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The rapid development of information technology in today's digital era has significantly impacted organizational performance, particularly in data management and resource planning. One organization that heavily relies on accurate data availability is the Indonesian Red Cross (PMI), especially its Blood Donor Unit (UDD). UDD PMI of Asahan Regency faces challenges in determining monthly blood donor targets to maintain stable blood stock. A shortage of blood supply can be fatal for patients requiring transfusions. Therefore, a system is needed to forecast the number of blood donors, allowing for more accurate decision-making. This study utilizes the Weighted Moving Average (WMA) method to predict the number of blood donors for the following month based on historical data from March 2024 to March 2025. The WMA method is chosen for its ability to assign greater weight to recent data, making the forecast more relevant and accurate. The results of this research are expected to assist UDD PMI Asahan Regency in anticipating blood needs and maintaining optimal stock availability.

Penelitian ini bertujuan untuk membuat sistem prediksi penjualan berbasis web pada Starmart dengan menerapkan dua metode, yaitu Linear Regression dan Weighted Moving Average. Penelitian dilakukan dengan beberapa tahap, yaitu pengumpulan data penjualan, pengolahan data, penerapan kedua metode prediksi, serta pengujian akurasi menggunakan Mean Squared Error (MSE) dan Mean Absolute Percentage Error (MAPE).Penulis melakukan pengumpulan data secara langsung yang diambil dari Starmart dengan cakupan data penjualan yang berjumlah 12 kategori produk,Kemudian data diproses untuk membangun aplikasi prediksi penjualan yang bertujuan untuk memprediksi penjualan produk pada Starmart untuk periode selanjutnya.Hasil penelitian menunjukkan bahwa implementasi metode Linear Regression memberikan hasil prediksi yang lebih stabil dan sesuai dengan pola data historis, dengan nilai MSE sebesar 2184.18 dan MAPE sebesar 10.88%. Sementara itu, metode Weighted moving average menghasilkan prediksi yang cenderung fluktuatif dengan nilai MSE sebesar 4715.66 dan MAPE sebesar 17.35%. Berdasarkan perbandingan kedua metode, dapat disimpulkan bahwa Linear regression lebih akurat dibandingkan Weighted Moving Average dalam memprediksi penjualan produk di Starmart.

Climate change has had a significant impact on various aspects of life, including air temperature in urban areas such as Medan City. This study aims to analyze changes in air temperature in Medan City during and after the COVID-19 pandemic, especially related to social restriction policies such as PPKM. Temperature data was obtained from UPT BMKG Medan for the period 2020 to 2025 and analyzed using I-MR (Individual-Moving Range), Cusum, and EWMA (Exponentially Weighted Moving Average) methods. The results of the analysis are expected to be able to show significant temperature differences as a result of the decline in human activities during the pandemic as well as the trend of post-pandemic temperature changes. This study is important as a basis for future environmental planning and climate change mitigation policies.

The A* Algorithm is a path-finding algorithm that primarily uses weighted graphs and focuses on the heuristic values of nodes. However, while effective in generating a near-optimal path in a static environment, the traditional algorithm faces limitations in navigating dynamic environments, often resulting in collisions due to its inability to recognize dynamic and moving obstacles. This limitation makes it inefficient especially in complex environments with real-world scenarios. To address these limitations, an Enhanced A* Algorithm is proposed. This algorithm utilizes Navigation Mesh data structure to generate a more optimal route with local path planning and to dynamically adjust the parameters in two-dimensional non-grid environments. The performance of the algorithms was evaluated using 12 benchmarks, each corresponding to a distinct test case and levels of complexity. Then, in terms of dynamic obstacle avoidance, a comparison between the Enhanced A* Algorithm and the traditional algorithm was conducted. Statistical analyses were also performed to assess the consistency and validity of the findings. The results demonstrated that the Enhanced A* Algorithm successfully avoided all dynamic obstacles and moving objects encountered along the path in all distinct test cases. In contrast to the traditional algorithm, which achieved an average obstacle avoidance rate of 8.33%, the enhanced algorithm consistently demonstrated a 100% average obstacle avoidance rate. The enhanced algorithm outperformed the traditional A* algorithm in generating a path in a complex environment by exhibiting optimal dynamic obstacle recognition and avoidance. The Enhanced A* Algorithm is subsequently applied to autonomous vehicle parking, following standard parking restriction laws.

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.

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.      

Forecasting demand for screen-printed clothing products at the UMKM "D'mitz Screen Printing" in Sobrah Village, Wungu District, Madiun Regency helps with production control planning to maximize supply chain management for screen-printed clothing products. To predict future product demand, it is very important for UMKM to forecast market demand. Forecasting future demand is very important to avoid sales prediction errors that can cause waste, such as increased production costs due to sales predictions being too large, or stock outs due to sales predictions being too small, which results in customers having to wait longer to get the goods they want. Based on this problem, the UMKM "D'mitz Screen Printing" carried out a demand forecasting analysis for screen printed clothing with the aim of reducing waste and maximizing value. Forecasting demand for screen printed clothing for the next five months using time series analysis and moving average methods. Forecasting results for the period March 2022 to February 2023 show sequential forecasting values of 3266.67; 3300; 3250; 3283.33; 3233.33; 3316.67; 3333.33; 3372.22; 3305.56; and 3272.22. From the Mean Absolute Error (MAE) and Mean Square Error (MSE) calculations that have been carried out, the MAE value is 94.44 and the MSE value is 16018.593.

This study proposes an approach for human fall classification utilizing a combination of Weighted Moving Average (WMA) and Convolutional Neural Networks (CNN) on the SisFall dataset. Falls among elderly individuals pose a significant public health concern, necessitating effective automated detection systems for timely intervention and assistance. The SisFall dataset, comprising accelerometer data collected during simulated falls and activities of daily living, serves as the basis for training and evaluating the proposed classification system. The proposed method begins by preprocessing accelerometer data using a WMA technique to enhance signal quality and reduce noise. Subsequently, the preprocessed data are fed into a CNN architecture optimized for feature extraction and fall classification. The CNN leverages its ability to automatically learn discriminative features from raw sensor data, enabling robust and accurate classification of fall and non-fall events. Experimental results demonstrate the efficacy of the proposed approach in accurately distinguishing between fall and non-fall activities, achieving high classification performance metrics such as accuracy, precision, recall, and F1-score. Comparative analysis with existing methods showcases the WMA-CNN hybrid approach's superiority in classification accuracy and robustness. Overall, the proposed methodology presents a promising framework for real-time human fall classification using sensor data, offering potential applications in wearable devices, ambient assisted living systems, and healthcare monitoring technologies to enhance safety and well-being among elderly individuals.

PT.XYZ is a company engaged in the business of trading industrial and fabricated goods. PT.XYZ often experiences excessive accumulation of thinner which will cause the storage warehouse to pile up. Therefore, this research aims to develop an effective forecasting model in anticipating the use of Thinner No.17 Jotun at Indonesia . The right forecasting method can help companies plan inventory, reduce the risk of overstock or stockout, and increase efficiency in the supply chain. The development of usage from month to month is increasingly uncertain, so the company wants to know how much Thinner expenditure will be used in the following month by referring to the previous month's expenditure using the single moving average, weighted moving average and single exponential smoothing methods. The software used to assist in this research is POM-QM. From the results of research using the Simple Moving Average method, this method has a MAPE value of 5,841%, the forecast size for the next period, namely period 13, is 285 pcs. This shows that the Simple Moving Average method is the most appropriate method to use to solve the problem of using Thinner No.17 Jotun experienced by PT. XYZ because it has the smallest error value. So it can be concluded that the application of this method provides the right solution for the needs of Thinner No.17 Jotun in the next period.  

Company CV. AM Nanda Putra is located in Sidoarjo and operates in the scaffolding industry. Currently the company is experiencing losses due to a lack of optimization in planning the amount and time of ordering raw materials, which results in shortages and excess material inventory. To overcome this problem, the company uses the MRP (Material Requirement Planning) method in optimizing raw material planning. In terms of the lot sizing approach, the company applies the LFL and EOQ methods. The forecasting methods used are Moving Average (MA), Weight Moving Average (WMA), and Exponential Smoothing (ES). The smallest MAD results were obtained using the Exponential Smoothing (ES) method for all scaffolding products. The forecasting results are obtained to determine the MPS (Master Production Schedule) for the next 10 months. After the determination of  MPS, the results of Material Requirement Planning (MRP) were obtained, namely the supply of raw materials for MF 170 AM scaffolding of 35402 units or 17700 sets, MF 170 K1 scaffolding of 28906 units or 14453 sets, MF 190 AM scaffolding of 16250 units or 8125 sets, and MF 190 K1 scaffolding of 7656 units or 3828 sets. From the results of calculating the cost of raw material requirements using the Lot for Lot (LFL) method and the Economic Order Quantity (EOQ) method, it can be seen that the total cost of planning the smallest raw material inventory with an amount of Rp. 12,975,818,022.

This study aims to obtain an effective solar panel tracking mechanism using energy-efficient electric actuators. Furthermore, we designed and implemented a semi-active solar tracking system. A tracking system is proposed to control solar panel orientation using a moving mass, a spring system, and an actuator. The weight of the moving mass and the spring constant are optimized to reduce actuator size. A stepper motor was used for this case. This electric drive is not the prime mover of the solar tracker; hence, it works against mass elements lighter than solar panel weight as used in the active solar tracker. Experimental results suggest that the average power required by the stepper motor is 0.21% of the energy generated by the solar tracking system. The results indicate that the proposed solar panel tracker works satisfactorily to control solar panel orientation.

The AWD Mranggen store is a store that is engaged in the sale of bags, belts, shoes with sales developments increasing from year to year, with fairly tight business competition, the AWD Mranggen store must be able to calculate the estimated number of items to be purchased based on previous sales data, the prediction is very influential on the decision to determine the number of items to be provided by the AWD Mranggen Store for the next sales period data. Inventory of goods that are not right cause some losses in terms of time and also costs, it is necessary to have a forecasting system. Forecasting is a technique to identify a model that can be used to predict conditions in the future. By using the weight moving average method, it can be seen that the error value is more than smaller than other methods and the estimated results can be more precise so that it can help owners make decisions in carrying out inventory.