Publication Search

This study aims to analyze the effect of capital structure, asset growth, and firm size on firm value in plastic and packaging sub-sector companies listed on the Indonesia Stock Exchange during the 2020–2024 period. Firm value is proxied by Price to Book Value (PBV), capital structure is measured using the Debt to Equity Ratio (DER), asset growth is measured by the asset growth ratio, and firm size is measured using the natural logarithm of total assets. This research employed an explanatory quantitative approach using secondary data obtained from financial statements, annual reports, and stock price data. The sample consisted of 11 companies observed over five years, resulting in 55 panel data observations. Data were analyzed using panel data regression through the Common Effect Model, Fixed Effect Model, and Random Effect Model, with model selection based on the Chow, Hausman, and Lagrange Multiplier tests. The results indicate that capital structure, asset growth, and firm size have no significant effect on firm value, either partially or simultaneously. These findings suggest that firm value in the plastic and packaging sub-sector is not sufficiently explained by financing structure, asset expansion, or company size. Investors are more likely to consider other factors, such as profitability, operational efficiency, cash flow, sales growth, raw material risk, and sustainability prospects. Therefore, companies should improve financial performance, asset efficiency, cost control, and sustainable innovation to enhance firm value.

The phenomenon of university students simultaneously undertaking academic studies and employment has become increasingly common, creating unique challenges in balancing work responsibilities and academic obligations. This study aims to examine the effects of job demands and time management on academic performance, with academic self-efficacy serving as a mediating variable among undergraduate and postgraduate students who work while pursuing higher education. The research employed a quantitative approach using a causal research design. Data were collected through an online questionnaire administered to 50 working students selected through purposive sampling. The study measured four variables, namely job demands, time management, academic self-efficacy, and academic performance. Data were analyzed using multiple regression analysis, Baron and Kenny’s causal steps approach, and the Sobel test to examine mediation effects. The findings reveal that time management has a positive and significant effect on both academic self-efficacy and academic performance. Academic self-efficacy also demonstrates a positive and significant influence on academic performance and emerges as the strongest predictor in the model. In contrast, job demands do not significantly affect either academic self-efficacy or academic performance. Furthermore, academic self-efficacy does not mediate the relationship between job demands and academic performance. However, academic self-efficacy fully mediates the relationship between time management and academic performance, indicating that effective time management enhances students’ confidence in their academic abilities, which subsequently improves their academic outcomes. These findings highlight the importance of strengthening time management skills and academic self-efficacy to support the academic success of working students in higher education institutions.

This study aims to analyze the influence of accountability and transparency in government financial reports on the achievement of Sustainable Development Goals (SDGs) 2 in Indonesia. SDGs 2 focuses on efforts to eradicate hunger, increase food security, and improve public nutrition. The study used a quantitative approach with panel data in 34 provinces in Indonesia during the 2021–2022 period. The sampling technique was purposive sampling, resulting in a total of 68 research observations. The research data were sourced from the National Development Planning Agency (BAPPENAS), the Supreme Audit Agency (BPK), and official local government websites related to the publication of regional financial reports. The accountability variable was measured using the BPK audit opinion on the LKPD, while transparency was measured based on the openness of the publication of seven components of the regional government financial report. Data analysis was performed using the Common Effect Model (CEM) with a robust standard error approach. The results showed that accountability has a positive and significant effect on the achievement of SDGs 2 with a coefficient value of 3.919 and a significance level of 0.000. Transparency also has a positive and significant effect on the achievement of SDGs 2 with a coefficient value of 2.415 and a significance level of 0.000. In addition, accountability and transparency simultaneously proved to have a significant effect on the achievement of SDGs 2. This finding indicates that the implementation of good governance through increased accountability and transparency of financial reports can support the effectiveness of food security and sustainable development programs in Indonesia.

This study aims to (1) describe the development of Dakpin media for learning the concepts of Greatest Common Factor (GCF) and Least Common Multiple (LCM) for fourth-grade students at MI Tarbiyatul Banin, and (2) determine the validity of the developed media. The research employed the ADDIE development model, consisting of Analyze, Design, Develop, Implement, and Evaluate stages. The study was conducted at MI Tarbiyatul Banin during the first semester, involving 11 fourth-grade students as research subjects. Data were collected through material and media expert validation questionnaires, student response questionnaires, tests, observations, and interviews. The results indicate that the Dakpin media was successfully developed through all stages of the ADDIE model and was considered highly valid for use in mathematics learning. Validation results showed an average score of 93.75% from material experts and 94.79% from media experts, both categorized as “very valid.” In addition, student responses toward the use of Dakpin media reached 91%, indicating a highly positive reception. These findings suggest that Dakpin media is appropriate and effective as a learning tool to support students’ understanding of GCF and LCM concepts in elementary mathematics learning.

Social Studies (IPS) learning in elementary schools plays an essential role in developing students’ critical thinking skills and civic attitudes as part of 21st-century competencies. However, in practice, IPS learning is still commonly teacher-centered and lacks contextual learning experiences, resulting in low student engagement and limited development of higher-order thinking skills. This study aims to analyze the implementation of Project-Based Learning (PjBL) in IPS learning to enhance critical thinking skills and civic attitudes of grade VI students at SDN Prekbun. The research employed a qualitative approach with a case study design. Data were collected through classroom observations, semi-structured interviews with teachers, students, school principals, and parents, as well as documentation of learning activities. Data analysis was conducted using the interactive model of Miles and Huberman, including data reduction, data display, and conclusion drawing and verification. The findings indicate that the implementation of PjBL effectively enhances students’ active participation, critical thinking skills in analyzing social issues, and civic attitudes such as responsibility, cooperation, and social awareness. Learning becomes more meaningful as students are directly involved in contextual, collaborative, and problem-based project activities. Despite these positive outcomes, several challenges were identified, including limited learning facilities, time management constraints, and variations in students’ confidence levels. This study implies that PjBL is an effective instructional model for strengthening character education and developing 21st-century skills in elementary social studies learning

This study is motivated by the importance of economic growth as a key indicator of regional development performance and public welfare improvement. Economic growth reflects the ability of local governments to manage resources and implement effective fiscal policies. This research aims to analyze the development and the influence of Local Own-Source Revenue, General Allocation Fund, and Special Allocation Fund on the economic growth of regencies/cities in Jambi Province during the period 2020–2024. The study employs a quantitative approach combined with descriptive analysis. Secondary data are obtained from official publications of relevant institutions and analyzed using panel data regression with the Common Effect Model approach. The results indicate that Local Own-Source Revenue and the General Allocation Fund have a significant effect on economic growth, showing that the increase in regional revenue and fiscal transfers contributes to economic performance. Meanwhile, the Special Allocation Fund does not have a significant effect, indicating that its allocation may not be optimally utilized in stimulating regional economic activities. These findings imply that strengthening regional fiscal capacity and improving the effectiveness of fund allocation are essential to promote sustainable economic growth. The study also highlights the need for better policy coordination and efficient financial management at the regional level to reduce disparities and enhance development outcomes.  

Differential equations involve derivatives of unknown functions and are widely used in mathematical modeling of various real-world problems. They can be classified based on linearity, homogeneity, coefficients, number of independent variables, degree, and order, thus requiring appropriate solution methods. One commonly used approach is the reduction of order method, which simplifies equations by reducing their order step by step. However, this method generally requires the solution of the corresponding homogeneous equation as an initial step. This study aims to solve nonhomogeneous linear ordinary differential equations of order with constant coefficients using the reduction of order method without determining the homogeneous solution. This research is a theoretical study based on relevant references concerning solution methods and types of differential equations. The procedure consists of two main stages: determining the fundamental symmetric polynomial variables based on the coefficients and constructing a sequence of solutions through first-order linear differential equations obtained from the reduction process. The results show that this method systematically produces the general solution of linear differential equations of order , making it an effective and efficient alternative approach

 This study was conducted to evaluate the influence of Total Asset Turnover (TATO), Debt to Equity Ratio (DER), and Net Profit Margin (NPM) in predicting stock return fluctuations. The study focuses on retail sector issuers listed on the Indonesian Stock Exchange (IDX) between 2021 and 2024. Through the application of panel data regression analysis, the study determined that the Common Effects Model (CEM) is the most appropriate estimation method. This decision was made based on a series of tests including the Chow Test and the Lagrange Multiplier. Although classical assumption testing showed symptoms of heteroscedasticity, this problem was addressed using the EGLS (cross-sector weighting) Panel method to ensure the validity of the estimates. Based on partial testing, it is found that TATO and NPM variables have a positive and significant contribution to stock returns, while DER is found to have no significant effect. Collectively, all independent variables had a significant effect, with the Adjusted R-Square value reaching 27.80%. This indicates that for investors in the retail sector, profitability and operational efficiency are important indicators in making investment decisions.

Software Defect Prediction (SDP) aims to identify defective modules early in the software development lifecycle to improve software quality and reduce maintenance costs. However, SDP datasets commonly suffer from high dimensionality, feature redundancy, and class imbalance, which can degrade model performance and stability. This study proposes a hybrid feature selection framework to address these challenges and enhance prediction performance. The proposed approach integrates Combined Correlation and Mutual Information (CONMI), which combines the Pearson Correlation Coefficient (PCC) and Mutual Information (MI) to capture both linear and nonlinear feature relevance. The selected features are further refined through Top-K selection, correlation-based filtering to reduce multicollinearity, and Backward Elimination (BE) to obtain an optimal feature subset. To address class imbalance, SMOTE-Tomek is applied by combining over-sampling and data cleaning techniques. Experiments are conducted on twelve NASA MDP datasets using Logistic Regression (LR) and Naïve Bayes (NB) classifiers. The results show that the proposed framework consistently achieves the best performance, with Logistic Regression combined with SMOTE-Tomek obtaining the highest average AUC of 0.7923 ± 0.0714, while NB achieves 0.7554 ± 0.0580. Statistical analysis using a paired t-test indicates that the proposed method significantly outperforms MI+SMOTE-Tomek and BE+SMOTE-Tomek for Logistic Regression, whereas no significant differences are observed for NB. In addition to improving overall classification performance (AUC), the proposed approach also enhances minority class detection, as reflected in improved Recall and F1-score. Overall, the proposed hybrid framework provides an effective and reliable solution for software defect prediction, particularly for high-dimensional and imbalanced datasets.

Multicollinearity is one of the common issues in multiple linear regression that can lead to instability in the estimation of regression coefficients. This study aims to examine the impact of multicollinearity on regression models and to evaluate the use of Ridge Regression as an alternative estimation method. The study employs simulated data consisting of 1,000 observations, including one dependent variable and four independent variables designed to exhibit high correlation. The analysis begins with model estimation using the Ordinary Least Squares (OLS) method, followed by multicollinearity testing using the Variance Inflation Factor (VIF). The OLS results indicate that most independent variables significantly influence the dependent variable, with a coefficient of determination (R²) of 0.9863. However, the high VIF values reveal the presence of strong multicollinearity in the model. To address this issue, Ridge Regression is applied, with the optimal penalty parameter determined through cross-validation, yielding a lambda value of 4.201589. The results show that the regression coefficients in the Ridge model undergo shrinkage, resulting in greater stability compared to the OLS estimates. Model evaluation indicates that the Mean Squared Error (MSE) for the OLS model is 24.77, whereas the Ridge model produces an MSE of 29.72. Although the Ridge model exhibits a slightly higher MSE, it effectively mitigates the impact of multicollinearity and provides more stable parameter estimates.

The rapid growth of TikTok Shop as a social commerce platform has led to the emergence of 'toxic content' produced by influencers as a marketing tactic that effectively influences Generation Z's shopping behavior. This study investigates the role of TikTok Shop influencers and their 'toxic content' in stimulating impulse buying behavior among Generation Z consumers in the fast fashion sector, focusing on the psychological mechanisms involved, the most frequently purchased product categories, and the broader impacts of these consumption patterns. A qualitative research approach with a phenomenological design was used, and data were gathered through in-depth interviews with eight informants: six Generation Z consumers aged 19–24 years and two active TikTok Shop fashion influencers, selected via purposive sampling. The data were analyzed using the interactive model of Miles, Huberman, and Saldaña (2014). Findings show that 'toxic content' acts as a stimulus within the Stimulus-Organism-Response (S-O-R) theoretical framework, triggering impulse buying within minutes by evoking positive emotions and urgency. The Fear of Missing Out (FOMO) mechanism, driven by emotional triggers like time scarcity, social proof, and exclusivity, was the most dominant psychological factor. The most commonly purchased items were tops, coordinated sets, aesthetic bottoms, locally-made footwear, and accessories. The effects of 'toxic content' are financial, psychological, social, and environmental. This study advances influencer marketing literature and provides insights into Generation Z’s consumer behavior in the social commerce age.

Academic anxiety is one of the problems commonly experienced by students due to the pressure of assignments, demands for achievement, and concerns about learning outcomes. This article aims to examine the effectiveness of mindfulness-based counseling in reducing students' academic anxiety and to explain the strategic role of guidance counselors in its application in schools. This study uses a literature review method with a literature review approach. Data were obtained from various national journals relevant to the topics of academic anxiety and mindfulness, then analyzed descriptively and qualitatively through a process of collecting, classifying, and synthesizing the results of previous studies. The results of the study show that mindfulness-based counseling is effective in helping students reduce their levels of academic anxiety through conscious breathing exercises, focusing attention, and accepting learning experiences without negative judgment. In addition, guidance counselors play an important role as facilitators, program developers, role models for mindfulness practices, mental health advocates, and liaisons between schools and parents in supporting the success of interventions. By applying the right methods, mindfulness-based counseling can be an effective alternative service in helping students overcome academic anxiety when implemented systematically and continuously  by guidance counselors in schools.

This community service activity aimed to strengthen business planning capacity among Tenaga Kerja Mandiri Lanjutan (TKML) in Kubu Raya Regency, West Kalimantan, where participants commonly face limitations in structured business planning that affect business sustainability. The activity applied a participatory mentoring approach through training, field visits, group discussions, and direct assistance tailored to the characteristics of each business. The program involved nine TKML groups designated by the Ministry of Manpower of the Republic of Indonesia. The results indicate that participants gained a clearer understanding of key business components, value creation processes, and internal strengths and weaknesses. The use of the Business Model Canvas supported participants in organizing business ideas, enhancing strategic thinking, and developing more structured and sustainable business plans. In addition, this activity reinforced the role of higher education institutions in supporting government initiatives on employment expansion through community service. Overall, the findings demonstrate that mentoring-based business planning is effective in improving TKML capacity and has the potential to be replicated in other regions to support independent workforce empowerment and job creation.

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 investigates the factors influencing export performance in five ASEAN countries Indonesia, Malaysia, the Philippines, Singapore, and Thailand during the 2014-2023 period. The topic is highly relevant given the vital role of exports in sustaining monetary stability and promoting long-term economic growth. The novelty of this research lies in its integrated approach, which simultaneously examines key export-related macroeconomic variables, namely foreign direct investment and inflation, while incorporating foreign exchange reserves as a moderating variable an approach that remains limited in existing ASEAN-focused studies. This analysis uses secondary data obtained from the World Bank and processed using panel data regression methods, including the Common Effect Model, Fixed Effect Model, and Random Effect Model, strengthened by a Moderated Regression Analysis (MRA) approach. The results show that foreign direct investment and inflation significantly influence foreign exchange reserves. Furthermore, foreign exchange reserves have been shown to play a strategic role in strengthening the economic resilience of ASEAN countries and can be used as a reference in formulating monetary and international trade policies.

This study investigates the factors influencing export performance in five ASEAN countries Indonesia, Malaysia, the Philippines, Singapore, and Thailand during the 2014-2023 period. The topic is highly relevant given the vital role of exports in sustaining monetary stability and promoting long-term economic growth. The novelty of this research lies in its integrated approach, which simultaneously examines key export-related macroeconomic variables, namely foreign direct investment and inflation, while incorporating foreign exchange reserves as a moderating variable an approach that remains limited in existing ASEAN-focused studies. This analysis uses secondary data obtained from the World Bank and processed using panel data regression methods, including the Common Effect Model, Fixed Effect Model, and Random Effect Model, strengthened by a Moderated Regression Analysis (MRA) approach. The results show that foreign direct investment and inflation significantly influence foreign exchange reserves. Furthermore, foreign exchange reserves have been shown to play a strategic role in strengthening the economic resilience of ASEAN countries and can be used as a reference in formulating monetary and international trade policies.

Private vehicles are a frequently used mode of transportation because they are considered more practical. However, using private vehicles carries several risks, such as traffic accidents due to drivers losing focus on the road due to other activities, such as making calls on smartphones, drinking, or operating the radio. Approximately 90% of accidents are caused by human error. Convolutional Neural Network (CNN) is a type of neural network commonly used on image data. CNN is often used for image classification due to its high performance and accuracy. Therefore, this study aims to analyze the performance of CNN for the classification of distracted driving activities. The results show that the CNN model is able to effectively classify images of distracted driving activities, with an accuracy of approximately 99% across all datasets and across all input image size variations. Furthermore, the results of this study also show that differences in right-hand and left-hand drive datasets do not significantly affect model accuracy. Variations in input image size also do not significantly affect model accuracy, but do affect the training duration.

Digital images often experience noise disturbances that can reduce visual quality and interfere with the image analysis process. One common type of noise is salt and pepper noise, especially in grayscale images, which is characterized by the random appearance of black and white dots. This study applied the Deep Convolutional Autoencoder (DCAE) method with a skip connection mechanism to eliminate salt and pepper noise in grayscale images measuring 256×256 pixels. The dataset used consists of 300 pairs of clean images and noisy images that have gone through the preprocessing stage, including normalization and data augmentation. The model was trained using an Adam optimizer with a Mean Squared Error (MSE) loss function and validated through a train-test split scheme to avoid overfitting. Model performance was evaluated using Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) metrics. The test results showed that the DCAE model with skip connections was able to effectively reduce noise while maintaining the main structure of the image based on the PSNR and SSIM values obtained, and showed better performance than conventional median filters. In addition, the model was successfully implemented into a Streamlit-based application to perform the image denoising process interactively, making it easier for users to experiment and visualize results in real-time.

The rapid development of information technology has provided significant opportunities to improve the efficiency of academic administration in schools. One of the common problems faced by educational institutions is the manual process of subject scheduling, which is time-consuming and prone to schedule conflicts among teachers, classes, and learning time. This problem is also experienced by SMPK Flos Carmeli, where the preparation of subject schedules has not yet been supported by an integrated computerized system. This study aims to design and develop a web-based subject scheduling application at SMPK Flos Carmeli using the Model–View–Controller (MVC) architecture. The research method employed is Research and Development (R&D) with the Waterfall software development model, which includes the stages of requirements analysis, system design, and application implementation. Data were collected through interviews, observations, and literature review to obtain system requirements that align with the school’s conditions. The application was developed using native PHP with the implementation of the MVC pattern to produce a structured, maintainable, and flexible system. The results show that the developed application is able to support the subject scheduling process in a faster, more accurate, and well-organized manner. The system provides features for managing teacher data, class data, time slots, schedule arrangement, and schedule printing, thereby minimizing schedule conflicts and improving the efficiency of school administrative work. Therefore, this subject scheduling application is expected to support the digitalization of academic administration and enhance the effectiveness of the teaching and learning process at SMPK Flos Carmeli.

This study aims to analyze the profitability level of the canteen at Nurul Islam 2 Islamic Boarding School and its contribution to the economy of the boarding school from a sharia perspective. The background of this study is based on the important role of Islamic boarding school business units as a source of economic independence for Islamic educational institutions. The research method used is a descriptive qualitative approach, with data collection techniques through in-depth interviews, direct observation, and documentation. The main informants of this study include the canteen manager and several students as consumers. The results of the study indicate that the canteen management has been running effectively, with a transparent financial recording system, separation of personal and operational cash, and a fair and affordable pricing strategy. The canteen's profitability level is relatively stable with an average daily income of between two and three million rupiah, and increases significantly during certain activities such as Sambangan. Business profits not only have a financial impact, but also contribute to financing the boarding school's activities and improving the welfare of students. From a sharia perspective, the economic practices of this canteen have implemented the principles of honesty (shiddiq), justice ('adl), trustworthiness, and blessings (barakah), so that it can be said that the Islamic boarding school canteen is a model of micro-economy based on sustainable Islamic values ​​and oriented towards the common good.