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This study compared the performance of SARIMA and Prophet models in forecasting daily close prices of three major Indonesian banking stocks: BBCA, BBRI, and BMRI, using data from January 2020 to March 2026. Data were retrieved via the yfinance library, preprocessed, and split into 80% training and 20% testing sets. SARIMA modeling followed the Box-Jenkins procedure, while Prophet was configured with a Lag-1 regressor, weekly and monthly seasonality, Indonesian public holidays, and log transformation. Model performance was evaluated using MAPE, MSE, and Dstat metrics. Results showed that SARIMA outperformed Prophet in MAPE and MSE across all six stock-variable combinations, with MAPE values ranging from 1.3368% to 1.9386% for SARIMA and 1.5992% to 2.2300% for Prophet. However, Prophet demonstrated marginally higher Dstat values in several series. Both models achieved "Very Good" forecasting accuracy. A web-based forecasting system was also developed using Streamlit to make the models accessible to investors.

Exports as a component of international trade contribute significantly to the development and growth of a country’s economy. In Indonesia, export activities are considered one of the key drivers in increasing national economic performance. This research seeks to evaluate the role of Indonesia’s exports to the United States within the Harmonized System (HS) Code 71 commodity classification, encompassing pearls, gemstones, precious metals, and jewelry products, in influencing Indonesia’s economic growth as reflected by Gross Domestic Product (GDP) at constant prices.The analysis focuses on three specific commodity groups: Pearls and Precious Stones (HS 71.01–71.05), Precious Metals and Metals Clad with Precious Metals (HS 71.06–71.12), and Jewelry and Related Articles (HS 71.13–71.18). A descriptive quantitative research design was employed, utilizing secondary time-series data collected for the 2015–2024 period. Data sources included the Indonesian Central Bureau of Statistics (BPS) and Trade Map. To assess the relationship between variables, this study adopted multiple linear regression analysis accompanied by classical assumption testing and hypothesis testing using both t-test and F-test procedures. The empirical findings demonstrate that export performance within the HS Code 71 category underwent considerable fluctuations throughout the observation period, reaching its peak in 2024.Furthermore, the statistical results indicate that each commodity sub-category contributes differently and exerts varying effects on Indonesia’s real GDP, both individually and collectively. The outcomes of this research are expected to provide useful references for government institutions in developing trade cooperation strategies and policy decisions, while also supporting business actors in strengthening the international competitiveness of Indonesian products.

This study aims to analyze the influence of Regional Original Revenue (PAD), General Allocation Fund (DAU), Special Allocation Fund (DAK), and Tax Revenue Sharing Fund (DBHP) on Regional Expenditure, as well as to analyze whether the flypaper effect phenomenon occurs in Regencies/Cities in Jambi Province during the 2017-2023 period. The data used in this study is panel data that combines time series data for 7 years and cross-section data from 11 Regencies/Cities in Jambi Province. The analysis method used is panel data regression with the selected model Fixed Effect Model (FEM). The results of the study show that simultaneously (F Test), the variables PAD, DAU, DAK, and DBHP have a significant effect on Regional Expenditure. Partially (t Test), PAD and DBHP do not have a positive and significant effect on Regional Expenditure, while DAU and DAK show a positive and significant effect on Regional Expenditure. This study also found a flypaper effect in regencies/cities in Jambi Province. This indicates that regional governments in Jambi Province tend to be more responsive in increasing regional spending using transfer funds from the central government rather than optimizing their own potential Regional Original Revenue (PAD).

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 bertujuan untuk menganalisis pengaruh investasi, konsumsi rumah tangga, dan pengeluaran pemerintah terhadap pertumbuhan ekonomi di Kabupaten Pamekasan tahun 2011–2025. Pertumbuhan ekonomi merupakan indikator penting dalam mengukur keberhasilan pembangunan daerah yang dipengaruhi oleh komponen pengeluaran agregat. Penelitian ini menggunakan pendekatan kuantitatif dengan jenis penelitian eksplanatori dan memanfaatkan data sekunder time series yang diperoleh dari Badan Pusat Statistik Kabupaten Pamekasan. Analisis data dilakukan menggunakan regresi linier berganda dengan metode Newey-West Heteroscedasticity and Autocorrelation Consistent (HAC) Standard Errors. Hasil penelitian menunjukkan bahwa investasi, konsumsi rumah tangga, dan pengeluaran pemerintah secara simultan berpengaruh signifikan terhadap pertumbuhan ekonomi. Secara parsial, investasi berpengaruh positif dan signifikan terhadap pertumbuhan ekonomi, sedangkan konsumsi rumah tangga dan pengeluaran pemerintah tidak berpengaruh signifikan. Nilai koefisien determinasi menunjukkan bahwa 98,03% variasi pertumbuhan ekonomi dapat dijelaskan oleh variabel independen. Temuan ini mengindikasikan bahwa peningkatan investasi merupakan strategi penting dalam mendorong pertumbuhan ekonomi yang berkelanjutan di Kabupaten Pamekasan.

Penelitian ini bertujuan untuk menganalisis pengaruh inflasi, suku bunga Federal Funds Rate (The Fed), dan pertumbuhan ekonomi Indonesia terhadap Indeks Harga Saham Gabungan (IHSG) selama periode 2005–2025. Penelitian menggunakan pendekatan kuantitatif dengan memanfaatkan data sekunder berbentuk deret waktu (time series) tahunan. Analisis dilakukan menggunakan metode regresi linear berganda dengan bantuan perangkat lunak IBM SPSS Statistics. Sebelum dilakukan estimasi model, terlebih dahulu dilakukan pengujian asumsi klasik yang meliputi uji normalitas, multikolinearitas, autokorelasi, dan heteroskedastisitas untuk memastikan bahwa model regresi memenuhi persyaratan statistik. Pengujian hipotesis dilakukan melalui uji F untuk menguji pengaruh variabel independen secara simultan, uji t untuk menganalisis pengaruh masing-masing variabel secara parsial, serta koefisien determinasi (R²) untuk mengukur kemampuan model dalam menjelaskan variasi IHSG. Hasil penelitian menunjukkan bahwa model regresi telah memenuhi seluruh asumsi klasik sehingga layak digunakan dalam analisis. Pengujian simultan membuktikan bahwa inflasi, suku bunga The Fed, dan pertumbuhan ekonomi secara bersama-sama berpengaruh signifikan terhadap IHSG. Sementara itu, hasil uji parsial menunjukkan bahwa hanya suku bunga The Fed yang berpengaruh negatif dan signifikan terhadap IHSG, sedangkan inflasi dan pertumbuhan ekonomi tidak menunjukkan pengaruh yang signifikan secara statistik. Nilai koefisien determinasi sebesar 52,4% mengindikasikan bahwa variasi IHSG dapat dijelaskan oleh ketiga variabel independen dalam model, sedangkan 47,6% sisanya dipengaruhi oleh faktor lain di luar penelitian. Temuan ini diharapkan dapat menjadi referensi bagi investor maupun pembuat kebijakan dalam memahami pengaruh kondisi makroekonomi terhadap dinamika pasar modal Indonesia.

Kemiskinan masih menjadi permasalahan yang dihadapi oleh Provinsi Jawa Timur. Kondisi ini dipengaruhi oleh berbagai faktor, diantaranya Indeks Pembangunan Manusia (IPM) dan Tingkat Pengangguran Terbuka (TPT). Penelitian ini menggunakan pendekatan kuantitatif dengan data sekunder berbentuk deret waktu (time series) periode 2010–2024 yang diperoleh dari Badan Pusat Statistik (BPS) Provinsi Jawa Timur. Teknik analisis yang digunakan adalah regresi linier berganda dengan bantuan program EViews. Hasil penelitian menunjukkan bahwa secara parsial IPM memiliki pengaruh negatif dan signifikan terhadap kemiskinan di Provinsi Jawa Timur, sedangkan TPT berpengaruh positif namun tidak signifikan. Secara simultan, IPM dan TPT berpengaruh signifikan terhadap tingkat kemiskinan di Provinsi Jawa Timur. Temuan ini menunjukkan bahwa peningkatan kualitas pembangunan manusia melalui aspek pendidikan, kesehatan, dan standar hidup yang layak dapat menjadi salah satu strategi dalam menurunkan tingkat kemiskinan di Provinsi Jawa Timur.

Motor Vehicle Tax (PKB) is a key pillar of Regional Original Revenue (PAD) that supports development funding. However, seasonal fluctuations in payment realization create uncertainties in local budget planning. This study aims to address the limitations of the standard Random Forestalgorithm, which suffers from extreme prediction failures on time-series data due to its inability to capture temporal transitions between months. The proposed solution implements feature engineering using a Cyclical Encoding approach (Sine and Cosine transformations) and Lagged Variables. The dataset comprises historical records of motor vehicle tax potential and realization from January 2021 to November 2025. The baseline model evaluation without feature engineering yields highly inaccurate predictions with a Mean Absolute Percentage Error (MAPE) of 203.47% (accuracy of -103.47%). Conversely, after integrating Cyclical Encoding and Lagged Variables, the proposed model's performance improves drastically, achieving a MAPE of 14.40% (an accuracy rate of 85.60%), an MAE of 9,317 units, and an RMSE of 12,638 units. Feature Importance analysis confirms that the cyclically encoded month feature contributes the highest weight to the model's decisions with a score of 0.5031, followed by the potential feature at 0.1798. This study demonstrates that time-based feature engineering effectively optimizes Random Forestfor precise tax revenue forecasting.

This study aims to analyze the effect of the Bank Indonesia interest rate on the Indonesian rupiah exchange rate against the United States dollar during the 2021–2026 period. A quantitative approach was employed using monthly time-series data obtained from official sources. The analysis was conducted using multiple linear regression with the assistance of EViews software. The dependent variable in this study was the rupiah exchange rate against the US dollar, while the independent variables included the Bank Indonesia interest rate, inflation, money supply (M2), and the Federal Reserve interest rate (Fed Rate). Prior to regression estimation, all data were tested for stationarity using the Augmented Dickey-Fuller (ADF) test to ensure compliance with time-series analysis requirements. Furthermore, the model was evaluated through classical assumption tests, including normality, heteroscedasticity, multicollinearity, and autocorrelation tests, to verify the validity and reliability of the estimation results. The findings reveal that the Bank Indonesia interest rate has a significant effect on the rupiah exchange rate, with a probability value of 0.0223. This result indicates that changes in the domestic interest rate can influence movements in the rupiah exchange rate against the US dollar. In contrast, inflation, money supply (M2), and the Fed Rate were found to have no significant effect on the rupiah exchange rate during the study period. Moreover, the classical assumption tests confirmed that the regression model satisfied all required criteria, indicating that the estimated results are reliable and can serve as a reference for economic policy formulation.

This study aims to analyze changes in Indonesia’s economic cycle patterns in the post-pandemic period, as reflected in inflation, interest rates, and the industrial production index over the 2018–2025 period. The study adopts a quantitative approach employing a multiple linear regression model. The data utilized consist of secondary time-series data obtained from Bank Indonesia and the Central Statistics Agency (Badan Pusat Statistik). The empirical results indicate that, simultaneously, the explanatory variables exert a statistically significant effect on economic growth. Partially, interest rates and the industrial production index demonstrate a positive and statistically significant influence. In contrast, inflation does not exhibit a statistically significant effect at the 10% significance level. The coefficient of determination (R-Squared) of 0.9327 suggests that the model possesses substantial explanatory power in accounting for variations in Indonesia’s economic growth. The descriptive analysis indicates that Indonesia’s economy experienced a contraction of -2,07% in 2020 due to the COVID-19 pandemic, followed by a gradual recovery throughout the 2021–2025 period. These findings imply that post-pandemic shifts in Indonesia’s economic cycle patterns are more prominently driven by interest rate policy measures and real sector recovery relative to the role of inflation.  

Manual management of customer receivables in manufacturing companies often leads to inefficiencies, including slow analysis processes, difficulty in monitoring outstanding balances, and limited ability to track payment trends comprehensively. This study designed a Business Intelligence Dashboard using Microsoft Power BI to address these issues at PT. XX Industry, employing a Software Development Life Cycle (SDLC) approach consisting of four stages: Planning, Analysis, Design, and Implementation. During the design phase, a data warehouse was built using a star schema model in SQL Server Management Studio, followed by an Extract, Transform, Load (ETL) process to ensure data accuracy and consistency prior to integration into Power BI. Five Key Performance Indicators (KPIs) were established collaboratively with company management through a series of online discussions, comprising Total Invoice, Total Payment, Total Receivables, Payment Rate, and Average Payment Time, all implemented using DAX expressions. The resulting dashboard features monthly trend visualizations, customer-based receivables distribution, and interactive filter capabilities. System testing confirmed that all components functioned correctly, while ETL validation verified consistency between processed and source data. User Acceptance Testing (UAT) conducted by management yielded an average score of 4.7 out of 5, reflecting a very high level of user acceptance. Overall, this dashboard proved effective in improving receivables monitoring efficiency and supporting faster, data-driven decision-making.

The increasing demand for electrical energy in military education facilities necessitates an efficient, reliable, and sustainable energy solution. This research aims to design a Solar Power Plant (PLTS) system to meet the street lighting needs in the Wangi-Wangi Complex of the Indonesian Naval Academy (AAL). The research method used is quantitative descriptive with an engineering design approach thru the stages of site survey, collection of solar energy potential data in the Surabaya area, calculation of electricity energy needs, calculation of solar panel capacity, calculation of battery capacity, and design of battery connection configuration. The research results show that the energy requirement for street lighting is 1,920 Wh/day, sourced from 8 units of 20 Watt LED lamps with an operating time of 12 hours per day. Based on the average solar radiation potential in Surabaya of 5 kWh/m²/day, the designed system requires 3 units of 200 Wp monocrystalline solar panels with a total area of approximately 4.89 m². For energy storage, 4 units of Yuasa N200 12 V 200 Ah batteries are used, configured in a series-parallel arrangement, capable of providing an effective energy of around 3,600 Wh with an estimated operating time of 22.5 hours. The research results indicate that the proposed solar power plant design is feasible to implement as an environmentally friendly, efficient alternative energy source that supports the green defense concept in the AAL environment.

This research investigates the effect of the amount of MSMEs, the number of buildings, and GRDP on regional tax revenue in selected cities and regencies in South Sulawesi, driven by the inconsistency between the growth of economic potential and the realization of tax revenue, where increases in MSMEs, buildings, and GRDP are not always followed by higher tax receipts. The study aims to analyze the effect of these variables and identify the most significant factors contributing to regional fiscal capacity. A quantitative approach is employed using panel data that combine time series and cross-sectional data from 2015-2024, analyzed through panel data regression with model selection based on Chow, Hausman, and Lagrange Multiplier test. The results show that partially, MSMEs and the number of buildings do not have a significant effect on tax revenue, while GRDP has a positive significant impact; however, simultaneously, all variables significantly influence tax revenue, as indicated by a high Adjusted R-squared value. These findings suggest that economic growth, as proxied by GRDP, plays a more dominant role in increasing tax revenue compared to the mere increase in the number of MSMEs and buildings, implying that optimizing tax revenue requires not only expanding economic potential but also enhancing tax compliance, administrative efficiency, and the quality of economic growth.

This study aims to analyze the effect of renewable energy, energy consumption, and Gross Domestic Product (GDP) per capita on carbon dioxide (CO2) emissions in Indonesia for the period 1995-2024. This study uses secondary data over time (time series) with the Ordinary Least Square (OLS) multiple linear regression analysis method corrected using the Newey-West Heteroskedasticity and Autocorrelation Consistent (HAC) approach. The results show that renewable energy does not have a significant effect on CO2 emissions, which is caused by the still low share of renewable energy in the national energy mix which only reaches 10.95% in 2024. Energy consumption has a positive and significant effect on CO2 emissions, where every 1% increase in energy consumption increases CO2 emissions by 84.23%. Gross Domestic Product (GDP) per capita has a positive and significant effect on CO2 emissions. Every 1% increase in GDP per capita increases CO2 emissions by 35.03%, indicating that Indonesia remains on the EKC curve. Simultaneously, all three variables have a significant effect, with an adjusted R-squared value of 53.63%. This finding confirms that Indonesia's energy mix, still dominated by fossil fuels, is a major factor in high carbon emissions. Comprehensive energy efficiency policies, accelerated renewable energy transitions, and greener and more sustainable economic growth strategies are needed.

 Cryptocurrency price prediction, particularly for highly volatile assets like Solana (SOL), is a crucial challenge in time series data analysis in digital finance. This study aims to compare the performance of the XGBoost machine learning algorithm with the Temporal Fusion Transformer (TFT) deep learning model in predicting Solana's daily closing price. The dataset used consists of historical Solana price data and network fundamentals data in the form of Total Value Locked (TVL). The research process includes data preprocessing, dividing training and test data, model training, and evaluation using the Root Mean Squared Error (RMSE) metric. The results show that using the same-day price feature has the potential to cause target leakage, resulting in invalid prediction accuracy. In testing using pure historical data without data leakage, the XGBoost model performed better than TFT with an RMSE of 4.27, while TFT produced an RMSE of 18.59. Furthermore, the integration of network fundamentals data in the form of TVL did not improve prediction accuracy and even caused a decrease in performance for the XGBoost model with an RMSE of 7.10. The results of this study show that the use of historical price action features is more effective than fundamental network indicators for short-term daily Solana price predictions.

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.

This community service activity aims to enhance public understanding of the role of infrastructure in supporting quality of life, particularly through the utilization of the Sub PPKBD Tambakkemerakan Community Hall in Krian as a social and educational space. The method used is Participatory Action Research (PAR), which emphasizes active community involvement in every stage of the activity, from problem identification to program implementation. The activities were carried out through a series of events that combined elements of togetherness, religiosity, and education, such as communal iftar (breaking the fast), congregational prayers, and an offline webinar featuring student speakers.The results indicate an improvement in community understanding of the importance of utilizing social infrastructure not only as a gathering place but also as a medium for learning and empowerment. In addition, the activity strengthened social interaction, increased community solidarity, and encouraged active participation in utilizing available facilities more productively. Despite several challenges, such as time constraints and differences in participants’ levels of understanding, the activity overall had a positive impact in promoting the optimization of social infrastructure functions. Therefore, the continuation of similar programs is necessary to support sustainable improvements in community quality of life.

This community service activity aims to enhance public understanding of the role of infrastructure in supporting quality of life, particularly through the utilization of the Sub PPKBD Tambakkemerakan Community Hall in Krian as a social and educational space. The method used is Participatory Action Research (PAR), which emphasizes active community involvement in every stage of the activity, from problem identification to program implementation. The activities were carried out through a series of events that combined elements of togetherness, religiosity, and education, such as communal iftar (breaking the fast), congregational prayers, and an offline webinar featuring student speakers.The results indicate an improvement in community understanding of the importance of utilizing social infrastructure not only as a gathering place but also as a medium for learning and empowerment. In addition, the activity strengthened social interaction, increased community solidarity, and encouraged active participation in utilizing available facilities more productively. Despite several challenges, such as time constraints and differences in participants’ levels of understanding, the activity overall had a positive impact in promoting the optimization of social infrastructure functions. Therefore, the continuation of similar programs is necessary to support sustainable improvements in community quality of life.

This study aims to analyze the influence of domestic and global factors on Indonesia’s economic growth, with variables including inflation, world oil prices, and geopolitical risk. The research employs a quantitative approach using a regression model combined with the Engle–Granger Error Correction Model (ECM) to capture both short-run and long-run dynamics among variables. The data used are time series data from 1986 to 2024, sourced from the World Bank and the Geopolitical Risk Index. The results show that in the short run, inflation and world oil prices have a positive and significant effect on economic growth, while geopolitical risk has a negative and significant effect. In the long run, inflation remains positive and significant, whereas geopolitical risk continues to have a negative and significant impact on economic growth. The error correction term coefficient (ECT(-1)), which is negative and significant, indicates the presence of a relatively fast adjustment mechanism toward long-run equilibrium. These findings confirm that the ECM approach is effective in explaining the dynamic relationship between domestic and global factors and Indonesia’s economic growth.

 Education in the 21st century demands a paradigm shift from passive learning to collaborative and student-centered learning, in line with the Merdeka Curriculum’s emphasis on the value of mutual cooperation. However, challenges arise due to the younger generation’s heavy reliance on technology, which tends to foster individualism. This study aims to examine the implementation of cooperative learning strategies as a solution to foster social interaction and student engagement. Cooperative learning strategies constitute a conceptual framework for a series of learning activities within heterogeneous small groups to achieve shared goals. Through a literature review, it was found that the implementation of these strategies involves systematic steps ranging from group formation, problem-solving discussions, to group recognition. The results of the analysis indicate that cooperative strategies have significant advantages in enhancing students’ self-confidence, independence, responsibility, and academic achievement. Although they have limitations regarding time management and the need for specific skills among educators, these strategies are considered highly relevant for fostering students’ character to be inclusive, tolerant, and capable of collaborating amidst differences.