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Economic growth in ASEAN countries has shown heterogeneous dynamics, particularly in the post-pandemic period. This study aims to analyze the effect of economic complexity, manufacturing value added, and foreign direct investment on economic growth in ASEAN-8 countries during 2015–2024. The study employs a quantitative explanatory approach using panel data regression analysis. The data were obtained from the World Development Indicators (World Bank) and Harvard Growth Lab. Based on the Chow and Hausman tests, the Fixed Effect Model (FEM) was selected as the best estimation model. The results indicate that economic complexity has a negative and significant effect on economic growth, suggesting that increasing economic sophistication does not automatically promote growth when industrial and institutional readiness remain limited. Meanwhile, the manufacturing sector has a positive but insignificant effect on economic growth. In contrast, foreign direct investment has a positive and significant effect on economic growth through capital accumulation and technology transfer. Simultaneously, all independent variables significantly affect economic growth in ASEAN-8 countries. These findings imply the importance of strengthening industrial capacity, institutional quality, and technological readiness to support sustainable economic growth in ASEAN countries.

This study examines the determinants of the Jakarta Composite Index (JCI) based on three main macroeconomic factors namely inflation, the USD/IDR exchange rate, and the SBI interest rate (BI Rate) covering the period January 2020 to December 2025, in the context of post-COVID-19 pandemic recovery and global economic turmoil. A quantitative approach was employed using the Ordinary Least Squares (OLS) method, with 72 monthly observations derived from secondary data sourced from official institutions including Bank Indonesia (BI), the Central Statistics Agency (BPS), the Indonesia Stock Exchange (IDX), and the Financial Services Authority (OJK). Classical assumption tests were applied comprising the Jarque-Bera normality test, Variance Inflation Factor (VIF) for multicollinearity, Breusch-Godfrey for autocorrelation, White Test for heteroscedasticity, and Ramsey RESET for model specification. Partially, inflation, exchange rate, and BI Rate each demonstrate a positive and significant effect on the JCI (p < 0.05). Simultaneously, all three variables exert a significant combined influence on the JCI, with a coefficient of determination R² = 0.4414, indicating that the model explains 44.14% of the variation in the JCI. The remaining 55.86% is attributed to other variables outside the model. Classical assumption test results reveal violations of normality, autocorrelation, and heteroscedasticity assumptions, although the model is free from multicollinearity. These findings confirm that Bank Indonesia's monetary policy has a significant and measurable impact on capital market performance. Further research is recommended using more advanced time series models such as GARCH or VECM to address violations of classical assumptions and improve estimation efficiency.

The Fréchet distribution is one of the commonly used Extreme Value Distributions (EVDs) in statistical modeling and heavy-tailed data analysis, where it plays an important role in describing product lifetimes as well as climatic and financial phenomena. The estimation of its two parameters, namely the shape parameter and the scale parameter, is traditionally based on the Maximum Likelihood Estimation (MLE) method. However, maximizing the likelihood function for this distribution involves numerical difficulties, which necessitates the use of numerical optimization methods. In this study, we propose the use of the Aquila Optimizer (AO), a recent metaheuristic algorithm inspired by the hunting behavior of eagles, as an efficient numerical tool for maximizing the likelihood function of the Fréchet distribution. The objective function was formulated as the negative log-likelihood function (-LogL), and the Aquila Optimizer was employed to obtain the optimal estimates of the distribution parameters. Several simulation experiments with different sample sizes were conducted to compare the performance of the proposed method with a conventional approach represented by the Nelder–Mead method, using the Mean Squared Error (MSE) criterion. The simulation results demonstrated that the Aquila Optimizer outperformed the Nelder–Mead algorithm in many cases, although the superiority was slight. The results also showed that both algorithms were consistent, as their MSE values decreased with increasing sample size. In addition, a practical application was carried out using real data, and the results of the survival function estimation indicated a good fit.

This research try to examine how investment choices in property and real estate sector businesses listed on IDX (2020-2024) are impacted by the cost of debt and equity.For the accuracy of the analysis, this study also uses firm size and profitability as controler. Purposive sampling was used in the sampling process, which produced seven qualifying organizations with a total of 35 observations over a five-year period. Panel data regression was used for data analysis, and the Common Effect Model was shown to be the best estimation model. The findings show that investment decisions are significantly influenced by firm size, profitability, cost of debt, and cost of equity all at the same time. Nonetheless, investment choices are not much impacted by the cost of debt. Similarly, it has been demonstrated that the cost of equity has no appreciable effect on the capital expenditures of the businesses. Firm size has a favorable and substantial impact, making it the main motivator for investment activity in the real estate industry. During the study period, investment decisions were not significantly impacted by profitability. These results show that, especially in the post-pandemic economic recovery era, asset capacity and economies of scale are more important for the viability of real investment projects for property firms on the IDX than yearly variations in capital costs.

This study aims to analyze and design the electrical installation requirements for a prototype residential building with dimensions of 11 × 19 meters. The research applies a technical calculation approach based on lighting standards, air conditioning capacity estimation, load analysis, and electrical protection planning. Lighting requirements were determined using the lumen method by considering room area, illumination standards, coefficient of utilization, and light loss factor. Air conditioning capacity was calculated based on room area and standard cooling load values in BTU/hr. Furthermore, total electrical loads were analyzed to determine installed capacity, nominal current, Miniature Circuit Breaker (MCB) ratings, and appropriate cable cross-sectional areas with safety factors applied. The results indicate that proper load grouping, appropriate MCB selection, and correct cable sizing significantly enhance system safety and reliability. The study provides a systematic planning model for residential electrical installations that meets safety, efficiency, and technical standards, and can serve as a reference for similar residential building designs.

Indonesia’s Oil and gas project construction continue face challenges notably delays that lead to underperformance. This study tries to describe the factors that causing delays in EPC oil and gas project in Indonesia from project management view. Started by overall study on cause of delays in construction worldwide, thus phenomenon on neighboring country (Malaysia) where Indonesian workers involved, then study continues with factors in Indonesia as overall, and include specific project which are South Sumatera and Sumbawa. Three main factors are always present in the cause of delays in EPC Project in Indonesia namely; contractor’s financial problem in funding the project, poor project planning and estimation leads to unrealistic determination of the duration, and ineffective change management by owner in project showed in resolving contract dispute, requirement changes, and dispute in understanding in contract articles. These factors are clearly present in EPC projects in Indonesia as main contributor in project delays together with two additional factors namely legal awareness and social community problems.

The foundation of turbine machines in industrial buildings must be able to withstand static and dynamic loads caused by operational vibrations. Generally, reinforced concrete block foundations are used due to their good stability. However, under certain conditions such as limited land, decreased soil bearing capacity, and the need for structural rehabilitation, their application becomes less effective. Therefore, this study aims to analyze the use of Micropile foundations as an alternative for turbine machine foundations at Candi Baru Sugar Factory, Sidoarjo.The research method includes literature studies, soil data collection through Cone Penetration Test (CPT), machine technical data analysis, and the design and analysis of Micropile foundations. The analysis covers bearing capacity, settlement, dynamic response, pile cap design, and cost estimation.The results indicate that Micropiles with a diameter of 0.20 m, a length of 2 m, five piles, and a pile cap size of 2 m × 2 m × 4.5 m are able to safely and stably support the machine load. In addition, this system is considered more efficient in terms of construction time and implementation. Therefore, Micropile foundations are recommended as an effective and economical alternative for turbine machine foundations.

This study examines the seismic performance of slender Air Traffic Control (ATC) towers in high‑hazard regions (PGA > 0.4g), where vertical taper, torsional eccentricity, and top‑heavy cab mass can significantly increase drift, base shear, and collapse risk relative to conventional buildings. Existing studies often rely on linear procedures and outdated provisions, leading to underestimation of nonlinear behaviour and limited guidance for ATC towers designed to SNI 1726:2019. The research aims to quantify these irregularity effects and formulate design recommendations that satisfy Immediate Occupancy, Life Safety, and Collapse Prevention performance targets. The methodology couples response spectrum analysis, using a site‑specific Padang spectrum consistent with SNI 1726:2019 and ASCE 7‑16, with nonlinear pushover analysis interpreted through FEMA/ATC performance‑based criteria. A parametric study is performed on three cab configurations small, medium, and large modelled as 5%, 15%, and 25% mass ratios at the tower head, while keeping a 10 m × 10 m hybrid core–frame shaft constant. Results indicate that larger cab mass produces systematic but moderate increases in global displacement, story drift, and base shear, while plastic hinges localize primarily in the upper stories and cab‑support region, yielding performance levels from Immediate Occupancy to Collapse Prevention. Overall, the tower meets code drift limits and acceptable performance if local strengthening is provided around the shaft–cab interface, offering a calibrated reference for top‑heavy ATC tower design in Indonesian high‑seismic settings and identifying priorities for future time‑history and soil–structure interaction studies.

The commercial sector, especially the hospitality industry, is one of the largest consumers of electrical energy, with energy costs often ranking as the second highest operational expense. This study aims to conduct a specific Electrical Energy Audit in the Office Engineering unit of Aston Denpasar Hotel & Convention Center to optimize electricity usage and improve energy efficiency. The research applies a detailed audit approach with a focus on lighting systems and air conditioning (AC), which are major contributors to energy consumption. The initial stage involves calculating the actual Energy Consumption Intensity (IKE) in kWh/m²/month and comparing the results with ASEAN and SNI standards to determine the efficiency classification of the building. Data collection is carried out through direct field measurements as primary data, using instruments such as a Clamp Meter and Lux Meter. The expected outcome of this study is the identification of detailed Energy Saving Opportunities (ESO), along with the estimation of potential monthly energy cost savings and the calculation of the investment Payback Period.

The study aimed to find out the nature of the budget, know the modern techniques of preparing and implementing the budget, and the consequences of preparing budget based on the modern techniques on performance of government. The descriptive-analytical approach was used to meet the objectives and hypotheses of the research. The data was gathered through a questionnaire whereby 60 questionnaires where distributed to a sample population of accountants working in the Financial Affairs Department at the General Directorate of Education in Wasit Governorate (Iraq), and 57 questionnaires were collected to establish the views of the accountants regarding the effect of preparing and implementing budgets under the modern techniques on the government performance. This paper came up with a number of conclusions. Preparation of the general budget of the state is a technical and political process that involves and is involving different actors in the state. The budget preparation process is a technical process since it involves numerous key calculations that can be used to quantify the anticipated revenues, the cost of activities likely to be obtained in the year and estimation of the planned expenditures. The process of preparing the budget through the traditional ways (line-item budgeting) does not contribute to the optimal planning and budget allocation of the state and, therefore, the fulfillment of the goals laid out by the countries. This paper advises on the need to focus on the current approaches in the budget preparation because they enhance governmental performance, enhance the effectiveness and efficiency of state expenditures, strive to diversify the income, and convert the policies and goals of the state into a definite and actualized reality on the ground, with the help of standards and indicators that modern means of preparing the budget have offered. And to act to modernize the budget system in Iraq, profiteering with the experience of other countries in this domain, and that modernization be a progress, commencing with the budgeting of some ministries or departments within ministries and bodies gradually with the aim of acquiring more experience and expertise, and then meet the impediments which will emerge to proceed to a complete transition.

This study examines the relationship between Corporate Social Responsibility (CSR) disclosure and tax avoidance, with CEO Overconfidence considered as a moderating factor. The research focuses on non-cyclical consumer goods companies listed on the Indonesia Stock Exchange during the 2022-2024 period. Using annual and sustainability reports, the analysis employs multiple linear regression and moderated regression analysis (MRA). Robust standard errors based on the Newey-West method are applied to ensure reliable estimation. The results indicate that CSR disclosure does not have a significant direct effect on tax avoidance. However, CEO Overconfidence significantly moderates the relationship between CSR disclosure and tax avoidance, highlighting the role of executive behavioral characteristics in corporate tax decisions. These findings suggest that CSR disclosure alone is insufficient to explain firms’ tax avoidance behavior without considering managerial traits. The study contributes to the literature by integrating behavioral perspectives into tax avoidance research and emphasizing the importance of executive oversight in aligning CSR practices with responsible tax behavior.

Nickel has become a strategic mineral in the global industrial value chain, particularly for stainless steel production and electric vehicle battery manufacturing. As one of the world’s largest nickel producers, Indonesia has implemented a downstream industrialization policy aimed at increasing value added and strengthening export performance. This study analyzes the effects of international nickel prices, destination countries’ GDP per capita, exchange rates, and the downstreaming policy on the value of Indonesia’s nickel exports (HS 75) over the period 2010–2023. The study employs a quantitative approach using panel data regression with secondary data covering five major export destination countries, namely China, Japan, South Korea, Thailand, and Singapore. Based on the Chow and Hausman tests, the Fixed Effects Model is selected as the most appropriate estimation technique, indicating the presence of country-specific heterogeneity among importing countries. The results show that destination countries’ GDP per capita and international nickel prices have a positive and statistically significant effect on Indonesia’s nickel export value. The downstreaming policy dummy variable also exhibits a positive and significant impact, suggesting that the nickel ore export ban implemented since 2020 has effectively shifted export composition toward higher value-added processed nickel products. In contrast, exchange rates are found to have no significant effect on export performance. Overall, the findings provide empirical evidence supporting the effectiveness of Indonesia’s downstream industrialization policy and highlight the importance of global demand conditions in driving the performance of processed nickel exports.

Pneumonia remains a leading cause of morbidity and mortality worldwide, particularly in resource-limited settings and among elderly populations, where timely diagnosis and continuous monitoring are often constrained by limited clinical infrastructure. This study presents an edge–cloud–integrated framework for early pneumonia risk monitoring, leveraging multimodal wearable sensors and deep learning to support continuous short-duration monitoring. The proposed system is designed to operate in near real time under simulated deployment conditions, continuously acquiring and analyzing physiological signals (respiratory rate, heart rate, SpO₂, and body temperature) alongside event-driven acoustic biomarkers (cough sounds) within a distributed architecture. A lightweight edge module performs local signal preprocessing and anomaly triage, selectively transmitting salient information to a cloud-based multimodal deep learning model for refined risk estimation and interpretability analysis. The framework was evaluated using a multi-source dataset comprising public repositories (MIMIC-III and Coswara) and a clinically supervised wearable study conducted in two Nigerian hospitals, resulting in 718  hours of quality-controlled multimodal monitoring data. In a pooled multi-source evaluation, the system achieved an AUC of 0.95, while in a clinically realistic local-only evaluation, the AUC was 0.86, reflecting a consistent but preliminary diagnostic signal. These results highlight the importance of local data adaptation for real-world applicability and suggest that multimodal AI can provide meaningful early risk indicators under resource constraints. Beyond predictive performance, this work demonstrates the feasibility of integrating multimodal learning, edge–cloud computation, and explainable analytics into a deployment-aware, privacy-preserving monitoring framework for low-resource healthcare environments.

Unemployment is a socio-economic problem that can threaten the stability of the Indonesian economy. This study analyzes the effect of minimum wages, exports, foreign investment, and the human development index (HDI) on the unemployment raefrom 1990 to 2023. Using the Ordinary Least Square (OLS) multiple linear regression estimation method, to correct bias in the estimation, the Newey-West HAC standard errors approach is used. Minimum wages and foreign investment have a significant negative effect on the open unemployment rate, confirming that wage increases can boost productivity, foreign investment creates direct jobs through the construction of production facilities and economic multiplier effects in supporting sectors. The most surprising finding of the HDI which has a positive effect and exports which are proven to be insignificant on the unemployment rate, this shows that human capital formation is not in line with existing job opportunities due to rapid technological changes, as well as export-increasing policies which focus more on capital intensity. The study provides important implications for policymakers, maintaining and optimizing minimum wage increases and foreign investment in a measurable manner because they have proven effective in reducing unemployment rates. Reorienting export strategies policy from capital-intensive to labor-intensive, increasing the human development index adjusted to technological developments, especially in the business and industrial world.

The implementation of the zoning-based admission policy (PPDB) in Semarang City continues to face challenges related to the accuracy of distance measurement and the transparency of information provided to the public. This study aims to examine the application of Geographic Information Systems (GIS) in defining zoning boundaries for public junior high schools in Semarang City and integrating the results into a web-based information platform. A quantitative descriptive approach was employed, incorporating spatial analysis through a 3-kilometer buffer radius using QGIS software. The results indicate that buffer analysis is effective in delineating priority domicile zones based on school coordinate data. These findings are integrated into a GIS-based website that presents visual information on school locations, enrollment capacity, and final score calculation mechanisms in accordance with current regulations. The proposed system contributes to improving information transparency, enabling the public to better understand admission opportunities while supporting government decision-making in promoting equitable access to education. For future development, the use of road network analysis is recommended to obtain more realistic distance estimations.

This study aims to analyze the operational and marketing strategies of UMKM Bolu Kambu Daeng Rosi in enhancing the competitiveness of traditional food products in Barru Regency, South Sulawesi. The research uses a descriptive qualitative approach with a case study method, through guided observation, semi-structured interviews, and documentation. The findings show that the enterprise's strengths lie in its culturallybased product differentiation and strong customer loyalty. However, the business still faces challenges in managerial aspects, digital promotion, and operational recording. SWOT analysis highlights the importance of strengthening production systems and applying simple marketing strategies to expand market reach. In addition, a daily profit and loss estimation shows a relatively high profit margin, though not yet supported by a documented financial system. The main recommendation is the need for gradual managerial transformation through simple financial record-keeping, basic visual promotion, and technology adaptation aligned with the owner's capacity. This research is expected to contribute to the development of local culinary-based MSMEs rooted in cultural heritage.

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.

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.