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A good sleep pattern is very important for our body's health both physically and mentally, while lifestyle habits such as physical activity and diet play a big role in influencing sleep quality. By using a decision tree, researchers aim to predict whether we have a healthy sleep pattern or not based on lifestyle. Healthy sleep patterns are regular and quality sleep habits to maintain our physical health. Healthy sleep patterns generally involve sleeping 8 hours – 9 hours per night, having a regular and consistent sleep time. The decision tree model was chosen because of the decision tree's ability to provide accurate predictions and produce rules that are easy to understand. This model can help us raise awareness of the importance of a healthy lifestyle in maintaining sleep quality.

A good sleep pattern is very important for our body's health both physically and mentally, while lifestyle habits such as physical activity and diet play a big role in influencing sleep quality. By using a decision tree, researchers aim to predict whether we have a healthy sleep pattern or not based on lifestyle. Healthy sleep patterns are regular and quality sleep habits to maintain our physical health. Healthy sleep patterns generally involve sleeping 8 hours – 9 hours per night, having a regular and consistent sleep time. The decision tree model was chosen because of the decision tree's ability to provide accurate predictions and produce rules that are easy to understand. This model can help us raise awareness of the importance of a healthy lifestyle in maintaining sleep quality.

This study examines the implementation of the fourth-order Runge-Kutta method in simulating bullet trajectories in the Earth's gravitational field. Bullet trajectory simulation is important in various fields such as ballistics and engineering, where the accuracy of predicting the trajectory of a moving object is crucial. The introduction explains the importance of using numerical methods in solving complex equations of motion, considering that analytical solutions are often inadequate. The purpose of this study is to apply the Runge-Kutta method to solve nonlinear differential equations describing the motion of a bullet under the influence of gravity. The research methods include modeling the motion system using Newton's laws and applying the Runge-Kutta method to predict the trajectory based on initial conditions such as velocity and firing angle. The simulation results show that the Runge-Kutta method provides accurate predictions of bullet trajectories, with low relative errors compared to other numerical methods. In conclusion, this method is effective and efficient in simulating bullet trajectories, providing reliable results in practical applications.

Customer churn prediction models have become an important tool in the telecommunications industry to reduce churn rates and improve customer retention. This research focuses on building an accurate customer churn prediction model using machine learning algorithms for TELCO Company. By applying diverse feature engineering techniques and prediction models such as RandomForestClassifier, DecisionTreeClassifier, and XGBoost, this study showcases a significant improvement in prediction accuracy compared to previously implemented rule-based methods. The findings of this research allow TELCO Company to identify high-risk customers more effectively and implement targeted retention strategies. Results show that the resulting model can identify customers at risk of churn more effectively, enabling more targeted retention actions..

Medical record data can be used as a benchmark and comparison in the health business to ascertain the rate at which a disease is developing in a given area. It would be beneficial, though, if this data could be transformed into useful information, like illness forecasts. Infectious diseases like malaria are common in tropical and subtropical regions. West Sumba Regency is the region with the highest number of malaria cases, and this figure rises year. Of the different Puskesmas labor locations, Lolo Wano Health Center has the largest number of positive cases of malaria. In order to apply information system technology and prevent malaria early, research was done at the Lolo Wano Community Health Center to predict malaria using the Naïve Bayes approach. This is because the Community Health Center does not currently have a malaria prediction system. Six of the 16 features in the patient dataset—a total of 27 patient data—were malaria symptoms. When there are suitable illness indicators, positive predictions are produced using the outcomes of Naïve Bayes computations. Before the patient proceeds with a direct medical evaluation, these anticipated results may be utilized as a provisional approximation. Naïve Bayes, Center, Prediction, Malaria

An airline is a company that offers air transportation services for passengers and cargo. This research aims to analyze bankruptcy predictions for airline companies listed on the Indonesia Stock Exchange (BEI) during the 2016-2023 period using the Altman Z-Score (Modified) model and the Springate model. The research population consists of 12 companies operating in the transportation subsector, focusing on the air transportation industry (airlines). The sample used includes 2 airline companies listed on the IDX, namely PT Garuda Indonesia (Persero) Tbk (GIAA) and PT Jaya Trishindo Tbk (HELI). The data collection technique involves documentation in the form of secondary data, specifically the financial reports of airline companies available on the IDX or each company's website. The results of the study show that PT Garuda Indonesia (Persero) Tbk demonstrates potential bankruptcy according to the Modified Altman Z-Score with an average Z-Score of 0.568. However, this company is considered healthy by Springate with an average S-Score of 0,913. Meanwhile, PT Jaya Trishindo Tbk (HELI) is in a grey area according to the Modified Altman Z-Score with an average Z-Score of 1,101, but shows potential bankruptcy according to Springate with an average S-Score of 0,806.

The Merdeka Belajar Kampus Merdeka (MBKM) program provides students the opportunity to study for one semester outside of their major, aiming to develop the soft and hard skills required in the workforce. One key component of this program is internships or practical work, which gives students hands-on experience in the professional world and the chance to build professional networks. This research uses the K-Nearest Neighbor (K-NN) method to predict the impact of MBKM activities on undergraduate students at STMIK Kaputama. Using the RapidMiner application, student data was tested to obtain the accuracy of predicting students' engagement in the MBKM program in the future. The test results show that the K-NN model has an accuracy of 75.34%, indicating that the model is fairly good at predicting the impact of the MBKM program on students.    

Every company is required to plan the need for goods as effectively as possible in order to maximize profits. Bintang Makmur Building Shop is a building shop that provides building materials, especially cement. Cement is one of the basic materials for buildings. The need for cement has recently continued to increase due to the large number of developments, both housing projects and road construction. In addition to the increasing demand for cement, cement prices also experienced price volatility which tended to fluctuate. This is done so that there is no stockpiling or even a shortage of cement. With prices that tend to go up and down if there is too much stock, it will cause losses if there is a price decrease. Vice versa if there is a shortage of cement stock, it can cause disappointment to customers. To deal with the above, it is necessary to build a prediction system that can predict cement needs in prosperous shops. The system that will be built uses an Artificial Neural Network (Artificial Neural Network) which is part of the science of artificial intelligence which has been widely used to solve various kinds of problems related to prediction or forecasting by utilizing the Backpropagation Method. The system is designed with the MATLAB programming application. From the results of the research that has been carried out, it was found that the total demand for Andalas cement for January of the following year is 0.2532 or 2532, thus the predicted total demand for Andalas cement is 2532 sacks.

Mental health is a state of well-being in which a person is aware of his or her abilities, can cope with normal life stresses, can work productively and contribute to his or her community. Mental health encompasses emotional, psychological and social well-being, and affects how a person thinks, feels and acts. It also determines how a person handles stress, relates to others and makes decisions. Prediction methods that can identify the level of mental health of students are important as a preventive measure. One promising method in this regard is the Naïve Bayes Method. This method has the advantage of being able to solve classification problems on complex datasets, such as student mental health data involving many independent variables. An expert system is a system that attempts to adopt human knowledge into computers so that computers can solve problems as is usually done by experts. The purpose of this study was to find out how to predict the level of mental health of students towards the end of school using the Naïve Bayes method. The results of this study are that the prediction of the level of mental health of students towards the end of school using the Naïve Bayes method can be used and the system created works well, without having to consult a doctor or psychologist.

Smartfren Telecom Tbk. is one of the telecommunications companies in Indonesia. The company has experienced losses in the last seven periods, from 2017 to 2023. It is feared that this condition will result in a high risk of a company experiencing financial distress or even bankruptcy. This research aims to find out, describe and explain the results of applying the analysis of the financial distress prediction model, namely the Altman Z”-Score model which is used to assess and predict potential bankruptcy with research objects at PT. Smartfren Telecom Tbk for the 2017-2023 period. The method used in this research is a descriptive method using a qualitative approach, and the operational variables used are independent variables, namely a bankruptcy prediction model with the dependent variable being financial ratios. The data used is secondary data in the form of PT's annual financial report. Smartfren Telecom Tbk for the 2017-2023 period. Results of financial distress analysis using the Altman Z”-Score model at PT. Smartfren Telecom Tbk for the 2017-2023 period, shows that the company is in a state of distress because the average Z"-Score value is -2.9 or Z < 1.1. This research shows that analysis of bankruptcy or financial distress using the Altman Z"-Score model at PT. Smartfren Telecom Tbk for the 2017-2023 period concluded that the company was in a state of distress.

Typhoid is an acute febrile condition caused by infection with Salmonella enterica bacteria, especially the Salmonella typhi variant. Typhoid fever or what we usually know as typhoid fever. However, this disease can also be caused by other types such as Salmonella paratyphi A, Salmonella typhi B, and Salmonella paratyphi C. Typoid fever or typhus abdominalis is an acute infectious disease of the small intestine with symptoms of fever for one week or more accompanied by disorders of the intestinal tract. digestion and with or without impaired consciousness. Bayes' theorem is a theory of probability conditions that takes into account the possibility of an event (hypothesis) depending on other events (evidence). Future events can be predicted if previous events have occurred. Bayes' theorem is a mathematical equation used in probability and statistics to calculate conditional probabilities. In other words, it is used to calculate the probability of an event based on its relationship to other events. Based on the weight value given by the expert to each patient's typhoid symptom data, from the results of the analysis carried out with the diagnosis results from the consultation, the symptoms are High fever (lasting up to two weeks), Headache, Chills, Skin rash, Muscle and joint pain, Extreme fatigue, Dry cough, Confusion or delirium, Nausea and vomiting, Swollen spleen, Abdominal pain with predicted results for Epidemic Typhus with a value of 76.26%.

PT Mass Rapid Transit Jakarta operates a mass transportation system from Lebak Bulus Station to Bundaran HI. One of the traction substations is located in Cipete Raya, with a voltage rating of 20kV/1.2kV. A critical piece of equipment in this substation is the traction transformer, with a capacity of 4850 kVA/2x2500 kVA. The purpose of this study is to predict the service life of the Cipete Raya traction transformer based on temperature and load using the linear regression method. This study employs direct observation, analyzing load data from traction transformers 1 and 2 at Cipete Raya from January 2022 to June 2024, along with transformer temperature measurements. Secondary data include the technical specifications of the Cipete Raya traction transformer. The linear regression analysis for transformer 1 yields the equation y = 687.42 + 11.97x, indicating a 5.75% annual increase over the next 5 years, with a very strong correlation coefficient of R = 0.919. For transformer 2, the equation is y = 815.4543 + 6.488x, showing a 3% annual increase, with a strong correlation coefficient of R = 0.814. Based on the transformer aging calculations for June 2024, Transformer 1 has a per unit aging value (V) of 0.0014 and an estimated service life (n) of 407.689 years, while Transformer 2 has a V of 0.0012 and an estimated service life of 496.77 years. The aging model evaluation using MAPE shows that the prediction accuracy for transformers 1 and 2 is 6% and 3%, respectively, indicating excellent modeling performance.    

The development of information technology that is currently developing serves to facilitate, accelerate, benefit and provide other alternatives for people who have businesses and have a big influence in the future. One of the things that is very influential is the sale of MSMEs. MSMEs are productive businesses owned by individuals or business entities that have met the criteria as micro businesses that have an important role in the economy because they provide employment, encourage local economic growth, and create innovation. MSMEs still face challenges such as limited access to financing, digital readiness, and marketing access that hinder the development of MSMEs. Therefore, it is necessary to take action to predict MSME sales in Binjai City using the backpropagation method so that later it can create new innovations and encourage community economic growth. Based on the process carried out using the backpropagation method, it can be seen that the value obtained has reached more than the predetermined target with a target value (t) of 0.26, learningrate 0.2, maximum epoch 10000 target error 0.01.

The development of time has changed the types of diseases from infectious to non-communicable or degenerative such as asthma, cancer, stroke, chronic kidney disease, diabetes, and hypertension that are influenced by lifestyle, nutrition, and physical activity. Insulin resistance or lack of pancreas triggers diabetes mellitus, causing hyperglycemia that damages the nervous system and blood vessels. International Diabetes Federation predictions show an increase in diabetes mellitus cases worldwide, including in Central Java, which ranks second only to hypertension. Diabetes mellitus can be identified through blood glucose monitoring and symptoms include hunger, thirst, and frequent urination. Risk factors for diabetes include age, genetics, obesity, inactivity, hypertension, dyslipidemia, and poor nutrition. Prevention and management of diabetes can be done through family care that involves educators, counselors, and collaborators to help families manage the disease well. This study examines the nursing care of Mr. K's family. K with endocrine system disorders Diabetes Militus in Kalibuntu Village, Losari District, Brebes Regency, with the results of the patient's lack of understanding about diabetes and rarely doing exercise.

Tropical diseases remain a serious public health challenge in Southeast Asia, particularly malaria, which has high morbidity and mortality rates. The complexity of their spread is influenced by various factors, including climate, environment, and population, requiring a spatially-based analytical approach to understand their distribution patterns. This study aims to develop a regression-based spatial model to predict the spread of tropical diseases and identify hotspots in high-risk areas. The data used include tropical disease case reports from national health agencies, climate data (temperature, rainfall, humidity) from BMKG and WorldClim, and population data (density and mobility) from  BPS and other official sources. The analysis was conducted using a Geographic Information System GIS for spatial mapping, as well as the application of spatial regression models, namely the Spatial Lag Model SLM and Spatial Error Model SEM. The results show that the developed model is able to predict disease distribution with a high level of accuracy, demonstrated by statistical validation through AIC, and Morans I. One of the main findings is the identification of malaria hotspots with a confidence level of 93, as well as the mapping of tropical disease risk predictions covering the Southeast Asian region. These results have significant implications for public health policy, particularly in resource allocation, prevention program planning, and priority area-based interventions. Furthermore, this study recommends the integration of big data and machine learning technologies to enrich predictive models and develop more adaptive early warning systems. Thus, this research contributes to strengthening tropical disease control strategies in Southeast Asia with a comprehensive spatial data-driven approach.

This study aimed to know the prediction of rice sales for Employee Cooperatives Republic of Indonesia Bina Warga Benjeng in the following month. Rice sales are often difficult to predict market demand. When consumer demand increases, rice supplies sometimes suffer from shortages. If consumer demand decreases, stock builds too much and results in a decrease in rice quality. In order for the rice sales process to run smoothly, it is necessary to have a sales prediction so that there are no excesses or shortage in rice supplies. The method of discussion used to predict in this study using the Single Moving Average method which is a prediction method that uses new actual data requests to raise the predictive value of the next month’s demand. The best results were using the Single Moving Average methods using rice sales data variant 25 kg variant were successfully implemented with an RMSE value of 9.3% which means this prediction accuracy of 90.7% accurate.

Stunting is a growth and development disorder in children caused by chronic malnutrition over a long period of time, especially in the first 1,000 days of life, namely from pregnancy to the first 2 years of life. There are more than 149 million (22%) toddlers worldwide who are stunted, of which 6.3 million are Indonesian toddlers. Based on data from the Ministry of Health, the stunting rate in Indonesia in 2023 was recorded at 21.5 percent, only down 0.1 percent from the previous year which amounted to 21.6 percent. Predicting the number of stunted toddlers is very important and necessary to know the stunting rate in Langkat Regency in 2024, and the prediction results can help health workers in handling and preventing the spread of stunting. The method applied to this prediction system is Multiple Linear Regression where this analysis determines whether each independent variable is positively or negatively related, the direction of the relationship between variables, and estimates the value of the dependent variable will increase or decrease.  The prediction system is carried out using the RapidMiner application because this application is very appropriate to produce information output in the form of prediction results for the coming year. The prediction results obtained are an increase and decrease in 2024 in each sub-district and there are sub-districts that do not experience an increase and decrease. The sub-district with the highest number was Secanggang with approximately 177 people, and the sub-district with the lowest number of stunted children was West Berandan with approximately 55 people. Then Stabat sub-district became the sub-district that experienced the most increase in the number of stunting, which was around 15 people, and the sub-district that experienced the most decrease was Kuala sub-district with a total of approximately 23 people. From the overall results it can be calculated that the number of stunting in all districts in Langkat Regency amounted to approximately 2453 people in 2024. And testing the error rate of prediction results using RMSE in the RapidMiner application of 7.63%, where the level of accuracy in the prediction of child stunting in Langkat Regency is 92.46%.

The restaurant industry in Indonesia has experienced significant growth, driving the need for data-driven strategies to remain competitive. This study aims to apply and compare time series methods in forecasting sales at "Nasi Goreng Bacot" restaurant. The methods used are Simple Moving Average (SMA), Weighted Moving Average (WMA), and Single Exponential Smoothing (SES), with a focus on sales data from the year 2023.The research results indicate that SMA provides the most accurate predictions, with a Mean Absolute Error (MAE) value of 296.67, Mean Squared Error (MSE) of 129055.6, and Mean Absolute Percentage Error (MAPE) of 3.02%. WMA and SES, although useful in certain data conditions, show higher error rates in this case. This study confirms the effectiveness of SMA in the context of stable and less fluctuating restaurant sales data. With these results, restaurants can plan their inventory of raw materials and workforce more efficiently, reduce waste, and improve customer satisfaction.      

Item-based collaborative filtering is a popular technique in recommendation systems that aims to provide suggestions for films to watch or services to users based on similarities between items. In this approach, the similarity between items is calculated using metrics such as cosine similarity, allowing the prediction of user preferences for items that have never been rated. This research implements Item-based collaborative filtering using datasets from Kaggle. Experimental results show that the resulting model is able to provide recommendations with significant improvements in evaluation metrics such as Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) of 3.05 and 3.26. This shows that the smaller the value, the better.

Accurate rainfall prediction is needed to improve the performance of land that always uses rainfall data. Data mining or often called knowledge discovery in databases (KDD) is an activity that includes collecting, using historical data to find regularities, patterns or relationships in large data. In predicting rainfall, there are several conditions that can be observed as reference data to predict rainfall, namely wind speed, temperature, and air humidity. In this research, a backpropagation artificial neural network prediction method is developed that can be used in predicting future rainfall. The backpropogation artificial neural network method that was built produced an accuracy value of 95.36%, a precision value of 90.50%, a recall value of 97.50% and an f-measure value of 92.00%