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(1) Background: The management of forest and land fires has become of concern to the Indonesian government, as demonstrated by the issuing of the Presidential Instruction of the Republic of Indonesia Number 11 of 2015, then replaced by Number 3 of 2020, concerning Forest and Land Fire Management. In 2002, the Ministry of Environment and Forestry (MoEF) established Manggala Agni, a forest fire management organization, at the central government level. This study aims to explain the development of the Indonesian Forest and Land Fire Prevention Patrol System. The system development was conducted by the Computer Science Department of IPB University, in collaboration with the Agency for Climate Change and Forest and Land Fire Management, as well as the Directorate of Forest and Land Fire Management, MoEF. (2) Methods: The system development adopted the steps in the prototyping method, namely communication, quick planning, rapid design modeling, and prototype construction, as well as dissemination, submission, and feedback. (3) Results: The web-based system for the real-time monitoring and analysis of the forest and land fire prevention patrol is integrated into a mobile application for recording field observations during patrol activities. The system testing was successfully conducted by involving the users. The test results show that all features in the system are working correctly and meet the user's requirements. The mobile application has saved 36.02% of the time for recording the patrol data and 40.32% for creating the patrol reports. In addition, using the web-based application has saved time in preparing the patrol reports by 56.48%. [ABSTRACT FROM AUTHOR]

Background & objective: Intensive care has been associated with high cost and resource-intensive medical care. Therefore, a risk prediction model is required to plan time allocation, human resources, and the required equipment. Various risk predictions for ICU mortality and 'Prolonged Length of Stay' (PLOS) scores are already available. Still, the established model, such as the APACHE IV score or SAPS II, sometimes became impractical since they required many laboratory parameters. A model based on co-morbidities and demographic factors may be more useful in limited resources setting. Hence, we developed a simple ICU mortality and PLOS risk prediction model based on comorbidities and demographic data. Methodology: This retrospective cohort study was performed to develop a risk scoring for mortality and PLOS, using data from Dr. Sardjito Hospital Yogyakarta database between January 01-December 31, 2019. Logistic regression and bootstrap methods were used to create a risk score for estimating the risk. The discrimination performance of the model was evaluated using the area under the curve (AUC) of the receiver operating characteristic (ROC). The Hosmer-Lemeshow test was employed to assess the model's calibration. Results: A total of 415 patients were included in this study. The risk factors for mortality were perioperative support medication, kidney failure, neurologic disorder, respiratory failure, and intraoperative blood transfusion. The mortality score of 6 was associated with a 100% probability of mortality. Medical cases, GCS < 8, vasoactive/inotropic medication, sepsis, respiratory failure, and kidney failure were the risk factors for PLOS. PLOS score of 3 was associated with a 100% probability of PLOS. The discrimination for either mortality or PLOS was considered excellent with the AUC (± 95% CI) for mortality 0.896 (0.853-0.94), while for PLOS 0.878 (0.80-0.90). The calibration test found that both models had good calibration with P values of 0.53 and 0.55 for mortality and PLOS, respectively. Conclusion: The 'Mortality and Prolonged Length of Stay Prediction Score' based on co-morbidities and demographic data upon admission to ICU had good accuracy and can be applied as a potential new scoring system in healthcare institutions. [ABSTRACT FROM AUTHOR]