Journal of Industrial Engineering & Management Research

Effective Strategies in Managing Educational Financing for Islamic Higher Education Institutions in the Contemporary Era

2025-04-07T08:07:56+00:00

This study examines effective strategies for managing educational financing in Islamic higher education institutions, emphasizing adherence to Islamic finance principles[3] and addressing contemporary challenges. Utilizing a mixed-methods approach, the research explores waqf revitalization[1], sukuk implementation[2], revenue diversification, and stakeholder engagement[4] as mechanisms to enhance financial sustainability and ethical alignment. Findings reveal that institutions leveraging waqf and sukuk demonstrate improved financial resilience and reduced reliance on tuition fees. Stakeholder collaboration and capacity-building initiatives[8] emerge as critical elements for optimizing financial strategies, ensuring accessibility, and advancing institutional missions in alignment with Shariah principles.

Regulatory Compliance Strengthening Strategy through Mitigating the Risk of Violation of Fiduciary Duty : Jiwasraya Case Study in Indonesia

2025-05-08T01:37:41+00:00

The Jiwasraya case in Indonesia is one of the biggest financial scandals that reflects weaknesses in the implementation of regulatory compliance and internal control, especially related to fiduciary duty violations. This study aims to analyze effective risk mitigation strategies in strengthening regulatory compliance through a case study based on a literature review. By using a qualitative research method through a literature review approach, this study explores various relevant documents, reports, and previous studies. The results of the study indicate that the violation of fiduciary duty in Jiwasraya was caused by weaknesses in investment supervision, lack of transparency in financial reports, and neglect of good corporate governance principles. To address this, the proposed strategies include strengthening internal control mechanisms based on the COSO framework, implementing a whistleblowing system for early detection of violations, and integrating real-time investment monitoring technology. This study provides theoretical contributions by expanding the literature on mitigating the risk of fiduciary duty violations in the context of regulatory compliance. Practically, this study recommends strategic steps for insurance companies and regulators to prevent similar cases from recurring in the future

Proposed design of the application of chemical warehouse control systems with the water fall method

2025-04-26T04:23:42+00:00

This study aims to make it easier for analysts to collect chemicals and maintain the accuracy of chemical inventory data because they no longer use manual recording. The research was conducted at the laboratory of PT. Mitsubishi Chemical Indonesia March 1 to May 30, 2024. Data were collected by interview and observation, and analyzed using the water fall method. Based on the research, it can be concluded that: 1) the system created can make it easier for the analyst when taking chemicals and the suitability of the inventory quantity data becomes accurate, 2) the application made can be easily understood by all analysts in the laboratory.

Analysis to predict diabetes Using Data Mining

2025-05-02T02:48:50+00:00

Abstract - Data mining is crucial for extracting patterns and valuable insights from extensive datasets, utilizing artificial intelligence and advanced data analysis techniques across various domains. Diabetes, a metabolic disorder characterized by elevated blood glucose levels, poses significant health risks, including cardiovascular and renal complications if untreated. Data mining plays a pivotal role in exploring and predicting diabetes by identifying high-risk populations, thereby enabling early intervention strategies such as lifestyle modifications and timely treatment initiation.

Analyzing comprehensive datasets encompassing diabetes-related factors such as weight, blood pressure, blood glucose levels, and genetic predispositions data mining constructs predictive models to assess risks and implement targeted interventions. In a comprehensive study involving 768 cases (268 positive and 500 negative) Logistic Regression achieved 70% accuracy, with a recall of 57% and an F1 score of 0.63 , Naive Bayes (GaussianNB) achieved 68% accuracy, with a recall rate of 54% and an F1 score of 0.61, Decision Tree Classifier achieved 66% accuracy, with a recall rate of 62% and an F1 score of 0.64 , Random Forest achieved 70% accuracy, with a recall rate of 59% and an F1 score of 0.64 , XGBClassifier achieved 66% accuracy, with a recall rate of 58% and an F1 score of 0.62.

The analysis underscores a trade-off between precision and recall, particularly in classifying high-risk diabetes cases. High precision reduces false positives but may lower recall, potentially missing true positive cases. Conversely, emphasizing recall may increase false positives. Achieving a balance between these metrics is critical for effective diabetes prediction and tailored healthcare strategies This abstract encapsulates the pivotal role of data mining in diabetes research, emphasizing its impact on predictive modeling and healthcare decision making.