Infrastructure development plays a crucial role in ensuring the sustainable economic growth of a country. For instance, Indonesia saw a 29.532% rise in infrastructure investments between 2015 and 2019, showcasing their heavy investment in infrastructure project. The Public Private Partnership (PPP) project in Indonesia is a collaborative effort between the government and private entities to enhance infrastructure project development. The Makassar-Parepare Railway Public Private Partnership (PPP) project is an example of a national strategic project on transportation sector, especially train transportation, which aims to improve the quality of national infrastructure and connectivity. However, the project faces various risks such as delays in determining the railway route and a lack of understanding of the Government Contracting Agency (GCA/PJPK) regarding the PPP concept. In order to ensure the success of this project, it is important to identify and manage these risks effectively. Utilizing the likert scale, a literature review uncovered 29 relevant risk factors, while the probability impact matrix (PIM) analysis aided in ranking the 18 main risks in the Makassar-Parepare Railway PPP project. This research can contribute to the development of more effective risk management strategies for similar infrastructure projects in the future.
This study proposes a geolocation microservices integration framework for immigration surveillance using UML and the sidecar pattern. The prototype was evaluated through multi-location subject testing, GNSS-based positional classification, descriptive error metrics, and comparative architectural experiments. Positional error was calculated using the Haversine formula and interpreted by subject rather than by a generalized percentage claim. The results show that six subjects were classified as Very High, one as High, and one as Moderate, with MAE of 43.34 m, RMSE of 75.20 m, median error of 16.75 m, and SD of 65.70 m. The comparative experiment indicates that microservices offer stronger modularity and fault isolation than a monolithic baseline, while the sidecar pattern improves observability and auditability without modifying the primary geolocation service. The framework is feasible as a decision-support layer for real-time immigration monitoring, subject to legal governance and further load testing.
Heart disease is a specific abnormal condition that includes heart-related and bloodstream-related illnesses. It can range from heart rhythm issues to blood vessel disorders. Medical practitioners use a patient''s medical history and tests like blood pressure, blood sugar, or cholesterol to make a diagnosis of heart disease. The Behavioral Risk Factor Surveillance System is the greatest telephone survey program in the United States for learning about Americans'' health-related risk behaviors, chronic illnesses, and use of preventative drugs (BRFSS). We may use the data to identify a variety of illness-related traits and forecast an individual's likelihood of developing a specific condition. Following that, we compare and contrast the accuracy of the outcomes obtained using the methods described in the journal, earlier methods for previous projects, and hybrid algorithms. The objective of this research is to surpass the result of the journal reference which is the minimum accuracy of 78% and the maximum of 90%. Meanwhile the results of our Hybrid Algorithm methods are 90% for XGBoost+Randomize Search and 91% for the other algorithms. Based on the results, we can conclude that by combining the classification algorithm with the hyperparameter optimization method, we may enhance classification accuracy. Because the presence of hyperparameters makes the limit more optimal and optimal for classification, resulting in more accurate findings. It can be seen that the results obtained from our project are slightly higher compared to the Journal. The highest accuracy from the Journal is 90% while our result is 91%.
OD matrix estimation under limited traffic count data in Indonesian Traffic Impact Analysis (Andalalin) remains fundamentally underdetermined, as the number of unknown OD pairs far exceeds the available traffic count observations. This study develops and empirically evaluates an integrated OD estimation system combining gravity model calibration, maximum entropy (MaxEnt) updating, and user equilibrium assignment within a Design Science Research framework. A stratified-coverage subsampling procedure (31 runs across four data availability scenarios) was applied to two datasets with contrasting characteristics. On the Magelang benchmark dataset (7 zones, known ground-truth OD), three key findings emerged: (1) reducing observed links from 29 to 18 via representative stratified selection improved GEH pass rate from 37.9% to 62.4%, indicating an optimal constraint count for small underdetermined networks; (2) GEH improvement did not coincide with improved OD accuracy (RMSE_OD increased 49%), empirically confirming the inherent limitation of flow-fit metrics as OD quality proxies; (3) result variability increased sharply below ~15 observed links, indicating a data threshold for reliable estimation. On the RSU Tabanan case study (4 zones, field data), all 31 runs produced identical outputs regardless of subset selection, revealing a prior-dominated regime caused by a 2.57× demand-volume structural inconsistency. These findings constitute a two-regime diagnostic framework applicable to Andalalin survey planning.
One promising solution to the global energy crisis is the utilization of biomass waste as an alternative fuel in the form of biobriquettes. A critical factor influencing the quality of biobriquettes is the type of binder used. Inorganic and composite binders often present drawbacks such as high ash content, low calorific value, and elevated production costs. To address these issues, this study explores the use of cassava peel waste as an organic binder. Cassava peel contains approximately 20% starch by dry weight, which acts as a natural polymer with adhesive properties when hydrolyzed into dextrin. The binder preparation involves washing, drying, and sieving the cassava peel to extract starch, followed by hydrolysis using 15 mL of 1N hydrochloric acid (HCl) at 110°C for 10 minutes to produce dextrin. The resulting solution is mixed with casein, cold water (15°C), and triethanolamine, then heated to 60°C and stirred until homogeneous. The resulting binder demonstrates high viscosity and strong adhesion, making it suitable for biobriquette production. The biobriquettes produced with this binder exhibit higher density (0.99–1.01 g/cm³) compared to commercial biobriquettes (0.97 g/cm³), indicating improved compactness and uniformity, which are essential for enhancing fuel quality and performance.
