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Abstract. From the perspective of Micro, Small, and Medium Enterprises (MSMEs), fluctuations in raw material prices are highly concerning as they can significantly impact business stability. While MSMEs may tolerate price fluctuations to some extent, from an industrial engineering perspective, such a passive approach contradicts the principles of continuous improvement. This study seeks to predict the price of large red chili peppers using five regression models implemented through Orange Data Mining: Linear Regression, Support Vector Machine, Decision Tree, k-Nearest Neighbors (kNN), and Gradient Boosting. Due to the limited availability of daily data, particularly within a daily timeframe, the study utilized weekly data spanning three years. The results of the Test and Score evaluation shows Gradient Boosting as the best-performing model, achieving a Mean Absolute Percentage Error (MAPE) of 0.7%. However, the MAPE for predictions in January 2025 increased to 15.8%. This error is expected to decrease as more weekly data becomes available to mitigate the inaccuracies inherent in this model. Keywords: prediction, red chilli, regression, supervised learning , orange data mining. Abstrak. Dalam perspektif UMKM, fluktuasi harga bahan baku adalah suatu hal yang paling ditakuti karena berakibat pada ketahanan usaha yang menjadi tidak menentu. Pada suatu kondisi, fluktuasi harga dapat diterima para UMKM, namun dalam perspektif teknik industri, sikap UMKM tersebut tidak sesuai prinsip continuous improvement. Penelitian ini mencoba untuk memprediksi harga cabai merah besar dengan menggunakan 5 model regresi dibantu Orange Data Mining. Yaitu Linear Regression, Support Vector Machine, Tree, kNN, Gradient Boosting. Data yang diperlukan sebagian besar tidak tersedia, khususnya dalam kerangka waktu harian sehingga penelitian ini menggunakan data mingguan selama 3 tahun. Hasil Test and Score menunjukkan model Gradient Boost terpilih menjadi model terbaik dengan tingkat MAPE 0.7% namun MAPE pada tahap Prediction di bulan Januari 2025 menjadi 15.8%. Error tersebut akan berkurang ketika data mingguan sudah cukup banyak untuk menambal kesalahan yang dihasilkan model ini Kata kunci: prediksi, cabai merah, regression, supervised learning , orange data mining.

Inventory control is a crucial element in supporting the smooth production process in manufacturing companies. PT Bonecom Tricom, a producer of automotive spare parts, faces challenges in managing SPP-C05 Black plastic pellets due to inaccurate estimation methods, resulting in material accumulation in storage. This study aims to propose inventory planning using time series and min-max methods to optimize material requirements. The results show that the Holt-Winter's Multiplicative method provides the best forecasting accuracy with the lowest error rate (MAPE) of 2%. Furthermore, implementing the min-max method can reduce inventory costs by 14.245%. This study is expected to serve as a reference for companies to manage material inventory more effectively and efficiently.

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.

XYZ is a manufacturing company that produces Yamaha brand motorbike parts such as electrical switches, electrical sockets, lever assembly and horns. Many of the products sent were returned by customers because they did not meet quality standards, for example in March 2024 there were 1,420 pcs out of a total of 19,900 pcs that were returned. This means that companies have to increase production time and costs to replace defective products that are returned because they do not have safety stock. Forecasting is needed to control the production system so that it does not experience over stock and safety stock shortages. This research aims to provide recommendations for forecasting methods that companies can use to minimize the occurrence of production excesses and shortages in the company. Forecasting is done based on historical company sales data for 12 months. The method used is the exponential smoothing method which is then tested whether this method can be used in the future. This calculation uses the help of POM-QM (Production Operation Management – Quantitative Method) software. Calculations are carried out by testing the MAD, MSE, and MAPE values to obtain calculation error values. The results obtained by forecasting the main sw srtg lock assembly product using the exponential smoothing method were 15,708 for demand for the next period, MAD 3.38, MSE 22.84 and MAPE value 26%. Based on these results, the exponential smoothing method can be a recommendation for companies to forecast future demand. This is because the value of forecasting accuracy or MAPE is reasonable. The smallest percentage of MAPE values has a fairly minimal possibility of error in forecasting results.

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.    

MSMEs are productive economic businesses run by individuals or small business entities to grow and develop their businesses in order to build the economy, so that MSMEs become the most important pillars in the Indonesian economy. The large number of competitors in the business world requires entrepreneurs to find strategies that can increase the sales cycle and fulfill the number of requests. Solusi Cash & Kredit is a company engaged in cash and credit sales of smartphones, furniture, and electronics. According to the owner of Solusi Cash & Kredit, Vivo smartphones are one of the best-selling brands on the market. The phenomenon that occurred in the company during August 2023 to June 2024 was the occurrence of fluctuations or instability in product sales levels. The purpose of this study was to determine the results of the comparison of sales forecasting with the Exponential Smoothingi and Least Square methods. The results obtained were that the exponential smoothing alpha 0.3 method had a MAPE value of 18.27% with a forecast value for the next period of 34 units per month. Alpha 0.5 had a MAPE value of 14.74% with a forecast value for the next period of 36 units per month. Alpha 0.7 had a MAPE value of 12.64% with a forecast value for the next period of 36 units per month. Alpha 0.9 had a MAPE value of 11.7% with a forecast value for the next period of 37 units per month. The least square method had a MAPE value of 7.2% with a forecast value for the next period of 41 units per month.

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 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.      

Sales forecasting is a technique that companies use to predict future sales volumes based on previous sales data. This research aims to help UMKM  XYZ determine the optimal production amount to maximize profits, by using forecasting methods in planning mocaf flour production. The methods used include the Time Series model with Moving Average, Exponential Smoothing, and Trend Analysis, which are calculated using POM QM Windows software. The analysis results show that the Trend Analysis method is the most accurate for forecasting, with the smallest error value, namely MAD of 76.997, MSE of 8161.672, and MAPE of 6.02%. The smaller the error value, the more accurate the forecasting results. Therefore, the Trend Analysis method is recommended for forecasting mocaf flour sales in XYZ UMKM in 2024, with the production of 15,100 kg to avoid excess stock and dead stock in meeting consumer demand.    

Amidst the growing competition in the industry, CV Berkah Jaya Klaten faces challenges in planning the production capacity of cleaning tools to meet market demand. This study aims to provide solutions to production capacity planning issues by applying the Rough Cut Capacity Planning (RCCP) method using the Capacity Planning Using Overall Factors (CPOF) technique and a system simulation approach. The planning process begins with demand forecasting using IBM SPSS Statistics 25 software, which produces the smallest Mean Absolute Percentage Error (MAPE) value using the Simple Seasonal method. These forecasting results are used to determine the Master Production Schedule (MPS). Processing RCCP data with the CPOF method requires MPS data, processing time for each workstation, and historical proportions calculated from standardized processing times. The system simulation of production capacity planning is conducted to model real conditions and evaluate various production scenarios. The simulation results reveal that the required production time capacity each month always exceeds the available time capacity, indicating the need for capacity adjustments to avoid bottlenecks and improve efficiency. With this approach, CV Berkah Jaya Klaten can plan production capacity more efficiently and effectively, ensuring product availability in accordance with customer demand.

Rock strength has an important role in the mining industry. These forces can determine many aspects of mining such as slope geometry, excavation, blasting, and drilling. Rock strength can include tensile strength, compressive strength, and shear strength. In this case, the test is carried out to determine the correlation of uniaxial compressive strength and indirect tensile strength. The method used to determine the correlation of compressive strength and indirect tensile strength is by linear regression approach, which will then be analyzed for accuracy through Root Mean Square Error (RMSE), and Mean Absolute Percentage error (MAPE). This study used quantitative and qualitative methods, starting from the coordinate data of rock sampling locations, physical properties and mechanical properties. In this study, there were 6 sampling locations located in 2 different areas, namely Loa Janan and Sanga-sanga. The secondary data used are regional geological maps and maps of the area where the study is located. Furthermore, secondary data is processed using Arcgis software for mapping, and using Microsoft Excel software to assist in calculations in determining the value of physical and mechanical properties of rocks. The results of the compressive and tensile strength tests in this study showed a perfect corelation using linear regression, namely UCS= 3.9582 σt - 0.4004, with a correlation coefficient (R) of 0.972 and a determination coefficient (R2) of 0.945. and obtained RMSE 0.033 and MAPE 5.89%.

The aim of this thesis is to: Explain how the Merdeka Curriculum is applied to the subjects of Islamic Religious Education and Character at Darunnajah Vocational School, Banjarmangu; and Provide an explanation of the design for the development of the Independent Curriculum in the subjects of Islamic Religious Education and Character at Darunnajah Vocational School, Banjarmangu.The author uses descriptive research with a qualitative approach and field research.  To collect data, this research uses observations, in-depth interviews (in-depth interviews), and documentation. On the other hand, the author uses data reduction methods, data visualization, conclusions, or verification in data analysis techniques which are carried out with the aim of facilitating understanding of problem phenomena that occur and providing solutions to problems.The results of the research show that the implementation of the Independent Curriculum in Islamic Religious Education and Character Education Subjects at Darunnajah Banjarmangu Vocational School has been carried out well. This starts with PAI teachers preparing themselves to apply learning, namely creating learning tools and learning flows, from initial, core, and closing. Students then carry out the Project for Strengthening the Pancasila Student Profile (P5) outside the classroom. At Darunnajah Banjarmangu Vocational School, the design for developing the implementation of the Independent Curriculum in PAI subjects is to take part in workshops, increase teacher creativity, and share to change the way of teaching to make it better.

Curriculum is a set of educational subjects and programs provided by an education provider institution which contains lesson plans that will be given to lesson participants in one educational level period. The method used in this research is qualitative research. According to Saryono (2010), qualitative research is research that is used to investigate, discover, describe and explain the qualities or features of social influence that cannot be explained, measured or described using a quantitative approach. The aim of this research is to discover the process of implementing the Curriculum at MTs IT At-Tauhid Kampung Tauhid Sriwijaya. The approach used in this research is qualitative with descriptive analysis methods. Data collection techniques were carried out through interviews and observation.

Furniture raw materials are still a major challenge in the industry, in line with the wishes of consumers to get good quality raw materials and soaring export demand, so there is a need for a control process to monitor the value of products using forecasting. The purpose of this study was to predict gross domestic product in the furniture industry in Indonesia in 2022. This study used secondary data on the quarterly trend of gross domestic product in the furniture industry in Indonesia 2010-2021 taken from the research industry data processed by BPS and Bank Indonesia, The method used is Double Exponential Smoothing-Holt. The results of the calculation using the double exponential smoothing-holt method obtained a value of α of 0.658 and β of 0.008 where the forecasting results for the 2022 period, namely the 1 quarter of 7.602 billion rupiah, quarter 2 of 7.676 billion rupiah, quarter 3 of 7.749 billion rupiah, and quarter 4 of 7.822 billion rupiah. Where the MAPE value is 0.737% which means forecasting has very good results.

This study examines the analysis of raw material inventory forecasting and buffer stock of vaname shrimp at one of the fishery companies in Makassar Industrial Estate (PT Bogatama Marinusa Makassar). The analysis method used to determine the supply of raw materials needed by the company is the ARIMA Box-Jenkins method. This method is used to forecast raw material inventory on time series data. Determination of buffer stock is done using standard deviation and policy factors. Vaname shrimp (Littopenaeus vannamei) raw material data was obtained from 2020 to 2022 (156 weeks). The results showed that the highest amount of raw material inventory occurred in October week 147 in 2022, amounting to 152,792 tons, while the lowest amount of raw material inventory occurred in May week 122 in 2022, amounting to 13,102 tons. The best vaname shrimp raw material inventory model is the ARIMA (1,1,1) model which has a Sum Square Error (SSE) value of 1.70250, Mean Square Error (MSE) value of 0.0112007. This model is used to forecast raw material inventory for the next 48 weeks. The forecasting results show that there will be a decrease in week 1 to week 6 and a relative increase in raw materials in week 7 to week 48 with a MAPE value of 4.54%. The amount of buffer stock that must be owned by the company is 37.311 tons.

In determining sales targets, each Dealer determines sales targets only based on previous sales, without any fixed calculation method. The aim of this research is to find a calculation method that is considered closest, so that it can be used to determine sales targets for the next period. The sample studied was sales of automatic motorbikes in October, November and December for the 2022 period, using actual company sales data as a reference as a comparison. The research results show that the Least Square Method can be implemented as a fixed calculation to determine sales targets for Honda brand motorbikes, with an average MAPE value of 5.3% or an accuracy value of 94.7%.    

The increasing complexity of modern software systems and the growing demand for real-time data processing have significantly contributed to higher energy consumption in computing infrastructures. This challenge is particularly evident in systems that rely on continuous monitoring, analytics, and adaptive decision-making. Addressing energy efficiency without compromising system performance has therefore become a critical concern in sustainable software engineering. This study proposes an energy-aware software approach that integrates real-time analytics with adaptive feedback mechanisms to optimize energy consumption while maintaining operational performance. The research adopts a design science oriented methodology, encompassing system design, implementation, and experimental evaluation. The proposed system architecture consists of real-time data acquisition, intelligent analytics, and an adaptive control layer based on the MAPE-K (Monitor, Analyze, Plan, Execute, Knowledge) feedback loop. Experimental evaluations were conducted under dynamic workload scenarios to compare the proposed adaptive system with a baseline non-adaptive system. Key performance indicators included energy consumption, response time, throughput, and adaptation latency. The results demonstrate that the proposed system achieves a substantial reduction in energy consumption while maintaining, and in some cases improving, system performance metrics. The adaptive feedback mechanism enables the system to respond effectively to workload fluctuations, reducing unnecessary energy usage during low-demand periods and ensuring stable performance during peak loads. These findings provide empirical evidence that real-time analytics and adaptive control can effectively support energy-efficient and sustainable software systems. This research contributes to the field of energy-aware software engineering by demonstrating that intelligent real-time adaptation is a viable strategy for achieving sustainability objectives in dynamic and performance-critical environments.

Sprite drink is a soda drink that is very popular among all groups. Demand is uncertain and always changes from time to time, making product availability difficult to control and often causes overstock or stockout problems. Therefore, inventory control is needed, which can be done by forecasting, determining safety stock and good re-order points. To obtain effective and efficient planning, the number of orders must be based on the number of past mass requests so as to reduce the occurrence of overstock or stockouts. With the problems experienced by PT. XYZ, the forecasting method used is the time series forecasting method. In this case, the time series methods used are Simple Average, Single Moving Average and also Single Exponential Smoothing. After carrying out several calculations, we obtained a Mean Absolute Centage Error (MAPE) value of 49.379%, a Mean Absolute Deviation (MAD) of 2297.145, a Root Mean Squared Error (RMSE) of 2912.495 and also a Mean Squared Error (MSE) of 8,482 .628 and forecasting results of 4504 pcs every month. Based on the calculation results, the proposal given is to reorder Sprite 250ML when the inventory in the warehouse reaches 1548 pcs with a safety stock of 540 pcs.

PT. Kalonika Bara Kusuma is a company operating in the mining sector located in the city of Samarinda, East Kalimantan province. To achieve maximum profits, PT. Kalonika Bara Kusuma adds or subtracts units according to the amount of turnover obtained in the previous month. However, after being evaluated, it turned out that this method was not effective. Because you only see at a glance the fluctuations in historical data. Sometimes when you have reduced units, it turns out that demand in the following month actually increases. This results in less than optimal profits because they cannot serve existing customer requests. Vice versa. This is what causes PT. Kalonika Bara Kusuma experienced difficulty in making a decision to add or subtract units. From this problem, the author created an application that can predict the amount of turnover in the next month and provide recommendations for deciding which camera units should be increased or decreased in number. To predict the amount of turnover using the Multiple Linear Regression method. After obtaining the predicted results for the amount of turnover, a test was carried out using the Mean Absolute Percentage (MAPE) with a result of 200%, which means that the Multiple Linear Regression method is not suitable to be used to predict the amount of turnover in the next period. Production forecasting is a form of decision making that is used as a basis in many manufacturing and service industries. Therefore, companies that are able to produce products on time and in the right quantities are companies that are able to survive the competition. This demand forecasting is used to forecast demand for products that are independent (not dependent), such as forecasting finished products. The multiple linear regression method is an analytical technique that tries to explain the relationship between two or more variables, especially between variables that contain cause and effect, called regression analysis. So in relation to the description above, this research aims to determine production forecasting using the multiple linear regression method at PT. Kalonica Bara Kusuma.The mining industry is a series of activities that have a long period of time and costs a lot of money, a series of industrial activities, namely mining activities which include digging, loading and hauling to obtain optimal profits from activities. One of the mining industries needs to be a study of operational costs for transportation equipment

Penelitian ini bertujuan untuk mengetahui problematika implementasi kurikulum merdeka di MA Al-Amin Tabanan tahun pelajaran 2023/2024. Metode penelitian yang digunakan yakni metode kualitatif dengan pendekatan kualitatif deskriptif dengan teknik pengumpulan data berupa wawancara, observasi dan dokumentasi. Informan pada penelitian ini yaitu Kepala Madrasah, Waka Kurikulum, Koordinator Proyek, Guru Mapel dan Tim Fasilitator Proyek. Hasil penelitian ini yaitu implementasi kurikulum di MA Al-Amin Tabanan telah berjalan cukup baik pada semester ganjil tahun pelajaran 2023/2024. Problematika implemantasi kurikulum merdeka di MA Al-Amin Tabanan yaitu perlunya adaptasi guru dengan paradigma baru terutama saat merancang perangkat pembelajaran, pemahaman guru terkait asesmen diagnostik perlu ditingkatkan, meningkatkan strategi pembelajaran, kurangnya pendampingan dan pelatihan terkait kurikulum merdeka, mendesain dan merencanakan modul ajar proyek P5PPRA masih membutuhkan pendampingan, penyusunan jadwal dan fasilitator proyek terutama pada tema kedua (suara demokrasi) masih kurang berjalan dengan baik, beberapa peserta didik mengandalkan temannya dan kurang aktif dalam melaksanakan proyek yang telah ditentukan.