Publication Search

Salah satu jenis kuliner yang banyak digemari masyarakat adalah kue kering. Kastengel, sebagai salah satu jenis kue kering warisan Belanda. Kastengel adalah salah satu jenis kue kering yang populer di Indonesia. Penelitian ini bertujuan untuk mengetahui pengaruh penambahan keju dangke terhadap hasil jadi produk kastangel ditinjau dari uji organoleptic meliputi warna, aroma, tekstur dan rasa, serta daya terima masyarakat terhadap olahan produk kastangel dengan tambahan keju dangke. Penelitian ini menggunakan metode eksperimen dengan tiga perlakuan konsentrasi keju dangke, yaitu 50%, 100%, dan 150% dari jumlah keju yang digunakan dalam resep standar.Data diperoleh melalui uji organoleptik oleh panelis terhadap lima parameter: rasa, aroma, tekstur luar,tekstur dalam dan warna. Selain itu, dilakukan uji daya terima untuk mengetahui tingkat kesukaan terhadap produk. Data dianalisis menggunakan uji One Way ANOVA untuk mengetahui pengaruh perlakuan terhadap masing-masing parameter yang diuji. Hasil penelitian menunjukkan bahwa 1). Penambahan keju dangke memberikan pengaruh yang signifikan (p < 0,05) terhadap parameter rasa dan aroma, namun tidak signifikan terhadap tekstur dan warna. 2). Perlakuan dengan penambahan keju dangke 100% merupakan yang paling disukai berdasarkan hasil uji daya terima masyarakat, dengan skor rata-rata tertinggi pada semua parameter.

This study examines the effects of poverty, the Human Development Index, and economic growth on the Labor Development Index across Indonesian provinces during 2020-2024. The issue is important because labor development reflects not only employment opportunities, but also labor quality, productivity, welfare, protection, and regional institutional capacity. The study uses balanced panel data consisting of 34 provinces over five years, producing 170 observations. The dependent variable is the Labor Development Index, while the independent variables are the poverty rate, the Human Development Index, and economic growth measured by the growth rate of gross regional domestic product. Data are analyzed using panel regression in Stata through pooled ordinary least squares, fixed effect, and random effect models. The individual effect test, Breusch-Pagan Lagrange multiplier test, and Hausman test indicate that the Fixed Effect model is the most appropriate specification. The final estimation uses Cluster-Robust standard errors at the provincial level. The results show that the Human Development Index and economic growth have positive and statistically significant effects on the Labor Development Index, whereas poverty has a positive but statistically insignificant coefficient. These findings imply that improving labor development requires more than reducing poverty. It also needs stronger human development and inclusive regional economic growth that can create productive and decent work.

This research investigates the effect of fundamental factors, namely the current ratio, debt to equity ratio, and return on equity on stock returns of mining firms listed on the Indonesia Stock Exchange (IDX) during 2021–2023. The research highlights the utility of understanding a firm’s financial performance in guiding investment selection within the capital market. Although the mining industry contributes significantly to Indonesia’s economy, stock movements in this sector are often subject to uncertainty due to market fluctuations and commodity price volatility. This research utilizes secondary data from annual financial statements and stock price records of 51 IDX-listed mining companies over the study period. Panel data regression, combined with descriptive and quantitative statistical techniques, was employed using E-Views 12 software. The findings reveal that stock returns are significantly influenced by the current ratio, debt to equity ratio, and return on equity. These results provide useful insights for investors, financial analysts, and corporate management by emphasizing the function of fundamental indicators in assessing stock performance, particularly within the mining sector.

This research aims to: (1) identify the variations of joyful learning in digital-based instruction using Interactive Flat Panel (IFP) tvs at SD Islam Nurul Ulum Kandangan, Kediri; and (2) determine the implications of joyful learning within digital-based instruction via IFP tvs at SD Islam Nurul Ulum Kandangan, Kediri. The data for this study were derived from visual records during lessons using IFP tvs and the students of SD Islam Nurul Ulum Kandangan. This study employed a qualitative method. Ata were gathered through observation and documentation. The analysis used a descriptive qualitative technique, presenting the subjects and research findings in a narrative form. The results from several classes utilizing IFP tvs based on joyful learning revealed various engaging learning activities. These include: Student digital literature, Digital learning matches, English language learning, Fine arts stencil printing, Honesty education based on Pancasila values. These activities integrate fun, game-based learning with IFP TV technology. The digitalization of learning through IFP tvs fosters the development of students' critical thinking, agility, accuracy, and precision in their work. Additionally, students become more enthusiastic and do not experience boredom during the learning process.

Income inequality and poverty continue to be major challenges in Indonesia's industrial areas, especially in DKI Jakarta, West Java, and Banten, although minimum wage policies continue to be developed as a form of protection for low-income workers. These policies theoretically serve as an instrument for income redistribution and improvement of labor welfare, but their effectiveness in reducing inequality and reducing poverty rates is still questionable, especially in areas with highly industrialized economic structures. This study aims to analyze the influence of income inequality, poverty rate, economic growth, and unemployment rate on the dynamics of the provincial minimum wage in the 2016–2023 period. The study used secondary data obtained from the Central Statistics Agency and analyzed using panel data regression to obtain a comprehensive empirical picture of the factors determining the minimum wage. The test results show that the Fixed Effect Model is the most suitable model for capturing variations between provinces and between times. Key findings reveal that poverty levels and economic growth have a significant influence on changes in the minimum wage, while income inequality and unemployment rates have no significant influence. The conclusion of the study emphasizes that the minimum wage policy is not effective enough in reducing income inequality and reducing poverty without the expansion of the formal sector, improving the quality of the workforce, and distributing economic growth more evenly between industrial areas.

This study examines the influence of income inequality and poverty on the prevalence of stunting in ten provinces across Sumatra Island during the 2016–2024 period. Using a panel dataset of 90 observations and applying a Fixed Effect Model, the results indicate that both income inequality—measured by the Gini Ratio—and poverty have a positive and significant effect on stunting. The Gini Ratio shows a coefficient of 1.46 (p = 0.0002), while poverty records a coefficient of 6.28 (p = 0.0140), jointly explaining 52% of the variation in stunting prevalence. Spatial analysis further supports these findings, with Moran’s I values exceeding 0.40, suggesting strong spatial autocorrelation and clustering of high-stunting regions. High-risk clusters—Aceh, Jambi, and Bengkulu—are characterized by Gini Ratios above 0.33 and poverty levels exceeding 12%, reinforcing the existence of an intergenerational poverty–stunting trap, particularly influenced by urban–rural disparities (rural 53.3% vs urban 34.9%). The study highlights that specific nutrition interventions such as supplementary feeding, micronutrient programs, and breastfeeding promotion are insufficient without accompanying structural reforms addressing economic inequality. Therefore, multisectoral convergence strategies are required, including expanded conditional cash transfers, progressive local taxation reforms, nutrition-focused social assistance, and universal basic infrastructure to accelerate stunting reduction toward the 14.2% target by 2029.

This study focuses on analyzing the welfare index in the ASEAN region (covering six major countries) by comparing two perspectives: objective welfare (Human Development Index/HDI) and subjective welfare (World Happiness Index). Using a balanced panel dataset from 2015–2023, the research applies different econometric approaches for each model, namely the Random Effect Model (REM) for HDI analysis and the Common Effect Model (CEM) for happiness analysis. Empirical findings indicate a striking welfare paradox across the six sample countries. In the objective dimension (HDI), economic stability (GDP) and governance free from corruption (CPI) are proven to be the main positive and significant drivers, while government expenditure (GovExp) shows no meaningful impact, suggesting budget inefficiency. Conversely, in the subjective welfare model, the Easterlin Paradox emerges, as GDP and the corruption index have no significant effect on the happiness index. The happiness levels in these six countries tend to be more influenced by government expenditure. This study concludes that strong economic fundamentals and clean governance free from corruption are essential to building a high quality of human life, whereas citizens’ life satisfaction is more determined by the direct presence of the state through public spending.

The purpose of this study is to investigate the factors that lead to poverty in different Indonesian regions between 2018 up to 2024. The Gross Regional Domestic Product per capita, the Human Development Index, and the Open Unemployment Rate are the independent factors used in this study, whereas poverty levels are the dependent variable. The Central Statistics Agency provided secondary data that was used in a quantitative manner. Using EViews 12 software, panel regression techniques were used to process the data. The study's conclusions show that, at a significance level of less than 0.05, economic and human development factors simultaneously significantly affect poverty rates across Indonesian regions. The coefficient of determination indicates that the variables in the model can account for the majority of the variations in poverty levels. These findings demonstrate how important a region's economic status and level of human development are to efforts to reduce poverty. It is anticipated that this research will help the government develop more effective and long-lasting methods for reducing poverty.

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

This study examines the impact of Pentagon Fraud factors on FSF, with WBS as a moderation variable, focusing on Indonesian State-Owned Enterprises (SOEs) from 2021 to 2024. The Pentagon’s Fraud Theory encompasses five key elements: pressure, opportunity, rationalization, competence, and arrogance, each of which is represented by financial stability, ineffective monitoring, the quality of auditors, the experience of directors, and CEO pictures. This study aims to determine how these factors affect financial reporting that contains fraud, and whether WBS can strengthen or weaken the relationship between the two. Using a quantitative approach with secondary data from the annual reports of 104 SOEs, thisi study applied panel data regression method. FSF was measured using the Beneish M-Score, while the effect of moderation was tested through moderated regression analysis. The results of this study are expected to provide deeper insights into the dynamics of fraud in the public sector and highlight the importance of WBS as a governance tool in reducing the risk of fraud. The study contributes to the previous literature by integrating a comprehensive fraud framework and testing it with moderation mechanisms, while also focusing on specific institutional contexts (SOEs), which have not been explicity explored in previous studies.

Poverty is still a major problem in regional economic development in Jambi Province, although economic growth has been relatively stable in recent years. This condition shows that economic growth has not been fully followed by an equitable distribution of development results. This study aims to analyze the influence of economic growth and income inequality on the poverty rate in Jambi Province. The research uses a quantitative approach with district/city panel data for the 2015–2024 period sourced from the Central Statistics Agency. The analysis was carried out using the panel data regression method to test the relationship between economic growth variables, income inequality, and poverty levels. The results of the study show that economic growth measured through the growth rate of Gross Regional Domestic Product (GDP) has a negative effect on the poverty rate, meaning that increasing economic growth can reduce the number of poor people. On the other hand, income inequality measured by the Gini Ratio has a positive effect on poverty levels, which means that the higher the income inequality, the greater the poverty rate. These findings indicate that poverty reduction strategies not only require sustainable economic growth, but must also be accompanied by income equity policies so that the benefits of development can be felt more inclusively by all levels of society.

The objective of this study is to directly analyze and illustrate the compositioneof the auditecommittee, which consists of financial knowledge, independence and the quantity of members on the committee, concerning the financial statement quality of energy sector industries listed on the IDX in 2023-2024.High-quality financial statements are a crucial component reflecting the outcome of the accounting process and are vital for stakeholders in decision-making. Despite regulatory requirements for audit committees, corporate financial statements in Indonesia often contain earnings management or accounting irregularities, indicating that the audit committee's very existence is insufficient to guarantee financial statements' quality. A numerical approach with a causal-comparative approach is utilized in this investigation. The secondary quantitative data are obtained from companies’ yearly financial statements, annual reports, and corporate governance disclosures published on the official IDX website. The data are examined using EViews software for panel data regression, going through many steps, including descriptive statistics, classical assumption testing, panel data model selection, and regression analysis for hypothesis testing. The audit committee's size, objectivity, and financial acumen make up the study's independent variables. Meanwhile, financial statement quality as the dependent variable is measured through earnings quality proxy using the discretionary accruals calculation approach (Jones model or Modified Jones model). Specifically, this research seeks to deliver theoretical and practical benefits for regulators in formulating corporate governance policies, give companies a comprehension of the importance of an effective audit committee, and help investors make informed investment choices.

This study aims to evaluate the effect of Return on Assets (ROA), Debt to Equity Ratio (DER), and Current Ratio on Firm Value, with Sales Growth as a moderator variable, in textile and garment companies listed on the Indonesia Stock Exchange (IDX) for the period 2020-2024. The population of this study consisted of 21 companies, and purposive sampling was used to select 19 companies according to the established criteria. The analytical method used was panel data regression analysis with the help of Eviews version 12.0. The results of this study indicate that Return on Assets (ROA) has a negative and significant effect on firm value, Debt to Equity Ratio (DER) and Firm Size have a positive and significant effect on firm value, Current Ratio (CR) does not have a significant effect on firm value, and Sales Growth cannot moderate the effect of Return on Assets on firm value. The condition of ROA that has a negative effect on firm value can describe a situation where the greater the company's profit in the form of assets, the lower the company's value will be, or conversely, the higher the company's value, the lower the company's assets will be.

This study investigates the impact of board characteristics on the financial performance of non-financial companies listed in the JII70 index in Indonesia. Motivated by the ongoing debate on the effectiveness of corporate governance mechanisms in enhancing firm outcomes, particularly within Sharia-compliant markets, this study focuses on three key board attributes: board size, board independence, and female representation on the board. Using a quantitative causal approach and panel data from 25 companies over the period 2020–2023, the study employs a fixed effect model to evaluate the relationship between board structure and financial performance measured by Return on Assets (ROA). The results show that board size has a positive and significant effect on firm performance, indicating that larger boards may enhance oversight capacity and provide broader resources beneficial to strategic decision-making. Conversely, board independence and board female representation do not exhibit significant effects on financial performance, suggesting that their roles may be more symbolic or constrained by institutional and contextual factors in the sampled companies. These findings highlight the importance of understanding corporate governance not merely in structural terms, but in relation to functional effectiveness and contextual maturity. The study offers implications for regulators, companies, and governance reform initiatives, particularly regarding strengthening substantive roles of independent and female commissioners in improving firm performance within Sharia-compliant markets.

Penelitian ini bertujuan untuk mengembangkan sistem telemetri guna memantau kinerja panel surya pada beberapa lokasi dengan menggunakan komunikasi jarak jauh dan platform Internet of Things (IoT). Metode pemantauan konvensional memiliki keterbatasan dalam menyediakan data secara real-time pada area yang luas, sehingga evaluasi kinerja jarak jauh menjadi kurang efisien. Untuk mengatasi permasalahan tersebut, dirancang sebuah sistem pemantauan menggunakan mikrokontroler ESP32, sensor INA219 untuk mengukur tegangan dan arus, modul GPS Neo-M8 untuk identifikasi lokasi, modul Real-Time Clock (RTC) DS3231 untuk pencatatan waktu, serta modul LoRa RA-02 sebagai media komunikasi nirkabel. Setiap node pengirim dilengkapi dengan modul MicroSD untuk menyimpan data pengukuran secara lokal. Data hasil pengukuran dikirimkan melalui LoRa ke unit penerima dan ditampilkan secara real-time pada platform Thinger.io. Hasil kalibrasi menunjukkan bahwa sensor INA219 memiliki rata-rata galat pengukuran arus sebesar 0,71% dan galat pengukuran tegangan sebesar 0,1%. Pengujian GPS menunjukkan koordinat lokasi yang stabil dengan tingkat akurasi sekitar ±3 hingga ±8 meter. Seluruh data pengukuran berhasil dikirim, disimpan, dan ditampilkan tanpa kehilangan data yang signifikan. Hasil penelitian menunjukkan bahwa sistem yang dikembangkan mampu menyediakan pemantauan parameter panel surya secara jarak jauh yang andal dan efisien dalam kondisi lapangan.

The development of renewable energy, particularly Solar Power Plants (PV), requires a reliable, real-time, and easily accessible electrical energy monitoring system to ensure optimal system performance. This study aims to design and implement an Internet of Things (IoT)-based electrical energy monitoring system for PV using the NodeMCU ESP32 microcontroller, the PZEM-004T sensor for measuring electrical parameters, and the Node-RED platform as the data visualization interface. The developed system is designed to monitor voltage, current, power, energy, frequency, and power loss in real time, and then display the data in the form of numerical values, graphs, and indicators on a dashboard accessible through a local network. The research method includes hardware design, software development (sensor reading, data processing, and communication), integration with Node-RED, and system testing on a small-scale PV installation. The test results show that the system is capable of monitoring electrical parameters in a stable and responsive manner. Variations in sunlight intensity were found to affect the current and power produced by the solar panels, whereas the inverter output voltage tended to remain within normal operating ranges. The Node-RED dashboard display was considered informative and helpful for users in monitoring and analyzing PV performance. Based on these results, it can be concluded that the IoT-based electrical energy monitoring system designed in this study functions well and is feasible for application in residential or educational-scale PV installations. The system still has the potential for further development through cloud service integration, the addition of environmental sensors, and enhancements to data analysis features and user interface design.

Tax avoidance can be detrimental to the country because it reduces the state's revenue. This study aims to analyze the effect of sales growth, capital intensity, and earnings management on tax avoidance with company size as a moderating variable. The population of this study comprises 221 manufacturing companies listed on the IDX in 2020-2024, with a sample of 64 companies selected via purposive sampling based on specific criteria, yielding a total of 320 observations analyzed using panel data regression (E-Views 12). The results show that sales growth directly affects tax avoidance, and company size moderates the relationship between sales growth and tax avoidance. However, capital intensity and earnings management do not have a significant effect, and company size cannot moderate the relationship between capital intensity and earnings management with tax avoidance. These findings emphasize that high sales growth can encourage companies to comply with tax regulations, thereby reducing tax avoidance, and that this effect can be suppressed by large company size due to greater reputational pressure and scrutiny. This study expands on previous research by making company size a moderating variable in the relationship between sales growth, capital intensity, and earnings management and tax avoidance.

This research examines the analysis of Return on Equity (ROE), Quick Ratio (QR), and Debt to Equity Ratio (DER) on corporate valuation, as assessed by Price-to-Book Value (PBV), within technology firms listed on the Indonesia Stock Exchange (IDX) during the period from 2022 to 2024. The primary aim of this investigation is to ascertain the effects of profitability, liquidity, and leverage both in isolation and in conjunction on market valuation in an industry characterized by innovation and intangible assets. This research employs panel data regression analysis utilizing EViews 13 as the quantitative methodology. The findings reveal that ROE significantly enhances PBV, indicating that investors place considerable importance on firms that are capable of generating substantial returns on equity for shareholders. Conversely, QR and DER appear to have no discernible impact on PBV. This observation can be attributed to the unique nature of technology companies, wherein investors prioritize factors other than short-term liquidity and leverage. Nonetheless, when assessed collectively, the three metrics illuminate the variations in corporate value. These results suggest that while financial stability indices exert a positive yet comparatively subdued effect on investor sentiment within the technology sector, profitability remains a paramount determinant. The study elucidates the financial determinants that influence corporate value in innovation-driven industries, providing valuable insights for managers and investors alike.

The increasing global energy demand drives the search for efficient and sustainable renewable energy solutions. Solar panels have become one of the most widely used technologies; however, their efficiency remains limited when installed in a static position. This research aims to analyze the performance of a single-axis auto tracking system on a 10WP solar panel integrated with the Internet of Things (IoT) for real-time monitoring, specifically in powering a portable powerbank. The research method employed was a quantitative experimental design with three testing scenarios: powerbank charging using an auto-tracking solar panel, a static solar panel, and conventional household electricity as a comparison. Charging data were collected via an IoT system integrated with the Blynk application in real-time. The results indicate that the auto-tracking system increased charging efficiency by around 10%, compared to only 6% with a static panel in one hour. This performance is nearly equal to household electricity charging, which reached approximately 10–11%. The study concludes that the single-axis IoT-based auto-tracking system significantly enhances the performance of small-scale solar panels and holds strong potential for portable energy solutions in remote areas.

This study seeks to unravel the complexities of applying the doctrine of culpa lata or gross negligence as the basis for criminal liability for state officials in corruption cases, an area that highlights gaps in the criminal law framework which tends to focus on intent. With the rise in corruption cases stemming from misguided strategic decisions that are difficult to prove as intentional, the urgency to explore alternative criminal liability grounds becomes crucial for establishing strong accountability in governance. This research specifically analyzes the application of culpa lata through an in-depth study of Decision Number 68/Pid.Sus-TPK/2025/PN Jkt Pst. Adopting a normative-empirical qualitative case study method, the analysis focuses on the court decision as a single unit of analysis, supported by primary data from the decision document and secondary data from legal literature. Through documentary evidence tracking, it was found that the panel of judges successfully applied the doctrine of culpa lata by identifying elements of negligence such as the disregard for risks that should have been known, subjective asset valuation, and strategic decision-making without adequate study, which causally led to state losses. These findings substantively show that gross negligence can effectively serve as a basis for criminal liability, indicating a shift in the pattern of corruption law enforcement that goes beyond proving mere intent. In conclusion, this study affirms the effectiveness of culpa lata in expanding the scope of criminal liability for state officials, offering theoretical contributions to the understanding of fault elements and practical implications in strengthening public accountability and the potential for corruption prevention through enhanced standards of caution.