it can be concluded that in this service program students have gained experience in implementing strategies for quick predictions in identifying the right answers.

Consensus problems need communication between two or more agents. The existence of time delay in communication makes every agent doesn’t get the real-time states of the other agents. The main problem of delay system is the response starts slowing down and oscillating when the gain is increasing. This paper proposes a predictor-feedback that reduces the effect of time delay. The predictor itself utilizes the complete subgraphs. Analytically the result generates faster response compared to the system without the predictor. Then, the proved solution is supported by a numerical solution.

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.