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In the increasingly competitive Industry 4.0 era, companies need to forecast product demand to meet consumer needs and improve operational efficiency. CV Mamifood Sukses Abadi, an MSME that produces milk and cheese-based foods, has faced sales fluctuations in the last two years, thus requiring accurate forecasting to plan production strategies and resource management. This research aims to forecast demand using the Fuzzy Mamdani method and the POM-QM application. Fuzzy Mamdani was chosen for its ability to handle decision-making with multiple criteria and balanced weights, while POM-QM was used to validate predictions through quantitative methods. Product sales data for the years 2022 and 2023 were analyzed to produce accurate forecasts. The methods used include Moving Average for forecasting and evaluation of the results using MAPE. The analysis results show that the Moving Average method with N = 2 produces a MAD value of 402.523 and a MAPE of 22.155%, while the results of Fuzzy Mamdani show that product demand in the next period tends to decrease. This research is expected to provide insight for CV Mamifood Sukses Abadi in planning a more efficient production strategy.

In recent decades, artificial intelligence (AI) has significantly advanced and shown great potential across various fields, including bioinformatics. This paper examines current trends in AI applications within bioinformatics, highlighting future potentials and the challenges of integrating these technologies. The research utilizes secondary data collection from scientific literature, books, conference reports, and official documents on AI and bioinformatics, sourced from reputable databases like Scopus, IEEE, PubMed, and Google Scholar. Through comparative analysis, similarities, differences, and technological advancements were identified and discussed. Descriptive narrative interpretation was employed to provide a holistic view of AI trends and potential in bioinformatics. Key findings indicate that AI, particularly machine learning and deep learning, is instrumental in genomic data analysis, protein structure prediction, drug discovery, and clinical bioinformatics. Furthermore, the study underscores the benefits of AI in enhancing data analysis accuracy and efficiency, while addressing ethical and technical challenges. Future prospects emphasize the importance of interdisciplinary collaboration to fully leverage AI's capabilities in bioinformatics.

Cardiovascular disease is a leading cause of death globally, necessitating effective predictive systems. This research aims to analyze the effectiveness of various machine learning (ML) models—Logistic Regression (LR), Random Forest (RF), Naive Bayes (NB), Support Vector Classifier (SVC), and K-Nearest Neighbors (KNN)—in predicting heart disease using publicly available health data. The study involved pre-processing data, training models, and evaluating them using accuracy, precision, recall, F1-score, and G-Mean metrics. The results show that KNN is the most reliable model, with the highest accuracy of 92%. Significant health features were identified, such as chest pain type and maximum heart rate. The study contributes to improving clinical decision support systems by identifying optimal ML models for heart disease prediction.

This work explores the potential of PennyLane and variational quantum-classical algorithms (VQCA) to forecast lung cancer using a structured dataset. The VQCA model performs exceptionally well, with flawless training, validation, and test accuracies of 1.0, demonstrating its capacity to identify patterns in the dataset and provide reliable predictions successfully. Contrarily, the accuracy of the quantum neural network (QNN) and classical neural network (NN) models is lower, demonstrating the benefits of utilizing quantum computing methods for enhanced predictive modeling. We provide a complete examination of the data, stressing the better performance of the VQCA model and its promise in correctly predicting lung cancer. The results highlight the importance of quantum-classical algorithms and help us understand the benefits and drawbacks of various strategies for predicting lung cancer. The study highlights the potential applications of quantum computing techniques in advancing the field of healthcare analytics. It shows the capability of the VQCA model to predict lung cancer using a tabular dataset accurately. Further research in this area is needed to explore scalability and practical implementation aspects. In summary, this study showcases the potential of VQCA and PennyLane in predicting lung cancer and underscores the benefits of quantum computing techniques in healthcare analytics.

Indonesia is committed to achieving zero hunger as one of the goals of fulfilling the Sustainable Development Goals (SDGs) where this commitment focuses on addressing the problem of food availability but also ensuring that every individual has access to sufficient, nutritious, and safe food throughout the year for everyone. However, reviewing the current conditions in Indonesia, there is still an imbalance in food availability that will cause food vulnerability. Therefore, a prediction of food vulnerability in the future is needed where discriminant analysis is one of the appropriate statistical methods to analyze qualitative dependent and quantitative independent variables. This study uses secondary data from the official website of the food agency and the central statistics agency. The results of the study show that the characteristics of the data have small variations, asymmetric distribution, and there are outliers in several categories. The assumptions of multivariate normality, the suitability of the dependent variables, and the identity of the variance-covariance matrix have been met. Through discriminant analysis, the variables of the percentage of poverty and the percentage of households with access to clean drinking water are proven to significantly affect the IKP category. The discriminant model produces one significant function that is able to group the IKP category with a model accuracy rate of 86.8% and a classification accuracy of 64.7%.

The increasing frequency and severity of tsunamis in coastal areas underscore the urgent need for efficient Tsunami Early Warning Systems (TEWS). This research aims to optimize TEWS by integrating fast computational tsunami wave modeling to enhance prediction speed and accuracy. The study utilizes numerical simulations employing finite volume methods, along with GPU acceleration, to model tsunami wave propagation and its impact on coastal areas. Machine learning techniques, such as regression trees, are incorporated to analyze large datasets of pre-computed tsunami simulations for accurate forecasting. The results reveal that by applying rapid computational methods, detection time can be reduced by up to 7 minutes, particularly for near-field tsunamis. This significant time-saving enables more effective evacuation procedures and better disaster mitigation efforts. In comparison to conventional systems, the fast computation model also provides more accurate predictions, including tsunami heights and arrival times. The implications of these findings suggest that fast computational methods can substantially improve the current TEWS, allowing for quicker and more reliable tsunami warnings. Moreover, the integration of advanced machine learning techniques ensures the system's adaptability and robustness in predicting tsunami behaviors based on varying data inputs. The potential for implementing this model in tsunami-prone regions worldwide is considerable, offering an improved approach to tsunami disaster preparedness and response. By reducing detection time and enhancing prediction accuracy, the optimized TEWS can significantly minimize loss of life and infrastructure damage, making it a valuable tool for global disaster management strategies.  

The purpose of integrating artificial intelligence (AI) technology into campus smart parking management systems is to improve user comfort, safety, and efficiency. To control car traffic in real-time, the system combines technologies including computer vision, machine learning, and the Internet of Things (IoT). Automatic vehicle detection, license plate recognition, parking lot availability prediction, and ideal vehicle flow regulation are some of the key characteristics. The system can effectively recommend parking to users through a mobile app by using pattern analysis and historical data. The implementation results showed a 25% reduction in traffic in the university's parking area and a 30% increase in parking time efficiency. According to the findings of the study, applying AI technology in parking management can be a creative way to overcome the difficulties associated with facility management in higher education environments.

Introducing alphabetical sign language is necessary to bridge communication between deaf and hard-of-hearing people and their surrounding environment. This research aims to develop a sign language alphabet letter detection system based on American Sign Language (ASL). The research methods include data collection, feature extraction with OpenCV and Mediapipe, model development with Random Forest algorithm, and real-time system testing. The test results show that the developed system can achieve 97% prediction accuracy in recognizing hand patterns that represent ASL letters. The system uses a webcam as real-time input, providing accurate responses in various environmental conditions. This research contributes significantly to developing communication support technology for the deaf community, with implications for increased inclusivity and social engagement.

The purpose of this research is to create a JST (artificial neural network) model that can forecast population growth at the Population and Civil Registration Office of West Sumba Regency. population growth at the Population and Civil Registration Office of West Sumba Regency. Regency. Regional development planning must consider the increasing number of population, therefore proper forecasting is essential to encourage sustainable policies and initiatives. sustainable policies and initiatives. Because it can identify complex patterns in past data and produce more accurate forecasts than traditional techniques, an ANN model is used. traditional techniques, the ANN model is used. The data used in this study is the population growth of Southwest Sumba Regency over the past including characteristics such as birth and death rates and population movements. deaths and population movements. The backpropagation algorithm is used to optimize the multilayer perceptron (MLP) architecture for ANN training. Separating the data into training and testing sets and assessing the models model using metrics such as Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) based on the error. Error (RMSE) based on the prediction error are the steps involved in the training process. involved in the training process. The research findings show that, with a low level of error, the artificial neural network model can estimate the population increase in Southwest Sumba Regency with a reasonable level of accuracy. reasonable level of accuracy. The model is expected to serve as a reference for relevant authorities to better manage population data and as a tool to create more focused and successful population policies.

The Water Quality Index (WQI) shows the condition of water quality in an area based on the status of water quality resulting from the measurement of physical, chemical and bacteriological parameters of a water body both rivers and lakes. Several machine learning techniques can be used to predict water quality in an area, one of which is through the prophet model approach which is able to provide fairly accurate predictions for the water quality index in Indonesia. The main objective of this research is to obtain a WQI prediction value as a baseline in the formulation of future environmental control activity policies using the prophet model. The result is that the predicted value of IKA for 2021-2023 generated through machine learning with the prophet model approach shows that the Mean Absolute Error (MAE) value: 7.01, Root Mean Square Error (RMSE): 8.61 and Mean Absolute Percentage Error (MAPE): 13.06%, which means that IKA prediction with the prophet model is effective in capturing annual patterns between historical data and future predictions.    

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.

KPRI KOKARNABA Baturraden faces challenges in managing increasingly complex sales data, particularly in identifying the most in-demand products to maximize profit. This study aims to analyze sales patterns using the Naïve Bayes algorithm as a probability-based classification method. The collected sales data were analyzed to identify categories of best-selling and less popular products within the cooperative. The results indicate that the Naïve Bayes algorithm has an accuracy rate of 77.56% in predicting product categories. This research is expected to assist the cooperative in optimizing stock management and improving member satisfaction.

Victory in Mobile Legends is influenced by various factors, such as player skills, strategy, and character selection. To predict game outcomes, the backpropagation algorithm is applied to process historical gameplay data and create an accurate predictive model. This study aims to apply the backpropagation algorithm to predict victory based on player attributes, including team role, experience level, and past performance. The research method involves training and testing the model using data from multiple gameplay sessions with varied outcomes. Findings show that the backpropagation algorithm can predict game results with high accuracy, especially when the data includes a more comprehensive range of attributes. The implications of this study suggest that a backpropagation-based predictive model can help players understand their chances of winning and optimize their gameplay strategies. Furthermore, future developments in this algorithm could provide benefits for similar applications in other digital gaming fields.

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.

Foodstuffs are raw materials in the form of agricultural, vegetable and animal products that are used by the food processing industry to produce a food product. Food ingredients consist of plant foods and animal foods. Food is the most basic need for human resources in a country. Food prices sometimes experience erratic increases or decreases. The aim of this research is to determine the results of food price predictions in the Deli Serdang Regency area using the Backpropagation algorithm. The data used in this research is food price data from 2020 to 2023 which comes from the official National Food Ingredients website. This research uses the Backpropagation algorithm artificial neural network method which uses several architectural models and the results of this test will produce the best accuracy values. The test results show that the best architecture for research on implementing the backpropagation algorithm in predicting food prices in Deli Serdang Regency is 2-10-1 with an accuracy of 87.5% and the 2-3-8-1 architecture with an accuracy of 87.5%.

Energy efficiency has become a critical focus in manufacturing plants due to rising operational costs and increasing environmental concerns. The growing importance of energy management is driven by the need to reduce energy consumption, lower emissions, and enhance overall operational efficiency. Traditional maintenance practices, such as reactive and preventive maintenance, often lead to unnecessary downtime, high repair costs, and inefficient energy usage. In contrast, predictive maintenance (PdM), supported by Internet of Things (IoT)-enabled sensor networks, offers a proactive approach to minimizing energy waste by predicting equipment failures before they occur. This study develops a predictive maintenance framework using IoT-based sensor networks to optimize energy usage and reduce energy losses in manufacturing plants. The research begins with an overview of IoT sensor network architectures and their applications in industrial automation, including sensors such as temperature, vibration, and pressure sensors. It explores predictive analytics techniques, such as machine learning and artificial intelligence, used for failure prediction, which are key to enhancing energy efficiency. The study emphasizes how predictive maintenance contributes to industrial sustainability by reducing carbon footprints and optimizing energy consumption. The research methodology involves the installation of IoT sensors in critical machinery, real-time data analysis using machine learning algorithms for failure prediction, and energy consumption measurement before and after implementing IoT-based interventions. The results show significant improvements in energy consumption efficiency and operational productivity. Predictive maintenance led to reduced unplanned downtime, increased equipment reliability, and a more sustainable manufacturing process. However, challenges such as sensor integration, initial setup costs, and data security concerns were identified. The study concludes with recommendations for integrating IoT-based predictive maintenance systems into manufacturing plants to further optimize energy usage and promote sustainability.

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.