Publication Search

The agricultural sector plays a strategic role in meeting food needs while maintaining environmental sustainability and community welfare. However, conventional farming practices that rely on chemical inputs have caused various problems, such as soil degradation, loss of biodiversity, and environmental pollution. This condition has encouraged the adoption of the Sustainable Agriculture concept, which emphasizes a balance between ecological, economic, and social aspects. Its successful implementation is strongly influenced by farmers’ perceptions as the main actors. This study aims to analyze rice farmers’ perceptions of the implementation of Sustainable Agriculture in swampy areas of Tebas District, Sambas Regency. The research was conducted from October to November 2025 using a survey method involving 95 farmers selected through the Slovin formula and proportional random sampling technique. Data were collected through observation, interviews, and Likert-scale questionnaires, and analyzed using descriptive analysis and binary logistic regression. The results show that farmers’ perceptions are generally positive, particularly regarding water management, variety selection, and cropping patterns. However, the use of organic fertilizers, environmentally friendly pest control, and post-harvest management are still considered difficult. Education level and non-farming occupations significantly influence farmers’ perceptions. Therefore, enhancing extension services, training, and support for business diversification is necessary to promote the adoption of Sustainable Agriculture.

Sustainability reporting has been increasingly popular in recent years as businesses become more aware of environmental challenges. 96% of the top businesses in the world have implemented sustainability reporting practices, according to a KPMG survey. The effect of sustainability reporting practices on environmental performance is examined in this study. Sustainability reports, the implementation of GRI standards, and external assurance are used to gauge sustainability reporting procedures; firm size is used as a control variable. In this study, 305 observational data from manufacturing firm over a five-year period (2018-2022) were analysed quantitatively using binary logistic regression. The findings indicate that while the use of GRI standards has a positive and significant effect on environmental performance, sustainability reports and external assurance have no significant effect. These findings show that the implementation in GRI standards encourages business commitment to sustainable practices and transparency, both of which have a significant impact on environmental performance. In the meanwhile, external assurance and sustainability reports tend to be mostly symbolic and do not demonstrate a real commitment to environmental improvement. Environmental performance is positively impacted by the control variable, firm size. These findings suggest that a company's environmental performance is correlated with its size. As a control variable, firm size contributes to maintaining and clarifying the relationship between environmental performance and sustainability practices while ensuring objective and valid study findings. This study emphasizes the importance of strengthening the quality of sustainability reporting and expanding the application of external standards and assurance to improve the credibility and accountability of corporate environmental performance in Indonesia.

Chronic Kidney Disease (CKD) is a major global health issue that can lead to serious complications and long-term medical care. This study aims to identify key clinical factors associated with CKD status using binary logistic regression analysis. The dataset, obtained from Kaggle, contains 400 patient records with various clinical and demographic attributes. The dependent variable is CKD status (positive or negative), while the independent variables include age, blood pressure, hemoglobin level, urine albumin level, and serum creatinine. Initial analysis involved descriptive statistics and multicollinearity checks, followed by model estimation and evaluation using likelihood ratio and Wald tests. The final model identified four significant predictors: blood pressure, hemoglobin, urine albumin, and serum creatinine. The model achieved a high classification accuracy of 95.50% and an Area Under the ROC Curve (AUC) of 98.78%, indicating excellent predictive performance. These results highlight the importance of these clinical indicators in early CKD detection and support their use in risk assessment models for kidney disease screening Keywords: Chronic Kidney Disease, Binary Logistic Regression, Likelihood Ratio Test, Wald Test, Classification Accuracy

The purpose of this research is to analyze the effect of financial ratios on financial distress in Islamic Commercial Banks in Indonesia. This study uses determinants financial distress that isDebt Ratio (DR), Financing to Deposit Ratio (FDR), Non Performing Financing (NPF), Return On Equity (ROE), and Operating Expenses Operating Income (BOPO). The population of this study is all Islamic Commercial Banks in Indonesia. The sample taken is the quarterly financial reports of Islamic commercial banks for 9 periods, namely the 2016-2024 period using the purposive sampling and using panel data binary logistic regression testing techniques. Based on the research conducted, the results show that Debt Ratio (DR), Non Performing Financing (NPF), Return On Equity (ROE), and Operational Expenses Operating Income (BOPO) have an effect on financial distress. Whereas Financing to Deposit Ratio (FDR) has no effect on financial distress.

This study aims to analyze the factors that affect the level of compliance of Micro, Small and Medium Enterprises (MSMEs) taxpayers in Kupang City. The type of research used is case study research with a quantitative approach. Data was collected through a questionnaire distributed to 189 respondents who were selected using the purposive sampling technique. Data analysis was carried out using binary logistic regression with the help of SPSS version 25 software. The results of this study show that taxpayer awareness, supervision and enforcement of tax laws, benefits felt by taxpayers, as well as tax support and incentives significantly affect taxpayer compliance; Meanwhile, understanding of tax regulations and ease of tax administration does not affect taxpayer compliance. However, overall, the six independent variables have a simultaneous effect on taxpayer compliance as a dependent variable. This finding is expected to be the basis for local governments and related institutions in formulating policies that support increasing the compliance of MSME taxpayers in Kupang City.

The purpose of this study is to analyze the effectiveness of green financing in sustainable financing for home industry players in Pematang Serai Village. Green financing is a financing scheme or lending to business actors that is environmentally friendly. Based on Government regulations to be able to receive financing with a green financing scheme. To achieve industrial, social and economic advantages to reduce the threat of global warming and prevent other environmental and social problems, the goal is to shift the goal to a competitive low-carbon economy, so that it can strategically promote environmental investment in various areas of business/economy. The research data is sourced from the results of interviews with industrial homes in Pematang Serai Village, Tanjung Pura District, Langkat Regency, North Sumatra Province. as borrowers/customers of the Green Financing Program with the help of questionnaires so that the questions in the interview are more systematic. Sampling was carried out using proportionate random sampling technique. The data analysis used was: Descriptive Statistical Analysis and Binary Logistic Regression Analysis. The results of the study explained that green credit and green technology partially did not have a significant effect on green finance in Pematang Serai Village, Tanjung Pura District, Langkat Regency, while green product innovation partially had a significant effect on green finance in Pematang Serai Village, Pematang Serai Village, Tanjung Pura District, Langkat Regency.

Educated unemployment in Indonesia is increasing, this phenomenon occurs when young, highly educated individuals find it difficult to get jobs according to their qualifications. In Central Kalimantan the proportion of educated unemployed will increase because low-educated workers still dominate labor absorption. This research aims to understand the factors that cause youth and educated unemployment in depth, which is important in order to formulate appropriate solutions, both from the government, educational institutions and individuals. Binary logistic regression is the analytical tool used in this research. The results obtained state that education level and gender have a positive and significant effect on the unemployment status of young and educated people in Central Kalimantan Province. Meanwhile, the Age variable has a negative and insignificant effect on the unemployment status of young and educated people in Central Kalimantan Province. And the location variable has a negative and significant influence on the unemployment status of young and educated people in Central Kalimantan Province.    

Indonesia is entering a critical period for mental health. Research results from the The Indonesia National Adolescent Mental Health Survey (2022), around 15,5 million Indonesian teenagers experience mental health disorders. Students are part of late adolescence and are vulnerable to mental disorders. The binary logistic regression model is used to examine in more depth what variables have a significant effect. So, this research aims to predict mental health of students in the Faculty of Health Sciences, Pekalongan University. This type of research is observasional with a cross-sectinal design. Data were collected using the SRQ-20 via the Google Form platform using simple random sampling of 186 students. There were 130 students who indicated mental health disordes (69,9%). Simultaneously age, gender, major, semester level, mother’s educational level, father’s educational level, social support and dependence on using smartphone influence student’s mental health status (P Value<0,05). Even though only a few variables were partially significant, the precision percentage of the model that could be predicted correctly was 71,5%. The accuracy of the predicted model is quite good, namely student mental health status (y) = -3,720 + 2,403 (Major) – 1,980 (Mother’s Educational Level) + 1,444 (Father’s Educational Level) + 0,888 (Dependence on using Smartphone). Promotive and preventive interventions such as further screening and education to support student’s healthy mental health.