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This study aims to analyze demand patterns and determine the most accurate forecasting method for the Kawachi KL 6167 A+ Emergency Lamp product to support inventory control decision-making. The data used in this study consist of product demand over 12 periods, showing an increasing trend with slight fluctuations in certain periods. The forecasting methods applied in this research include the Linear Trend Method, Quadratic Trend Method, and Moving Average (MA), while forecasting accuracy was evaluated using Mean Squared Error (MSE). The results indicate that the linear trend method provides a more suitable forecasting model compared to the quadratic trend method. The MSE value of the linear method is 69.31, whereas the quadratic method produces an MSE of 81.50, indicating that the linear method is more accurate due to its lower forecasting error. In addition, a 3-period Moving Average (MA) method was applied to forecast demand from period 13 to period 24. The forecasting results show that demand tends to stabilize within the range of 276–277 units, with the forecast for period 24 reaching 276.66 units, rounded to 277 units. Based on the findings, it can be concluded that the demand pattern for the Kawachi KL 6167 A+ Emergency Lamp demonstrates a relatively stable upward trend, making the linear trend method the most appropriate forecasting approach for predicting future demand. These forecasting results are expected to serve as a reference for companies in optimizing inventory planning to minimize the risks of stock shortages and overstocking.

Penelitian ini dilatarbelakangi oleh tingginya biaya penyimpanan dan meningkatnya stok mati pada Delyana Hijab, yang menunjukkan belum optimalnya pengelolaan persediaan bahan baku. Ketidaksesuaian antara jumlah bahan baku dan kebutuhan produksi menyebabkan pemborosan biaya serta menurunkan efisiensi operasional. Penelitian ini bertujuan untuk menentukan kebutuhan material yang optimal melalui pendekatan forecasting dan Material Requirement Planning (MRP). Metode yang digunakan adalah deskriptif kuantitatif dengan teknik pengumpulan data berupa observasi, wawancara, dan dokumentasi. Tahapan analisis meliputi peramalan permintaan, penyusunan Master Production Schedule (MPS), Bill of Materials (BOM), perhitungan kebutuhan bersih (MRP), serta penentuan ukuran pemesanan melalui metode lot sizing. Hasil penelitian menunjukkan bahwa metode regresi linier menghasilkan tingkat kesalahan peramalan terendah sehingga mampu memproyeksikan kebutuhan produksi dengan lebih akurat. Penerapan MRP menghasilkan perencanaan kebutuhan bahan baku yang lebih terarah dan sesuai dengan jadwal produksi. Pada tahap lot sizing, metode Lot For Lot (LFL) menjadi yang paling efisien dengan total biaya persediaan sebesar Rp108.669.000. Dalam penerapannya, jumlah pemesanan bahan baku mengikuti kebutuhan bersih tiap periode, misalnya kebutuhan kain katun berkisar 5-7 roll per minggu dan dipesan dalam jumlah yang sama tanpa kelebihan stok. Pola ini mampu menekan penumpukan persediaan dan mengurangi risiko stok mati karena bahan baku langsung digunakan sesuai kebutuhan. Dengan demikian, tujuan penelitian untuk menentukan kebutuhan material yang optimal telah terjawab melalui penerapan metode Lot For Lot yang mampu menghasilkan kuantitas pemesanan yang tepat dan efisien.

This study aims to analyze the fluctuations in chili production in Kabanjahe District, Karo Regency, which affect market price instability and uncertain supply. One approach applied in this study is the Single Exponential Smoothing (SES) method to forecast chili production. SES was chosen for its simplicity, ease of implementation, and its ability to generate accurate predictions even when the data lacks significant seasonal patterns. The data used is secondary data on chili production obtained from official publications by the Karo Regency BPS for the period of 2020–2024. The analysis results show that a smoothing parameter (α) of 0.8 produced the lowest Mean Absolute Percentage Error (MAPE) of 3.08%. These findings indicate that applying a higher α makes the model more responsive to recent data changes, thus yielding more accurate forecasts. This study demonstrates the effectiveness of the SES method in forecasting chili production in areas with significant seasonal fluctuations.

This study examines the development of a Crude Palm Oil (CPO) price forecasting model using an artificial neural network algorithm, specifically the backpropagation algorithm. As one of Indonesia’s main export commodities, CPO has a significant economic impact and influences the income of oil palm farmers. The CPO price data used in this study were obtained from CIF Rotterdam, covering the period from January 2019 to December 2023. The research methodology consists of several stages, including data collection, preprocessing, model design, and model implementation using Python programming. The training results of the backpropagation algorithm show an error value of 0.537829578 after 1,000 epochs, while the evaluation using Mean Squared Error (MSE) indicates an MSE of 0.022709 during the training process and 0.017604 during the testing process. The model also produces CPO price predictions for the next three months, namely 932.578 for the first month, 949.568 for the second month, and 774.855 for the third month. These findings indicate that the developed model is capable of predicting future CPO prices with adequate accuracy, which can assist companies in making better financial decisions and managing risks associated with CPO price fluctuations.

The dynamic fluctuations in stock prices present a major challenge for investors in making informed decisions. To anticipate such uncertainties, forecasting methods that can provide accurate predictions are required. This study compares two time series forecasting methods Autoregressive Integrated Moving Average (ARIMA) and Double Exponential Smoothing (Holt) in predicting the stock prices of PT Telkom Indonesia (TLKM). The dataset consists of monthly closing prices from January 2018 to December 2023. The performance of each model is evaluated using three error metrics: Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE). The results show that the ARIMA(1,1,1) model yields higher predictive accuracy than the Holt method, with MAE of 787.71, MSE of 771,844.2, and RMSE of 878.55. In contrast, the Holt method records a MAE of 837.19, MSE of 878,393.4, and RMSE of 937.23. These findings confirm that ARIMA is superior in capturing the complex patterns of stock price movements and is more effective in volatile market conditions such as the stock exchange.

Perkembangan kecerdasan buatan (AI) mendorong inovasi dalam analisis keuangan, termasuk prediksi harga saham yang fluktuatif. Penelitian ini bertujuan memprediksi harga saham PT Garudafood Putra Putri Jaya Tbk menggunakan model ARIMA dengan penanganan Outlier sebagai pendekatan awal menuju sistem prediksi yang lebih adaptif. Data harga penutupan harian dari Yahoo Finance dianalisis melalui uji stasioneritas, identifikasi model ARIMA, deteksi Outlier berbasis log-return, serta evaluasi performa menggunakan RMSE, MAE, dan MAPE. Hasil penelitian menunjukkan bahwa ARIMA Outlier memberikan performa lebih baik dibandingkan ARIMA dasar. ARIMA standar menghasilkan MAPE 1.32% dan AIC –899.46, sedangkan ARIMA dengan tiga dummy Outlier mencapai MAPE 1.16% dan AIC –900.37. Peramalan 14 hari ke depan menunjukkan pola yang stabil pada kisaran Rp 370–371. Pada data uji, ARIMA dasar memberikan akurasi terbaik pada pertengahan Agustus, sedangkan ARIMA Outlier mencapai akurasi tertinggi pada akhir Agustus dengan prediksi Rp 370.2 yang sangat dekat dengan harga aktual Rp 370.4. Hasil ini menunjukkan bahwa penanganan Outlier meningkatkan ketepatan model, sehingga ARIMA Outlier dapat digunakan sebagai fondasi awal menuju pengembangan sistem prediksi keuangan berbasis AI.

The relatively high volatility of Robusta coffee prices creates uncertainty for farmers, business actors, and policymakers in making economic decisions. This study aims to analyze the price movement patterns of Robusta coffee, determine the most appropriate Autoregressive Integrated Moving Average (ARIMA) model, and conduct short- to medium-term price forecasting for Robusta coffee. The data used consist of monthly Robusta coffee price data from January 2023 to September 2025, sourced from the World Bank Commodity Price Data. The analytical method employed is ARIMA using EViews software, beginning with stationarity testing using the Augmented Dickey-Fuller (ADF) test, model identification through ACF and PACF, parameter estimation, and residual diagnostic testing. The results show that Robusta coffee price data are non-stationary at the level but become stationary at the first difference, indicating integration of order one I(1). Based on model identification and diagnostic testing, the ARIMA (0,1,0) model is found to be the most appropriate and satisfies the white noise assumption. Forecasting results indicate that Robusta coffee prices are projected to remain relatively stable with a moderate upward trend through December 2026. These findings are expected to serve as a reference for decision-making by farmers, business actors, and the government in responding to Robusta coffee price dynamics.

Demam berdarah dengue merupakan tantangan kesehatan masyarakat yang terus berulang di wilayah tropis, termasuk Indonesia. Penelitian ini bertujuan untuk memprediksi jumlah kasus tahunan dengan memanfaatkan lima algoritma pembelajaran mesin, yaitu Regresi Linier, Decision Tree, Random Forest, Support Vector Machine (SVM), dan Neural Network. Data historis tahun 2017–2024 diolah menggunakan teknik windowing deret waktu untuk menghasilkan fitur lag yang sesuai bagi pembelajaran terawasi. Evaluasi kinerja dilakukan melalui metrik Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), serta koefisien determinasi (R²). Model Decision Tree menunjukkan performa paling unggul pada sebagian besar indikator. Prediksi untuk tahun 2025 mengindikasikan adanya peningkatan moderat jumlah kasus. Namun, rendahnya nilai R² pada seluruh model mengisyaratkan perlunya pendekatan multivariat yang lebih kompleks dengan mempertimbangkan faktor iklim, lingkungan, dan demografi. Hasil penelitian ini menegaskan pentingnya kualitas data dan pemilihan fitur yang tepat dalam peramalan epidemiologis guna mendukung perencanaan kesehatan yang lebih efektif.

Polusi udara partikulat halus (PM2,5) merupakan ancaman serius bagi kesehatan masyarakat di Kabupaten Brebes, Jawa Tengah. Faktor penyumbang utamanya adalah emisi kendaraan di jalur Pantura, aktivitas industri perikanan, serta konsentrasi tinggi selama musim kemarau (Juni–November). Tidak adanya model peramalan sub-jam yang akurat menghambat pengembangan sistem peringatan dini yang efektif. Penelitian ini mengembangkan dan mengevaluasi model deep learning berbasis Transformer untuk memprediksi konsentrasi PM2,5 dengan resolusi waktu 15 menit. Data yang digunakan berasal dari NASA GEOS-CF (band PM25_RH35_GCC) yang diakses melalui Google Earth Engine menggunakan API Python. Dataset mencakup periode 1 Januari hingga 22 November 2025, menghasilkan 7.813 observasi per jam, yang kemudian diinterpolasi linear menjadi 31.249 titik data dengan resolusi 15 menit. Arsitektur Transformer terdiri dari 3 lapis enkoder, 4 kepala perhatian multi-head, dimensi embedding 128, dimensi feed-forward 256, panjang sekuen 60 timestep, dan augmentasi fitur menggunakan rerata bergulir (*rolling mean*, jendela = 3) dan beda pertama (*first difference*). Pelatihan dilakukan dengan TensorFlow-Keras, pengoptimal Adam, penjadwal peluruhan kosinus (*cosine decay scheduler*), dan fungsi kerugian Huber. Pembagian data dilakukan secara kronologis: 70% pelatihan, 30% validasi. Evaluasi pada set uji independen (16 Agustus–21 November 2025, 9.357 observasi atau 97 hari 11 jam 15 menit) menghasilkan MAE 0,7691 µg/m³, RMSE 1,2052 µg/m³, R² 0,9945, dan *Explained Variance Score* 0,9948. Model ini mampu menggambarkan variasi diurnal dan anomali musiman secara akurat, jauh melampaui model LSTM dan GTWR konvensional. Penelitian ini memberikan kontribusi signifikan di bidang Teknologi Informasi melalui kerangka kerja pengolahan *big data* satelit untuk aplikasi lingkungan.

This study aims to estimate changes in the Indonesian Sharia Stock Index (ISSI) from 2020 to 2025 using the Autoregressive Integrated Moving Average (ARIMA) model. The growth of the Islamic stock market in Indonesia has increased rapidly, driven by public awareness of investments that follow sharia principles, as well as changes in macro and microeconomic conditions, especially during the COVID-19 pandemic which has had a significant impact on the financial market. This study relies on monthly ISSI data taken from official sources and analyzed with a quantitative approach using the time series method using EViews version 13 software. Statistical analysis and stationarity tests indicate that the ISSI data exhibits an increasing trend pattern and quite high volatility, so that a differentiation process is necessary to achieve stationarity. Based on the results of model testing and the selection of optimal information criteria, the ARIMA (1,1,1) model was selected as the most appropriate to capture the autocorrelation pattern and produce accurate short-term predictions. Projections indicate a stable growth trend until the end of 2025, with an estimated index of more than 8.3 million. The findings of this study indicate that the ARIMA model is an effective tool for forecasting ISSI movements and can be a strategic consideration for investors, financial institutions, and policymakers in developing sustainable investment strategies in the Indonesian Islamic stock market.

Rubber prices experience significant and prolonged fluctuations, which impact farmer incomes and management decisions. Understanding historical patterns and price predictions is considered crucial for production planning, marketing, and farmer protection policies. This study aims to identify the characteristics of rubber price time series in Lahat Regency and develop a reliable forecasting model to support short- to medium-term decision-making. This study uses secondary data on monthly average producer prices for the period January 2019–December 2023. The analysis includes the Augmented Dickey–Fuller stationarity test to determine the need for transformation, differencing, and/or logarithmic transformation when necessary, identification of autocorrelation patterns using ACF/PACF, model estimation on the processed data, and evaluation of residual diagnostics (Ljung–Box, normality test) and forecasting accuracy metrics (RMSE, MAE, MAPE, Theil). The level data shows non-stationarity and becomes stationary after the first differencing; The model on log-transformed data had significant parameters and higher explanatory power than the model on de-differenced data, with RMSE and MAPE values ​​within a reasonable range. Forecast confidence intervals widened at longer time horizons, indicating increased projection uncertainty. Conclusion: Validated forecasts can inform farmers and policymakers to manage price risk and design market interventions.

This study aims to analyze and project consumer prices of cabbage commodities at four levels: Ngawi Regency, Pacitan Regency, East Java Province, and nationally, using the additive Holt–Winters forecasting model. Monthly price data for the period January 2020–December 2024 were used to capture the dynamics of levels, trends, and seasonal patterns that affect price fluctuations. Model performance was evaluated using the Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) indicators. The results showed differences in model accuracy between regions. East Java Province produced the best performance with the lowest MAE and RMSE values, indicating a more stable price pattern that was easier for the model to capture. In contrast, Ngawi Regency showed the highest volatility, resulting in greater forecasting errors. Pacitan Regency displayed a relatively consistent seasonal pattern with moderate accuracy, while national data showed smoother fluctuations due to the aggregation effect. Overall, the additive Holt–Winters model is effective for short-term projections in regions with low to moderate variability, but is less optimal in regions with highly volatile price dynamics.

This study aims to forecast beef prices in Palembang City and at the national level in Indonesia using the Autoregressive Integrated Moving Average (ARIMA) method. The data used are the monthly average beef prices for the period January 2019 to December 2024. The analysis involves stationarity tests using Augmented Dickey-Fuller (ADF), model identification through Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF) plots, parameter estimation with Maximum Likelihood Estimation (MLE), and residual diagnostics with the Ljung-Box and Jarque-Bera tests. The results show that beef prices at both regional levels are not stationary at the level but become stationary after the first differencing (I(1)). The best ARIMA models obtained are ARIMA(0,1,1) for Palembang City and ARIMA(1,1,0) for the national level. Both models successfully predict price fluctuations with a low error rate and show a moderate price increase trend. These findings provide practical implications for price stabilization policy making and beef-related business planning. The forecast results state that beef prices in Palembang City and nationally are predicted to tend to rise in 2025 from January to December.  

Penelitian ini bertujuan untuk membuat sistem yang bisa memprediksi jumlah mahasiswa menggunakan pendekatan machine learning dengan Python dan antarmuka Streamlit sebagai alat interaktif. Masalah utama yang dibahas adalah kebutuhan lembaga pendidikan tinggi dalam meramalkan jumlah mahasiswa di masa depan agar bisa membantu dalam merencanakan kapasitas dan mengalokasikan sumber daya secara tepat. Solusi yang ditawarkan adalah dengan menggabungkan dua metode prediksi, yaitu Regresi Linear dan ARIMA. Regresi Linear digunakan untuk mencari pola dasar dalam data masa lalu, sedangkan ARIMA dipakai untuk menganalisis data yang memiliki perubahan atau ketergantungan terhadap data sebelumnya. Data yang digunakan berasal dari Pangkalan Data Pendidikan Tinggi (PDDIKTI) untuk program studi Ilmu Komputer di Universitas Negeri Medan pada periode tahun 2019 sampai 2024. Sistem ini dirancang agar orang yang tidak ahli di bidang teknologi bisa melakukan analisis secara visual melalui grafik dan metrik evaluasi yang ditampilkan langsung di aplikasi Streamlit. Hasil pengujian menunjukkan bahwa Regresi Linear memberikan akurasi tinggi pada data yang memiliki tren stabil dengan nilai MAPE di bawah 10%, sedangkan ARIMA memberikan prediksi yang lebih baik pada data yang tidak stabil. Penerapan sistem ini menunjukkan bahwa menggabungkan metode statistik dan pembelajaran mesin bisa meningkatkan efisiensi dan ketepatan dalam analisis data akademik. Temuan ini memberikan manfaat nyata dalam membantu pengambilan keputusan strategis terkait perencanaan jumlah mahasiswa, serta membuka kemungkinan pengembangan model yang lebih canggih di masa depan.

Bitcoin is one of the most prominent digital assets in the modern financial era due to its high volatility and huge profit potential. However, its extreme price volatility also makes it a high-risk asset, so a reliable forecasting approach is needed to help investors make more rational decisions. This study aims to forecast Bitcoin price using the Moving Average (MA) method, specifically MA3, by utilizing monthly historical data of Bitcoin price in USD currency obtained from investing.com website. The MA3 method was chosen for its ability to smooth out short-term fluctuations and identify the direction of price trends. The forecasting process is performed by calculating the average of the last three months' prices for each point in time and compared to the actual price to evaluate its accuracy. The evaluation is done using various prediction error metrics, namely Error, Absolute Error, Squared Error, and Percentage Error. The results of the analysis show that the MA method provides a fairly representative picture of price trends and can be used as an early indicator in short-term investment strategies. Thus, the Moving Average method proves to be a simple but effective prediction tool, especially for novice investors in the dynamic crypto asset market.

The SMEs sector has an important role in supporting economic development in Indonesia. One of the sub-districts in Gresik, namely Sidayu, has many SMEs that are members of an association called ASUMSI. Almost all SMEs in these locations have not carried out a structured production planning. Production planning plays a vital role in a product-producing industry, including SMEs. Production planning can be started by forecasting demand. The results of this demand forecasting become the basis for production planning. This community service takes the theme of introducing the concept of demand forecasting which is expected to improve the production planning process for SMEs. This community service is carried out through counseling regarding the basic concept of demand forecasting for SMEs actors who are incorporated in ASUMSI. This community service is a form of academic participation in increasing the capacity of local resources, namely SMEs

The increasing competition in the bottled drinking water distribution business in Tarakan City compels companies to improve sales forecasting accuracy as a basis for strategic decision-making. This study aims to analyze the effectiveness of the Exponential Smoothing with Trend method implemented through the POM-QM software in predicting the sales volume of CV Tirta Anugerah Abadi. Monthly sales data over the past two years were quantitatively analyzed using the Exponential Smoothing model with various smoothing parameter combinations. The results indicate that the parameter combination of α = 0.7 and β = 0.1 yields the lowest forecasting error, with a MAPE value of 1.24%. These findings demonstrate that the applied method is highly accurate and reliable for sales forecasting, thus supporting the company in more efficient stock, production, and distribution planning. The implications of this research not only enrich the literature on the application of modern forecasting methods in the local distribution sector but also provide practical contributions to company management in data-driven decision-making.

Accurate sales forecasting is essential for stakeholders to make strategic decisions. This study aims to compare the performance of two deep learning models, namely Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN), in forecasting domestic motorcycle sales produced by AISI member manufacturers. The forecast is based on historical data from January 2021 to December 2024. The model was trained using time series data and the forecasting results for the period January to March 2025 were evaluated using the metrics Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE). The results show that the LSTM model produces lower MAE and MAPE values than CNN, which shows its superiority in providing more accurate and consistent predictions. On the other hand, the CNN model has lower RMSE and MSE values, thus being able to reduce large prediction errors. By comparing the results of LSTM, CNN, and actual data forecasting, the LSTM model is more suitable for forecasting motorcycle sales in Indonesia

The forecasting of advertisement tax payments at the Medan City Revenue Agency aims to support planning and decision-making regarding advertisement tax revenue from 2021 to 2023, covering the period from January to December. In this process, historical data on advertisement tax payments is analyzed to determine the most suitable ARIMA model by considering the Autoregressive (AR), Differencing (I), and Moving Average (MA) parameters. The research indicates that the ARIMA model can provide accurate predictions of advertisement tax payment trends, thereby serving as a tool to enhance the effectiveness of local tax management. For the period from January to October 2024, it is estimated that 1,141 individuals will make advertisement tax payments, with the lowest forecasted number occurring in January 2024 at 1,128 individuals.

This study aims to determine the forecasting of stock return volatility of energy companies listed on the Indonesian Sharia Stock Index (ISSI) using the ARCH/GARCH method. This study uses purposive sampling method and uses secondary data in the form of daily stock returns from January 2022 to June 2024 on 10 selected stocks. Data processing is done using Stata software. The results showed that of the 10 selected stocks, only 6 stocks, namely BYAN, ADRO, GEMS, PTBA, AKRA, and BSSR, were suitable for analysis using the ARCH/GARCH model. Meanwhile, PGAS, ITMG, PTRO, and HRUM do not show ARCH effect or do not contain heteroscedasticity. Statistical evaluation of volatility prediction shows that the selected models provide good predictions. Among the six stocks analyzed, ADRO, PTBA, and BSSR show high volatility, while BYAN, GEMS, and AKRA show low volatility. Therefore, investors should consider investment risk when evaluating stocks with different levels of volatility.