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Companies often face various obstacles related to managing raw material inventory to meet demand, one of which is Peuyeum Ketan Istimewa. Working in the food processing industry, of course, raw material inventory management needs to be planned optimally to avoid various risks that can harm the company. The Quantity Discount model is used to take advantage of cost savings provided by suppliers when purchases are made in large quantities, while other efforts that can help manage raw materials in a company are by knowing the safety stock and reorder point of raw materials and also forecasting demand to predict future demand. This study will use the Quantity Discount model which optimizes inventory levels by considering storage costs, ordering costs, and quantity discounts. The calculations carried out are also to find the value of the company's Safety Stock and Reorder Point. The results of this study indicate that the use of the Quantity Discount method can reduce total costs by Rp26,319,267/year, while forecasting using the seasonality method increases the accuracy of demand predictions, thus enabling more efficient inventory management. The implementation of this model is expected to provide a significant contribution to operational efficiency and cost reduction at Peuyeum Ketan Istimewa

Bioinformatics can help identify cancer risk factors, predict cancer, and develop effective prevention strategies. The development of bioinformatics technologies such as genetic data analysis, development of prediction models, and personalization of treatment have opened up new opportunities in cancer prevention. This research aims to examine the role of bioinformatics in preventing cancer and building a better health future. By understanding the potential of bioinformatics, we can develop effective prevention strategies and improve people's quality of life. Prevention and efforts to control breast cancer were discovered using bioinformatics technology. This research shows that the implementation of bioinformatics has a positive impact on efforts to prevent breast cancer for the future of health.

Dangeu dengue fever or what we often call dengue fever is a disease transmitted by the Aedes aegypti mosquito and caused by the dengue virus. This disease can potentially cause serious complications if it does not receive proper treatment. In this research, the author uses the application of artificial neural networks with the Hebb rule approach to predict the risk level of dengue fever. Predictions are made based on factors such as weather conditions, population density and historical case data that influence this disease. The Hebb rule is used in this research because of its ability to strengthen connections between neurons based on the input patterns they receive, so it is hoped that it can produce more accurate predictions. Test results show that this method has a fairly high level of accuracy in predicting the pattern of dengue fever cases in an area. This research indicates that the application of artificial neural networks with the Hebb rule can be an effective tool for related parties in taking preventive measures to minimize the number of dengue cases in the future.    

In the midst of increasingly rapid technological developments, Artificial Intelligence (AI) technology has also been formed as a form of development. The presence of AI technology helps many people complete their work. Including strategic activities in the decision-making process for business development. This research aims to discuss the role of artificial intelligence technology in improving decision making in the business development process by focusing on the Management Information Systems (SIM), Micro, Small and Medium Enterprises (MSMEs), and finance sectors. With a literature review used as a comprehensive research method, it involves collecting and analyzing articles related to the topic from various academic sources. The research results show that Artificial Intelligence (AI) can improve the efficiency and accuracy of decision making through in-depth analysis and algorithm-based predictions. In the Management Information Systems (MIS) sector, artificial intelligence contributes to business automation processes and information management, in the MSME sector, artificial intelligence helps in understanding consumer behavior and market trends. Meanwhile, in the financial sector, AI plays an important role as a risk analyst, financial manager and investment manager. This research is expected to provide knowledge for corporate organizations about the role of artificial intelligence technology in improving the decision-making process.

The use of Artificial Neural Networks (JST) for weather prediction is one of the innovative approaches in climate data analysis. This study aims to apply JST in predicting weather, especially rainfall and the number of rainy days in the North Sumatra region. Historical weather data obtained from BMKG Region I for 2022-2023 is used as input to train the JST model. With a training process that involves processing rainfall data, this model is expected to provide accurate predictions regarding weather patterns. The results of this research can help in agricultural sector planning, disaster risk mitigation, and natural resource management. JST has proven to be effective in identifying dynamic and complex weather patterns, so it has the potential to be used in long-term weather prediction.

Credit approval prediction is one of the critical challenges in the financial industry, where the accuracy and efficiency of credit decision-making can significantly affect business risk. This study proposes an outlier detection method using the Gaussian Mixture Model (GMM) combined with Extreme Gradient Boosting (XGBoost) to improve prediction accuracy. GMM is used to detect outliers with a probabilistic approach, allowing for finer-grained anomaly identification compared to distance- or density-based methods. Furthermore, the data cleaned through GMM is processed using XGBoost, a decision tree-based boosting algorithm that efficiently handles complex datasets. This study compares the performance of XGBoost with various outlier detection methods, such as LOF, CBLOF, DBSCAN, IF, and K-Means, as well as various other classification algorithms based on machine learning and deep learning. Experimental results show that the combination of GMM and XGBoost provides the best performance with an accuracy of 95.493%, a recall of 91.650%, and an AUC of 95.145%, outperforming other models in the context of credit approval prediction on an imbalanced dataset. The proposed method has been proven to reduce prediction errors and improve the model's reliability in detecting eligible credit applications.

Islamic banking financing has become a crucial component of Indonesia's financial sector, providing a Sharia-compliant alternative to conventional financing. Despite its rapid growth, assessing the feasibility of Islamic banking financing remains a major challenge, particularly in terms of risk management, financial sustainability, and regulatory compliance. Previous studies have assessed financing feasibility using various methods, including the 5C approach (Character, Capacity, Capital, Collateral, and Conditions). However, research in this area remains fragmented, with a lack of systematic analysis of key trends, methodologies, and influencing factors. This study uses a Systematic Literature Review (SLR) to synthesize and analyze existing research on the feasibility of Islamic banking financing in Indonesia. The review covers studies published between 2020 and 2022, focusing on research distribution, analytical techniques, and key determinants affecting financing feasibility. The findings reveal that most studies emphasize credit risk assessment, financial literacy, and regulatory frameworks, but lack a unified approach to measuring feasibility. Furthermore, this study highlights gaps in the application of digital technologies, such as big data and machine learning, that can be used to strengthen the financing eligibility assessment system. The application of these technologies not only improves the accuracy of risk predictions but also enables Islamic banking institutions to reach more customers, particularly MSMEs and the informal sector, which have historically been underserved. The results of this study provide valuable insights for Islamic financial institutions, regulators, and researchers, highlighting the need for integrated risk assessment models, a better regulatory framework, and enhanced financial literacy initiatives to strengthen Islamic banking financing in Indonesia. This research contributes to the development of a more structured and comprehensive framework for evaluating financing eligibility, ensuring sustainable growth and financial inclusion in the Islamic banking sector.

Dengue Hemorrhagic Fever (DHF) is a disease caused by the Dengue virus and has a significant impact on public health, especially in tropical areas. Early diagnosis and prediction of DHF risk are essential to prevent complications and improve medical care. This study aims to develop a DHF risk prediction model using the Decision Tree method based on clinical symptoms and laboratory data. The data used include symptoms such as fever, joint pain, rash, and laboratory results such as platelet count and hematocrit. The Decision Tree model was chosen because of its ability to handle data with various variables and provide easy-to-understand interpretations. The research data were taken from patients diagnosed with DHF in several hospitals during a certain period. The dataset was then analyzed to find relevant patterns that could predict a high risk of DHF. The model training and testing process was carried out using cross-validation techniques to ensure prediction accuracy. The results showed that the Decision Tree model had an accuracy rate of 96.95% and consistent results from cross-validation which produced an average accuracy of 92.8%,, with good sensitivity and specificity in predicting DHF risk based on a combination of clinical symptoms and laboratory data. Factors such as low platelet count and fever symptoms lasting more than three days were found to be significant predictive variables. In conclusion, this Decision Tree model has the potential to be used as a tool in early prediction of DHF risk, which can help medical personnel in clinical decision making and patient management. Further development can be done by adding other variables such as epidemiological data to improve model performance.

Dengue Hemorrhagic Fever (DHF) is a disease caused by the Dengue virus and has a significant impact on public health, especially in tropical areas. Early diagnosis and prediction of DHF risk are essential to prevent complications and improve medical care. This study aims to develop a DHF risk prediction model using the Decision Tree method based on clinical symptoms and laboratory data. The data used include symptoms such as fever, joint pain, rash, and laboratory results such as platelet count and hematocrit. The Decision Tree model was chosen because of its ability to handle data with various variables and provide easy-to-understand interpretations. The research data were taken from patients diagnosed with DHF in several hospitals during a certain period. The dataset was then analyzed to find relevant patterns that could predict a high risk of DHF. The model training and testing process was carried out using cross-validation techniques to ensure prediction accuracy. The results showed that the Decision Tree model had an accuracy rate of 96.95% and consistent results from cross-validation which produced an average accuracy of 92.8%,, with good sensitivity and specificity in predicting DHF risk based on a combination of clinical symptoms and laboratory data. Factors such as low platelet count and fever symptoms lasting more than three days were found to be significant predictive variables. In conclusion, this Decision Tree model has the potential to be used as a tool in early prediction of DHF risk, which can help medical personnel in clinical decision making and patient management. Further development can be done by adding other variables such as epidemiological data to improve model performance.

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..

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.

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 research applies the Monte Carlo simulation method to predict the movement of Apple Inc.'s stock price over a long period of time. Using historical data of Apple's stock price from 12 December 1980 to 24 March 2022, this study aims to generate a probability distribution of the future stock price. The method involves several steps, including data collection, log return calculation, parameter estimation, and simulation of the stock price path through random iterations based on the log return distribution. The simulation results show that the closing price of Apple stock can be predicted by following the historical trend, although there are differences with the real data due to the stochastic nature of the Monte Carlo technique. This research also applies a variance reduction method to improve simulation efficiency. The findings provide a valuable perspective for investors and financial analysts in identifying investment risks and opportunities through an in-depth understanding of the dynamics of stock price movements using Monte Carlo simulation. Suggestions for future research include the use of VaR methods with historical variance and covariance approaches, as well as considering longer data periods and more stock indices for more comprehensive results.

Planning a business can be used as a basis for making decisions that will be taken for the future. With this planning, MSMEs can identify various risks that will arise so that their business can improve its operational performance. The aim of this service is to determine sales and income projections for MSMEs in Batulaya Village. The results of the service concluded that if there is a plan, the company can ensure the continuity of the company for the long term. Apart from good planning, a company also needs to prepare a budget, so that the activities the company will carry out can be planned properly. The company budget plays a very important role in planning all company activities for a certain period of time in the future. A sales budget can provide an overview of estimates or predictions about the number of products or services that will be sold in a certain time period in the future.

The rapid advancement of deep learning technologies has significantly transformed healthcare analytics, particularly in medical data prediction and classification. This study proposes a hybrid Convolutional Neural Network–Long Short-Term Memory (CNN–LSTM) framework for multi-modal healthcare data analysis, integrating medical imaging, structured electronic health records (EHRs), and IoT-generated time-series physiological signals. The proposed architecture combines spatial feature extraction through CNN with temporal dependency modeling via LSTM to enhance predictive accuracy and clinical decision support. A quantitative experimental design was employed, utilizing multi-source healthcare datasets that underwent preprocessing, normalization, and feature engineering prior to model training. The performance of the hybrid model was evaluated using Accuracy, Precision, Recall, F1-Score, AUC-ROC, and Mean Absolute Error (MAE), and compared with conventional machine learning models and standalone deep learning architectures. Experimental results demonstrate that the proposed CNN–LSTM model achieves superior performance, with improved classification accuracy and reduced prediction error, while maintaining strong generalization capability. The findings indicate that integrating spatial and temporal feature learning significantly enhances disease detection, risk stratification, and personalized treatment planning. This approach supports the development of intelligent clinical decision support systems and scalable smart healthcare environments. The proposed framework offers a reliable and efficient solution for advanced healthcare analytics in IoT-enabled systems.