Abstract:Penelitian ini bertujuan untuk menganalisis penerapan peramalan (forecasting) penjualan pada Toko Sembako Alaudin yang berlokasi di Pasar Rumah Tiga, Teluk Ambon, serta bagaimana hasil peramalan tersebut digunakan dalam…
menentukan strategi bisnis. Penelitian menggunakan pendekatan kualitatif deskriptif dengan metode pengumpulan data melalui observasi, wawancara mendalam, dan dokumentasi data penjualan toko selama periode Januari hingga Desember 2025. Hasil penelitian menunjukkan bahwa Toko Sembako Alaudin belum menerapkan metode peramalan secara sistematis dan masih mengandalkan pengalaman serta intuisi pemilik dalam menentukan jumlah stok barang. Pola penjualan menunjukkan tren peningkatan menjelang hari besar keagamaan dan akhir tahun, namun belum dimanfaatkan secara optimal untuk perencanaan strategi pengadaan barang. Penelitian ini merekomendasikan penerapan metode peramalan sederhana berbasis data historis penjualan guna mendukung pengambilan keputusan bisnis yang lebih akurat dan efisien.
Abstract:In recent years, the housing market has faced significant challenges, including fluctuating prices and declining sales. To solve this issue, there was an increasing need for more sophisticated methods to predict housing…
prices accurately. This study aimed to provide real estate marketers with a tool to enhance their pricing tactics and mitigate the decline in home sales by predicting house prices using machine learning techniques. Several parameters were considered in this study, such as location, number of bedrooms, number of bathrooms, land area, building area, and number of carports. Linear regression and neural network methods were used to develop predictive models. The findings showed that the neural network method was more accurate than linear regression, which made it a better tool for real estate pricing strategies, with land area and number of carports being the most influential aspects in house price prediction.
Abstract:This study investigates the influence of artificial intelligence (AI) integration on strategic financial management in large corporations. Focusing on a sample of 20 Fortune 500 companies from diverse industries, the research…
earch employs a quantitative, descriptive-analytical approach utilizing secondary data from financial reports and AI system logs. The findings reveal that AI adoption significantly enhances forecasting accuracy, risk identification, and operational efficiency, while also enabling financial managers to redirect resources toward creative and strategic initiatives. However, the study also identifies challenges related to data quality, ethical considerations, and skill gaps in AI utilization. The results highlight the importance of a balanced approach that combines AI-driven insights with managerial intuition to maximize value creation in the digital age.
Abstract:Inflation is a fundamental measure of macroeconomic stability that negatively impacts the nation's economy and affects many other macroeconomic variables. Thus, in this study, the major objective was based on modeling and…
d forecasting inflation in Ethiopia and its components using a VAR model. The analysis was based on annual data from 1992 to 2021, encompassing 30 years. The results indicated that all five series were non-stationary at the level but stationary after their first differencing at a 5% level of significance. Johansen's cointegration tests were conducted. The results indicated the presence of at least one co-integration relationship between the variables. The Vector Error Correction Model (VECM) was fitted to model short run and long run relationships among inflation and other macro-econometric series such as GDP growth, government expenditure, money supply, and imports, and the result indicated that the coefficient of error correction term is negative (-0.279), which indicated that the fitted VECM model continues to move toward long run equilibrium and converges. Granger causality tests were employed to explore potential causal relationships. Impulse response analysis and variance decomposition were used to determine the short-run interactions among the variables. Finally, using the fitted model, out-of-sample forecasts were produced, yielding forecasted plots and values for the endogenous variables. According to the forecasted inflation rates for the next five years, prices are projected to decrease by approximately 18.0% in 2022, 6.8% in 2023, and then increase by approximately 8.3% in 2024. Subsequently, prices are expected to decrease by approximately 2% in 2025 and 5% in 2026 over the specified time periods.
Abstract:This study explores how incorporating artificial intelligence improves institutional resilience and overcomes the rigidity of conventional, data-based methods to alter financial risk management. To find patterns in AI applications,…
applications, resilience theory, and integration pathways, a qualitative systematic literature review was carried out utilizing theme synthesis in accordance with PRISMA peer-reviewed protocols. Findings show that AI techniques, machine learning for tail-risk detection, deep learning for high-frequency forecasting, and explainable AI for transparent decisions, yield up to 28% reductions in forecasting errors and halve recovery times during crises. The hybrid CNN Transformer architectures and transformer-based NLP models significantly enhance predictive accuracy and forward-looking insights. The study suggests financial institutions adopt integrated AI frameworks, invest in data quality and human–AI collaboration, and implement principle-based governance to balance innovation with fairness and stability. Limitations include reliance on published literature and limited representation of emerging AI models, warranting future longitudinal and context-specific empirical research.
Abstract:The rapid growth of the hotel industry requires hotels to improve operational planning, one of which is by forecasting room reservations. Inaccurate forecasting may cause an imbalance between room availability and customer…
er demand. This study aims to compare the Weighted Moving Average and Single Exponential Smoothing algorithms in forecasting room reservations at Raz Hotel and Convention Medan using historical data from January 2025 to May 2026. The research method consisted of data collection, forecasting using both algorithms, and accuracy evaluation through Mean Absolute Deviation, Mean Squared Error, and Mean Absolute Percentage Error. The results indicate that the Single Exponential Smoothing algorithm achieved a Mean Absolute Percentage Error of 15.24%, which is lower than the 15.63% obtained by the Weighted Moving Average algorithm. Furthermore, the Single Exponential Smoothing algorithm predicted 835.90 room reservations for June 2026. Therefore, it can be concluded that the Single Exponential Smoothing algorithm provides better forecasting accuracy and is more suitable for predicting room reservations at Raz Hotel and Convention Medan.
Abstract:Sales forecasting is a crucial component of operational strategy and inventory management in the culinary industry, particularly for businesses dealing with raw materials that have short shelf lives. The Sate Padang Hapis…
s business faces the challenge of unpredictable monthly fluctuations in consumer demand, which often lead to supply imbalances, overproduction, or lost profit opportunities due to stockouts. This study aims to implement and analyze the accuracy of the Single Moving Average (SMA) quantitative forecasting method in predicting Sate Padang Hapis sales volumes for June 2026. Model performance was evaluated by assessing mathematical accuracy across two time-interval variations: 3-month and 5-month moving averages. Projection error rates were rigorously measured using Mean Absolute Deviation (MAD), Mean Squared Error (MSE), and Mean Absolute Percentage Error (MAPE) parameters. The analysis reveals that the Single Moving Average model with a 5-month interval yields projections closest to actual data, achieving the lowest error rates (MAD: 28.00; MSE: 1,304.00; MAPE: 2.28%). Implementing this forecasting model provides management with an objective basis for decision-making, enabling the effective and efficient optimization of raw material logistics and supply management.
Abstract:Improper rice stock management in wholesale businesses can lead to shortages or overstock that negatively impact operational efficiency. This study aims to forecast rice stock needs at Warung Grosir Giran using the Weighted…
ted Moving Average method with a weighting of 1:2:3, based on historical data from January 2025 to June 2026. A quantitative approach with time series forecasting technique was applied. Accuracy was evaluated using Mean Absolute Deviation, Mean Squared Error, and Mean Absolute Percentage Error. The results show that the forecasted rice stock requirement for July 2026 is 1,456.67 kg. The accuracy evaluation yielded a Mean Absolute Deviation of 78.22, Mean Squared Error of 9,288.15, and Mean Absolute Percentage Error of 5.71%, which is classified as highly accurate. In conclusion, the Weighted Moving Average method is suitable as a decision-support tool for rice stock management at Warung Grosir Giran.
Abstract:Angga Colection adalah sebuah usaha yang bergerak di bidang penjualan fashion, terletak di Jalan Besar Sei Silau Timur, Kabupaten Asahan. Produk fashion yang ditawarkan meliputi baju, celana, tas, sepatu, aksesoris, dan…
gamis. Namun, saat ini Angga Colection menghadapi kesulitan dalam memprediksi permintaan produk fashion karena masih mengandalkan perkiraan dari bagian penjualan. Untuk mengatasi masalah ini, penelitian ini bertujuan untuk menerapkan metode single moving average pada sistem peramalan penjualan produk fashion di Angga Colection. Metode ini melibatkan pengambilan sekelompok nilai pengamatan, mencari nilai rata-rata, dan menggunakan hasilnya sebagai ramalan untuk periode yang akan datang. Penelitian ini menggunakan metode penelitian kuantitatif dan hasilnya menunjukkan bahwa menerapkan metode single moving average pada sistem peramalan penjualan produk fashion di Angga Colection dapat memudahkan proses peramalan dan meningkatkan akurasi prediksi. Dengan demikian, Angga Colection dapat membuat keputusan yang lebih tepat dan efektif dalam menghadapi permintaan produk fashion.
Abstract:Abstract: Palm oil is a key commodity that requires accurate production planning to support budget and operational efficiency. PT Buana Sawit Indah, a company managing palm oil plantations, faces challenges in forecasting…
g future production, necessitating a computerized forecasting system. This study aims to develop a forecasting system using the Single Exponential Smoothing (SES) and Single Moving Average (SMA) methods to predict palm oil production. The results indicate that both methods are effective; however, SMA with a Moving Average (MA) of 5 provides the best forecasting results, with a MAD of 165,941.64, an MSE of 65,823,372,491.27, and an MAPE of 15.66%, categorized as "good" forecasting. Meanwhile, the SES method with α = 0.9 yields the lowest error compared to other alpha values, with a MAD of 154,586.25, an MSE of 33,192,818,696.83, and an MAPE of 16.91%, also classified as "good" forecasting. Based on these findings, the SMA method with MA 5 is recommended as it produces lower forecasting errors than the SES method. Therefore, this forecasting system can assist the company in planning palm oil production more accurately and efficiently, supporting better decision-making in the future.
Keywords: palm oil; forecasting; production; single exponential smoothing; single moving average
Abstrak: Kelapa sawit merupakan komoditas utama yang memerlukan perencanaan produksi yang akurat untuk mendukung efisiensi anggaran dan operasional. PT Buana Sawit Indah, salah satu perusahaan yang mengelola perkebunan kelapa sawit, sedang menghadapi kesulitan dalam memperkirakan hasil produksi di masa mendatang, sehingga diperlukan sistem peramalan berbasis komputer. Penelitian ini bertujuan mengembangkan sistem peramalan menggunakan metode Single Exponential Smoothing (SES) dan Single Moving Average (SMA) untuk memprediksi produksi kelapa sawit. Hasil penelitian menunjukkan bahwa kedua metode cukup efektif, namun SMA dengan Moving Average (MA) 5 memberikan hasil peramalan terbaik dengan nilai MAD sebesar 165.941,64, MSE sebesar 65.823.372.491,27, dan MAPE sebesar 15,66%, yang dikategorikan sebagai peramalan “bagus.†Sementara itu, metode SES dengan α = 0,9 menunjukkan nilai kesalahan lebih rendah dibanding nilai alpha lain, dengan MAD sebesar 154.586,25, MSE sebesar 33.192.818.696,83, dan MAPE sebesar 16,91%, yang juga termasuk kategori peramalan “bagus.†Berdasarkan hasil tersebut, metode SMA dengan MA 5 lebih direkomendasikan karena menghasilkan tingkat kesalahan yang lebih rendah dibandingkan metode SES. Dengan demikian, sistem peramalan ini dapat membantu perusahaan dalam merencanakan produksi kelapa sawit secara lebih akurat dan efisien untuk mendukung pengambilan keputusan yang lebih baik di masa mendatang.
Kata kunci: kelapa sawit; peramalan; produksi; single exponential smoothing; single moving average