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Work volume calculations are essential for project cost estimation. Many projects, such as the Laboratory Building of the Faculty Engineering at Tanjungpura University, calculate work volumes conventionally. Conventional calculation considered less efficient and prone to errors. Building Information Modeling (BIM) provides a solution that produces more accurate and efficient calculations than conventional methods. This research aims to compare structural work volume results produced by BIM using Autodesk Revit against conventional methods and project’s BOQ. This research also describes the benefits and challenges of BIM implementation based on the researcher’s experience applying BIM with Autodesk Revit in work volume calculation. The comparison between BIM and conventional method shows a maximum difference of 2% across all work items. Meanwhile, the comparison between BIM and the BOQ shows significant differences: 81% in column formwork area, 24% in grade beam/beam concrete volume, 25% in column reinforcement weight, 25% in steel beam weight, and 10% in the steel plate weight. This research proves that BIM implementation produces more accurate and efficient calculations and serves as an effective BOQ cross-check tool. Based on the researcher’s experience in implementing BIM with Autodesk Revit, challenges found in procurement aspects, modeling aspects, and model dependency on reference drawings.    

This study examines the application of double integrals in calculating the volume of cylindrical concrete piles as a basis for estimating material requirements in building foundation structures. The volume calculation was carried out using a double-integral approach in polar coordinates for three pile segments with lengths of 4 m, 3.9 m, and 4 m, each having a diameter of 60 cm. The results were then validated using the standard geometric formula to ensure consistency and mathematical reliability. The obtained concrete volume was subsequently used to estimate material needs based on a 1:1.5:3 mix proportion consisting of cement, sand, and gravel. The findings indicate that double integrals can be effectively applied to generate accurate estimations of both volume and material requirements, supporting logistical planning in construction. This approach also highlights the strong connection between mathematical concepts—particularly multivariable calculus—and practical applications in civil engineering. Furthermore, the study emphasizes that double integrals may serve as a relevant alternative when structural modeling requires deeper analytical exploration or validation beyond conventional geometry. Therefore, the implementation of double integrals not only reinforces theoretical understanding but also enhances precision in evaluating structural components within building foundation planning.

This study aims to analyze the factors affecting real wages of construction workers across provinces in Indonesia from 2010 to 2023 using panel data analysis. The independent variables include Provincial Minimum Wage (UMP), Consumer Price Index (CPI), Open Unemployment Rate (TPT), and Performance Pay (Balas Jasa). A panel dataset of 476 observations from 34 provinces over 14 years was analyzed using three model approaches: Common Effect Model (CEM), Fixed Effect Model (FEM), and Random Effect Model (REM). The best model was determined through Chow Test, Hausman Test, and Lagrange Multiplier Test, which confirmed that the Fixed Effect Model (FEM) is the most appropriate for analyzing this research data. FEM estimation results show that simultneously, all independent variables (UMP, CPI, TPT, and Performance Pay) have a significant effect on real wages with an F-statistic value of 436,465.9 (p-value = 0.0000 < 0.05), indicating that the model as a whole is highly valid and capable of explaining the variation in real wages collectively. However, partial tests reveal that only the Real Wage variable has a positive and statistically significant effect on Performance Pay (coefficient = 106.3320; t-statistic = 1276.083; p-value = 0.0000), while UMP (p-value = 0.1472), CPI (p-value = 0.6460), and TPT (p-value = 0.6934) show no significant effects at the 5% significance level. The research model demonstrates very high predictive ability with an R-squared value of 0.999735 (99.97%), indicating that the variables studied can explain nearly all variation in real wages of construction workers at the provincial level. This research provides policy implications that improving real wages in the construction sector requires an integrated approach that focuses not only on minimum wage setting but also on regional inflation control, human capital quality improvement, and creating conducive labor market conditions through unemployment reduction

Software development effort estimation is crucial as it is one of the key factors for successful software development. This research employs Random Forest to estimate software development effort. To achieve better results, the study combines the Random Forest method with Genetic Algorithm. The results show that the China dataset provides more accurate estimation compared to the Desharnais dataset, because the China dataset uses relevant feature selection for estimation.

This study examines the implementation of digital health services via the Mobile JKN application at the Payung Sekaki Community Health Center in Pekanbaru City and explores factors affecting service effectiveness, especially complaints about long waiting times. Using a descriptive qualitative approach based on the Mobile Health Acceptance Model by Handayani et al. (2021), the study focuses on five constructs: ease of use, system availability, system responsiveness, health workers’ digital skills, and user trust. The results show that Mobile JKN has significantly simplified administrative processes, accelerated registration, and improved service efficiency at the health center. Effectiveness is supported by factors such as the application’s general ease of use, faster queue data processing, and adequate digital skills among staff. Users also show a high level of trust, though queue time estimation still needs improvement. Despite these benefits, complaints about long waits persist due to patients who register online but still queue manually and misunderstandings between Mobile JKN and e-Puskesmas queue numbers. Late patient arrivals also contribute to delays. Overall, Mobile JKN proves effective in enhancing digital health services, but further optimization is needed through better socialization of service procedures, accurate queue information, and improved system integration to maximize the advantages of digitalization.

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 examines the stock market reaction to the Christmas and New Year holidays by analyzing abnormal return and trading volume activity for companies consistently listed in the LQ45 Index during 2021–2023. Using a quantitative causal approach and an event study design, the research observes market behavior within a 10 day estimation window and a 10 8day event window surrounding the holiday period. The findings show that abnormal return exhibits limited but notable reactions, with a significant decline observed before the holiday, indicating that investors tend to reduce risk exposure prior to market closure. After the holiday, significant movements still appear, but they remain negative, suggesting that investor activity and confidence have not fully recovered. In contrast, trading volume activity does not show significant differences either before or after the holiday, implying that changes in prices are influenced more by sentiment and price adjustments rather than shifts in trading intensity. These results indicate that the Indonesian capital market demonstrates characteristics of a semi-strong form efficiency, where public information such as national holidays is largely anticipated and absorbed by the market.

Accurate wind turbine power curve modeling plays a crucial role in performance evaluation, energy yield estimation, and data-driven control strategies. However, actual power curves often exhibit non-linear behavior influenced by atmospheric variability, measurement noise, and SCADA anomalies, making conventional modeling approaches less effective. This study proposes an optimized logistic power curve model whose parameters are tuned using Particle Swarm Optimization (PSO) to improve predictive accuracy. The analysis uses the Wind Turbine SCADA Dataset from Kaggle, which undergoes extensive preprocessing including physical rule filtering, outlier detection with the Interquartile Range (IQR) method, anomaly removal, and smoothing of the power signal. A three-parameter logistic model is selected due to its ability to capture the typical S-shaped relationship between wind speed and power output. PSO is applied to identify optimal model parameters by minimizing the Mean Squared Error (MSE), utilizing 40 particles over 200 iterations. The optimized model achieves strong predictive performance with RMSE of 404.09, MAE of 179.96, and R² of 0.904 on the test set, indicating that more than 90% of the variability in actual power can be explained by wind speed. Residual analysis reveals heteroscedastic patterns and slight overestimation in mid-range wind speeds, yet overall model consistency remains high. Comparative evaluation against Linear Regression, Random Forest, and logistic modeling using curve_fit shows that the Logistic–PSO approach provides the most accurate and stable predictions. These findings demonstrate that combining logistic modeling with PSO offers an effective and robust method for data-driven wind turbine power curve optimization.

The increasing demand for sustainable energy solutions in rural areas has prompted the utilization of biogas and bio-slurry as alternative resources. This study aims to evaluate the economic feasibility of household-level biogas systems by integrating Cost-Benefit Analysis (CBA), Net Present Value (NPV), Benefit-Cost Ratio (BCR), and Undiscounted Payback Period (UPBP), complemented with sensitivity analysis. Primary data were collected from 16 households operating biogas systems, while secondary data supported the estimation of cost and benefit components. Results show that biogas adoption provides positive economic returns, with average NPV reaching Rp 12,749,000, BCR above 1.0, and UPBP within four years, indicating financial viability. Sensitivity analysis reveals that variations in LPG prices and livestock numbers significantly affect economic outcomes, demonstrating the importance of market and production factors in ensuring project sustainability. The findings conclude that household biogas systems are economically feasible and resilient under certain conditions. Future studies are suggested to expand the scope by incorporating environmental and social benefits,a s well as exploring scalability at the community level.

The implementation of risk management is an important method that is carried out in order to identify risk factors that may arise during the implementation of the project. However, the implementation of risk management still faces some obstacles in its implementation. Therefore, this study aims to analyze the barriers to the implementation of risk management in construction projects in Surabaya. A total of 80 respondents filled out questionnaires from construction service providers including contractors and consultants. Respondents involved include professional experts such as Project Managers, Site engineers, Site managers, implementers, estimators, General Managers and Company Directors. Data processing using fuzzy AHP method as a data processing tool and decision making. The results of the study revealed that the main factors that can hinder the implementation of cost risk management are inaccurate cost estimates (Y4) with the highest weight of 0.433, lack of quality Control (Qc) supervision criteria (Y5) is ranked second with a weight of 0.288, poor coordination between stakeholders (owner,contractor and consultant) (Y1) is ranked third with a weight of 0.274, lack of risk management training (Y3) is ranked fourth with a weight of 0.005, and some, old age) (Y2), the work can not be done according to the work drawings (Y6), limited skilled human resources (Y7), materials not according to specifications (Y8), improper initial cost estimation (Y9), late progressive payment from the owner (Y10) ranked fifth jointly because it has an equivalent weight value of 0.These findings conclude that accurate cost estimation (Y4) is very important in construction projects because it becomes the main basis in budget planning, decision making, and risk management.

Rapid developments in information technology have made laptops an essential device for students, especially those in their final year of study. Choosing the right laptop plays an important role in supporting academic productivity, such as writing theses, analyzing data, and developing software. This study aims to classify the preferences of mathematics students at Gorontalo State University in choosing laptops based on usage characteristics and factors that influence purchasing decisions. The method used is Kernel Discriminant Analysis (KDA) with a Gaussian kernel function and an optimal bandwidth of 0.8. The research data involved 268 respondents divided into training and testing data. The analysis results show that the KDA model has an accuracy rate of 60% on the training data and 52% on the testing data, which indicates the model's ability to recognize student preference patterns despite a decrease in accuracy on new data. Based on the kernel density estimation results, Acer is the most widely used laptop brand, while Zyrex and Apple are rarely chosen. The most influential factor in purchasing decisions is processor specifications, with a contribution of 35.739%, followed by brand, warranty, and price. These findings indicate that hardware characteristics are the main consideration in laptop selection, with most students choosing laptops with Intel Core i5 processors, a minimum of 8GB of RAM, and SSD storage. The results of this study can also be used by universities to provide recommendations for selecting laptops that suit students' academic needs.  

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 designs and implements Bank Jali, a web-based bank queue system that applies a queue data structure with the First In First Out (FIFO) principle to improve service efficiency. The research and development method includes requirement analysis, design, implementation, testing, and evaluation. The application is built with HTML, CSS, and JavaScript, storing queue data temporarily through localStorage so the entire process runs client-side without a dedicated server. Key features include online queue registration, digital ticket generation, real-time status monitoring with waiting-time estimation, and an admin dashboard for managing teller and customer service queues. Testing shows the system operates reliably, processes queues according to FIFO, and provides a practical, lightweight user experience across devices.

A sump is a water reservoir that functions to accommodate runoff water in the pit before the water is pumped. This study was conducted to determine the location plan and sump capacity based on the direction of mine progress and the actual estimation of runoff discharge that enters the pit at PT. Wahyu Murti Garuda Kencana, Central Kalimantan. There is a water problem that interferes with production activities because the actual sump dimensions are insufficient to accommodate the daily discharge of water that enters the sump. So that the impact on the mining front is flooded or there is waterlogging that disrupts mining activities. The progress of the mine is directed towards the north of the sump so that the distance and dimensions of the actual sump are ineffective and unable to accommodate the incoming water discharge in accordance with the direction of the mine progress due to the mining location in the pit which is easily submerged by water. The drainage condition at PT. Wahyu Murti Garuda Kencana uses the mine dewatering method which is a method of removing water that flows into the mining sump using a pump. Theoretically, by conducting direct research on the sump area of PT. Wahyu Murti Garuda Kencana, the actual sump dimensions are insufficient to accommodate the water discharge that enters the pit. The design carried out pays attention to the mining plan so that the sump design to be designed can be applied in the field in accordance with the mine design. The shape of the sump designed is a trapezoidal shape with dimensions of 80 m x 70 m cross-section, 74 m x 64 m bottom section, depth of 4 m and 55 degree tilt angle and a total sump volume capacity of 20,672 m3.

The daily exchange of informatics over the Internet has both eased the widespread proliferation of resources to ease accessibility, availability and interoperability of accompanying devices. In addition, the recent widespread proliferation of smartphones alongside other computing devices has continued to advance features such as miniaturization, portability, data access ease, mobility, and other merits. It has also birthed adversarial attacks targeted at network infrastructures and aimed at exploiting interconnected cum shared resources. These exploits seek to compromise an unsuspecting user device cum unit. Increased susceptibility and success rate of these attacks have been traced to user's personality traits and behaviours, which renders them repeatedly vulnerable to such exploits especially those rippled across spoofed websites as malicious contents. Our study posits a stacked, transfer learning approach that seeks to classify malicious contents as explored by adversaries over a spoofed, phishing websites. Our stacked approach explores 3-base classifiers namely Cultural Genetic Algorithm, Random Forest, and Korhonen Modular Neural Network – whose output is utilized as input for XGBoost meta-learner. A major challenge with learning scheme(s) is the flexibility with the selection of appropriate features for estimation, and the imbalanced nature of the explored dataset for which the target class often lags behind. Our study resolved dataset imbalance challenge using the SMOTE-Tomek mode; while, the selected predictors was resolved using the relief rank feature selection. Results shows that our hybrid yields F1 0.995, Accuracy 0.997, Recall 0.998, Precision 1.000, AUC-ROC 0.997, and Specificity 1.000 – to accurately classify all 2,764 cases of its held-out test dataset. Results affirm that it outperformed bench-mark ensembles. Result shows the proposed model explored UCI Phishing Website dataset, and effectively classified phishing (cues and lures) contents on websites.

Oil palm is a leading commodity that makes a major contribution to Indonesia’s economy, yet a significant productivity gap remains between actual and potential yields. A principal cause is suboptimal water management, which leads to flooding during the rainy season and drought in the dry season. This study develops a Conceptual Design (CD) for water management to map existing problems, analyse root causes, formulate improvement measures, and present a macro-level cost estimate for the study site. The research was conducted at an oil palm plantation in East Kalimantan anonymized as “PT XYZ.” The site was selected due to recurrent flooding and a recent change in ownership that limited data availability, making it well-suited for a CD-stage assessment. The objective is to identify water management issues and propose effective recommendations. A quantitative approach integrates primary data from field observations and measurements with secondary data. The analyses cover flood problem assessment, Water Management Zoning (WMZ/ZPA), rainfall analysis, hydrology, hydraulics, improvement proposals, and macro cost estimation. Results indicate that challenges are driven by swampy land conditions and inadequate channel and hydraulic structure capacity. Micro-watershed delineation using DEMNAS identified four ZPAs totalling 479–4,061 ha. Design rainfall was derived from CHIRPS satellite data using a log-normal distribution. Hydrologically, peak discharges range from 3.87–22.58 m³/s for the 2-year return period and 4.46–26.31 m³/s for the 5-year return period. Hydraulically, the proposed dimensions for rivers, outlet drains, carrier drains, and field-edge drains are 4×3×2 m to 9×7×3 m (T=2 years) and 4×3×2 m to 10×8×3 m (T=5 years), while collection and main drains are proposed at 3×2×2 m for both return periods. The total estimated investment for the 5-year design scenario is IDR 27,999,263,000.

This study uses a quantitative associative approach to analyze the influence of exports, imports, inflation, and exchange rates on economic growth in five ASEAN member countries: Indonesia, Malaysia, Singapore, Thailand, and Vietnam. The data used are secondary data obtained from the World Bank for the period 2013–2023. The analysis technique used is the Panel Autoregressive Distributed Lag (Panel ARDL) Model, which begins with stationarity and cointegration tests. Results The ARDL Panel Model estimation in this study is declared valid because it meets the main requirements, namely having a cointegrated lag with a negative coefficient value of -0.831550 and significant at the 5% significance level (probability 0.0000 < 0.05). The long-term estimation results indicate that only the inflation variable has a significant influence on Gross Domestic Product (GDP) in the 5 ASEAN countries studied. Meanwhile, in the short term, no variables were found to have a significant influence on GDP in the 5 countries. Furthermore, country-level estimations show varying results. Indonesia is the only country that shows a significant influence of exports, imports, inflation, and exchange rates on GDP. Thailand shows a significant influence of exports and exchange rates, while Malaysia, Singapore, and Vietnam do not show any significant influence of exports, imports, inflation, and exchange rates on GDP. These findings reflect that the relationship between macroeconomic variables and economic growth in ASEAN countries is heterogeneous and is strongly influenced by the structural characteristics of each country.

The maintenance of non-aviation defense equipment (main weapon system) is a critical aspect in maintaining operational readiness. However, the Maintenance, Repair, and Overhaul (MRO) system in Indonesia still faces limitations due to manual reporting, inefficiency in spare parts management, and the lack of integration of the Life Cycle Cost (LCC) approach. This study aims to design and develop the Integrated Cavalry Monitoring and Maintenance System (ICMMS) based on a web application that integrates sensors, real-time data analytics, and LCC calculation. The prototyping method was used, involving design, development, integration, and testing phases on the Maung Tactical Vehicle and Anoa Armoured Personnel Carrier at PT Pindad. The results of the prototype implementation showed a significant increase in maintenance efficiency: damage reporting time decreased from ±3 hours to ±1 minute, critical component identification became 95% faster, and maintenance scheduling shifted from reactive to predictive. Additionally, the integration of the LCC algorithm allows for more accurate maintenance cost estimation, supporting technical and strategic decision-making. This study demonstrates that ICMMS based on LCC can be an innovative digital solution to enhance MRO effectiveness and operational readiness of non-aviation defense vehicles in Indonesia. It is expected that this system will improve the resilience and cost-effectiveness of managing Indonesia’s military vehicle fleet.

This study aims to compare the results of student living cost estimates over a four-year study period using two approaches in financial mathematics, namely the discrete model and the continuous model. The background of the study is based on the need for students to manage their personal finances effectively amidst rising living costs due to inflation. The discrete model is used to predict expenses at certain time intervals, while the continuous model assumes that changes in the value of money occur continuously at all times. This study uses a quantitative descriptive-comparative method with controlled simulations on 100 student data with variations in monthly living costs between Rp2,000,000–Rp4,000,000 and a random inflation rate of 0%–20%. The data were analyzed using discrete and continuous growth formulas, then a Paired Sample t-Test was performed to determine significant differences between the two models. The results show that both models produce very similar living cost estimates with an average difference of only about 1–3% of the total four-year costs. The continuous model produces slightly higher results than the discrete model due to its exponential and continuous nature of calculations. However, the statistical test results showed a p-value > 0.05, indicating no statistically significant difference between the two. Practically, both approaches can be used equally in student financial planning, with the discrete model being more appropriate for short-term projections and the continuous model being more appropriate for long-term projections.