Publication Search

Mining operations rely heavily on the performance and reliability of heavy equipment used in the production process. One of the most important hauling units in open-pit mining is the dump truck, which functions to transport overburden and coal from the mining front to disposal areas. Due to high operational intensity, dump trucks require effective maintenance management to ensure equipment reliability and reduce unexpected downtime. However, maintenance activities are often carried out based only on routine service schedules without analytical planning based on historical data. This study aims to analyze the implementation of forecasting methods in maintenance management to improve the effectiveness of dump truck maintenance planning in mining operations. The research was conducted during field work practice at PT Putra Perkasa Abadi Jobsite BIB, Tanah Bumbu, South Kalimantan. The data used were historical maintenance records of dump truck units obtained from the maintenance department. The research method used a quantitative approach with time series forecasting analysis to identify maintenance patterns and estimate future maintenance needs. The results show that forecasting-based maintenance planning can help companies predict maintenance requirements more accurately and prepare maintenance resources more efficiently. Furthermore, the implementation of forecasting methods can reduce unexpected equipment failures and support operational efficiency in mining activities.

The instability of the climate is becoming increasingly prominent across Southeast Asia, creating uncertainty in agricultural systems that are highly dependent on seasonal weather patterns. Indonesia, where rice remains the primary staple food, is particularly vulnerable to the effects of rising temperatures and rainfall deficits. This study applies the Seasonal Autoregressive Integrated Moving Average (SARIMA) model to predict rice production while incorporating indicators of extreme climate anomalies. Using publicly available datasets, including FAOSTAT production statistics, NOAA rainfall and temperature anomalies, and climate indices from the World Bank, this model was developed following the Box-Jenkins procedure. Among the configurations tested, the SARIMA model (1,1,1)(0,1,1)₁₂ showed the strongest performance, reflected in a MAPE of 4.62% and low RMSE values. The model indicates that significant El Niño events can reduce annual rice production by 3–7%, while wetter La Niña conditions may support production recovery. These findings highlight the importance of integrating climate-sensitive data into agricultural forecasting. The model presented here could support early warning systems, adaptive farming strategies, and long-term food security planning in Indonesia.

Economic forecasting is becoming increasingly important year after year, especially during crises such as the pandemic of COVID-19 and the Russia-Ukraine war. Its development can be seen from the use of basic statistical models to the increasingly widespread use of machine learning technology. Economic forecasting plays an important role in helping to formulate policies and is also a reliable tool for researchers in dealing with uncertainty. Global crises, such as inflationary pressures due to the pandemic and supply chain disruptions from the Russia-Ukraine conflict, have prompted increased research in this field in an effort to anticipate economic shocks and emphasize the urgency of forecasting to prepare strategies for dealing with future uncertainty. This literature review uses the Scopus database with 2561 publications from 2020 to 2025, analyzed using R Studio with a bibliometrix approach (specifically biblioshiny) and VOSviewer to map relevant thematic connections. This analysis shows that economic forecasting is greatly influenced by market uncertainty and geopolitical factors, and at the same time influences public policy formulation and financial stability. Research contributions from Indonesia are still limited, with only 40 documents, thus emphasizing the need to strengthen economic forecasting studies in Indonesia to support monetary policy and national financial stability.

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.

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.

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 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.  

This study develops a strategic information system plan to improve customer service at the Food Mood MSME in the food and beverage sector. The Ward and Peppard framework is used to map the business and technology environment through Value Chain, SWOT, PEST, and Porter's Five Forces analyses, which are then broken down into Critical Success Factors and measurable key performance indicators. The research design is a qualitative case study with semi-structured interviews with the owner and employees, observations during peak hours, and a review of operational documents. The mapping results in a prioritized portfolio that places a cloud-based point-of-sale system integrated with QRIS, a lightweight inventory and procurement module, a kitchen display system, and basic accounting as the foundation, followed by a mini customer relationship management and loyalty program, online channel integration, a sales dashboard, and simple demand forecasting. The formulated performance targets include a wait time of no more than eight minutes, an order error rate below one percent, stock-outs of less than one day per month, and 100% transaction recording. The suggested three-month roadmap is operational and provides immediate benefits in terms of increased service speed, data accuracy, and potential customer retention, while also confirming the relevance of Ward and Peppard's approach for the Indonesian MSME context.

Economic forecasting is becoming increasingly important year after year, especially during crises such as the pandemic of COVID-19 and the Russia-Ukraine war. Its development can be seen from the use of basic statistical models to the increasingly widespread use of machine learning technology. Economic forecasting plays an important role in helping to formulate policies and is also a reliable tool for researchers in dealing with uncertainty. Global crises, such as inflationary pressures due to the pandemic and supply chain disruptions from the Russia-Ukraine conflict, have prompted increased research in this field in an effort to anticipate economic shocks and emphasize the urgency of forecasting to prepare strategies for dealing with future uncertainty. This literature review uses the Scopus database with 2561 publications from 2020 to 2025, analyzed using R Studio with a bibliometrix approach (specifically biblioshiny) and VOSviewer to map relevant thematic connections. This analysis shows that economic forecasting is greatly influenced by market uncertainty and geopolitical factors, and at the same time influences public policy formulation and financial stability. Research contributions from Indonesia are still limited, with only 40 documents, thus emphasizing the need to strengthen economic forecasting studies in Indonesia to support monetary policy and national financial stability.

Academic planning is one form of planning the teaching and learning process in state universities, aimed at achieving educational goals based on the standards set. One important aspect of academic planning is forecasting the number of new students. This study compares two forecasting methods, Fuzzy Time Series (FTS) and Autoregressive Integrated Moving Average (ARIMA), in predicting the number of new students in the Statistics Study Program at Universitas Negeri Gorontalo. Forecasting the number of new students is crucial for determining various policies, such as resource allocation and providing adequate facilities. The results of the study show that the ARIMA method produces more accurate predictions with a Mean Absolute Percentage Error (MAPE) of 0.35%, which is lower than the FTS method. This indicates that ARIMA is more effective in predicting the number of new students in the Statistics Study Program at Universitas Negeri Gorontalo and can serve as a reference to improve academic planning quality in higher education institutions.

Effective management of fixed assets plays a crucial role in maintaining the reliability and transparency of a company’s financial reporting. Errors in the capitalization process can lead to misstatements in financial statements and affect investment decisions. This study aims to analyze and forecast asset capitalization trends using the Autoregressive Integrated Moving Average (ARIMA) model. The research utilizes monthly recap data of asset capitalization recorded during the Settlement to Fixed Asset process from January 2021 to August 2025. The data were processed through several stages, including stationarity testing, model identification, parameter estimation, and model accuracy evaluation. The findings indicate that the data are stationary without differencing (d = 0). From several candidate models, ARIMA(0,0,3) was identified as the best model based on the lowest AIC value of 39.76. The selected model was then applied to predict asset capitalization values for the next ten periods, resulting in forecasts ranging from 1.12 to 1.56 trillion rupiah. Model evaluation showed a MAPE of 29.01%, which implies a moderate forecasting accuracy. Consequently, the ARIMA model can be considered a suitable analytical tool for monitoring and forecasting asset capitalization quantitatively.

This study aims to analyze the role of inventory management in improving financial efficiency at CV. Sumber Jaya Putra Perkasa. The main problems faced by the company are manual inventory management, technological limitations, dependence on certain suppliers, and suboptimal demand planning, which affect distribution effectiveness and financial efficiency. This study uses a quantitative descriptive approach with data collection techniques through interviews, observation, and documentation. The analysis was conducted on the stock management process, inventory turnover, and its impact on storage costs and operational efficiency. The results show that good inventory management contributes significantly to increased financial efficiency. With proper stock planning, companies can minimize the risk of excess and shortage of goods, reduce storage costs (holding costs), and increase inventory turnover so that working capital can circulate more quickly. However, the inventory management system currently used by CV. Sumber Jaya Putra Perkasa still has limitations, especially in terms of digitization and information integration. This study recommends the implementation of a technology-based inventory management system, a multi-supplier strategy, and the application of demand forecasting methods to improve stock planning accuracy. With this strategy, it is hoped that the company can achieve more optimal financial efficiency and strengthen its competitiveness in the distribution industry.

Drug stock-outs are an indicator of pharmaceutical management failure that directly affects patient safety and the quality of hospital services. Gatoel Hospital Mojokerto experienced an increase in the percentage of drug debt from 3.14% in January to 6.20% in July 2025, with 1,607 patients affected. This study aims to identify the factors causing drug stock-outs and formulate preventive strategies through the optimization of the Minimum-Maximum Stock Level (MMSL) system based on the Hospital Information System. A mixed-method approach was used, combining secondary data analysis (January–July 2025) and in-depth interviews. Fishbone analysis was applied to identify root causes, USG analysis to determine priorities, and SWOT analysis to formulate intervention strategies. Priority drug classification was carried out using the ABC-VEN method. The intervention involved implementing an MMSL pilot project for 150 drug items under Pareto category A. The analysis identified six dimensions of stock-out causes: man, materials, methods, machines, measurement, and environment. The highest priority issue was drug demand forecasting based on historical data (USG score: 125). SWOT analysis placed the organization in quadrant II, recommending a Weakness-Opportunities (WO) strategy. MMSL implementation was initiated through the development of SOPs and the entry of 150 priority drug items into the system. Drug stock-outs are caused by multifactorial issues that require systemic intervention. MMSL optimization has the potential to serve as a long-term solution, provided there is expanded coverage, strengthened human resource capacity, and comprehensive system integration.

This study aims to analyze investment efficiency in Bandung Regency from 2011 to 2024 and project it for the years 2025 to 2030. Investment efficiency is measured using the Incremental Capital-Output Ratio (ICOR) based on data from Gross Regional Domestic Product (PDRB) and Gross Fixed Capital Formation (PMTB) at constant 2010 prices. Forecasting is performed using the Autoregressive Integrated Moving Average (ARIMA) model. The analysis results show fluctuating ICOR values, reflecting annual variations in investment efficiency. Projections for 2025–2030 indicate a potential decline in efficiency, which signals important considerations for regional development planning. The findings highlight the need for the Investment and Integrated One-Stop Service Office (DPMPTSP) to use ICOR as a key performance indicator when formulating more effective and efficient investment policies to support quality economic growth in Bandung Regency. This study recommends improving future investment policies by utilizing the ICOR indicator to monitor and evaluate the effectiveness of regional investments.

This study examines the application of Educational Data Mining (EDM) to predict the academic per-formance of first-year students at the Catholic University of Bukavu and the Higher Institute of Edu-cation (ISP) in the Democratic Republic of Congo. The primary objective is to develop a model that can identify at-risk students early, providing the university with a tool to enhance student support and academic guidance. To address the challenges posed by data imbalance (where successful cases outnumber failures), the study adopts a hybrid methodological approach. First, the SMOTE algorithm was applied to balance the dataset. Then, a stacking classification model was developed to combine the predictive power of multiple algorithms. The variables used for prediction include the National Exam score (PEx), the secondary school track (Humanities), and the type of prior institution (public, private, or religious-affiliated schools), as well as age and sex. The results demonstrate that this approach is highly effective. The model is not only capable of predicting success or failure but also of forecasting students' performance levels (e.g., honors or distinctions). Moreover, the use of the Apriori association rule mining algorithm allowed the identification of faculty-specific success profiles, transforming prediction into an interpretable decision-support tool. This research makes several significant contributions. Practically, it provides the University of Bukavu with a tool for student orientation and early risk detection. Methodologically, it illustrates the effectiveness of a combined approach to EDM in an African context. However, the study acknowledges certain limitations, including the non-public nature of the data and the geographical specificity of the sample. It therefore proposes avenues for future research, such as the integration of Explainable AI (XAI) techniques for more refined and transparent analysis of the results.

Electricity has emerged as an essential requirement in modern life. As demand escalates, electricity costs rise, making wastefulness a drain on financial resources. Consequently, forecasting electricity usage can enhance our management of consumption. This study presents an IoT-based monitoring and forecasting system for electricity consumption. The system comprises two NodeMCU micro-controllers, a PZEM-004T sensor for collecting real-time power data, and three relays that regulate the current flow to three distinct electrical appliances. The data gathered is transmitted to a web application utilizing the k-Nearest Neighbor (k-NN) algorithm to forecast future electricity usage based on historical patterns. We evaluated the system's performance using four weeks of electricity consumption data. The results indicated that predictions were most accurate when the user’s daily consumption pattern remained stable, achieving a Mean Absolute Error (MAE) of approximately 1 watt and a Mean Absolute Percentage Error (MAPE) ranging from 1% to 1.7%. Additionally, predictions were notably precise during the early morning hours (3:00 AM to 8:00 AM) when k=6 was employed. This study demonstrates the effectiveness of integrating IoT-based systems with machine learning for real-time energy monitoring and forecasting. Furthermore, it emphasizes the application of data mining techniques within embedded IoT environments, providing valuable insights into the implementation of lightweight machine learning for smart energy systems.

Information technology is a technology used to manage data, including processing, acquiring, organizing, storing, and manipulating data in various ways to produce high-quality information—namely, information that is relevant, accurate, and timely. This information is used for personal, business, and governmental purposes, serving as strategic information in decision-making. To anticipate changes in weather conditions, particularly rainfall, a valid and accurate report is needed that can be useful for the public. So far, the correlation or relationship between the factors influencing weather conditions—especially rainfall—has not been precisely determined, making it mathematically difficult to create a model that can describe the correlation among all these factors. This is where Artificial Neural Networks (ANN) come into play: to create such models and map out the existing problems purely based on the input data provided. One of the capabilities of neural networks is to make predictions based on previously learned data using the backpropagation method.

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.

Poverty is one of the main problems that hinder regional development. Deli Serdang Regency shows a fluctuating trend in the number of poor people from year to year. To support government policies in overcoming poverty, an accurate forecasting method is needed to predict the number of poor people in the future. This study uses the Single Moving Average (SMA) method with two period variations, namely n = 2 and n = 3, based on data from the Central Statistics Agency (BPS) of Deli Serdang Regency for 2017–2023. The forecasting results show that the SMA method with n = 3 provides better accuracy than n = 2, as indicated by the Mean Squared Error (MSE) value of 21.38, Mean Absolute Deviation (MAD) of 4.44, and Mean Absolute Percentage Error (MAPE) of 3.52%. These findings indicate that the SMA method is capable of providing fairly accurate predictions and can be used as a basis for regional development policy planning to reduce poverty in Deli Serdang Regency in 2024.